National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.
Food Chem. 2022 Dec 1;396:133672. doi: 10.1016/j.foodchem.2022.133672. Epub 2022 Jul 12.
Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC-MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.
食品的品种和产地真实性越来越受到消费者的关注。本研究采用顶空气相色谱-质谱联用（HS-GC-MS）和快速气相色谱电子鼻（fast GC e-nose）成功区分了中国 7 个主要产区的干姜品种和产地。通过化学计量学分析，基于 HS-GC-MS 实现了两种生姜品种的明显分离。此外，快速 GC e-nose 提取的风味信息实现了产地的区分，并选择了一些潜在的风味成分作为产地认证的重要因素。此外，还比较了几种模式识别算法在品种和地区识别中的应用，随机森林（RF）算法的判别准确率最高。总之，本研究开发了一种结合多元化学计量学和算法的快速、准确的方法，用于确定生姜的品种和产地，也可应用于其他农产品。
