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PMC11276206_p19 | PMC11276206 | sec[1]/sec[1]/p[3] | Based on the above analysis of the theoretical and practical paths, research hypotheses H 4 and H 5 are formulated as follows: | en | 2.2. Analysis of the Moderating Mechanism of Risk Preference | other | other | 1.584961 | [
0.255126953125,
0.0012912750244140625,
0.74365234375
] | [
0.3076171875,
0.685546875,
0.005725860595703125,
0.0011959075927734375
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p20 | PMC11276206 | sec[1]/sec[1]/p[4] | Figure 1 illustrates the path of actions of risk preference. | en | 2.2. Analysis of the Moderating Mechanism of Risk Preference | other | other | 1.405273 | [
0.231689453125,
0.0033740997314453125,
0.76513671875
] | [
0.057586669921875,
0.93603515625,
0.004726409912109375,
0.0017337799072265625
] | 0.999999 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p21 | PMC11276206 | sec[2]/sec[0]/p[0] | This research uses data from the Chinese Family Database (CFD) of Zhejiang University and the China Household Finance Survey (CHFS) conducted by the Survey and Research Center for China Household Finance at the Southwestern University of Finance and Economics, China. Wheat price data are from the National Food and Materials Reserve Administration’s weekly market monitoring reports from 2017 to 2018 and the Bric Agricultural Database from 2015 to 2018. | en | 3.1. Data | other | other | 1.010742 | [
0.0091705322265625,
0.00041031837463378906,
0.990234375
] | [
0.10430908203125,
0.892578125,
0.0017671585083007812,
0.001392364501953125
] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p22 | PMC11276206 | sec[2]/sec[0]/p[1] | Due to the availability of data, the Bric Agricultural Database only includes monthly wheat prices in some provinces, and the State Administration of Food and Material Reserves weekly market monitors wheat prices in some regions and years. We chose farmers in the six main wheat-producing provinces of Hebei, Jiangsu, Anhui, Shandong, Henan, and Hubei covered by both databases as the study sample and eliminated the harvested food used exclusively for rations and agricultural production and cases of zero income from food sales, resulting in 1388 samples. | en | 3.1. Data | other | study | 1.34375 | [
0.0693359375,
0.0006670951843261719,
0.93017578125
] | [
0.916015625,
0.08282470703125,
0.000728607177734375,
0.00067901611328125
] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p23 | PMC11276206 | sec[2]/sec[0]/p[2] | Also, to mitigate the effect of potential outliers, this paper shrinks the continuous variables at the 1% and 99% levels. | en | 3.1. Data | biomedical | study | 1.985352 | [
0.9228515625,
0.0008902549743652344,
0.07635498046875
] | [
0.9453125,
0.05303955078125,
0.0011873245239257812,
0.00037598609924316406
] | 0.999996 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p24 | PMC11276206 | sec[2]/sec[1]/p[0] | In order to examine the effect of risk perception on farmers’ choice of timing of food sales, let y denote the timing of food sales chosen by farmers, and the explanatory variable x i varies only with individual i and not with mode j . It is a multivariate unordered choice problem requiring a control group, and we use a multinomial logit model for empirical estimation. The general form of the model can be expressed as follows: (8) P y i = j | x i = 1 1 + ∑ k = 2 J exp x i ′ β k j = 1 exp x i ′ β j 1 + ∑ k = 2 J exp x i ′ β k j = 2 , … , J | en | 3.2. Measurement Modeling | other | study | 2.177734 | [
0.13525390625,
0.0005197525024414062,
0.8642578125
] | [
0.921875,
0.0771484375,
0.0006995201110839844,
0.0003802776336669922
] | 0.999995 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p25 | PMC11276206 | sec[2]/sec[1]/p[1] | This model represents the probability that the i farmer chooses mode j , where j is the timing of the food sale and x i is farmer’s risk perception. | en | 3.2. Measurement Modeling | other | other | 1.222656 | [
0.0477294921875,
0.0008883476257324219,
0.951171875
] | [
0.0301666259765625,
0.96826171875,
0.000827789306640625,
0.0006227493286132812
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p26 | PMC11276206 | sec[2]/sec[2]/sec[0]/p[0] | The dependent variable in this paper is the timing of food sales by farmers, the food type is wheat, the choice of intertemporal food sales (in this paper, the raw food processed and sold by farmers is classified as intertemporal food sales) is assigned a value of 1, the choice of two-phase food sales is assigned a value of 2, and the choice of current food sales (in this paper, farmers’ choices for rations + current sales are categorized as current food sales) is assigned a value of 3. The distribution of choices of the sample farmers is reported in Table 1 . | en | 3.3.1. Dependent Variable | other | study | 1.081055 | [
0.016448974609375,
0.0004394054412841797,
0.98291015625
] | [
0.72998046875,
0.266845703125,
0.0015506744384765625,
0.0015592575073242188
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p27 | PMC11276206 | sec[2]/sec[2]/sec[0]/p[1] | Table 1 shows that 89.77% of the sample farmers chose the timing of current sales, indicating that most of the farmers choose to sell all their food after harvesting or leave their rations and then sell them immediately, showing negativity to food sales. The statistical results also reflect that only 1.37% of the sample farmers chose intertemporal sales. Although some farmers will choose to keep rations in the current sales, the part used to reserve for sale is the main body of China’s private food reserves, which is consistent with the conclusion of some scholars that farmers’ food storage in China has declined . In contrast, the proportion of farmers who chose two-phase sales is higher than that of intertemporal sales. The important influence of this difference lies not only in liquidity but also in farmers’ choice of the two, which, to a certain extent, is affected by their risk preference and risk perception. Farmers who choose two-phase sales tend to be risk averse and seek more secure returns, and their risk perception may be relatively high, while farmers who choose intertemporal sales are less risk averse and seek higher returns, and their risk perception may be relatively low. In normal years when food prices are stable, farmers tend to choose intertemporal sales to obtain higher returns. In the case of fluctuating or even inverted prices, farmers may choose two-phase or current sales to avoid risk. | en | 3.3.1. Dependent Variable | other | study | 1.125 | [
0.00849151611328125,
0.00034165382385253906,
0.9912109375
] | [
0.869140625,
0.12841796875,
0.0013723373413085938,
0.001201629638671875
] | 0.999999 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p28 | PMC11276206 | sec[2]/sec[2]/sec[1]/p[0] | The core independent variable in this paper is farmers’ risk perception. The concept of risk perception belongs to the field of psychology and originated in the theory of “perceived risk”, which was proposed by Bauer at Harvard University for consumer behavior . Subsequently, many scholars have conducted in-depth studies on the basis of this foundation in terms of the nature, dimensions, individual differences, and quantification of risk perception, respectively . The more representative ones are by Cunningham, Cox, and Slovic. In particular, Cunningham’s two-factor theory and Cox’s multidimensional theory have been recognized by most scholars and have been widely used in various types of research. The two-factor theory considers risk perception to be composed of the uncertainty of an event and the severity of the adverse consequences. The greatest contribution of this theory is that it builds on Bauer’s theory by quantifying risk perception in both objective and subjective terms , and much of the subsequent quantitative research on risk perception has built on this approach. Slovic measured risk perception using a psychometric paradigm based on two-factor theory. Multidimensional theory suggests that risk perception consists of multiple dimensions and that the combination of different dimensional elements realizes a measure of risk perception. Despite the differences in the understanding of risk perception among different scholars, the types of dimensions are basically the same. Table 2 reports the main dimensions of previous research on risk perception. | en | 3.3.2. Core Independent Variable | other | study | 2.568359 | [
0.085205078125,
0.0006775856018066406,
0.9140625
] | [
0.9677734375,
0.0189971923828125,
0.01271820068359375,
0.0004489421844482422
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p29 | PMC11276206 | sec[2]/sec[2]/sec[1]/p[1] | Combining the analysis of risk dimensions in the classical literature in Table 2 , the exploration of farmers’ risk perception in food sales can be categorized into four types: market risk , liquidity risk , transaction cost risk , and information risk . Based on Equation (9), we refer to Cunningham’s two-factor model and quantify the magnitude of farmers’ risk perception by assigning weight scores to the four types of risk. (9) P = A B = ∑ i = 1 n a i j m ∗ n ∗ b i j n ∗ 1 = P i m ∗ 1 | en | 3.3.2. Core Independent Variable | other | study | 1.506836 | [
0.0262603759765625,
0.00032782554626464844,
0.9736328125
] | [
0.5576171875,
0.4384765625,
0.0027923583984375,
0.0010747909545898438
] | 0.999994 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p30 | PMC11276206 | sec[2]/sec[2]/sec[1]/p[2] | The two-factor theory considers risk perception P to be composed of A , the uncertainty of an event, and B , the severity of the adverse consequences. The solutions are as follows. | en | 3.3.2. Core Independent Variable | biomedical | other | 1.919922 | [
0.59130859375,
0.002758026123046875,
0.40576171875
] | [
0.0894775390625,
0.904296875,
0.005382537841796875,
0.0009360313415527344
] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p31 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[0] | Calculating weights according to the degree of variability of the indicator data can better reflect the uncertainty of risk, and we choose the entropy weight method to objectively quantify the uncertainty of risk. First of all, the risk perception measurement indicators of farmers’ food sales are consistently processed (the indicators have been positively normalized when they are set; see the Appendix A for a description of the indicators), and the matrix X i j = ( x i j ) m ∗ n is as follows: X i j = x 11 x 12 ⋯ x 1 n x 21 x 22 ⋯ x 2 n ⋮ ⋮ ⋱ ⋮ x m 1 x m 2 ⋯ x m n | en | The Solution of A | other | study | 2.173828 | [
0.343994140625,
0.000598907470703125,
0.6552734375
] | [
0.8662109375,
0.1326904296875,
0.0008230209350585938,
0.00044465065002441406
] | 0.999995 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p32 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[1] | The matrix X i j is then normalized as follows: X ~ i j = ( x ~ i j ) m ∗ n = x i j − min { x 1 j , x 2 j , … , x m j } max { x 1 j , x 2 j , … , x m j } − min { x 1 j , x 2 j , … , x m j } | sq | The Solution of A | biomedical | other | 2.796875 | [
0.83251953125,
0.0011224746704101562,
0.166259765625
] | [
0.2568359375,
0.7412109375,
0.0011749267578125,
0.0006694793701171875
] | 0.428571 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p33 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[2] | Yield the weight of the j th indicator value for the i th farmer as follows: p i j = x ~ i j ∑ i = 1 m x ~ i j | en | The Solution of A | other | other | 1.333984 | [
0.07025146484375,
0.0008339881896972656,
0.9287109375
] | [
0.025909423828125,
0.97314453125,
0.0006880760192871094,
0.0004620552062988281
] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p34 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[3] | Yield the information entropy as follows: e j = − 1 l n m ∑ i = 1 m p i j ln ( p i j ) | en | The Solution of A | biomedical | other | 2.833984 | [
0.89599609375,
0.0013294219970703125,
0.1025390625
] | [
0.29052734375,
0.70751953125,
0.00118255615234375,
0.0006880760192871094
] | 0.999996 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p35 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[4] | Yield the information entropy redundancy as follows: d j = 1 − e j | en | The Solution of A | biomedical | other | 2.197266 | [
