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Rapid identification of rice geographical origin and adulteration by excitation-emission matrix fluorescence spectroscopy combined with chemometrics based on fluorescence probe

化学计量学 荧光 主成分分析 线性判别分析 可追溯性 模式识别(心理学) 基质(化学分析) 分析化学(期刊) 水溶液中的金属离子 化学 生物系统 人工智能 离子 色谱法 计算机科学 数学 生物 物理 统计 量子力学 有机化学
作者
Leqian Hu,Yan Zhang,Ying Ju,Xiangru Meng,Chunling Yin
出处
期刊:Food Control [Elsevier BV]
卷期号:146: 109547-109547 被引量:23
标识
DOI:10.1016/j.foodcont.2022.109547
摘要

In general, fluorescent probes are used to determine certain metal ions due to their high selectivity, whereas the weakly selective probe could produce different fluorescence spectra after interacting with numerous metal ions. Based on the different species and contents of metal ions in rice, the weakly selective fluorescence probe combined with chemometrics for rice origin traceability and adulteration identification was studied in this study. Excitation-emission matrix spectra (EEMs) of rice extracts from different geographical origins (including adulterated rice) combined with the weakly selective probe were collected. Considering the three-dimensional (3D) characteristics of the EEMs, multi-dimensional principal component analysis (M-PCA) and unfold partial least squares discriminant analysis (U-PLS-DA) pattern recognition methods were used to extract useful information from complex 3D fluorescence data. And the models were built to classify the origin and adulteration of rice. The results of the M-PCA analysis showed that rice from different origins could not be completely distinguished from each other, but there was a clustering trend. It was suggested that the 3D fluorescence data measured after the reaction of the weakly selective probe with rice extracts may be used for rice origin traceability combined with pattern recognition. The analysis of 3D fluorescence data based on U-PLS-DA showed that the classification accuracy of training sets was 100%, and the accuracy of predicted sets was 98%. The results of the M-PCA analysis also showed that rice with different adulteration ratios had a clustering trend. The accurate recognition rate of training sets and predicted sets after U-PLS-DA analysis of adulterated rice was 99% and 95%, respectively. The results showed that the weakly selective probe could be used for rice origin traceability and adulteration identification after the reaction with rice extracts, combined with pattern recognition methods. This study broadens the application range of the fluorescent probe, which can be used for origin traceability and adulteration recognition of foods that cannot produce fluorescence themselves.
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