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Abstract：
The quality of milk is tightly linked to its brand. A famous brand of milk always has good quality. Therefore, this study seeks to design a new fuzzy feature extraction method, called fuzzy improved null linear discriminant analysis (FiNLDA), to cluster the spectra of collected milk for identifying milk brands. To elevate the classification accuracy, FiNLDA was applied to process the near-infrared (NIR) spectra of milk acquired by the portable near-infrared spectrometer. The principal component analysis and Savitzky-Golay (SG) filtering algorithm were employed to lower dimensionality and eliminate noise in this system, respectively. Thereafter, improved null linear discriminant analysis (iNLDA) and FiNLDA were applied to attain the discriminant information of the NIR spectra. At last, the K-nearest neighbor classifier was utilized for assessing the performance of the identification system. The results indicated that the maximum classification accuracies of LDA, iNLDA and FiNLDA were 74.7%, 88% and 94.67%, respectively. Accordingly, the portable NIR spectrometer in combination with FiNLDA can classify milk brands correctly and effectively.
摘要：
牛奶的质量与其品牌密切相关。一个知名品牌的牛奶总是质量很好。因此,本研究旨在设计一种新的模糊特征提取方法，称为模糊改进零线性判别分析(FINLDA)，对收集的牛奶光谱进行聚类，以识别牛奶品牌。为了提高分类精度，将FiNLDA应用于处理便携式近红外光谱仪获得的牛奶近红外（NIR）光谱。主成分分析和Savitzky-Golay（SG）滤波算法用于降低该系统的维数并消除噪声。分别。此后,应用改进的零线性判别分析（iNLDA）和FiNLDA来获得近红外光谱的判别信息。最后,K最近邻分类器用于评估识别系统的性能。结果表明，LDA的最大分类精度，iNLDA和FiNLDA为74.7%，88%和94.67%，分别。因此，便携式近红外光谱仪与FINLDA相结合，可以正确有效地对牛奶品牌进行分类。