0.61083984375,
0.0018978118896484375,
0.38720703125
] | [
0.134521484375,
0.86279296875,
0.0016851425170898438,
0.0008625984191894531
] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p36 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[5] | Calculate the entropy weights for each indicator as follows: w j = d j ∑ j = 1 n d j | en | The Solution of A | biomedical | other | 2.332031 | [
0.7431640625,
0.0013065338134765625,
0.25537109375
] | [
0.155517578125,
0.8427734375,
0.0010528564453125,
0.0004763603210449219
] | 0.999996 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p37 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[0]/p[6] | That is, A = ( a i j ) m ∗ n = ∑ j = 1 n w j ∗ ( x ~ i j ) m ∗ n is obtained. The measurement indexes and weights are shown in Table 3 . | en | The Solution of A | other | other | 1.700195 | [
0.4130859375,
0.0012273788452148438,
0.58544921875
] | [
0.401611328125,
0.5947265625,
0.0024204254150390625,
0.0012369155883789062
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p38 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[1]/p[0] | Yu L. and Zheng K. pointed out that blindly choosing the objective assignment method may misjudge the importance of the indicators, so this paper chooses the AHP method to subjectively assess the severity of risk through the risk perception dimension. Based on the reality of food sales by farmers, the primary constraint on whether to sell food during the harvest season is liquidity to first meet the financial needs of credit, debt, production, and life. Secondly, most farmers choose to engage in part-time business in order to increase their income, so the transaction cost of intertemporal food sales becomes an important consideration for them. Again, based on the analysis of utility theory, farmers take a negative attitude towards market risk, which is dominated by price fluctuation, and information risk is relatively last. Then, the farmers’ perception of risk severity in descending order is liquidity risk, transaction cost risk, market risk, and information risk. The comparative judgment matrix is constructed according to Saaty’s 1–9 scale method, and the criterion-level judgment matrix is as follows: 1 2 3 5 1 / 2 1 3 4 1 / 3 1 / 3 1 2 1 / 5 1 / 4 1 / 2 1 | en | The Solution of B | other | study | 1.727539 | [
0.0311737060546875,
0.00045990943908691406,
0.96826171875
] | [
0.9638671875,
0.03448486328125,
0.0011463165283203125,
0.0005784034729003906
] | 0.999999 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p39 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[1]/p[1] | This matrix CR = 0.0212 < 0.1 meets the consistency test, and the weight vector of the criterion layer is as follows: B = ( b i j ) n ∗ 1 = 0.4667 0.3146 0.1392 0.0795 | en | The Solution of B | other | other | 1.891602 | [
0.275146484375,
0.0009775161743164062,
0.72412109375
] | [
0.207275390625,
0.79052734375,
0.0013055801391601562,
0.0008330345153808594
] | 0.999996 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p40 | PMC11276206 | sec[2]/sec[2]/sec[1]/sec[1]/p[2] | Combine (1) and (2) for A and B using the definition of risk perception P = A B = ∑ i = 1 m ( a i j ) m ∗ n ∗ ( b i j ) n ∗ 1 = ( P i ) m ∗ 1 . It can be derived as P i , which is the risk perception of the individual farmer. | en | The Solution of B | other | other | 1.708984 | [
0.1512451171875,
0.0008120536804199219,
0.84814453125
] | [
0.13525390625,
0.8623046875,
0.0014867782592773438,
0.0007944107055664062
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p41 | PMC11276206 | sec[2]/sec[2]/sec[2]/p[0] | The moderating variable in this paper is the risk preference of farmers. Farmers’ risk preference is measured by the following question: “If you have a sum of money to invest, what kind of investment program are you most willing to choose?” If farmers choose option 1, “high risk and high return”, it means that farmers’ risk preference is risk seeking. Similarly, if farmers choose option 2, “medium risk and medium return”, or option 3, “low risk and low return”, it means that farmers’ risk preference type is risk neutral and risk averse, respectively. The results of the survey show that only 2.02% of the 1388 farmers are risk seeking, while 70.53% are risk averse, i.e., most of the farmers are risk averse to possible risks. | en | 3.3.3. Moderating Variable | other | study | 1.076172 | [
0.0113677978515625,
0.0004191398620605469,
0.98828125
] | [
0.72705078125,
0.269775390625,
0.0015001296997070312,
0.0016794204711914062
] | 0.999995 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p42 | PMC11276206 | sec[2]/sec[2]/sec[3]/p[0] | Referring to related studies , we selected other factors affecting the decision of farmers’ food sales as control variables, including the characteristics of the head of the household (the head of the household’s gender, age, education, physical condition, and whether or not he or she is a village official) and the characteristics of the family (household size, and non-farm income). | en | 3.3.4. Control Variables | other | study | 1.264648 | [
0.06146240234375,
0.0007357597351074219,
0.93798828125
] | [
0.89111328125,
0.1068115234375,
0.0013475418090820312,
0.0009169578552246094
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p43 | PMC11276206 | sec[2]/sec[2]/sec[4]/p[0] | Table 4 shows the definition of each variable and the results of descriptive statistics. The proportion of male heads of the sample households is 66.5%, and the education level of most farmers is elementary school and junior school. The physical condition of the sample is basically general and poor, and 5.5% of the sample households have had the experience of being village officials. The average age of the head of households is about 57 years old, the average household size in the sample households is nearly four persons, and the average household non-farm income is CNY 3730, with the highest household non-farm income being CNY 100,000. | en | 3.3.5. Descriptive Statistics | other | study | 1.336914 | [
0.0582275390625,
0.0005521774291992188,
0.94140625
] | [
0.65966796875,
0.33837890625,
0.001007080078125,
0.000995635986328125
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p44 | PMC11276206 | sec[3]/sec[0]/p[0] | Table 5 reports the impact of risk perception on farmers’ timing of food sales. Columns (1) and (2) report that relative to current sales, risk perception has negative effects on both intertemporal and two-phase sales at a 1% significance level. In terms of relative risk ratios (RRRs), for each unit increase in risk perception compared with current sales, the odds of farmers choosing intertemporal and two-phase sales decreases to 0.409 and 0.739 times the original ratio, respectively. Columns (3) and (4) report that the risk perception variable remains significant with negative coefficients after adding the control variable (the result is conservative because the realities of credit and storage costs reduce the likelihood that farmers will store for future sales). That is, the higher the risk perception of farmers, the more they are inclined to forego intertemporal and two-phase food sales. Hypothesis H 1 is verified. | en | 4.1. Benchmark Regression | other | study | 1.253906 | [
0.0172576904296875,
0.0005044937133789062,
0.982421875
] | [
0.97216796875,
0.0262451171875,
0.00101470947265625,
0.00066375732421875
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p45 | PMC11276206 | sec[3]/sec[0]/p[1] | Among the control variables, the gender variable has a significant and positive coefficient on two-phase sales and a positive coefficient on intertemporal sales compared to current sales, which may be due to the fact that male farmers have a higher risk preference compared to female farmers, and even though the perception of risk increases, they still seek to make a profit and thus tend to choose two-phase or intertemporal sales. | en | 4.1. Benchmark Regression | other | other | 1.058594 | [
0.007396697998046875,
0.00036334991455078125,
0.9921875
] | [
0.333251953125,
0.66259765625,
0.0022983551025390625,
0.0019168853759765625
] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p46 | PMC11276206 | sec[3]/sec[0]/p[2] | Table 6 reports the average marginal effect of the sample (AME). The results show that risk perception has a significant impact on farmer’s food sales. Columns (1) to (3) show, respectively, that for every 0.1 unit increase in risk perception, the probability of farmers choosing intertemporal sales, two-phase sales, and current sales decreases by 11%, decreases by 23%, and increases by 34%, validating the results of benchmark regression. | en | 4.1. Benchmark Regression | other | study | 1.233398 | [
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0.95556640625,
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p47 | PMC11276206 | sec[3]/sec[1]/p[0] | There is no reverse causality between the dependent and independent variables of the article, but missing variables can have an impact on the estimated results. On the one hand, some farmers have not encountered significant risks in the short term, and the objectivity of risk perception is low; on the other hand, farmers’ food sales decisions may be affected by other unobservable exogenous factors. For this reason, we use the propensity score matching method to mitigate the endogeneity problem between risk perception and the timing of farmers’ food sales. | en | 4.2. Endogenous Issues | other | study | 1.637695 | [
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] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p48 | PMC11276206 | sec[3]/sec[1]/p[1] | Firstly, the relevant variables affecting risk perception and farmers’ timing of food sales are treated as covariates to ensure that the negligibility assumption is satisfied. According to the average value of risk perception, the sample is divided into two groups, low risk perception and high risk perception, respectively, which are assigned the values 0 and 1. We use the seemingly uncorrelated model to test the intergroup differences, and the test results in Table 7 show that there are intergroup differences between the different risk perceptions on the timing of food sales by farmers at the 1% significance level, and coefficient comparisons can be made. | en | 4.2. Endogenous Issues | other | study | 1.452148 | [
0.0654296875,
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p49 | PMC11276206 | sec[3]/sec[1]/p[2] | The logit model is then used to estimate the propensity score, which is matched using k-nearest neighbor matching and caliper matching methods to estimate the average treatment effect of risk perception on farmers’ timing choices for food sales. Since the traditional propensity matching score method is biased in estimating standard errors, we use the “teffects psmatch” command to derive robust standard errors following Abadie and Imbens . Table 8 reports the estimated average treatment effects and “A-I” robust standard errors. The estimation results show that after controlling for the effects of covariates, a high risk perception significantly and positively influences farmers’ choice of current food sales compared to a low risk perception. Overall, the omitted variables did not seriously interfere with the model estimation results in this paper. | en | 4.2. Endogenous Issues | other | study | 2.886719 | [
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] | 0.999996 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p50 | PMC11276206 | sec[3]/sec[2]/sec[0]/p[0] | Since the risk perception of farmers is calculated based on the AHP and entropy method weights, in order to test the reliability of the results, this paper replaces the dependent variable with “residents’ happiness”, which takes a value of 1~5, indicating that the degree of unhappiness is increasing in turn, and it uses the same control variables to estimate the impact of risk perception on the “residents’ happiness”. Logically, the higher the risk perception, the lower the resident’ happiness. Since the dependent variable becomes an ordered variable, we estimate it using an ordered logit model. The results, as shown in column (1) in Table 9 , show that risk perception positively affects the type of residents’ happiness at the 1% significance level, i.e., the higher the risk perception, the lower the residents’ happiness may be. In summary, the article’s treatment of the dependent variable does not seriously interfere with the robustness of the findings. | en | 4.3.1. Replacement of the Dependent Variable | other | study | 1.538086 | [
