Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China.
Agro-product Safety Research Center, Chinese Academy of Inspection and Quarantine, China.
Food Res Int. 2020 Nov;137:109714. doi: 10.1016/j.foodres.2020.109714. Epub 2020 Sep 21.
In this paper, we report a newly developed non-target H NMR detection associated with chemometrics method to classify the botanical and geographical origins of the monofloral Chinese honey. H NMR tests of 218 monofloral honey samples of 8 classes (Acacia, Jujube, Linden, Longan, Orange, Rape, Sunflower, Vitex) collected in 2017-2019 across China were conducted under the optimal sample preparation conditions and NMR acquisition parameters. The whole profiles of NMR spectra instead of individual or partial signals from specific components were processed and extracted, then fed to SIMCA-P to classify the botanical and geographical origins through non-target statistical analysis. For the botanical origins, most of them could be classified clearly according to Principal Component Analysis (PCA) with both R and Q close to 1. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could classify the honey floral types successfully with RY and Q greater than 0.85. It is found that the integral bin for data extraction has no obvious influence on the classification. For the geographical origins, the classification at different geographical levels (providence and town) could be successfully distinguished by OPLS-DA model. The promising preliminary results with the geographical classification at 40 km level unambiguously demonstrate the application of this NMR-based multi-species non-targeted method for the honey authenticity. Successful result is obtained on a pilot prediction of the geographical classification. Comparing with the methods based on other techniques, the advantages of this reported one are less sample amount needed, simple preparation, short test time, and non-targeted multi-species detection.
在本文中，我们报告了一种新开发的非靶向 H NMR 检测方法结合化学计量学方法，用于对单花蜜源的植物学和地理起源进行分类。在最佳样品制备条件和 NMR 采集参数下，对 2017-2019 年在中国各地采集的 8 类（刺槐、枣、椴树、龙眼、橙、油菜、向日葵、荆条）218 个单花蜜样本进行了 H NMR 测试。对 NMR 光谱的全谱而不是特定成分的单个或部分信号进行处理和提取，然后输入 SIMCA-P 通过非靶向统计分析对植物学和地理起源进行分类。对于植物学起源，根据主成分分析（PCA），大多数可以根据 R 和 Q 接近 1 进行清晰分类。正交偏最小二乘判别分析（OPLS-DA）模型可以成功地对蜂蜜花型进行分类，RY 和 Q 大于 0.85。发现数据提取的积分箱对分类没有明显影响。对于地理起源，通过 OPLS-DA 模型可以成功区分不同地理水平（省和城镇）的分类。在 40 公里水平上进行地理分类的有希望的初步结果明确证明了基于 NMR 的多物种非靶向方法在蜂蜜真实性方面的应用。对地理分类的初步预测取得了成功。与基于其他技术的方法相比，该方法的优点是所需样本量少、制备简单、测试时间短、非靶向多物种检测。
