Zhang Jiawei, Wu Xiaohong, He Chengyu, Wu Bin, Zhang Shuyu, Sun Jun
Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China.
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Foods. 2024 May 7;13(10):1439. doi: 10.3390/foods13101439.
The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
菊花茶的品质与其品种有很大关联。不同种类的菊花茶具有截然不同的功效和作用。此外，菊花茶品种的鉴别是茶叶行业中的一个重要问题。因此，为了对菊花茶样本进行正确且无损的分类，本研究尝试基于模糊集理论和改进的直接线性判别分析（IDLDA）设计一种新颖的特征提取方法，即模糊IDLDA（FIDLDA），用于从菊花茶的近红外（NIR）光谱数据中提取判别特征。首先，使用便携式近红外光谱仪收集了五个品种菊花茶的近红外数据，共400个样本。其次，通过四种不同的预处理方法对原始近红外光谱进行处理，以减少噪声和冗余数据。第三，通过主成分分析（PCA）进行近红外数据降维。第四，通过线性判别分析（LDA）、IDLDA和FIDLDA从近红外光谱中提取特征。最后，应用K近邻（KNN）算法评估判别系统的分类准确率。实验结果表明，LDA、IDLDA和FIDLDA的判别准确率分别可达87.2%、94.4%和99.2%。因此，近红外光谱与FIDLDA的结合在菊花茶品种无损鉴别领域具有巨大的应用潜力和前景。
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