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0.0006694793701171875,
0.95947265625
] | [
0.9873046875,
0.01152801513671875,
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p51 | PMC11276206 | sec[3]/sec[2]/sec[1]/p[0] | In order to test the robustness of the model, we conduct a robustness test of the baseline regression by changing the model form. Since farmers’ food sales choices move in the opposite direction to their risk perceptions, farmers’ food sales choices can be approximated as an ordered arrangement, so the ordered ologit model is used for replacement. Column (2) in Table 9 shows that risk perception positively affects farmers’ food sales choices at 1% significance level. That is, the higher the risk perception is, the more farmers tend to give up intertemporal sales and choose current sales. | en | 4.3.2. Replacement of the Estimation Model | other | study | 1.112305 | [
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0.92822265625,
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p52 | PMC11276206 | sec[3]/sec[2]/sec[2]/p[0] | To test the robustness of the marginal effects analysis, we use the mprobit model to verify the impact on marginal effects. Columns (3) and (4) show that both the significance and sign of the variables are consistent with the mlogit model. | en | 4.3.3. Verification of Marginal Effects | biomedical | study | 1.845703 | [
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p53 | PMC11276206 | sec[3]/sec[2]/sec[2]/p[1] | Overall, the above tests indicate the robustness of the results of the model analysis used in this paper. | en | 4.3.3. Verification of Marginal Effects | biomedical | study | 1.871094 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p54 | PMC11276206 | sec[3]/sec[3]/p[0] | The effect of risk perception on the timing of the food sales of farmers may be significant heterogeneity in different planting scales and the proportion of food sales income to total household income, This paper attempts to reclassify the sample through the characteristics of the planting scale and the percentage of food sales income, through which the heterogeneity of the effect of the timing of the food sales of farmers can be analyzed. | en | 4.4. Heterogeneity Analysis | other | study | 1.389648 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p55 | PMC11276206 | sec[3]/sec[3]/sec[0]/p[0] | Farmers with different planting scales have different characteristics in their timing choices for food sales. We divided the sample into three groups, small scale, medium scale, and large scale, based on quartiles and conducted econometric regressions separately, and Table 10 reports the effects of risk perception on the timing choices of the food sales of farmers with different planting scales. | en | 4.4.1. Planting Scale | other | study | 1.011719 | [
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] | [
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p56 | PMC11276206 | sec[3]/sec[3]/sec[0]/p[1] | From the regression results, it can be summarized that small-scale farmers’ intertemporal food sales are more affected by farmers’ risk perception, while medium- and large-scale farmers’ two-phase sales are more significantly affected by their risk perception. Small-scale farmers have insignificant returns from intertemporal food sales due to the fragmentation of their plots and tend to give up intertemporal food sales and switch to two-phase or current sales if risk perception increases. Medium- and large-scale farmers, due to their large production scale, insisted on intertemporal sales to pursue returns or chose current sales to avoid risks when risk perception increased. | en | 4.4.1. Planting Scale | other | study | 1.06543 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p57 | PMC11276206 | sec[3]/sec[3]/sec[0]/p[2] | The above analysis shows that heterogeneity in planting size significantly differentiates farmers’ timing choices for food sales, thus reinforcing the practical basis of the relevant discussion in this paper. | en | 4.4.1. Planting Scale | other | other | 1.135742 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p58 | PMC11276206 | sec[3]/sec[3]/sec[1]/p[0] | The percentage of income from food sales to total household income implies the importance of food sales to the farm household in terms of income. There are differences in the characteristics of food sales among farm households with different percentages of income from food sales. We divide the sample into two groups, a low percentage of food sales income and a high percentage of food sales income, based on the median and run separate econometric regressions. Table 11 reports the effect of risk perception on the timing of food sales by farm households with different shares of food sales income. | en | 4.4.2. Percentage of Revenue from Food Sales | other | study | 0.985352 | [
0.00827789306640625,
0.00038123130798339844,
0.9912109375
] | [
0.7177734375,
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] | 0.999997 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p59 | PMC11276206 | sec[3]/sec[3]/sec[1]/p[1] | Meanwhile, the regression results also show that among farmers with a low percentage of income from food sales, intertemporal food sales are more affected by risk perception than two-phase food sales. In the case of farmers with a high share of income from food sales, the impact of risk perception is more significant in the case of two-phase sales. Farmers with a low share of income from food sales do not value the premium income from opportunistic sales, and if they have a high perception of risk after harvesting, they will choose to sell in the current period and give up intertemporal sales and two-phase sales, especially giving up intertemporal sales. When risk perception increases, farmers with a high percentage of income from food sales will insist on intertemporal sales to pursue income or choose current sales to avoid risk, which validates the choice of medium- and large-sized farmers in the heterogeneity of planting size. | en | 4.4.2. Percentage of Revenue from Food Sales | other | study | 1.196289 | [
0.00873565673828125,
0.00041604042053222656,
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] | [
0.81494140625,
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p60 | PMC11276206 | sec[3]/sec[3]/sec[1]/p[2] | To summarize, unlike the attitude of farmers who value food sales with a high percentage of income from food sales, a low percentage of income from food sales is an important factor that induces farmers to be negative toward food sales. | en | 4.4.2. Percentage of Revenue from Food Sales | other | other | 1.082031 | [
0.004940032958984375,
0.000530242919921875,
0.99462890625
] | [
0.00553131103515625,
0.9931640625,
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] | 0.999998 | [
"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p61 | PMC11276206 | sec[3]/sec[4]/p[0] | When examining the moderating mechanism of risk preference on risk perception affecting the timing of food sales by farmers, this paper estimated the effects by gradually adding risk preference, the interaction terms of risk preference, and risk perception to the equation. Table 12 reports the effects of risk preference on the timing of farmers’ food sales and the results of the moderating effects. | en | 4.5. Test of the Moderating Mechanism of Risk Preference | other | study | 1.366211 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p62 | PMC11276206 | sec[3]/sec[4]/p[1] | Columns (1) and (2) normalize risk perception and risk preference with the inclusion of only risk preference and examine the effects of risk perception and risk preference on farmers’ food sales choices. The estimation results show that both risk perception and risk preference significantly and negatively affect farmers’ choices of intertemporal sales and two-phase sales. Hypothesis H 2 is tested. The standardized coefficients of risk perception in columns (1) and (2) are larger than risk preference, indicating that risk perception has a greater impact on farmers’ food sales choices. Hypothesis H 3 is tested. | en | 4.5. Test of the Moderating Mechanism of Risk Preference | other | study | 1.138672 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p63 | PMC11276206 | sec[3]/sec[4]/p[2] | Columns (3) and (4) incorporate the interaction term between risk preference and risk perception. In terms of the timing of food sales, we may categorize intertemporal and two-phase food sales into two groups. Within the group, the estimation in column (3) shows that the coefficients on risk perception and risk preference are both significantly negative, consistent with the estimation in column (1). The coefficient of the interaction term is significantly positive at the 5% confidence level, which not only indicates that risk preference negatively moderates the effect of risk perception on farmers’ intertemporal food sales but also leads to the following inference: there is a relationship between risk preference and risk perception on farmers’ intertemporal food sales, i.e., the substitution effect. Hypothesis H 4 is verified. Column (4) estimates a non-significant interaction term and a non-significant main effect of risk perception, suggesting that risk preference has no significant moderating role in the effect of risk perception on farmers’ two-phase food sales compared to current food sales. | en | 4.5. Test of the Moderating Mechanism of Risk Preference | other | study | 1.463867 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p64 | PMC11276206 | sec[3]/sec[4]/p[3] | Between the groups, the test found that the marginal effect of risk perception was −4.8%, which was significant at the 5% level in the intertemporal sales group, where the risk of food sales was higher, and 3.4% in the two-phase sales group, where the risk of food sales was lower, which was not statistically significant. This indicates that in the case of two-phase sales, farmers’ choice of food sales is no longer associated with risk perception. Intuitively, the reverse causal and omitted variables do not differ significantly between the two groups. This suggests that the causal effect of risk perception on farmers’ food sales choices is heterogeneous across risk preference. Further analyzed by simple slope plots, Figure 2 shows that as the R value increases, risk preference decreases and the negative selection trend of risk perception on farmers’ inter-phase food sales is significantly weakened, which indicates that there is a substitution effect of farmers’ risk preference on risk perception in intertemporal food sales compared to the current sales. Figure 3 shows that different risk preference groups are parallel to each other, i.e., there is no moderating effect of risk preference in the relationship of risk perception on farmers’ intertemporal food sales. Hypothesis H 5 is verified. | en | 4.5. Test of the Moderating Mechanism of Risk Preference | other | study | 2.398438 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p65 | PMC11276206 | sec[3]/sec[4]/p[4] | The analysis of the moderating mechanism shows that risk preference significantly negatively moderates the effect of risk perception on farmers’ intertemporal food sales compared to current food sales, and there is a substitution effect on risk perception, which further confirms that the increase in risk perception is the cause of farmers’ negativity in food sales. There is no moderating effect of risk preference in the effect of risk perception on farmers’ two-phase sales. The causal effect of risk perception on farmers’ timing of food sales is heterogeneous across risk preferences, and the evidence of differences in risk preferences