化学计量学
主成分分析
偏最小二乘回归
线性判别分析
原儿茶酸
银杏
色谱法
支持向量机
模式识别(心理学)
校准
人工智能
高效液相色谱法
近红外光谱
计算机科学
化学
生物系统
分析化学(期刊)
数学
机器学习
植物
统计
物理
生物
量子力学
抗氧化剂
生物化学
作者
Sijie Zhang,Xingchu Gong,Haibin Qu
摘要
To investigate the feasibility of using near-infrared spectroscopy for rapid determination of main organic acids in Ginkgo biloba leaf extract (EGBL).Main organic acids in EGBL were assayed using the HPLC method. Critical factors of the chromatographic separation were optimized by a novel analytical quality by design approach. Partial least squares-discriminant analysis (PLS-DA) was performed to screen the marker components, and principal component analysis (PCA) was utilized to distinguish the different samples. Then, spectral quantification potential was investigated using PLS and support vector machine (SVM) approaches. For modelling, different spectral preprocessing and wavelength selection methods were systematically compared.It was found that quinic acid, protocatechuic acid and 6-hydroxykynurenic acid were identified as possible index components. PLS-DA based on contents and PCA based on near-infrared spectra can both effectively distinguish the different EGBL samples. The calibration models with wonderful prediction performance can be both developed by the PLS and SVM algorithms.NIR spectroscopy combined with chemometrics can realize the rapid and non-destructive qualitative and quantitative analysis of EGBL. The proposed method may be applied to quality control of EGBL and other natural products in commercial use.
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