基于嗅觉可视化技术和气相色谱-质谱联用鉴别霉变小麦

采用气相色谱-质谱联用技术对不同霉变程度小麦的挥发性气体进行检测，利用主成分分析法对检出的气体成分进行分析能有效地将不同霉变程度的小麦进行区分，为霉变小麦的可视化鉴别提供实验基础；采用嗅觉可视化技术与最近邻域和线性判别结合的方法，对不同霉变程度小麦进行检测，建立的最近邻域模型和线性判别模型的识别率分别为95.83%和85.40%。结果表明，嗅觉可视化技术可实现对霉变小麦快速、无损、准确检测，具有很大的应用潜力。

GC-MS of Volatile Organic Compounds for Identification of Moldy Wheat Based on Olfactory Visualization

The volatile organic compounds of wheat with different mildew degrees were detected using gas chromatography-mass spectrometry (GS-MS). Principal components analysis (PCA) of the volatile organic compounds was performed to effectively distinguish the different degrees of moldy wheat in order to provide an experimental basis for visual identification of moldy wheat. Olfactory visualization as a rapid and convenient method was employed to detect fresh and moldy wheat with different mildew degrees, and the data obtained were processed by PCA followed by linear discriminant analysis (LDA) and K-nearest neighbor (KNN) algorithm. The KNN and LDA models gave a recognition rate of 95.83% and 85.40%, respectively. These results conclusively show that olfactory visualization technology can allow fast, non-destructive and accurate detection of moldy wheat.