provides a stronger test, showing that a high risk perception contributes to farmers’ negativity regarding food sales. | en | 4.5. Test of the Moderating Mechanism of Risk Preference | other | study | 1.527344 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p66 | PMC11276206 | sec[4]/p[0] | The effect of risk perception on the timing of food sales of farmers may vary significantly due to different characteristics. The heterogeneity of planting sizes and percentage of food sales revenue allows us to further explore farmers’ sale decisions. | en | 5. Discussion | other | other | 1.1875 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p67 | PMC11276206 | sec[4]/p[1] | Existing studies generally concluded that large-scale and small-scale farmers tend to choose current sales, while medium-scale farmers tend to choose two-phase sales . Table 10 divides the scale of cultivation into small scale, medium scale, and large scale. The estimation results show that risk perception has a negative effect at a 5% level of significance on small-scale farmers’ choice of intertemporal sales compared to current sales, and the coefficient on two-phase sales is negative but not significant; it is clear that small-scale farmers have a low utility of intertemporal sales due to the small size of cultivation and tend to sell their food directly after harvesting. The results show that medium- and large-scale farmers have similar choices, and the effect of risk perception on their choice of intertemporal food sales is not significant compared to current sales but tends to abandon two-phase sales. The possible reason for this is that some medium- and large-scale farmers, because of the importance of income from food sales, would store food for sale even if there is risk in order to pursue the income from food sales, while some medium- and large-scale farmers are constrained by storage, capital, and other constraints and have difficulty in realizing the sale of food in installments and can only sell it in the current period. | en | 5. Discussion | other | study | 1.238281 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p68 | PMC11276206 | sec[4]/p[2] | We also want to observe the effect of risk perception on farmers’ grain sale timing decisions at different shares of food sale income in household total income. The estimation results in Table 11 show that risk perception has a negative effect at a 5% level of significance on the choice of intertemporal and two-phase sales by farmers, with a low share of income from food sales compared to current sales, while in the group with a high percentage of income from grain sales, this effect is not so significant, and even the effect on intertemporal sales is not significant. Logically, the higher the percentage of grain sales income in total household income, the greater the importance of grain sales income to the household, so farmers with a low percentage of grain sales income have a stronger willingness to sell directly, while those with a high percentage of grain sales income have to deliberate for the sake of profit. | en | 5. Discussion | other | study | 1.033203 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p69 | PMC11276206 | sec[4]/p[3] | These findings have two implications. First, compared with large-scale farmers, small-scale farmers tend to sell their food immediately after harvest because the quantity is too little to bring enough benefit. Second, farmers will seriously consider the timing of the sale if food sale income is really important to their family. Conversely, farmers with a low share of income from food sales tend to choose current sales. All in all, farmers’ food sale decisions could be fully explained by expected utility theory, and farmers value risk more than the pursuit of excess returns. | en | 5. Discussion | other | other | 1.075195 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p70 | PMC11276206 | sec[5]/p[0] | The focus on food prices under the profit maximization framework in the literature has led many researchers to overlook the importance of sales risk and farmers’ sales decisions. We study Chinese farmers’ timing of food sales and demonstrate that farmers’ negativity in food sales is essentially based on increased risk perception under constraints and the substitution of risk preference. We also find that the effect of risk perception on the timing of food sales has significant heterogeneity in different planting scales and the proportion of food sales income to total household income. | en | 6. Conclusions | other | study | 1.019531 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p71 | PMC11276206 | sec[5]/p[1] | The findings make a number of contributions. First, we break the shackles of price primacy assumptions and reveal the importance of the risk to farmers by utility comparison. The profit maximization theory is generalized while the farmers are more concerned with risk, even though they do not pay much attention to price due to the constraints of risk factors. Risk perception has also led to passive grain sales by peasant households. The dimensions of risk perception are both constraints and pain points in reality, which provide some insights for improving farmers’ situations. | en | 6. Conclusions | other | other | 1.040039 | [
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"Tan Tian",
"Xia Zhao"
] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p72 | PMC11276206 | sec[5]/p[2] | Second, previous studies, including prospect theory, discussed risk perception and risk preference together without directly examining the causal mechanisms of behavioral effects, and our findings reveal the relationship between them in food sales. In terms of farmers’ food sales psychology, unlike the existing research that mainly explains farmers’ economic behavior from the perspective of risk preference, our results validate the point in the debate that risk perception plays a more important role in risk-averse individual decisions, which helps to enrich the literature. | en | 6. Conclusions | other | study | 1.580078 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p73 | PMC11276206 | sec[5]/p[3] | Third, due to risk perception being difficult to measure and data being not trustworthy , more studies in recent years involving farmers’ food sale decisions are mainly based on risk preference. While there is some explanatory power, it clearly ignores the important relationship between farmers’ food sales and risk perception. We quantify the risk perception for farmers’ food sales and examine the effects, providing a reference for farmers’ risk research. | en | 6. Conclusions | other | study | 1.171875 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p74 | PMC11276206 | sec[5]/p[4] | Finally, this paper finds that there is a substitution relationship between risk preference and risk perception in the context of farmers’ intertemporal food sales. This finding has important policy implications. While designing policies to support agriculture to reduce farmers’ risk perception, it should also be noted that risk aversion may be increased, resulting in policy failure. Moreover, the substitution effect between risk preference and risk perception is common in other agricultural activities and may raise similar issues. | en | 6. Conclusions | other | study | 1.047852 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p75 | PMC11276206 | sec[5]/p[5] | Our results also suggest the potential importance of experimenting and evaluating policies that focus on farmers’ risk in food sales. Food sales have the most direct and important impact on the ability of farmers to realize the revenue of food production, and most farmers are risk averse towards uncertain returns. While reducing the risk of farmers’ sales, improving the ability to manage risk for farmers is needed. Only by approaching risk and managing it can farmers seize the opportunity to make higher profits and thus break out of the circle of the poor being always poor. In the context of substitution effects, the design of policies to support agriculture to reduce farmers’ risk perceptions should also be cognizant of the potential for elevated risk aversion to cause policy failure. | en | 6. Conclusions | other | other | 1.199219 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p76 | PMC11276206 | sec[5]/p[6] | The study of related issues is of great significance for guaranteeing food supply and promoting common prosperity in the context of rural revitalization. Farmers’ food sale is the first step for food to realize the circulation of the commodity market and increase the income of food farmers. It is also the basis for building a modern food circulation industry. With urbanization, rural aging, hollowing, and the socialization of farm machinery, the burden of ensuring food security will fall on large-scale growers in the near future. Only when the risks are low and the benefits are sufficient will they be willing to grow food well and take responsibility for ensuring food security. If sufficient attention is not paid to the sale of food by farmers, farmers will feel that the food business is risky and low profit, and the incentives for food operation will inevitably suffer. | en | 6. Conclusions | other | other | 1.130859 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276206_p77 | PMC11276206 | sec[5]/p[7] | Since the sample farmers basically have no credit behavior and the storage conditions are almost the same, this paper assumes that the credit and storage conditions of farmers are the same and costless. In the future, we can further consider the storage conditions and the impact of storage losses on food sale behavior. In addition, the interaction mechanism between borrowing costs and farmers’ returns can be examined by the model in this paper. The management of risks to farmers could be replicated in other areas of agriculture. | en | 6. Conclusions | other | other | 1.035156 | [
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"Tan Tian",
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] | https://doi.org/10.3390/foods13142243 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p0 | PMC11276218 | sec[0]/p[0] | Nelumbo nucifera , a perennial aquatic, rhizomatous herbaceous plant, also known as lotus or “He” or “Fuqu” in Chinese, possesses an underground stem known as a “lotus root”. This ancient angiosperm originated in China and India and is a prominent aquatic vegetable in Chinese cuisine . In China, the lotus is categorized into three main groups: rhizome lotus, seed lotus, and flower lotus, each with distinct characteristics. The rhizome lotus is distinguished by its tall stature, infrequent blooms, and tubers suitable for consumption. Hubei Province is a critical center for lotus cultivation, boasting a diverse collection of nearly 200 lotus root varieties, solidifying its position as a crucial region within the industry . | en | 1. Introduction | other | other | 1.841797 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p1 | PMC11276218 | sec[0]/p[1] | The lotus root, a significant export vegetable for China, boasts a rich composition of dietary fiber, starch, sugars, proteins, amino acids, minerals, vitamins, and phenolic compounds. It also holds both ornamental value and the unique characteristic of being both medicinal and edible . Phenolic compounds and secondary plant metabolites offer benefits not only to the plant itself but also to human health. These compounds can contribute to the alleviation of skin disease symptoms and hinder their progression. Furthermore, consuming fruits and vegetables rich in phenolics can potentially reduce the risk of diseases mediated by oxidative stress, such as cardiovascular disease and cancer . While current research on the lotus primarily focuses on the qualitative and quantitative analysis of phenolic compounds in lotus flowers , it should be acknowledged that lotus roots are also rich in phenolics and other functionally active substances . Structurally characterized phenolic compounds identified in the lotus root include catechol, gallic acid, (+)-catechin, (−)-epicatechin, (+/−)-gallocatechin, chlorogenic acid, rutin, and more . | en | 1. Introduction | biomedical | review | 4.03125 | [
