Shuilan Liao

Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp²) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products.

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400–1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky–Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R_P² = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R_P² = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea.

Mustard is an important cash crop of the genus Brassica in the family Cruciferae. Low temperature is an important environmental factor limiting the growth of mustard. In this study, 65 leafy mustard cultivars were used as experimental materials, 25 °C was set as the control temperature, and 5 °C was set as chilling stress temperature to investigated the physiological response of chlorophyll (Chl) content, soluble sugar (SS) content, proline (Pro) content, antioxidant enzyme activity, malondialdehyde (MDA) content, and chlorophyll fluorescence to chilling injury. The chilling tolerance coefficients of each individual index were measured and correlation analysis, principal component analysis (PCA), the membership function method, and cluster analysis were applied to evaluate chilling tolerance. In a comprehensive analysis, the most chilling-tolerant cultivar was SJTKJ, the least chilling-tolerant cultivar was DX. Stepwise regression was used to establish a mathematical model for evaluating the chilling tolerance of mustard, and four chilling tolerance identification indices, including Fv/Fm, ΦPSII, POD activity, and Rfd were screened. This study provides a reference for the evaluation of the chilling tolerance of mustard and the breeding of new chilling-tolerant cultivars.