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 lD-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 lD-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.
关键词
高光谱成像/陈皮/陈化年份/多层梯度加权类激活映射/特征波段
Key words
hyperspectral imaging/Chenpi/the aging year of Chenpi/multi-layer gradient-weighted class activation mapping(M-Grad-CAM)/feature band
维普
万方数据