1：Experimental Design and Method Selection:

Hyperspectral imaging system was used to analyze DON levels in wheat kernels. Spectral preprocessing methods included standard normal variate transformation and multiplicative scatter correction (MSC). The successive projections algorithm (SPA) and random frog algorithm were used for optical wavelength selection. Support vector machine (SVM) and partial least squares discriminant analysis were applied to establish models for determining DON levels.

2：Sample Selection and Data Sources:

Wheat kernel samples were obtained from the Food Inspection Institute of the Jiangsu Provincial Academy of Agricultural Sciences. The DON contents were predetermined using liquid chromatography–tandem mass spectrometry (LC–MS/MS).

3：List of Experimental Equipment and Materials:

Hyperspectral system composed of a spectrometer (ImSpector V10E, Spectral Imaging Ltd., Oulu, Finland), a CCD camera (GEV-B1621M-TC000, Imperx, USA), a 150-W halogen lamp light source (model 3900-ER, Illuminator, USA), a precision mobile platform with a stepper motor (Isuzu Optics, Taiwan, China), and a computer.

4：Experimental Procedures and Operational Workflow:

Hyperspectral images were recorded for each sample. Spectral data analysis was performed by Matlab 2012a.

5：2a. Data Analysis Methods:

5. Data Analysis Methods: The SVM and PLS–DA models were each run with a training set of 132 samples. For each model, a testing set of 48 samples was used to evaluate the accuracy.