TY - JOUR T1 - Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS). AU - Zhang,Xuan, AU - Li,Wei, AU - Yin,Bin, AU - Chen,Weizhong, AU - Kelly,Declan P, AU - Wang,Xiaoxin, AU - Zheng,Kaiyi, AU - Du,Yiping, Y1 - 2013/05/29/ PY - 2013/01/04/received PY - 2013/04/08/revised PY - 2013/05/10/accepted PY - 2013/6/22/entrez PY - 2013/6/22/pubmed PY - 2014/3/4/medline KW - Caffeine KW - Multivariate calibration KW - Near-infrared spectroscopy KW - Roasted coffee KW - Variable selection SP - 350 EP - 6 JF - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy JO - Spectrochim Acta A Mol Biomol Spectrosc VL - 114 N2 - 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. SN - 1873-3557 UR - https://www.unboundmedicine.com/medline/citation/23786975/Improvement_of_near_infrared_spectroscopic__NIRS__analysis_of_caffeine_in_roasted_Arabica_coffee_by_variable_selection_method_of_stability_competitive_adaptive_reweighted_sampling__SCARS__ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1386-1425(13)00542-8 DB - PRIME DP - Unbound Medicine ER -