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"Wanyu Dong",
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"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p2 | PMC11276218 | sec[0]/p[2] | The quality of fruits and vegetables can vary significantly due to factors such as variety, growing period, and harvest time. These factors primarily influence the nutritional composition and functional bioactive substance content of the produce . For example, a recent study compared two lotus root varieties, “Elian No. 5” and “Elian No. 6”. Their evaluation of pasting and textural properties revealed that “Elian No. 5” exhibited a reduced firmness and a lower capacity to resist shear forces and thermal stress during cooking than “Elian No. 6”. Similarly, another study investigated the accumulation of phenolic compounds in different parts of two pomelo varieties during their development. They found that the total phenolic content changed with the degree of maturation, initially decreasing and then increasing, reaching a peak on the 60th day. Additionally, significant differences in total phenolics were observed between the two pomelo varieties. Several studies have shown that the growing period and harvesting period also affect the flavor, processing performance, functional bioactive substance content, and antioxidant and anti-inflammatory properties of fruits and vegetables . Recent research published by Wu et al. analyzed the nutrient content and changes in lotus roots at different harvesting periods. They determined the optimal harvesting times as follows: September for “August-farinose”, October for “Elian No. 6”, and February for both “Elian No. 10” and “Elian No. 11”. Notably, the comprehensive nutritional quality of lotus roots harvested in March across all four varieties was found to be relatively lower. Similarly, other studies evaluated the optimal harvesting period of Dendrobium officinale , finding that its dry matter and chemical composition differed at different harvest times, with December being the optimal harvest period. As a fruit and vegetable with significant nutritional value in China, the lotus root has been extensively studied for its nutritional components across various varieties and harvest times . However, research on the phenolic composition and content at different varieties and harvest periods remains limited. Therefore, this study aims to conduct a systematic assessment of the nutritional components, phenolic profiles, and contents of the predominant lotus root varieties in Hubei Province during distinct harvest periods. | en | 1. Introduction | biomedical | study | 4.191406 | [
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"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p3 | PMC11276218 | sec[0]/p[3] | This study investigated six lotus root varieties: “Xinanwu”, “Wuzhi No. 2”, “Baiyuzhan”, “Huaqilian”, “Elian No. 6”, and “Elian No. 5”. The research aimed to assess the appearance (color difference, browning degree), texture, nutrient profile (total soluble solids, water content, soluble protein, soluble sugar, starch, and vitamin C), phenolic composition (total phenols, total flavonoids, and specific phenolic compounds), and antioxidant capacity (DPPH radical scavenging rate and ABTS free radical scavenging activity) across different varieties and harvest periods. This evaluation will contribute to a comprehensive scientific understanding of lotus root quality and inform strategies for optimizing both cultivation and post-harvest handling practices. | en | 1. Introduction | biomedical | study | 4.09375 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p4 | PMC11276218 | sec[1]/sec[0]/p[0] | Six lotus root varieties, “Xinsanwu”, “Wuzhi No. 2”, “Baiyuzhan”, “Huaqilian”, “Elian No. 6”, and “Elian No. 5”, were harvested at different harvest periods (H1–H5) from 16 December 2023, to 16 April 2024, in Hankou North. Samples were collected monthly, with one sample per period (H1, H2, H3, H4, H5). The corresponding temperature ranges for each harvest period were as follows: H1 (1–11 °C), H2 (3–5 °C), H3 (2–12 °C), H4 (8–15 °C), and H5 (12–20 °C). Following harvest, the lotus roots were promptly transported to the laboratory for a 24 h pre-cooling period at 4 °C. | en | 2.1. Materials and Instruments | biomedical | study | 2.90625 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p5 | PMC11276218 | sec[1]/sec[0]/p[1] | Analytical grade chemicals, including anhydrous ethanol, sodium nitrate, sodium hydroxide, aluminum nitrate, anhydrous sodium carbonate, glucose, concentrated sulfuric acid, anhydrous disodium hydrogen phosphate, anhydrous disodium dihydrogen phosphate, glacial acetic acid, and 1,1-diphenyl-2-picrylhydrazyl, were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China) Folin–Ciocalteu’s phenol was obtained from Wuhan Feiyang Bio-tech Co., Ltd. (Shanghai, China) High purity methanol, acetonitrile, and glacial acetic acid (≥99.9%, chromatographic grade) were purchased from Sigma-Aldrich. Standards (≥98%) including coumarin, pyrogallol, (+)-catechin, gastrodin, hyperin, rutin, quercetin, and hydroquinone were obtained from Aladdin Reagent (Shanghai) Co., Ltd. (Shanghai, China) Additional standards (≥98%) including quercetin, (−)-epicatechin, chlorogenic acid, and (+/−)-gallocatechin were purchased from Shanghai Yuanye Bio-technology Co., Ltd. (Shanghai, China) Assay kits for total starch, vitamin C, soluble protein, and total antioxidant capacity (ABTS method) were obtained from Beijing Solarbio Science & Technology Co., Ltd. (Beijing, China) Nanjing Jiancheng Bioengineering Institute, and Beyotime Biotechnology (Shanghai, China), respectively. All unspecified reagents were of analytical grade. | en | 2.1. Materials and Instruments | biomedical | other | 1.469727 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p6 | PMC11276218 | sec[1]/sec[0]/p[2] | The experimental setup utilized various instruments: a EOS 550D digital camera for image capture (Canon Co., Ltd., Beijing, China); a low-temperature refrigerator (Sanyo, Japan); a JZ-500 general colorimeter manufactured (Shenzhen Jinhuai Instrument Equipment Co., Ltd., Shenzhen, China); an IMS-20 ice-making machine (Changshu Xueke Electric Appliance Co., Ltd., Changshu, China); an XHF-D high-speed disperser and an ultrasonic cleaner(Ningbo Xinzhi Biotechnology Co., Ltd., Ningbo, China); an A360 UV-Vis spectrophotometer (Aoyi Instruments Co., Ltd., Shanghai, China); a high-speed refrigerated centrifuge (Shanghai Anting Scientific Instrument Factory, Shanghai, China); a multifunctional enzyme-linked immunosorbent assay (ELISA) reader (PerkinElmer, Waltham, MA, USA); an HPLC 1269 II system(Agilent Technologies Co., Ltd., Beijing, China); a texture analyzer (Shanghai Bao Sheng Industrial Development Co., Ltd., Shanghai, China); and an HH-8 digital display constant temperature water bath (Jintan District Bai Ta Xinbao, Jintan, China). | en | 2.1. Materials and Instruments | biomedical | other | 1.519531 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p7 | PMC11276218 | sec[1]/sec[1]/sec[0]/p[0] | To prepare the lotus root samples, surface mud was removed with tap water. The roots were then sectioned using sterilized knives, ensuring complete cuts at the nodes to minimize air exposure to the internal flesh. | en | 2.2.1. Sample Pretreatment | biomedical | other | 2.84375 | [
0.98583984375,
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p8 | PMC11276218 | sec[1]/sec[1]/sec[0]/p[1] | Samples were selected from lotus roots harvested at different periods (H1, H2, H3, H4, H5). To account for potential variations in material condition across harvesting times, a preliminary treatment process was applied. This involved peeling and dicing the samples, followed by rapid freezing using liquid nitrogen. The frozen samples were then stored at −80 °C in an ultra-low-temperature freezer for further analysis. | en | 2.2.1. Sample Pretreatment | biomedical | study | 3.263672 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p9 | PMC11276218 | sec[1]/sec[1]/sec[1]/p[0] | The assessment of lotus root appearance quality was conducted using a digital camera (Canon EOS 550D) to capture standardized photographs of the whole lotus root. | en | 2.2.2. Appearance | biomedical | study | 2.232422 | [
0.9580078125,
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"Wanyu Dong",
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"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p10 | PMC11276218 | sec[1]/sec[1]/sec[2]/p[0] | In accordance with the methodology described in the literature , a representative area of the lotus root skin was randomly chosen, and its edible segment’s surface color parameters (L value, a value, and b value) were measured at five different points using a JZ-500 general colorimeter. The color difference (ΔE) was calculated using the following equation: (1) Δ E = ( L * − L 0 * ) 2 + ( a * − a 0 * ) 2 + ( b * − b 0 * ) 2 where L 0 , a 0 , and b 0 were all values on the H1 period, and L*, a*, and b* were readings at each sampling point during the harvest period. | en | 2.2.3. Color Difference | biomedical | study | 4.042969 | [
0.97998046875,
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] | 0.999998 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p11 | PMC11276218 | sec[1]/sec[1]/sec[3]/p[0] | The water content was determined following the methodology outlined in the literature , with some modifications: A tray was placed in a 105 °C drying box and weighed to a constant weight (±2 mg). Then, two slices of lotus root were weighed and placed into the tray and dried in a 105 °C drying box to a constant weight (±2 mg). The water content was expressed in % after three repetitions for each sample. | en | 2.2.4. Water Content | biomedical | study | 4.015625 | [
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0.9990234375,
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p12 | PMC11276218 | sec[1]/sec[1]/sec[4]/p[0] | The browning degree was evaluated following the procedure described in the literature . Briefly, at 4 °C, 30 mL distilled water was mix with a 3.0 g sample, homogenized, and then centrifuged for 5 min at 10,000× g . The supernatant in the centrifuge tube was collected. The absorbance is measured at 410 nm after zeroing the spectrophotometer with distilled water; this measurement was repeated three times. The results are then expressed as A410 × 10. | en | 2.2.5. Browning Degree | biomedical | study | 4.0625 | [
0.99951171875,
0.000164031982421875,
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] | [
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0.0011386871337890625,
0.00033164024353027344,
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p13 | PMC11276218 | sec[1]/sec[1]/sec[5]/p[0] | The total soluble solids content was determined using a portable refractometer . To prepare the sample, 10 g of tissue was ground in a mortar with an ice bath. The homogenate was then transferred to a centrifuge tube and centrifuged at 10,000 rpm for 5 min. The supernatant was measured thrice on the prism of the refractometer to obtain the reading. | en | 2.2.6. Total Soluble Solids | biomedical | study | 4.011719 | [
0.99951171875,
0.00015497207641601562,
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] | [
0.9951171875,
0.00455474853515625,
0.00037670135498046875,
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] | 0.999996 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p14 | PMC11276218 | sec[1]/sec[1]/sec[6]/p[0] | The texture analysis followed the method reported in the literature . Lotus root flesh was cut into cubes measuring approximately 1 cm on each side (1 cm × 1 cm × 1 cm). A texture analyzer was set to TPA mode with a P/45 probe, a 100 g trigger force, and the following speeds: 10.0 mm/s for initial speed, 0.5 mm/s for compression speed, and 10.0 mm/s for end ascending speed. A 5 s dwell time was set between two compressions, and the maximum deformation was set to 35%. The experiment was conducted with 10 biological replicates. | en | 2.2.7. Texture | biomedical | study | 4.066406 | [
0.9990234375,
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] | [
