Pan Tao, Li Jiaqi, Fu Chunli, Chang Nailiang, Chen Jiemei
Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
Department of Biological Engineering, Jinan University, Guangzhou, China.
Front Nutr. 2022 Jul 18;9:796463. doi: 10.3389/fnut.2022.796463. eCollection 2022.
The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RAR ) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency ( = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.
识别优质葡萄酒品牌可以避免掺假和欺诈行为，保护生产者和消费者的权益。由于葡萄酒的主要成分大致相同，能够区分葡萄酒品牌的特征成分通常是痕量的且并非独一无二。传统的品牌识别定量检测方法复杂且困难。朴素贝叶斯（NB）分类器是一种基于概率分布的算法，它简单且特别适用于多类判别分析。然而，光谱波长之间的吸光度概率不一定是强独立的，这限制了贝叶斯方法在光谱模式识别中的应用。本研究提出了一种基于波长优化的贝叶斯分类器算法。首先，对等距组合（EC）进行大规模波长筛选，然后进行波长逐步淘汰（WSP）以降低波长之间的相关性并提高贝叶斯判别的准确性。所提出的EC-WSP-贝叶斯方法应用于基于可见和近红外（Vis-NIR）光谱的葡萄酒品牌识别的五类判别分析。其中，从正规销售渠道收集了四种葡萄酒品牌作为识别品牌。第五类样本由21种其他商业品牌葡萄酒和来自各种来源的自酿酒组成，作为干扰品牌。选择了最优的EC-WSP-贝叶斯模型，相应的波长组合为位于可见光、短波近红外和组合频率区域的404、600、992、2070、2266和2462nm。在建模和独立验证中，总识别准确率（RAR）分别达到98.1%和97.6%。该技术快速简便，对规范酒类市场具有重要意义。所提出的少波长高效（=6）模型可为小型专用仪器提供有价值的参考。所提出的综合化学计量学方法可以降低波长之间的相关性，提高识别准确率，并提高贝叶斯方法的适用性。
