Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes

In order to ensure the edibility of potatoes, fluorescence hyperspectral images of potato samples were obtained to predict the solanine content in potatoes. For the best ROI (region of interest), the S‐component of saturation was extracted by the HSI colorimetric technology to characterize the bud eye of potatoes in three‐dimensional geometric space. The effective bud eye was located as the geometric center of ROI and the average spectral information was obtained. After pretreatment and selection of feature wavelengths, the predicting mode of SVR was established and was optimized by adjusting the penalty coefficient c and the core coefficient g of radial basis function (RBF). Finally, the determinant coefficient of the model was 0.9143 and the root mean square error was 0.0296, which could basically meet the application requirements. It was concluded that the method based on hyperspectral fluorescence image and HSI colorimetry could predict the solanine content in potatoes accurately through the optimized SVR model. PRACTICAL APPLICATIONS: The solanine content in potatoes is an important indicator for judging the edibility of potatoes. The traditional manual observation method has a high misidentification rate and is prone to waste. The chemical detection processes are also tedious and time‐consuming. Compared with traditional methods and chemical methods, hyperspectral imaging technology can detect the solanine content in potatoes rapidly, accurately, and nondestructively. In this study, we generated the best prediction model by SVR, which was established a base on characteristic wavelengths through best ROI selection. After comparing, it was found that the bud eye region is the best ROI and the spectral data obtained here has the best predictive ability. All these results provided a theoretical basis for the portable detection of solanine content in potatoes.