文献类型：期刊文献

英文题名：Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

作者：Zhang, Xuan[1,2];Li, Wei[1,2];Yin, Bin[3];Chen, Weizhong[3];Kelly, Declan P.[3];Wang, Xiaoxin[3];Zheng, Kaiyi[1,2];Du, Yiping[1,2]

机构：[1]E China Univ Sci & Technol, Shanghai Key Lab Funct Mat Chem, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Res Ctr Anal & Test, Shanghai 200237, Peoples R China;[3]Philips Res China, Shanghai 200233, Peoples R China

年份：2013

卷号：114

起止页码：350

外文期刊名：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

收录：;EI(收录号：20134416925507);WOS:【SCI-EXPANDED(收录号:WOS:000323396800049)】；

基金：This work was supported by the National Natural Science Foundation of China (20975039).

语种：英文

外文关键词：Near-infrared spectroscopy; Roasted coffee; Caffeine; Variable selection; Multivariate calibration

摘要：Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. (C) 2013 Elsevier B.V. All rights reserved.