Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy
Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy
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DOI：
10.1016/j.aca.2004.08.057
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发表时间：
2004-11-08
影响因子：
6.2
通讯作者：
Pizarro, C
中科院分区：
文献类型：
--
作者：
Esteban-Díez, I;González-Sáiz, JM;Pizarro, C
35个代表性的和适当选择的烘焙咖啡样品的特征在于近红外（NIR）光谱，并用于制备相应的浓缩咖啡样品，随后由训练有素的小组成员进行感官评价。的主要目的是调查的某些关键感官属性的浓缩咖啡，包括感知酸度，苦味，后味，和近红外光谱的原始烘焙咖啡样品之间的关系，在这样一种方式，非破坏性的近红外反射率测量将被用来预测所有这些感官特性的决定性影响，从质量保证的角度来看。基于偏最小二乘回归（PLS）的单独的校准模型，相关的近红外光谱数据的烘焙咖啡样品与每个感官属性的浓缩咖啡样品的研究，开发。波长的选择也进行了应用迭代预测加权PLS（IPW-PLS），以考虑到只有显着的和特征的光谱特征，试图提高最终的回归模型的质量。使用IPW-PLS回归，预测的四个感官反应建模进行了高精度，与交叉验证（RMSECV）的残差的均方根误差范围从4.7%到7.0%。因此，在本研究中提出的高品质的校准模型，提供的结果，在准确性方面提供了一个训练有素的感官小组的评价相媲美，是有前途的，并证明了使用类似的方法在在线或常规应用程序的可行性，通过各自的近红外烘烤咖啡光谱未知的浓缩咖啡样品的感官质量预测。(C)2004 Elsevier B. V.保留所有权利。
Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and after-taste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting-PLS (IPW-PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW-PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra. (C) 2004 Elsevier B.V. All rights reserved.