1：Experimental Design and Method Selection:

The study used reflectance near-infrared hyperspectral imaging (NIR-HSI) in the 1000–2500 nm range to non-destructively detect fungal decay in strawberries. It involved analyzing sugar constituents (fructose, glucose, sucrose, TWSS) via HPLC as a reference method. Key wavelengths were selected using the successive projection algorithm (SPA), and support vector machine (SVM) regression was employed for prediction models.

2：Sample Selection and Data Sources:

Strawberries (Fragaria × ananassa Duch. 'Hongyan' variety) were harvested at commercial maturity from a local orchard in Nanjing, China. Samples were inoculated with Botrytis cinerea spores for the infected group, with a control group using sterile saline. Samples were stored at 20°C and 85% relative humidity, with data collected at various time points over 120 hours.

3：List of Experimental Equipment and Materials:

Equipment included an NIR-HSI system (Xeva-T2SL-9928 camera, Xenics Ltd.), HPLC system (Shimadzu LC-20A), halogen lamps, transport unit (IRCP0076, Isuzu), computer (Dell Vostro-3667), and software (SPSS

4：0, MATLAB 2014, PLS Toolbox 5). Materials included strawberries, Botrytis cinerea spores, trichloroacetic acid, ultrapure water, acetonitrile, sugar standards (fructose, glucose, sucrose), and PTFE for white reference. Experimental Procedures and Operational Workflow:

Samples were prepared by inoculation and storage. Hyperspectral images were captured using the NIR-HSI system with calibration using dark and white references. Sugar content was measured destructively via HPLC after sample freezing and extraction. Spectral data were pre-processed with standard normal variate correction (SNV). Key wavelengths were selected using SPA, and SVM models were built for prediction and classification of decay stages.

5：Data Analysis Methods:

Statistical analysis used ANOVA with LSD in SPSS. SVM regression models were evaluated using correlation coefficients (R2), root mean square errors (RMSE), and relative percentage of deviation (RPD). Classification accuracy for decay stages was assessed based on key wavelengths.