Nondestructive detection of adulterated wolfberry (Lycium Chinense) fruits based on hyperspectral imaging technology

Abstract In order to detect adulterants on Lycium Chinense species effectively, a rapid, clean, and nondestructive detection method based on hyperspectral imaging (HSI) technology was conducted in a wavelength range of 400.68–1001.60 nm. Industrial sulfur particles were chosen as a dye to prepare three groups of adulterated L. Chinense samples as the research object. The whole L. Chinense was considered the region of interest. First, a multiple scatter correction (MSC) method was used to preprocess spectra data. The competitive adaptive reweighted sampling (CARS) and linear discriminant analysis approaches were contrasted for optimal extraction of wavelengths characteristic. Then, two models were established: K‐nearest neighbor (KNN) and support vector machine (SVM). Furthermore, the performance accuracies of KNN and SVM models were compared. According to the outcomes, the SVM model built on CARS provided the best classification impact. The accuracy for the prediction set was 98.75%, and the accuracy for the training set was 100%. Also, the kernel parameters c and g of the SVM model were enhanced by genetic algorithm (GA) optimization. The values parameters ( c , g) were set at 14.975 and 0.224, respectively, and the results improved by 1.25% at an elapsed time of 1.887 s, with the accuracy reaching 100% on both training and test sets. This study aimed to detect and classify sulfur‐adulterated wolfberries using an improved SVM and HSI. Finally, the results demonstrate that a combination of HSI and the CARS‐GA‐SVM model could be used for the rapid detection of foreign entities' in wolfberry fruits. Practical applications Dried wolfberry adulteration has a direct link to the overall quality of the fruits, and potentially compromises the health of the fruits consumers. The traditional methods of testing adulterants on Lycium Chinense are arduous, require a lot of time, and are highly impacted by biased elements, necessitating new techniques. HSI technology, on the other hand, is nondestructive, quick/fast, accurate, subjective, reproducible, and pollution‐free. The study findings proved to be recommendable for initiating a feasible mobile system for rapidly detecting adulteration on L. Chinense .