Supplementary data and algorithms associated with the article of deep learning for predicting TVC in peeled shrimp

Python codes:
1.SAEs.py #this file is used to train the SAEs model
2.SAEs-PLSR.py #use this file to train PLSR model based on deep spectra features, and evaluate the model.
3.SPA-PLSR.py #use this file to train PLSR model based on characteristic wavelengths selected by SPA, and evaluate the model.
4.F-PLSR.py #use this file to train PLSR model based on full spectra, and evaluate the model.
Data:
1.ramdonpixel_1.pkl #60060 spectra for training SAEs model
2.ramdonpixel_2.pkl #60060 spectra for validating SAEs model
3.Fullspectra.csv #200 samples with 230 bands spectra and reference TVC values
4.SPAspectra.csv #200 samples with 18 characteristic wavelengths and reference TVC values
Logs:
saved model files
Results:
experimental results files

Python代码：
1. SAEs.py #该文件用于训练稀疏自编码器（SAEs）模型
2. SAEs-PLSR.py #使用该文件基于深度光谱特征训练偏最小二乘回归（PLSR）模型，并对模型进行评估
3. SPA-PLSR.py #使用该文件基于连续投影算法（SPA）筛选出的特征波长训练偏最小二乘回归（PLSR）模型，并对模型进行评估
4. F-PLSR.py #使用该文件基于全光谱训练偏最小二乘回归（PLSR）模型，并对模型进行评估
数据：
1. ramdonpixel_1.pkl #包含60060条光谱数据，用于训练稀疏自编码器（SAEs）模型
2. ramdonpixel_2.pkl #包含60060条光谱数据，用于验证稀疏自编码器（SAEs）模型
3. Fullspectra.csv #包含200个样本，每个样本带有230个波段的光谱数据及对应的TVC参考值
4. SPAspectra.csv #包含200个样本，每个样本带有18个特征波长的光谱数据及对应的TVC参考值
日志：
保存的模型文件
结果：
实验结果文件