Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy
Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy
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DOI：
10.1016/j.foodchem.2018.02.076
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发表时间：
2018-07-30
期刊：
影响因子：
8.8
通讯作者：
Yeretzian, Chahan
中科院分区：
文献类型：
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作者：
Dias, Rafael Carlos Eloy;Valderrama, Patricia;Yeretzian, Chahan
化学分析和感官评价是烘焙和研磨咖啡（RG）质量控制中最常用的方法。然而，更快的替代品将非常有价值。在这里，我们应用红外光声光谱（FTIR-PAS）RG粉末。将特定缺陷豆的混合物与健康（无缺陷）小粒咖啡和中粒咖啡基料以特定比例混合，形成不同类别的混合物。主成分分析允许预测混合物中的缺陷的量/分数和性质，而偏最小二乘判别分析揭示了混合物（=样品）之间的相似性。使用六类共混物获得了成功的预测模型。该模型可以将100%的样本分为四类。特异性高于0.9。应用FTIR-PAS对RG咖啡进行表征和分类混合物已被证明是一种准确、简单、快速和“绿色”的替代方法。
Chemical analyses and sensory evaluation are the most applied methods for quality control of roasted and ground coffee (RG). However, faster alternatives would be highly valuable. Here, we applied infrared-photoacoustic spectroscopy (FTIR-PAS) on RG powder. Mixtures of specific defective beans were blended with healthy (defect-free) Coffea arabica and Coffea canephora bases in specific ratios, forming different classes of blends. Principal Component Analysis allowed predicting the amount/fraction and nature of the defects in blends while partial Least Squares Discriminant Analysis revealed similarities between blends (= samples). A successful predictive model was obtained using six classes of blends. The model could classify 100% of the samples into four classes. The specificities were higher than 0.9. Application of FTIR-PAS on RG coffee to characterize and classify blends has shown to be an accurate, easy, quick and "green" alternative to current methods.