The nutritional content of tiger nut (Cyperus esculentus L.) is abundant, rich in oil, protein, and starch. Conventional methods for assessing the nutrient composition of tiger nuts (TNs) are time-consuming and labor-intensive. Near-infrared spectroscopy (NIR) combined with chemometrics has been widely applied in rapidly predicting the nutritional content of various crops, but its application to TNs is rare. In order to enhance the practicality of the method, this study employed a portable NIR in conjunction with chemometrics to rapidly predict the contents of crude oil (CO), crude protein (CP), and total starch (TS) from TNs. In the period from 2022 to 2023, we collected a total of 75 TN tuber samples of 28 varieties from Xinjiang Uyghur Autonomous Region and Henan Province. The three main components were measured using common chemical analysis methods. Partial least squares regression (PLSR) was utilized to establish prediction models between NIR and chemical indicators. In addition, to further enhance the prediction performance of the models, various preprocessing and variable selection algorithms were utilized to optimize the prediction models. The optimal models for CO, CP, and TS exhibited coefficient of determination (R²) values of 0.8946, 0.8525, and 0.8778, with root mean square error of prediction (RMSEP) values of 1.1764, 0.7470, and 1.4601, respectively. The absolute errors between the predicted and actual values for the three-indicator spectral measurements were 0.80, 0.59, and 0.99. The results demonstrated that the portable NIR combined with chemometrics could be effectively utilized for the rapid analysis of quality-related components in TNs. With further refinements, this approach could revolutionize TN quality assessment and be used to determine optimal harvest times, as well as facilitate the graded marketing of TNs.

Fritillaria spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. To rapidly and accurately identify the origin and adulteration of Fritillaria spp., a handheld near-infrared spectrometer was combined with a convolutional neural network (CNN) to establish an efficient and convenient quality assessment method. First, for the origin of Fritillaria spp., the CNN could achieve high accuracy, with 100 ± 0%. The features contributing to the origin of Fritillaria spp. were visualized using gradient-weighted class activation mapping (Grad-CAM). For the adulteration of Fritillaria spp., compared with partial least squares regression (PLSR), the CNN yielded the best performance, with the R² of the test set being 0.9897. Additionally, to improve the interpretability of the adulteration model, a CNN model was established using data whose dimensions had been reduced by PCA (PCA–CNN), which also achieved an R² of 0.9876. The features extracted by PCA focused on 1400–1500 nm, which was consistent with Grad-CAM. The visualization of Grad-CAM and the adulteration detection model achieved mutual validation, showing the effectiveness of both methods in analyzing the samples. The experimental results demonstrated that the integration of a handheld near-infrared spectrometer with a CNN enabled both reliable authentication of Fritillaria spp. geographical origins and quantitative determination of adulteration levels, establishing a novel analytical framework for rapid quality evaluation of Fritillaria spp. materials.

A new method for identification of AQMs in passenger's luggage using IMS combined with CNN.