高光谱成像
脂肪酸
偏最小二乘回归
残余物
化学
食品科学
绿茶
环境科学
计算机科学
人工智能
生物化学
算法
机器学习
作者
Yiyi Zhang,Lunfang Huang,Guojian Deng,Yujie Wang
出处
期刊:Foods
[MDPI AG]
日期:2023-01-07
卷期号:12 (2): 282-282
被引量:5
标识
DOI:10.3390/foods12020282
摘要
The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of green tea freshness during storage. Hyperspectral images of green tea during storage were acquired, and fatty acid profiles were detected by GC-MS. Partial least squares (PLS) analysis was used to model the association of spectral data with fatty acid content. In addition, competitive adaptive reweighted sampling (CARS) was employed to select the characteristic wavelengths and thus simplify the model. The results show that the constructed CARS-PLS can achieve accurate prediction of saturated and unsaturated fatty acid content, with residual prediction deviation (RPD) values over 2. Ultimately, chemical imaging was used to visualize the distribution of fatty acids during storage, thus providing a fast and nondestructive method for green tea freshness evaluation.
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