Background: This work describes a fast, simple, sensitive, and low-cost method for the identification of resveratrol in different brands and varieties of red wines.

Methods: It was developed based on a comparison of the UV-VIS spectra of the samples and samples enriched with different concentrations of the trans-resveratrol standard. The spectra were analyzed by chemometric principal component analysis (PCA) and multivariate calibration.

Results: The PCA data indicated that only 4 main components made possible group samples based on the grape variety characteristics and/or production region.

Conclusion: From the construction of partial least squares (PLS) and multiple linear regression (RLM) models, it was possible to predict the sample trans-resveratrol content with that sample showing similarities between the groups observed in the PCA and the samples used in the model constructions. The predicted trans-resveratrol present in these samples ranged from 0.29 to 23.3 mg L^-1. This multivariate method suggested a good predictive capacity of determination of resveratrol concentrations in commercial red wines.

背景：这项工作描述了一种快速、简单、灵敏且低成本的方法，用于鉴定不同品牌和品种的红葡萄酒中的白藜芦醇。

方法：它是基于样品和富含不同浓度反式白藜芦醇标准品的样品的 UV-VIS 光谱的比较而开发的。通过化学计量学主成分分析(PCA)和多变量校准分析光谱。

结果：PCA 数据表明，根据葡萄品种特征和/或产区，只有 4 个主要成分可以成为可能的组样本。

结论：根据偏最小二乘法 (PLS) 和多元线性回归 (RLM) 模型的构建，可以预测样品反式白藜芦醇的含量，该样品显示出 PCA 中观察到的组与实验中使用的样品之间的相似性。模型结构。这些样品中存在的预测反式白藜芦醇的范围为 0.29 至 23.3 mg L ^-1。这种多变量方法对商业红葡萄酒中白藜芦醇浓度的测定具有良好的预测能力。