School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China.
China National Institute of Standardization, Beijing, China.
J Food Sci. 2021 Nov;86(11):4922-4931. doi: 10.1111/1750-3841.15933. Epub 2021 Oct 12.
Red Huajiao was the most important Zanthoxylum species in China, and its quality was highly determined the geographical region. This study was aimed to establish a determination method for the geographical origin recognition of Red Huajiao by using the electronic nose and ensemble recognition algorithm. Six origins of samples were detected by the electronic nose, and two categories of electronic nose sensors characteristic values, named as "optimized characteristic value" and "filtered characteristic value," were obtained by the principal component analysis and discrimination index method and Filter-Wrapper method. Based on the two categories of characteristic values, 22 kinds of model analysis methods, which belonged to five categories of ensemble recognition algorithms were used to recognize the geographical origin. The total recognition accuracy rate of the two categories of characteristic values were 83.9% and 85.7%, respectively. Furthermore, during 22 kinds of model analysis method, the ensemble Subspace KNN and Bagged Trees methods in Ensemble Learning algorithm exhibited the best distinguishing ability with the accuracy rate more than 90%. Therefore, the electronic nose combined with Ensemble Learning would be promising for the geographical origin determination application. PRACTICAL APPLICATION: This work demonstrates that the Red Huajiao can be simply and rapidly determined by using electronic nose combined with ensemble recognition algorithm, allowing to effectively distinguish geographical origin of Red Huajiao, which can provide an important reference for the quality assessment of Huajiao.
红花花椒是中国最重要的花椒属物种,其质量高度取决于地理区域。本研究旨在建立一种基于电子鼻和集成识别算法的红花花椒地理起源识别方法。采用电子鼻对来自 6 个产地的样品进行检测,通过主成分分析和判别指数法以及 Filter-Wrapper 方法获得两类电子鼻传感器特征值,分别命名为“优化特征值”和“过滤特征值”。基于这两类特征值,采用 22 种属于 5 类集成识别算法的模型分析方法对其地理起源进行识别。两类特征值的总识别准确率分别为 83.9%和 85.7%。此外,在 22 种模型分析方法中,集成学习算法中的集成子空间 KNN 和袋装树方法表现出最好的区分能力,准确率均在 90%以上。因此,电子鼻结合集成学习方法有望应用于地理起源的测定。实际应用:本研究表明,利用电子鼻结合集成识别算法可以简单、快速地对红花花椒进行测定,有效区分红花花椒的地理来源,为花椒质量评价提供重要参考。