Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics
Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics
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使用电子鼻和舌头相结合并借助化学计量学按品种区分中国罗布斯塔咖啡
DOI：
10.1016/j.foodchem.2017.02.149
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发表时间：
2017-08-15
期刊：
影响因子：
8.8
通讯作者：
Tan, Lehe
中科院分区：
文献类型：
--
作者：
Dong, Wenjiang;Zhao, Jianping;Tan, Lehe
应用电子鼻舌传感器和化学计量学多元分析对7个不同烘焙程度的中国罗布斯塔咖啡品种进行了表征和分类。分析数据来自分布在中国海南省的126个烘焙咖啡豆样品。采用湿化学法测定了发酵液的pH值、可滴定酸（TA）、可溶性固形物（TSS）、总固形物（TS）和TSS/TA比值等理化指标。研究了数据融合策略，以提高模型相对于单一技术的性能。通过主成分分析、K-近邻分析、偏最小二乘判别分析和反向传播人工神经网络对所有研究的咖啡样品进行了明确的分类。采用偏最小二乘回归（PLSR）建立了传感器响应与参考理化质量之间的定量模型。具有融合数据集的PLSR模型被认为是用于确定质量参数的最佳模型。(C)2017爱思唯尔有限公司版权所有
Electronic nose and tongue sensors and chemometric multivariate analysis were applied to characterize and classify 7 Chinese robusta coffee cultivars with different roasting degrees. Analytical data were obtained from 126 samples of roasted coffee beans distributed in the Hainan Province of China. Physicochemical qualities, such as the pH, titratable acidity (TA), total soluble solids (TSS), total solids (TS), and TSS/TA ratio, were determined by wet chemistry methods. Data fusion strategies were investigated to improve the performance of models relative to the performance of a single technique. Clear classification of all the studied coffee samples was achieved by principal component analysis, K-nearest neighbour analysis, partial least squares discriminant analysis, and a back-propagation artificial neural network. Quantitative models were established between the sensor responses and the reference physicochemical qualities, using partial least squares regression (PLSR). The PLSR model with a fusion data set was considered the best model for determining the quality parameters. (C) 2017 Elsevier Ltd. All rights reserved.