Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
State Key Laboratory Breeding Base of Systematic Research, Development and Utilization of Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Sensors (Basel). 2018 Jan 15;18(1):241. doi: 10.3390/s18010241.
Origin traceability is an important step to control the nutritional and pharmacological quality of food products. mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 192 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of mushrooms.
起源可追溯性是控制食品产品营养和药理质量的重要步骤。蘑菇是世界上著名的食物资源。其营养价值和药用价值因地理起源而有很大差异。在这项研究中，结合化学计量学，应用了三个传感器系统（电感耦合等离子体原子发射光谱仪（ICP-AES）、紫外-可见（UV-Vis）和傅里叶变换中红外光谱（FT-MIR））对 192 个蘑菇样本（帽和茎）进行了起源追溯。基于单一传感器技术，分别清楚地说明了帽和茎之间的差异。来自三种仪器的特征变量用于追溯起源。应用了两种有监督分类方法，偏最小二乘判别分析（FLS-DA）和网格搜索支持向量机（GS-SVM），来开发数学模型。使用两步法（内部交叉验证和未知样本的外部预测）来评估分类模型的性能。结果令人满意，准确率从 90.625%到 100%不等。这些模型也具有最佳参数的出色泛化能力。基于三个感官系统的组合，我们的研究为蘑菇的起源提供了一种多感官和综合的追溯方法。
