A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for Fritillaria spp. Quality Control

<i>Fritillaria</i> 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 <i>Fritillaria</i> 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 <i>Fritillaria</i> spp., the CNN could achieve high accuracy, with 100 ± 0%. The features contributing to the origin of <i>Fritillaria</i> spp. were visualized using gradient-weighted class activation mapping (Grad-CAM). For the adulteration of <i>Fritillaria</i> 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 <i>Fritillaria</i> spp. geographical origins and quantitative determination of adulteration levels, establishing a novel analytical framework for rapid quality evaluation of <i>Fritillaria</i> spp. materials.