Shen Fei, Wu Qifang, Shao Xiaolong, Zhang Qiang
1College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing, 210023 China.
2Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6 Canada.
J Food Sci Technol. 2018 Mar;55(3):1175-1184. doi: 10.1007/s13197-018-3033-1. Epub 2018 Jan 12.
The applicability of near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was explored in this study to develop rapid, low-cost and non-destructive spectroscopic methods for classification and quantification of aflatoxins in brown rice. A total of 132 brown rice samples within the aflatoxin concentration range of 0-2435.8 μg/kg were prepared by artificially inoculated with and strains of fungus. For the classification of samples at varying levels of aflatoxin B, the linear discriminant analysis model obtained correct classification rate of 96.9 and 90.6% for NIR and MIR spectroscopy, respectively. For the simultaneous determination of aflatoxins B, B, G, G and the total aflatoxins, partial least squares regression also showed good predictive accuracy for both NIR ( = 0.936-0.973, RPD = 2.5-4.0) and MIR spectroscopy ( = 0.922-0.970, RPD = 2.5-4.0). The overall results indicated that the two spectroscopic techniques offered the feasibility to be used as alternative tools for rapid detection of various aflatoxin contaminations in grain.
本研究探讨了近红外(NIR)和中红外(MIR)光谱结合化学计量学的适用性,以开发快速、低成本且无损的光谱方法,用于糙米中黄曲霉毒素的分类和定量。通过人工接种 和 菌株,制备了132个黄曲霉毒素浓度范围为0 - 2435.8 μg/kg的糙米样品。对于不同水平黄曲霉毒素B的样品分类,线性判别分析模型对近红外和中红外光谱分别获得了96.9%和90.6%的正确分类率。对于同时测定黄曲霉毒素B、B、G、G和总黄曲霉毒素,偏最小二乘回归对近红外光谱( = 0.936 - 0.973,RPD = 2.5 - 4.0)和中红外光谱( = 0.922 - 0.970,RPD = 2.5 - 4.0)也显示出良好的预测准确性。总体结果表明,这两种光谱技术作为快速检测谷物中各种黄曲霉毒素污染的替代工具具有可行性。