化学计量学
偏最小二乘回归
线性判别分析
支持向量机
绿茶
茶氨酸
化学
数学
分光计
近红外光谱
儿茶素
人工智能
质量(理念)
模式识别(心理学)
食品科学
色谱法
分析化学(期刊)
红茶
校准
检出限
质量评定
计算机科学
统计
多酚
物理
抗氧化剂
量子力学
生物化学
作者
Yujie Wang,Menghui Li,Luqing Li,Jingming Ning,Zhengzhu Zhang
出处
期刊:Food Chemistry
[Elsevier]
日期:2021-05-30
卷期号:345: 128816-128816
被引量:31
标识
DOI:10.1016/j.foodchem.2020.128816
摘要
Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.
科研通智能强力驱动
Strongly Powered by AbleSci AI