The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China; Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, SC 29634, USA.
The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2017 Jul 5;182:17-25. doi: 10.1016/j.saa.2017.03.074. Epub 2017 Apr 2.
The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.
食品质量的起源和真实性是两个至关重要的方面。在这项工作中，建议采用傅里叶变换近红外光谱和化学计量学的综合质量评估方法来解决中国灵芝（GL）的地理起源和真实性问题。使用近红外光谱和间隔组合一对一最小二乘支持向量机（IC-OVO-LS-SVM）对 25 组 GL 样本（来自 15 个生产区的 7 种常见品种）进行分类。对 7 种 GL 品种中的每种品种，通过单类偏最小二乘（OCPLS）建模对 4 种较便宜蘑菇的掺杂物进行非靶向分析。经过离群值诊断和比较不同预处理方法和光谱区间对分类的影响后，IC-OVO-LS-SVM 与标准正态变量（SNV）光谱相结合，总分类准确率为 0.9317，平均灵敏度和特异性分别为 0.9306 和 0.9971。对于 SNV 或二阶导数（D2）光谱，OCPLS 可以检测到至少 5 种 GL 品种中 2%或更多的掺杂物，在另外 2 种 GL 品种中可以检测到 5%或更多的掺杂物。本研究证明了使用新的化学计量学和近红外光谱对 GL 地理起源和品种进行精细分类以及对多种掺杂物进行非靶向分析的可行性。
