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利用傅里叶变换红外光谱建立模型预测葡萄酒的酒石酸稳定性。

Use of Fourier transform infrared spectroscopy to create models forecasting the tartaric stability of wines.

机构信息

IASMA Fondazione Edmund Mach, Centro Trasferimento Tecnologico, Via E. Mach 1, 38010 San Michele all'Adige, Italy.

出版信息

Talanta. 2013 Dec 15;117:505-10. doi: 10.1016/j.talanta.2013.08.036. Epub 2013 Sep 7.

Abstract

Tartaric instability of wines still represents a serious problem in terms of the commercial value of bottled wines, particularly whites, leading consumers to be suspicious as regards the effective healthiness or wholesomeness of products. The study, carried out on 536 Italian wines, investigated the potential of using Fourier Transform Infrared Spectroscopy, distinguishing between white and red/rosé wines, to create models predicting the instability of wines, assessed in comparison to two of the most widespread methods of reference: the "mini-contact test" (10 min, 0 °C, KHT) and the "cooling test" (5 days, -4 °C). The models proposed, constructed using 80% of the samples and based on Partial Least Squares-Regression and Artificial Neural Networks, were shown to work well in terms of correct classification (from 89% to 97%) of the external validation subset (20%). As regards the more problematical question of technical management of wines before bottling, in the worst cases only 4-6% of unstable samples were erroneously classified as stable.

摘要

葡萄酒的酒石酸不稳定性仍然是瓶装葡萄酒商业价值方面的一个严重问题,特别是对白酒而言,这导致消费者对产品的实际健康性或安全性产生怀疑。本研究对 536 种意大利葡萄酒进行了研究,调查了使用傅里叶变换红外光谱法(FTIR)区分白葡萄酒和红/桃红葡萄酒的潜力,建立模型预测葡萄酒的不稳定性,并与两种最广泛使用的参考方法(“微型接触测试”(10 分钟,0°C,KHT)和“冷却测试”(5 天,-4°C))进行了比较。所提出的模型是使用 80%的样本构建的,基于偏最小二乘回归和人工神经网络,对于外部验证子集(20%)的正确分类(从 89%到 97%)效果良好。至于装瓶前葡萄酒的技术管理这一更具争议性的问题,在最坏的情况下,只有 4-6%的不稳定样本被错误地归类为稳定样本。

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