Salt stress has become a major problem in the tomato planting process, of which peroxidase (POD) activity is an important parameter reflecting the antioxidant capacity of plants. In order to explore the dynamic changes of catalase in leaves under different concentrations of NaCl stress, it is necessary to establish a rapid detection technology for changes of POD activity in micro-areas of leaves. In this study, a total of 139 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. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining POD activity. Various spectral pre-treatment combined with partial least-squares regression models was compared to original spectrum combined with partial least-squares regression model. The characteristic wavelength was extracted by four methods, and partial least-squares regression (PLSR) and principal component regression (PCR) were established according to the characteristic wavelength. The results show that multiple scattering correction (MSC) is optimized as the pre-treatment method. The partial least-squares regression model based on the interval variable iterative space contraction method is the best, and the coefficient of determination and root mean square error of prediction set (RMSEP) are 0.66 and 18.94 U/g·min, respectively. The results show that it is feasible to detect the peroxidase activity in tomato leaves by micro-hyperspectral imaging combined with stoichiometry.

In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m²·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (R_P = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories.

With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

The time involved and the high economic cost of using oak barrels to age wines have led to a significant price difference compared to non-oak barrel aged wines and may lead to some fraudulent sales in the market. In this study, an untargeted metabolomic strategy was developed to detect the metabolite composition of oak barrel aged and non-oak barrel aged wines in both positive and negative ion modes by using UHPLC–HRMS combined with the recently developed chemometric method AntDAS. The results of partial least squares discrimination analysis (PLS-DA) showed that the samples were characterized into two categories. Finally, 51 compounds were identified in positive ion mode, while 26 compounds were identified in negative ion mode. The results indicate that combining UHPLC–HRMS with AntDAS can reveal the material basis of wines and has excellent potential to differentiate between oak barrel aged and non-oak barrel aged wines.

Goji berries (Lycium barbarum L.) are valued for their nutritional and medicinal properties; however, the systematic biochemical impact of drying on their quality remains poorly understood. This study applied an untargeted metabolomics approach based on UHPLC-HRMS and AntDAS to profile metabolic changes during sun-drying. Multivariate analyses (PCA and PLS-DA) revealed distinct time-dependent clustering, indicating significant shifts in the metabolome. Key metabolites, including betaine, galactose, and trans-ferulic acid, increased significantly (p < 0.05), whereas choline, allantoin, and huperzine isomers decreased. Pathway analysis highlighted glycine, serine, threonine, galactose, and phenylpropanoid metabolism as the central pathways that were affected. These differential metabolites could potentially be used as quality biomarkers. Our findings establish untargeted metabolomics as an effective tool for elucidating the evolution of goji berry quality during drying, offering a theoretical basis for process optimization.