Study on CAT activity of tomato leaf cells under salt stress based on microhyperspectral imaging and transfer learning algorithm

Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a micro-scopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400-1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (Rp) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the Rp and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by micro-hyperspectral imaging combined with stoichiometry.