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首页> 外文期刊>CyTA Journal of Food >Application of artificial neural networks coupled to UV?￠????VIS?￠????NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures
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Application of artificial neural networks coupled to UV?￠????VIS?￠????NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures

机译：人工神经网络与紫外光谱，可见光谱，近红外光谱的结合，用于快速定量分析含水混合物中的酒类化合物

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Ultraviolet?￠????visible (UV?￠????VIS) and near-infrared (NIR) spectroscopy coupled to artificial neural networks (ANNs) was used as a non-destructive technique to quantify ethanol, glucose, glycerol, tartaric acid, malic acid, acetic acid and lactic acid in aqueous mixtures. Spectral data were obtained for 152 samples. Differing pre-treatments were applied to the spectra and ANN models were obtained using raw and pre-treated data to evaluate several spectral wavelength groupings and ANN training conditions. Feasible calibration models were obtained for ethanol, malic acid and tartaric acid. To validate the process, 120 new samples were measured using the best ANN models. The determination coefficients for the three compounds using this validation set were above 0.9. The results showed the importance of good parameter selection when training the ANN to obtain reliable models. Coupling UV?￠????VIS?￠????NIR spectroscopy to ANN could provide an alternative to conventional chemical methods for determining ethanol, tartaric acid and malic acid in wines.
机译：紫外可见光谱（UV）和近红外光谱（NIR）结合人工神经网络（ANN）被用作定量分析乙醇，葡萄糖，甘油，水混合物中的酒石酸，苹果酸，乙酸和乳酸。获得了152个样品的光谱数据。将不同的预处理应用于光谱，并使用原始数据和预处理的数据获得ANN模型，以评估几种光谱波长分组和ANN训练条件。获得了乙醇，苹果酸和酒石酸的可行校准模型。为了验证该过程，使用最佳的ANN模型测量了120个新样品。使用该验证集的三种化合物的测定系数均高于0.9。结果表明，在训练人工神经网络以获得可靠模型时，良好的参数选择非常重要。将UV-VIS-VIS-NIR光谱与ANN耦合可以为测定葡萄酒中乙醇，酒石酸和苹果酸的常规化学方法提供一种替代方法。

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