Rapid Discrimination of Aging Year of Chenpi Based on Hyperspectral Images

Chenpi, or sun-dried mandarin orange peel, held significant economic and medicinal value, yet counterfeit and substandard products were prevalent in the current market. The aging year of Chenpi was a crucial quality indicator, but accurately determining it through manual inspection was challenging. This study proposed a rapid, non-destructive method to discern the aging year of Chenpi by integrating hyperspectral imaging with deep learning. A total of 480 Chenpi samples across four aging years were collected, and their near-infrared hyperspectral data (wavelength range: 935.61~1720.23 nm) were obtained. A lightweight 1D-Rep network was utilized to develop a classification model enhanced by a feature band selection technique using multi-layer gradient-weighted class activation mapping (M-Grad-CAM). This approach evaluated the importance of spectral bands across multiple Rep-block layers, indicating band significance while considering inter-band and remote correlations. To validate the effectiveness of the proposed method, feature bands obtained from machine learning methods such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) were used as comparative methods. The results showed that, the 1D-Rep full-spectrum model achieved an accuracy of 98.55%. When employing M-Grad-CAM for feature band selection and establishing a classification model based on the first nine feature bands, an accuracy greater than 90% could be achieved in feature band modeling. The accuracy reached 96.82% with 20 feature bands, significantly higher than that of the comparative models. This research effectively distinguishes Chenpi of different years using hyperspectral imaging technology, providing a methodological and theoretical basis for the development of portable detection instruments.