0.99951171875,
0.0004622936248779297,
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] | 0.999996 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p15 | PMC11276218 | sec[1]/sec[1]/sec[7]/p[0] | The total phenolic content of the fresh-cut lotus root was determined using the Folin–Ciocalteu method . Briefly, a 3.0 g sample of flesh tissue was homogenized with 30 mL 60% ethanol and centrifuged, and the absorbance was triply measured at 760 nm after the reaction with the Folin–Ciocalteu reagent. A standard curve prepared with gallic acid was used to calibrate the results, expressed as milligrams of gallic acid equivalents (GAE) per 100 g. | en | 2.2.8. Total Phenolic Content | biomedical | study | 4.101563 | [
0.99951171875,
0.0001780986785888672,
0.00039577484130859375
] | [
0.9990234375,
0.0005068778991699219,
0.0003135204315185547,
0.00004661083221435547
] | 0.999998 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p16 | PMC11276218 | sec[1]/sec[1]/sec[8]/p[0] | The total flavonoid content was determined using the method described in the literature . Five grams of flesh tissue and 50 mL of 60% ethanol were used to prepare the extraction mixture, which was centrifuged for 10 min (4 °C, 10,000× g ). Following sample preparation, the absorbance was triply measured at a wavelength of 510 nm. Rutin was employed as a standard for quantification, and the total flavonoid content was expressed as milligrams of rutin per 100 g (mg rutin/100 g). | en | 2.2.9. Total Flavonoid Content | biomedical | study | 4.089844 | [
0.99951171875,
0.0001971721649169922,
0.00022733211517333984
] | [
0.9990234375,
0.00044155120849609375,
0.00034308433532714844,
0.000056684017181396484
] | 0.999997 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p17 | PMC11276218 | sec[1]/sec[1]/sec[9]/p[0] | The soluble sugar content was determined as described in the literature with few modifications. Absorbance was measured at 485 nm after three repetitions for each sample. A standard curve was prepared using an anhydrous glucose solution, and the mass fraction of soluble sugar was then calculated using the following formula: (2) Soluble sugar mass fraction ( % ) = m ′ × V × N V S × m × 10 6 × 100 where m′ represents the mass of the soluble sugar determined from the standard curve, μg; V denotes the complete volume of the extracted sample, mL; N signifies the dilution ratio of the extracted sample; V s is the volume of the sample extract used for the measurement, mL; and m is the weight of the sample, g. | en | 2.2.10. Soluble Sugar | biomedical | study | 4.125 | [
0.99951171875,
0.00016307830810546875,
0.00028014183044433594
] | [
0.9990234375,
0.0004131793975830078,
0.00035119056701660156,
0.000046253204345703125
] | 0.999998 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p18 | PMC11276218 | sec[1]/sec[1]/sec[10]/p[0] | The starch content was determined following the manufacturer’s instructions for the total starch assay kit obtained from Beijing Solarbio Science & Technology Co., Ltd. A total of 3 biological replicates were established. | en | 2.2.11. Starch Content | biomedical | study | 2.988281 | [
0.99755859375,
0.00031685829162597656,
0.0021419525146484375
] | [
0.986328125,
0.01259613037109375,
0.000621795654296875,
0.00022792816162109375
] | 0.999999 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p19 | PMC11276218 | sec[1]/sec[1]/sec[11]/p[0] | The soluble protein content was determined following the manufacturer’s instructions for the commercially available soluble protein assay kit obtained from Nanjing Jiancheng Bioengineering Institute. A total of 4 g of flesh tissue and 36 mL of phosphate buffered solution (0.1 mol/L, pH = 7.0) were used to prepare the extraction mixture, which was centrifuged for 10 min (4 °C, 10,000× g ); then, the supernatant was saved for subsequent measurement. Absorbance was measured three times at 595 nm, and the results were expressed in mg/g. | en | 2.2.12. Soluble Protein | biomedical | study | 4.089844 | [
0.99951171875,
0.0001933574676513672,
0.00023043155670166016
] | [
0.9990234375,
0.0005583763122558594,
0.000255584716796875,
0.000059604644775390625
] | 0.999997 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p20 | PMC11276218 | sec[1]/sec[1]/sec[12]/p[0] | The vitamin C content was determined according to the instructions of the kit (Nanjing Jiancheng Bioengineering Institute). The sample was homogenized with phosphate buffered solution (0.1 mol/L, pH = 7.0) at a ratio of 1:9 under ice bath conditions. After centrifugation at 4 °C at 10,000× g for 10 min, the supernatant was saved for later use. Absorbance was measured three times for each sample at 536 nm, and the results were expressed in μg/g. | en | 2.2.13. Vitamin C Content | biomedical | study | 4.066406 | [
0.99951171875,
0.00017392635345458984,
0.00023412704467773438
] | [
0.9990234375,
0.0006542205810546875,
0.00026297569274902344,
0.00006151199340820312
] | 0.999996 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p21 | PMC11276218 | sec[1]/sec[1]/sec[13]/p[0] | The DPPH radical scavenging rate was determined according to the method described in the literature . A total of 2 g of the sample was homogenized in 25 mL ethanol and sonicated (50 °C) for 30 min. After centrifuging for 10 min (4 °C, 10,000× g ), the supernatant was diluted ten times. Absorbance was triply measured for each sample at 517 nm, with anhydrous ethanol used to blank the instrument. The results were expressed as a percentage (%). | en | 2.2.14. DPPH Radical Scavenging Rate | biomedical | study | 4.078125 | [
0.99951171875,
0.00019252300262451172,
0.00023317337036132812
] | [
0.9990234375,
0.0005650520324707031,
0.0003123283386230469,
0.00006008148193359375
] | 0.999997 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p22 | PMC11276218 | sec[1]/sec[1]/sec[14]/p[0] | The ABTS radical scavenging ability was measured following the manufacturer’s instructions for the total antioxidant capacity assay kit. A total of 5 g of the sample was weighted, homogenized in 25 mL phosphate buffered solution (0.1 mol/L, pH = 7.0) in ice mortar, and centrifuged for 10 min (4 °C, 10,000× g ). The absorbance of each sample was measured at 734 nm with three biological replicates and the results were expressed as Trolox equivalent antioxidant capacity (mM Trolox/g). | en | 2.2.15. ABTS Radical Scavenging Ability | biomedical | study | 4.101563 | [
0.99951171875,
0.0002111196517944336,
0.00021028518676757812
] | [
0.9990234375,
0.0005164146423339844,
0.00029754638671875,
0.00006586313247680664
] | 0.999996 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p23 | PMC11276218 | sec[1]/sec[1]/sec[15]/p[0] | The lotus root phenolic extraction method was conducted as described in the literature , with few modifications. Briefly, 32 g of the ground lotus root sample was homogenized with 160 mL of the pre-cooled 40% ethanol solution adjusted to pH 3.0. The mixture was then ultrasonicated for 72 min followed by centrifugation at 4500 rpm for 10 min. The supernatant was collected by filtration, and the residue was re-extracted with 200 mL of pre-cooled 40% ethanol (pH 3.0) using ultrasonication for 10 min and centrifugation. The combined supernatants were concentrated using vacuum rotary evaporation and re-dissolved in methanol to a final volume of 10 mL. A high-performance liquid chromatography (HPLC) analysis was performed using two separate programs for the optimal detection of different phenolic compounds. The first program, designed for pyrogallol, gastrodin, coumarin, (+/−)-gallocatechin, hydroquinone, (+)-catechin, chlorogenic acid, (−)-epicatechin, and quercetin, utilized a mobile phase consisting of solvent A (methanol) and solvent B (0.4% glacial acetic acid) at a flow rate of 1.0 mL/min. The column temperature was maintained at 30 °C with UV detection at 280 nm. An injection volume of 20 μL was used with a linear elution program: 0–40 min (5–25% A), 40–50 min (25–50% A), 50–65 min (50–70% A), 65–66 min (70–100% A), 66–72 min (100% A), 72–73 min (100–5% A), and 73–80 min (5% A). The second program, designed for quercetin, rutin, and hyperin, employed a mobile phase composed of solvent A (acetonitrile) and solvent B (0.4% glacial acetic acid) at a flow rate of 1.0 mL/min with a column temperature of 30 °C and UV detection at 280 nm. The injection volume remained at 20 μL, and the linear elution program was as follows: 0–10 min (5–25% A), 10–20 min (25–35% A), 20–21 min (35–100% A), 21–25 min (100% A), 25–26 min (100–5% A), and 26–30 min (5% A). The sample was measured thrice. | en | 2.2.16. Monomeric Phenol Content | biomedical | study | 4.207031 | [
0.99951171875,
0.000274658203125,
0.0002930164337158203
] | [
0.9990234375,
0.0004220008850097656,
0.0002930164337158203,
0.00006371736526489258
] | 0.999999 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p24 | PMC11276218 | sec[1]/sec[2]/p[0] | Data were processed, analyzed, and visualized using Microsoft Excel and Origin software . Statistical significance testing, correlation analysis, and principal component analysis were performed using SPSS (version 20.0, IBM Corporation, Armonk, NY, USA). | en | 2.3. Statistical Analysis | biomedical | study | 1.838867 | [
0.99365234375,
0.0007996559143066406,
0.005443572998046875
] | [
0.58154296875,
0.414794921875,
0.002010345458984375,
0.00144195556640625
] | 0.999996 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p25 | PMC11276218 | sec[2]/sec[0]/sec[0]/p[0] | Table 1 illustrates the visual differences in lotus root appearance across varieties and harvest times. “Wuzhi No. 2” and “Baiyuzhan” exhibited a slender oval shape, while the remaining four varieties possessed larger and more rounded nodes . Table 1 presents the L* values, indicating lightness, of all varieties. All L* values were above 56 ( p < 0.05), but generally showed a downward trend throughout the harvest period. Notably, “Baiyuzhan” and “Wuzhi No. 2” maintained a higher and less pronounced decrease in L* values compared to “Elian No. 5” and “No. 6”, whose L* values experienced a significant decline. The a* and b* values, representing redness–greenness and yellowness–blueness, respectively, generally exhibited an upward trend across all varieties during the harvest period. “Wuzhi No. 2” and “Baiyuzhan” possessed higher a* values, suggesting a redder skin color compared to other varieties. Conversely, “Elian No. 5” and “No. 6” exhibited significantly higher b* values, indicating a tendency towards a more yellowish skin color. The ΔE values of lotus roots across all varieties exhibited a general upward trend with the extension of the harvest period, and distinctions could be discerned among different harvest periods ( p < 0.05). This trend was corroborated by the visual appearance of the intact lotus roots as depicted in photographs taken throughout the various harvest periods. | en | 3.1.1. Appearance | biomedical | study | 4.097656 | [
0.95263671875,
0.0007190704345703125,
0.046539306640625
] | [
0.99951171875,
0.0002999305725097656,
0.0002340078353881836,
0.000036716461181640625
] | 0.999995 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p26 | PMC11276218 | sec[2]/sec[0]/sec[1]/p[0] | Table 2 reveals a consistent upward trend in hardness, chewiness, and cohesiveness for all lotus root varieties as the harvest period progresses. Significant differences ( p < 0.05) were observed in these textural properties within the same variety at different harvest times. However, the cohesiveness between different varieties at the same harvest time did not exhibit significant differences ( p > 0.05). Previous research suggests that rising temperatures lead to the expansion of water volume within fruits and vegetables. This expansion, in turn, promotes increased cell strength, ultimately contributing to greater hardness at later harvest stages. Similarly, a recent study demonstrated that a decrease in inherent water content within the lotus root leads to increased cohesiveness between tissues, resulting in enhanced hardness. These findings align with the observations of this study. Hardness is critical in determining the commercial quality, storability, and shelf life of fruits and vegetables. Many studies have established a positive correlation between hardness and storage performance. Therefore, the observed increase in hardness with extended harvest periods suggests potentially improved storability for lotus roots. Cohesiveness refers to the degree of tight binding between internal components, while chewiness represents the energy required to chew food to a swallowable state . As the harvest period progresses, the textural data suggest a strengthening of the internal bonds within all lotus root varieties, consequently increasing the energy needed for mastication. | en | 3.1.2. Texture | biomedical | study | 4.15625 | [
