化学计量学
偏最小二乘回归
主成分分析
化学
高效液相色谱法
色谱法
近红外光谱
傅里叶变换红外光谱
线性判别分析
分析化学(期刊)
数学
统计
物理
量子力学
作者
Hui Yan,Penghui Li,Guisheng Zhou,Yingjun Wang,Beihua Bao,Qinan Wu,Shen-Liang Huang
出处
期刊:Food Chemistry
[Elsevier BV]
日期:2020-10-01
卷期号:341 (Pt 1): 128241-128241
被引量:80
标识
DOI:10.1016/j.foodchem.2020.128241
摘要
A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger) and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared spectroscopy (FT-NIR) and chemometrics. FT-NIR provided a reliable method to qualitatively assess ginger samples and batches of S-ginger (41) and NS-ginger (39) were discriminated using principal component analysis and orthogonal partial least squares discriminant analysis of FT-NIR data. To generate quantitative methods based on partial least squares (PLS) and counter propagation artificial neural network (CP-ANN) from the FT-NIR, major gingerols were quantified using high performance liquid chromatography (HPLC) and the data used as a reference. Finally, PLS and CP-ANN were deployed to predict concentrations of target compounds in S- and NS-ginger. The results indicated that FT-NIR can provide an alternative to HPLC for prediction of active components in ginger samples and was able to work directly on solid samples.
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