Yu Dai-Xin, Guo Sheng, Zhang Xia, Yan Hui, Zhang Zhen-Yu, Chen Xin, Chen Jiang-Yan, Jin Shan-Jie, Yang Jian, Duan Jin-Ao
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 X. 2022 Sep 17;15:100450. doi: 10.1016/j.fochx.2022.100450. eCollection 2022 Oct 30.
Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.
姜粉（GP）是一种在全球都很受欢迎的香料。由于其营养价值，姜粉被视为易被掺假的诱人目标，且掺假不易被察觉。在本研究中，开发了色度分析和傅里叶变换近红外（FT-NIR）光谱结合化学计量学的方法来鉴定和定量姜粉及其掺假物。结果表明，通过色度分析无法完全区分纯姜粉和掺假姜粉。然而，优化后的近红外光谱能够准确地区分纯姜粉和掺假样品。随机森林和梯度提升算法在分类中表现出最高的准确率（100%）。此外，成功建立了一个定量模型来预测姜粉中的掺假水平。对于纯姜粉和掺假姜粉，预测偏差的最佳参数分别为8.92、13.68、14.61和4.30。总体而言，傅里叶变换近红外光谱是一种很有前景的工具，它可以快速识别姜粉中潜在的掺假情况并追踪掺假物的类型。
