通过分析糙米在储藏过程中风味物质变化规律,建立一种新的快速判断糙米储藏期和新鲜度的方法。研究通过气相离子迁移谱技术(gas chromatography-ion mobility spectrometry, GC-IMS)采集糙米储藏过程中特征挥发性有机物组成及含量的变化,采用动态主成分分析对不同储藏时间糙米的挥发性有机物(volatile organic compounds, VOCs)数据降维处理并利用K均值聚类分析建立判别模型。研究表明,GC-IMS联用技术可有效分离不同储藏时间和不同包装方式的8组糙米样品中VOCs的单倍体和二倍体,快速筛选出59种离子峰强度变化明显的VOCs,通过二维数据可视化方式可直观区分不同糙米样品中VOCs的差异性。经数据降维处理后所构建的聚类判别模型,可将糙米样品根据储藏时间的不同归类到对应的时间簇,为糙米储藏期和新鲜度的快速鉴定提供新的思路和方法。
A novel detection method to quickly judge the storage time and freshness of brown rice by analyzing the changes of flavor substances of brown rice was established. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to collect the volatile organic compounds of brown rice at different storage time. Dynamic principal component analysis (PCA) was used to reduce the dimensionality of volatile organic compounds (VOCs) data and the processed data were applied to establish clustering analysis model by K-means algorithm. The results showed that the GC-IMS combination technique could effectively separate haploid and diploid of the VOCs in 8 groups of brown rice samples with different storage time and different packaging methods. Meanwhile, it could rapidly screen out 59 characteristic ion peaks with significant intensity changes and also discriminated the VOCs differences in different brown rice samples by two-dimensional data visualization. The clustering discriminant model constructed by data dimensionality reduction can classify brown rice samples into corresponding time clusters according to different storage time, which provides new ideas and methods for rapid identification of brown rice storage period and freshness.
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