Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy

被引:50
作者
Magalhaes, Luis M. [2 ]
Machado, Sandia [1 ]
Segundo, Marcela A. [2 ]
Lopes, Joao A. [3 ]
Pascoa, Ricardo N. M. J. [1 ]
机构
[1] Univ Porto, Fac Pharm, Dept Chem Sci, LAQV,REQUIMTE, P-4050313 Oporto, Portugal
[2] Univ Porto, Fac Pharm, Dept Chem Sci, UCIBIO,REQUIMTE, P-4050313 Oporto, Portugal
[3] Univ Lisbon, Fac Farm, Dept Farm Galen & Tecnol Farmaceut, IMed, P-1649003 Lisbon, Portugal
关键词
Spent coffee grounds; Phenolic compounds; Methylxanthines; Near-infrared spectroscopy; Caffeine; Chlorogenic acid; Partial least squares regression; Chemometrics; NEAR-INFRARED-SPECTROSCOPY; ANTIOXIDANT ACTIVITY; CAFFEINE; CHEMOMETRICS; OPTIMIZATION; ESPRESSO; FIGURES; SAMPLES; TOOL;
D O I
10.1016/j.talanta.2015.10.022
中图分类号
O65 [分析化学];
学科分类号
070302 [分析化学];
摘要
Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (R-p(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, ( +)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid,)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:460 / 467
页数:8
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