应用近红外光谱（NIR）分析新的 PLS 方法对葡萄酒挥发性化合物进行特征描述。
New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR).
机构信息
CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.
CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal; Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal.
出版信息
Food Chem. 2018 Apr 25;246:172-178. doi: 10.1016/j.foodchem.2017.11.015. Epub 2017 Nov 6.
This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm to 6357 cm. Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines.
本工作旨在探索近红外（NIR）光谱技术在量化绿酒（Vinho Verde）挥发性化合物方面的潜力，这些化合物通常采用气相色谱法进行测定。为此，使用傅里叶变换近红外（FT-NIR）透射光谱法对 105 个绿酒样本进行了分析，测量范围为 5435cm 至 6357cm。采用箱线图和主成分分析（PCA）对聚类进行识别和异常值剔除。然后采用偏最小二乘（PLS）回归法通过一种新的迭代方法建立校准模型。通过外部验证程序，采用独立样本集对模型的预测能力进行了确认。结果表明，所得到的模型具有较好的预测能力，决定系数（R）的变化范围为 0.94 至 0.97。本研究采用近红外光谱和化学计量学方法，为快速测定绿酒中的挥发性化合物提供了一种很有前途的新方法。
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