Wu Xiaohong, Wang Yixuan, He Chengyu, Wu Bin, Zhang Tingfei, Sun Jun
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China.
Foods. 2024 Jun 6;13(11):1783. doi: 10.3390/foods13111783.
Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky-Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk.
牛奶是一种营养价值很高的乳制品。追溯牛奶的来源可以维护消费者的利益以及乳制品市场的稳定。在本研究中，提出了一种模糊直接线性判别分析（FDLDA），通过将模糊集理论与直接线性判别分析（DLDA）相结合来提取牛奶的近红外光谱信息。首先，使用便携式近红外光谱仪收集牛奶样品的光谱数据。然后，通过Savitzky-Golay（SG）和标准正态变量（SNV）对数据进行预处理以降低噪声，并通过主成分分析（PCA）降低光谱数据的维度。此外，采用线性判别分析（LDA）、DLDA和FDLDA将光谱数据转换到特征空间。最后，使用k近邻（KNN）分类器、极限学习机（ELM）和朴素贝叶斯分类器进行分类。研究结果表明，当使用KNN分类器时，FDLDA的分类准确率高于DLDA。FDLDA、DLDA和LDA的最高识别准确率分别可达97.33%、94.67%和94.67%。当使用ELM和朴素贝叶斯分类器时，FDLDA的分类准确率也高于DLDA，但最高识别准确率分别为88.24%和92.00%。因此，KNN分类器优于ELM和朴素贝叶斯分类器。本研究表明，将FDLDA、DLDA和LDA与近红外光谱相结合是确定牛奶来源的有效方法。
