Huang Luelue, Liu Miaoling, Li Bin, Chitrakar Bimal, Duan Xu
School of Food and Drug, Shenzhen Polytechnic University, No. 2190, Liuxian Road, Shenzhen 518055, China.
College of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China.
Foods. 2024 Jan 24;13(3):389. doi: 10.3390/foods13030389.
In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.
在本研究中，提出了太赫兹时域光谱技术（THz-TDS）来鉴别三种不同品种以及同一品种三种不同烘焙度的咖啡。应用主成分分析（PCA）从频域光谱数据中提取特征，并将提取的特征通过线性判别（LD）、支持向量机（SVM）、朴素贝叶斯（NB）和k近邻（KNN）用于分类预测。通过混淆矩阵分析模型的分类效果和误分类情况。使用浅度干燥条件下的卡蒂姆、铁皮卡1和铁皮卡2这三个咖啡品种进行对比试验。结合PCA的LD分类模型具有最佳的降维分类效果，其速度和准确率分别达到20毫秒和100%。通过比较铁皮卡1三种不同烘焙度的分类结果发现，LD模型的速度最高（25毫秒）且准确率最高（100%）。基于THz-TDS结合建模分析方法的咖啡豆品质检测方法具有更高的准确率、更快的速度和更简单的操作，有望成为咖啡鉴别的一种有效检测方法。