0.9951171875,
0.0004553794860839844,
0.00433349609375
] | [
0.99951171875,
0.0001316070556640625,
0.0004200935363769531,
0.00003629922866821289
] | 0.999998 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p27 | PMC11276218 | sec[2]/sec[0]/sec[2]/p[0] | The degree of browning in freshly-cut produce significantly influences consumer purchasing decisions, making it a crucial quality metric . As shown in Figure 1 a, browning increases with delayed harvest. By harvest period H5 (April), the browning degree at the center of the lotus root exhibited a significant rise, particularly in varieties Elian No. 5 and No. 6, reaching 75.52% and 66.82%, respectively. Soluble solids, essential nutrients in fruits and vegetables, serve as a key indicator of lotus root quality . Figure 1 b reveals that “Wuzhi No. 2” and “Baiyuzhan” consistently possessed lower soluble solids than other varieties throughout the harvest period. However, all varieties exhibited the highest soluble solids content by harvest period H5 (April). This finding aligns with recent research , which reported that crop cycle, harvest month, and variety could affect the soluble solids content in cucumbers. Similarly, another research study reported increasing soluble solids content with ripening in “Orin” apples, which is consistent with our observations. As shown in Figure 1 c, the water content of all varieties tended to decrease with delayed harvest, with the highest water content observed during harvest period H1 (December). “Wuzhi No. 2” and “Baiyuzhan” showed a significantly lower water content than other varieties throughout the harvest period. In daily practice, water content is an experience-based indicator for measuring lotus root crispness; a higher water content suggests crispiness, while a lower water content indicates a powdery texture . Therefore, “Wuzhi No. 2” and “Baiyuzhan” can be considered more powdery than other varieties. The data also suggest that water content changes can influence the texture within the same lotus root variety as the harvest period extends. | en | 3.1.3. Browning Degree, Total Soluble Solids, Water Content | biomedical | study | 4.046875 | [
0.88134765625,
0.0007929801940917969,
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] | [
0.9990234375,
0.0005183219909667969,
0.00028443336486816406,
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p28 | PMC11276218 | sec[2]/sec[0]/sec[3]/p[0] | Figure 2 a illustrates the soluble sugar content across various lotus root varieties throughout the harvest period. A consistent pattern emerged, with all varieties exhibiting the lowest content initially. The content then increased, peaked in the middle to late stages, and subsequently decreased. This peak ranged from 1.29% to 2.50%. The greatest difference in soluble sugar content between varieties occurred during harvest period H1. “Baiyuzhan” boasted a content 1.53 times higher than “Elian No. 5.” However, “Elian No. 5” demonstrated the most significant change in soluble sugar content as the harvest progressed. Its content reached a high of 2.40% in period H3 (February), representing a 1.85-fold increase compared to H1. Seasonal temperature fluctuations likely contributed to this observed pattern. Previous studies have shown that decreasing temperatures can lead to the accumulation of soluble sugars , accounting for the peak in soluble sugar content observed during the middle of the harvest period. | en | 3.1.4. Soluble Sugar, Starch, Soluble Protein, and Vc Content | biomedical | study | 4.128906 | [
0.994140625,
0.0003838539123535156,
0.00531005859375
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0.99951171875,
0.00019979476928710938,
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0.000031828880310058594
] | 0.999999 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p29 | PMC11276218 | sec[2]/sec[0]/sec[3]/p[1] | Figure 2 b reveals significant differences ( p < 0.05) in starch content across lotus root varieties at various harvest times. The data demonstrated a general trend of initial increase followed by a subsequent decrease, with the starch content ranging from 20.18 to 119.43 mg/g. Notably, “Wuzhi No. 2”, “Xinsanwu”, and “Baiyuzhan” consistently exhibited a higher starch content compared to other varieties. These findings were consistent with recent research , which reported a range of a 1.3% to 13.7% starch content across 10 different lotus root varieties from various growing regions. Previous studies suggest seasonal variations in the carbohydrate composition of plant tissues, with the peak starch content occurring in autumn and the lowest levels observed in winter . During this period, starch undergoes a conversion into soluble sugars. Conversely, early spring witnesses the reconversion of these soluble sugars back into starch. However, multiple factors influence this starch conversion process, including fruit weight, soluble solids content, flesh firmness, and variations in absorbance . | en | 3.1.4. Soluble Sugar, Starch, Soluble Protein, and Vc Content | biomedical | study | 4.144531 | [
0.99853515625,
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0.001220703125
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0.99951171875,
0.0001264810562133789,
0.00041937828063964844,
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] | 0.999997 | [
"Wanyu Dong",
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"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p30 | PMC11276218 | sec[2]/sec[0]/sec[3]/p[2] | Figure 2 c illustrates the range of soluble protein content in lotus root varieties, varying from 0.39 to 2.58 mg/g. A recent study investigated the yield and quality of seven lotus root varieties, reporting a soluble protein content range of 0.7 to 3.3 mg/g. This observed difference might be attributed to variations in both the specific varieties studied and the sampling period employed. The data for all varieties, except for “Baiyuzhan”, revealed a recurring pattern of a low point in the soluble protein content during harvest periods H3 and H4, followed by an increase, then a decrease, and finally another increase. In contrast, “Baiyuzhan” maintained a consistently higher level of soluble protein content throughout the harvest period. | en | 3.1.4. Soluble Sugar, Starch, Soluble Protein, and Vc Content | biomedical | study | 4.082031 | [
0.9970703125,
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0.99951171875,
0.00018084049224853516,
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0.000031054019927978516
] | 0.999998 | [
"Wanyu Dong",
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"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p31 | PMC11276218 | sec[2]/sec[0]/sec[3]/p[3] | As shown in Figure 2 d, the Vc content in lotus root varieties ranged from 231.28 to 741.96 μg/g. Interestingly, the Vc content patterns differed between varieties. While “Baiyuzhan” and “Elian No. 6” demonstrated an initial rise followed by a decline, others exhibited a decrease at the beginning of the harvest period. Notably, “Xinsanwu” and “Elian No. 5” maintained a relatively stable and high Vc content throughout. Similar trends in vitamin C content variation have been observed in other studies. Li et al. reported that the vitamin C content in edible peonies peaked at stage S2, exhibiting a rise-and-fall pattern. This aligned to some extent with the observed trend in “Baiyuzhan” and “Elian No. 6.” However, the influence of various factors on vitamin C content should be borne in mind, including plant maturity, variety, temperature, and environment . These factors contribute to the observed variations in Vc content change across the six lotus root varieties throughout the harvest period. | en | 3.1.4. Soluble Sugar, Starch, Soluble Protein, and Vc Content | biomedical | study | 4.132813 | [
0.99853515625,
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0.99951171875,
0.00013315677642822266,
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0.0000324249267578125
] | 0.999997 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p32 | PMC11276218 | sec[2]/sec[0]/sec[4]/p[0] | Figure 3 a presents the total phenolic content (TPC) of lotus root varieties, ranging from 10.65 to 19.39 mg GAE/100 g. Across all six varieties, the TPC exhibited a general trend of increasing followed by a decrease, and then another increase as the harvest period progresses. This pattern aligned broadly with the findings of Mijin et al. regarding TPC variations in jackfruit at different harvest times. Notably, “Xinsanwu”, “Baiyuzhan”, “Huaqilian”, and “Elian No. 6” reached their peak TPC during period H2 (January). Additionally, “Wuzhi No. 2” and “Baiyuzhan” consistently exhibited a significantly higher TPC compared to other varieties ( p < 0.05). Previous research has investigated the influence of temperature and light on phenolic compound production in cranberry fruits and leaves. These studies suggest that cooler temperatures and light exposure promote the biosynthesis of these compounds . This finding may explain the observed peak in TPC for lotus root during period H2 in this study, which likely coincides with cooler temperatures and potentially greater light exposure. | en | 3.1.5. Phenolic Compounds and Antioxidant Ability | biomedical | study | 4.144531 | [
0.99853515625,
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0.99951171875,
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] | 0.999997 | [
"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p33 | PMC11276218 | sec[2]/sec[0]/sec[4]/p[1] | Figure 3 b depicts the variations in total flavonoid content (TFC) across lotus root varieties throughout the harvest period. The data revealed distinct patterns between varieties. “Baiyuzhan”, “Huaqilian”, and “Elian No. 6” exhibited an initial increase in TFC followed by a subsequent decrease. Conversely, “Xinsanwu”, “Elian No. 5”, and “Wuzhi No. 2” demonstrated an initial decline in TFC, followed by an increase, and finally another decrease. The total flavonoid content of lotus root ranged from 25.33 to 58.26 mg rutin/100 g. Notably, “Wuzhi No. 2” maintained a consistently high level of total flavonoids throughout the harvest period, with the exception of period H2. A recent study investigated changes in flavonoid compounds within dried tangerine peel at different harvest times. Their findings revealed an increasing and then decreasing trend in total flavonoid content as the harvest period progressed, which aligned generally with the observations in this experiment for some varieties. | en | 3.1.5. Phenolic Compounds and Antioxidant Ability | biomedical | study | 4.125 | [
