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Prediction of anthocyanin concentrations during red wine fermentation: development of Fourier transform infrared spectroscopy partial least squares models

机译:红酒发酵过程中花色苷浓度的预测:傅里叶变换红外光谱偏最小二乘模型的发展

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Aim: The aim of the present study was to use Fourier transform infrared (FT–IR) spectroscopy with chemometrics to develop partial least squares (PLS) models to predict the concentrations of various anthocyanins during red wine fermentation. Methods and results: Must and wine samples were collected during fermentation. To maximize diversity, 12 different fermentations, of two different vintages and two different varieties, were followed. The anthocyanin composition of the samples was characterized by using different methods described in the literature: the concentration of free anthocyanins was determined by bisulphite bleaching, the concentration of molecular anthocyanins was determined by high-performance liquid chromatography with ultraviolet–visible detection, and the ratio of monomeric anthocyanins to polymeric anthocyanins was determined using the Adams–Harbertson assay. Finally, the data were analysed statistically by PLS regression to quantify laboratory-determined anthocyanin from FT–IR spectra. The correlations obtained showed good results for a large percentage of parameters studied, with the determination coefficient ( R ~(2)) for both calibration and cross-validation exceeding 0.8. The models for molecular anthocyanins appeared to overestimate their prediction, owing to intercorrelation with other parameters. Comparison of the data for each vintage indicated no apparent matrix effect per year, and data for other vintages should be compared to validate this hypothesis. The best models were those for monomeric or polymeric pigments and free anthocyanins. Conclusions: By using FT–IR spectroscopy coupled with chemometrics, it is possible to create predictive models to estimate concentrations of anthocyanins and changes in global anthocyanin parameters during winemaking. Significance and impact of the study: These results improve our understanding of anthocyanin prediction using FT–IR spectroscopy with chemometrics and pave the way for its use as a process control tool for the winemaker. They also highlight the propensity of chemometrics to overestimate certain predicted values when close parameters intercorrelate.
机译:目的:本研究的目的是使用傅里叶变换红外光谱(FT-IR)和化学计量学来建立偏最小二乘(PLS)模型,以预测红酒发酵过程中各种花色苷的浓度。方法和结果:发酵过程中收集了葡萄汁和葡萄酒样品。为了最大限度地提高多样性,进行了两个不同年份和两个不同品种的12种不同发酵。样品中的花色苷成分采用文献中描述的不同方法进行表征:游离的花色苷浓度通过亚硫酸氢盐漂白法测定;分子花色苷的浓度通过高效液相色谱-紫外可见检测法测定。使用Adams–Harbertson分析测定单体花色苷与聚合花色苷的含量。最后,通过PLS回归对数据进行统计分析,以量化FT-IR光谱中实验室测定的花色苷。所获得的相关性对于所研究的大部分参数显示出良好的结果,校准和交叉验证的测定系数(R〜(2))均超过0.8。由于与其他参数的相互关系,分子花色苷的模型似乎高估了它们的预测。比较每个年份的数据表明,每年没有明显的基质效应,应将其他年份的数据进行比较以验证这一假设。最好的模型是单体或聚合物颜料和游离花青素的模型。结论:通过使用FT-IR光谱结合化学计量学,可以创建预测模型来估计葡萄酒酿造过程中花色苷的浓度和全球花色苷参数的变化。研究的意义和影响:这些结果增进了我们对结合化学计量学的FT-IR光谱学对花色苷预测的理解,并为将其用作酿酒师的过程控制工具铺平了道路。他们还强调了当接近的参数相互关联时,化学计量学倾向于高估某些预测值。

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