Create README.md (#3)

- Create README.md (776a9fce07ce12f2cc2c0149f8706da9d1e60840)


Co-authored-by: Tomiris_R <[email protected]>

README.md

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+# Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam
+<img width="4271" height="2484" alt="Picture1" src="https://github.com/user-attachments/assets/524db8f0-99a2-414a-85c5-cc3b3d959f6a" />
+
+This repository accompanies our study on **non-destructive sugar content estimation** in apple jam using **VNIR hyperspectral imaging (HSI)** and machine learning. It includes a reproducible set of Jupyter notebooks covering preprocessing, dataset construction, and model training/evaluation with classical ML and deep learning.
+
+
+---
+
+## Dataset
+The Apples_HSI dataset is available on Hugging Face:
+[issai/Apples_HSI](https://huggingface.co/datasets/issai/Apples_HSI).
+
+### Dataset structure
+
+```text
+Apples_HSI/
+├── Catalogs/                                    # per-cultivar & sugar-ratio sessions
+│   ├── apple_jam_{cultivar}_{sugar proportion}_{apple proportion}_{date}/   # e.g., apple_jam_gala_50_50_17_Dec
+│   │   ├── {sample_id}/                         # numeric sample folders (e.g., 911, 912, …)
+│   │   │   ├── capture/                         # raw camera outputs + references
+│   │   │   │   ├── {sample_id}.raw              # raw hyperspectral cube
+│   │   │   │   ├── {sample_id}.hdr              # header/metadata for the raw cube
+│   │   │   │   ├── DARKREF_{sample_id}.raw      # dark reference (raw)
+│   │   │   │   ├── DARKREF_{sample_id}.hdr
+│   │   │   │   ├── WHITEREF_{sample_id}.raw     # white reference (raw)
+│   │   │   │   └── WHITEREF_{sample_id}.hdr
+│   │   │   ├── metadata/
+│   │   │   │   └── {sample_id}.xml              # per-sample metadata/annotations
+│   │   │   ├── results/                         # calibrated reflectance + previews
+│   │   │   │   ├── REFLECTANCE_{sample_id}.dat  # ENVI-style reflectance cube
+│   │   │   │   ├── REFLECTANCE_{sample_id}.hdr
+│   │   │   │   ├── REFLECTANCE_{sample_id}.png  # reflectance preview
+│   │   │   │   ├── RGBSCENE_{sample_id}.png     # RGB scene snapshot
+│   │   │   │   ├── RGBVIEWFINDER_{sample_id}.png
+│   │   │   │   └── RGBBACKGROUND_{sample_id}.png
+│   │   │   ├── manifest.xml                     # per-sample manifest
+│   │   │   ├── {sample_id}.png                  # sample preview image
+│   │   │   └── .validated                       # empty marker file
+│   │   └── …                                    # more samples
+│   └── …                                        # more cultivar/ratio/date folders
+│
+├── .cache/                                      # service files (upload tool)
+├── ._.cache
+├── ._paths.rtf
+├── .gitattributes                               # LFS rules for large files
+└── paths.rtf                                    # path list (RTF)
+```
+
+## Repository structure
+This repository contains:
+- **Pre-processing**: `1_preprocessing.ipynb` (import HSI, calibration, masking (SAM), ROI crop, grid subdivision).
+- **Dataset building**: `2_dataset preparation.ipynb` (train/val/test splits, sugar concentration/apple cultivar splits, average spectral vectors extraction).
+- **Model training & evaluation**:
+  - `3_svm.ipynb` — SVM, scaling, hyperparameter search.
+  - `4_xgboost.ipynb` — XGBoost, tuning & early stopping.
+  - `5_resnet.ipynb` — 1D ResNet training loops, checkpoints, metrics.
+
+
+
+## Preprocessing → Dataset → Models (How to Run)
+
+### 1) **Preprocessing**  
+
+Inputs to set (near the bottom of the notebook)
+```python
+input_root = "path/to/input"     # root that contains the dataset folders (e.g., Apples_HSI/Catalogs)
+output_root = "path/to/output"   # where the NPZ files will be written
+paths_txt = "path/to/paths.txt"  # text file with relative paths to .hdr files (one per line)
+```
+- Run all cells. The notebook:
+  - reads `REFLECTANCE_*.hdr` with `spectral.open_image`
+  - builds a SAM mask (ref pixel `(255, 247)`, threshold `0.19`)
+  - crops ROI and saves `cropped_{ID}.npz` under `output_root/...`
+
+- Each NPZ contains: `cube` (cropped H×W×Bands), `offset` (`y_min`, `x_min`), `metadata` (JSON).
+
+
+### 2) **Dataset building**  
+
+Run all cells. The notebook:
+- loads each NPZ (`np.load(path)["cube"]`)
+- extracts **mean spectra per patch** for grid sizes **1, ..., 5**
+- creates tables with columns `band_0..band_(B-1)`, `apple_content`, `apple_type`
+- writes splits per grid:
+  - **apple-based:** `{g}x{g}_train_apple.csv`, `{g}x{g}_val_apple.csv`, `{g}x{g}_test_apple.csv`
+  - **rule-based:** `{g}x{g}_train_rule.csv`, `{g}x{g}_val_rule.csv`, `{g}x{g}_test_rule.csv`
+
+
+### 3) **Model training**  
+
+Classical ML — `3_svm.ipynb`
+Run all cells. The notebook:
+- loads pre-split CSVs (e.g., `{g}x{g}_train_apple.csv`, `{g}x{g}_test_apple.csv`)
+- scales inputs and targets with **MinMaxScaler**
+- fits **SVR** with hyperparameters: `C=110`, `epsilon=0.2`, `gamma="scale"`
+- reports **RMSE / MAE / R²** on Train/Test (targets inverse-transformed)
+
+Classical ML — `4_xgboost.ipynb`
+Run all cells. The notebook:
+- loads Train/Val/Test CSVs and scales inputs with **MinMaxScaler**
+- builds **DMatrix** and trains with:
+    objective = "reg:squarederror", eval_metric = "rmse",
+    max_depth = 2, eta = 0.15, subsample = 0.8, colsample_bytree = 1.0,
+    lambda = 2.0, alpha = 0.1, seed = 42
+    num_boost_round = 400, early_stopping_rounds = 40
+- evaluates and prints **RMSE / MAE / R²** (Train/Test)
+
+Deep model — `5_resnet.ipynb`
+Run all cells. The notebook:
+- builds a **ResNet1D** and DataLoaders (`batch_size=16`)
+- trains with **Adam** (`lr=1e-3`, `weight_decay=1e-4`), **epochs=150**, **MAE** loss
+- uses target **MinMaxScaler** (inverse-transforms predictions for metrics)
+- early-stopping on **Val MAE**; saves best checkpoint to **`best_resnet1d_model.pth`**
+- reports **RMSE / MAE / R²** on the Test set
+
+
+## If you use the dataset/source code/pre-trained models in your research, please cite our work:
+
+Lissovoy, D., Zakeryanova, A., Orazbayev, R., Rakhimzhanova, T., Lewis, M., Varol, H. A., & Chan, M.-Y. (2025). Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam. Foods, 14(21), 3585. https://doi.org/10.3390/foods14213585