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] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p34 | PMC11276218 | sec[2]/sec[0]/sec[4]/p[2] | Lotus root is rich in phenolic compounds, known for their potent antioxidant properties and efficient free radical scavenging abilities . Prior research has established a significant positive correlation between the antioxidant capacity of plant materials and their phenolic content. The DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging rate and ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) radical scavenging activity serve as valuable indicators for evaluating the antioxidant capacity of lotus root, reflecting the anti-free radical activity of phenolic compounds . Figure 3 c illustrates the DPPH free radical scavenging rates of the six lotus root varieties, exhibiting a general trend of decrease followed by an increase and, finally, another decrease. The scavenging rates ranged from 15.48% to 38.84%. Notably, “Wuzhi No. 2” and “Baiyuzhan” consistently maintained a higher level of DPPH activity throughout the harvest period. Additionally, the DPPH scavenging rates for most varieties reached their lowest point during period H5. This observed trend aligned broadly with the pattern of total phenolic content depicted in Figure 3 a. Figure 3 d presents the ABTS free radical scavenging activity of all lotus root varieties, demonstrating a generally increasing trend as the harvest period progressed. The activity ranged from 0.33 to 0.67 Mm Trolox/g. This pattern deviated slightly from the trends observed for both total phenolic content and DPPH scavenging rate. However, it aligns with a past study , which reported an increase in antioxidant capacity with increasing ripeness. Conversely, another study observed a decreasing trend in DPPH and ABTS scavenging rates for different pomelo varieties with extended harvest periods. Research on other produce, such as cashews, has shown an increase in antioxidant capacity with ripeness . | en | 3.1.5. Phenolic Compounds and Antioxidant Ability | biomedical | study | 4.226563 | [
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"Hongxun Wang",
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] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p35 | PMC11276218 | sec[2]/sec[0]/sec[5]/p[0] | Table 3 details the changes in individual phenolic compound content across the six lotus root varieties at various harvest times. Chlorogenic acid was the predominant phenolic acid identified in all varieties, followed by coumarin. The content of these phenolic acids exhibited a recurring pattern of decrease, increase, and then another decrease, mirroring the trend observed for total phenolic content . Notably, peak levels of phenolic acids were generally reached during the middle and late harvest periods. “Wuzhi No. 2” demonstrated a significantly higher content of chlorogenic acid compared to other varieties throughout the harvest period ( p < 0.05), reaching a maximum of 2.27 μg/g during period H5. The primary flavonoids detected in the lotus root were (+/−)-gallocatechin and (+)-catechin. “Elian No. 6” exhibited the highest content of (+/−)-gallocatechin at 55.44 μg/g during period H4, which was 2.4 times the content found in “Huaqilian” (23.07 μg/g) for the same period. (−)-Epicatechin, while exhibiting a high content, was only detected in some varieties during periods H3 and H4. The trend observed for flavonoid content change aligned generally with that of total flavonoids . Similarly, “Wuzhi No. 2” and “Baiyuzhan” maintained consistently high levels of flavonoids throughout the harvest period. Gastrodin, a non-flavonoid substance, was the most abundant phenolic compound detected across all varieties and at all harvest times. During period H5, “Huaqilian” exhibited the highest gastrodin content, reaching 191.31 μg/g, which was 2.29 times greater than the content observed in “Baiyuzhan” (83.70 μg/g) for the same period. Similar to other phenolic substances, the gastrodin content in all varieties peaked during the middle and late harvest periods. Hydroquinone was primarily detected during periods H3 to H5, exhibiting a relatively low content and also reaching its peak later in the harvest period. The statistical analysis revealed significant differences ( p < 0.05) in the content of phenolic substances in lotus roots at different harvest times. Similar observations have been reported for wheat, where variety and maturity stage significantly impact the phenolic content . Additionally, Fernandes et al. documented changes in the flavonoid content of calendula with an extended harvest period. Various environmental factors, including relative humidity, temperature, and light intensity, can influence the content of phenolic substances . The content of the main phenolic acids (e.g., chlorogenic acid), flavonoids ((+/−)-gallocatechin), and non-flavonoids (e.g., gastrodin) in the lotus root varied throughout the harvest period. Each substance generally peaked during the middle and late harvest periods. This trend might be attributable to lower temperatures, which are typically experienced during these later stages. | en | 3.1.6. Monomeric Phenolic | biomedical | study | 4.253906 | [
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"Wanyu Dong",
"Xueting Liu",
"Yang Yi",
"Limei Wang",
"Wenfu Hou",
"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p36 | PMC11276218 | sec[2]/sec[1]/p[0] | Principal component analysis (PCA) has emerged as a valuable tool for quality assessment in agricultural products, as evidenced by its application to winter jujubes and broccoli . PCA streamlines the assessment process by reducing the dimensionality of data and eliminating redundant information from multiple sources. This simplification leads to faster and more accurate evaluations than individual assessments of each quality metric. Furthermore, PCA effectively addresses potential biases arising from intercorrelations among traits, which could otherwise skew assessment outcomes . In this study, an integrative assessment model was constructed to quantitatively evaluate the quality attributes of lotus roots across various cultivars and harvest periods. By employing PCA, ten quality metrics—water content, total soluble solids, soluble sugars, starch, soluble protein, Vc, total phenolic content, total flavonoid content, DPPH radical scavenging capacity, and ABTS radical scavenging activity—were condensed into four principal components ( Table 4 ). This approach facilitated a more comprehensive evaluation of the lotus root’s overall quality profile. The eigenvalues of the first four principal components, all exceeding 1, were 3.269, 2.106, 1.440, and 1.174, respectively. These components collectively accounted for 79.89% of the total variance in the data. | en | 3.2. Principal Component Analysis | biomedical | study | 4.132813 | [
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"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p37 | PMC11276218 | sec[2]/sec[1]/p[1] | The first principal component (PC 1 ) primarily captured the variation associated with active components, such as total phenols, total flavonoids, and the DPPH free radical scavenging rate. This suggests a strong positive correlation between these factors. The second principal component (PC 2 ) was mainly associated with soluble solids and soluble sugars, indicating a potential link between these two variables. The third principal component (PC 3 ) focused on indicators related to the lotus root’s physiological state, such as water content, starch content, and soluble protein content. These factors likely covary and influence aspects of lotus root texture and composition. Finally, the fourth principal component (PC 4 ) primarily reflected the content of Vc. By utilizing the comprehensive factor scores obtained from the PCA, the following Formulas (3)–(7) were derived for further data processing ( Table 5 ): PC 1 = −0.354X 1 − 0.140X 2 − 0.105X 3 + 0.090X 4 − 0.242X 5 − 0.060X 6 + 0.497X 7 + 0.514X 8 + 0.444X 9 + 0.254X 10 (3) PC 2 = −0.004X 1 + 0.522X 2 + 0.519X 3 − 0.249X 4 + 0.123X 5 − 0.360X 6 + 0.175X 7 − 0.042X 8 − 0.061X 9 + 0.464X 10 (4) PC 3 = −0.561X 1 − 0.093X 2 − 0.032X 3 + 0.510X 4 + 0.491X 5 − 0.056X 6 − 0.143X 7 − 0.010X 8 − 0.298X 9 + 0.248X 10 (5) PC 4 = 0.025X 1 + 0.192X 2 + 0.389X 3 + 0.477X 4 − 0.449X 5 + 0.556X 6 − 0.132X 7 − 0.106X 8 + 0.061X 9 + 0.203X 10 (6) PC = 0.409PC 1 + 0.264PC 2 + 0.180PC 3 + 0.147PC 4 (7) | en | 3.2. Principal Component Analysis | biomedical | study | 4.214844 | [
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] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p38 | PMC11276218 | sec[2]/sec[1]/p[2] | The principal components likely represent the following: PC 1 , PC 2 , PC 3 , and PC 4 correspond to the first, second, third, and fourth principal components, respectively. There is no “composite principal component” denoted by “PC” in standard PCA analysis. Data for various nutritional and functional indicators of the lotus root (water content, total soluble solids, soluble sugars, starch, soluble protein, Vc, total phenols, total flavonoids, DPPH free radical scavenging rate, and ABTS radical scavenging activity) were standardized as X 1 through X 10 . | en | 3.2. Principal Component Analysis | biomedical | study | 3.939453 | [
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"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p39 | PMC11276218 | sec[2]/sec[1]/p[3] | Table 5 reveals that lotus roots from harvest period H4 generally scored higher in the first and third principal components compared to other months. This observation could be attributed to its higher content of both common functional active substances and starch. In the second principal component, the H5 lotus root exhibited the highest scores, indicating a higher carbohydrate content during this harvest period. The fourth principal component highlighted a higher Vc content in the H4 lotus root compared to other periods. The comprehensive scores identified “Wuzhi No. 2” harvested in H1 and H5, “Huaqilian” harvested in H2, and “Baiyuzhan” harvested in H3 and H4 as having superior nutritional value and a functional active substance content. Notably, the overall highest comprehensive score belonged to the H4 lotus root, suggesting a relatively higher concentration of both nutritional components and functional active substances during this harvest period. | en | 3.2. Principal Component Analysis | biomedical | study | 4.050781 | [
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"Youwei Ai",
"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
PMC11276218_p40 | PMC11276218 | sec[3]/p[0] | This study investigated the nutritional composition and functional active substance content of six lotus root varieties across different harvest periods. Significant variations were observed between varieties and harvest times. The L value and ΔE of lotus roots from different varieties generally showed an upward trend as the harvest period extended, while the trends of a and b values for different varieties did not align. Meanwhile, textural indicators exhibited an increasing trend with the extension of the harvest period. “Wuzhi No. 2” and “Baiyuzhan” exhibited a higher phenolic and starch content. Additionally, their lower water content translated into a more powdery and waxy texture, making them well-suited for soups and lotus root starch production. Conversely, the two “Elian” series varieties, with their higher water content, vitamin C content, and crisper texture, were found to be more suitable for stir-frying after purchase. The principal component analysis identified “Wuzhi No. 2” harvested in H1 and H5, “Huaqilian” harvested in H2, and “Baiyuzhan” harvested in H3 and H4 as having a superior nutritional and functional active substance content. Notably, the overall highest score belonged to the lotus root harvested during H4. This suggests a peak in the nutritional and functional active substance content during the H4 harvest period compared to others. However, it is important to acknowledge that H4 also coincided with peak hardness and browning in the lotus root, which might negatively impact consumer preferences despite the higher nutritional value. This study provides valuable insights into the development of advanced lotus root processing techniques and the differentiation of varieties for the marketplace. Consumers and the industry can leverage these findings to make informed choices regarding variety selection and optimal harvest times based on specific needs. Future research can explore targeted product development by strategically selecting varieties and harvest times to address diverse consumer demands and enrich the variety of lotus root products available in the market. | en | 4. Conclusions | biomedical | study | 4.199219 | [
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"Limei Wang",
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"Hongxun Wang",
"Ting Min"
] | https://doi.org/10.3390/foods13142297 | https://creativecommons.org/licenses/by/4.0/ |
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