## 2 HSI Procedure
### Preprocessing
#### 2.2.9 Log (1/R)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Preprocessing Method** & **Objective** & **Advantage** & **Limitation and Challenges** & **Reference** \\ \hline MSC & Detect foreign matter in meat products & Achieves 95\% accuracy with SVM + PCA + MSC + SPA model & Requires complex modeling; performance may vary by contaminant & [42] \\ \hline MSC & Origin classification & Improved model accuracy (91.01\%) with spectra reconstruction from RGB images & High device costs, long imaging times, and low image resolution hinder broader use & [43] \\ \hline SNV & Detection of adulteration & High accuracy with SVM models, enhanced by feature wavelength selection & Parameter optimization (GA, grid search) adds complexity; requires pretreatment and variable selection & [48] \\ \hline SNV + SG & Freshness classification & Achieved high classification accuracy using SVM models, with feature wavelength selection techniques improving model performance & Parameter optimization using GA computational complexity; requires data pretreatment and variable selection & [47] \\ \hline SG & Estimation of water content & High accuracy with FOSGD and PLS models; reduced RMSE & Complex preprocessing with multiple wavelength selection methods; computationally intensive & [57] \\ \hline SG & Rapid determination of chemical compositions (moisture, protein, ash) & High prediction accuracy and visual chemical distribution maps & Requires multiple preprocessing steps and feature selection for optimal performance & [58] \\ \hline OSC & Predict soluble solid content (SSC) during ultrasound-assisted osmotic dehydration & High accuracy with OSC-CARS-SVM model, capable of visualizing SSC distribution & Involves complex wavelength selection (CARS) and computationally intensive full-spectrum modeling & [60] \\ \hline OSC & Non-destructive assessment of protein content & High prediction accuracy, aiding precise quality control of dried layer & Involves multiple preprocessing steps and advanced regression, increasing computational complexity & [61] \\ \hline AS (Standard Scaler) & Non-destructive assessment of protein content & Enhanced model accuracy combined with CARS and other preprocessing techniques & Involves multiple preprocessing steps and complex wavelength selection, increasing computational demands and may complicate implementation & [64] \\ \hline MC & Prediction of energy and macronutrient content & Accurate predictions of protein and nutritional values with Ridge regression & Lower accuracy for carbohydrates and ash predictions; model selection and evaluation can be complex & [64] \\ \hline MA & Prediction of protein content & Enhances accuracy of regression models like PLSR for food composition & May lead to loss of fine spectral details and may require additional techniques for feature extraction & [59] \\ \hline MA & Droplets detection & Improves classification accuracy of hyperspectral images for detecting diluted spinach juice with SVM models & Requires optimization to reduce wavelengths and computation time; may underperform with highly diluted samples & [66] \\ \hline MNF & Detection of adulteration & Effectively reduces noise and brand interference, enhancing the clarity of adulterant signals in hyperspectral data & Requires complementary algorithms for improved classification accuracy and quantification of adulterants & [73] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the application of spectral pre-processing techniques used in hyperspectral imaging system in the food industry.