Phenolics contents in wine grapes are key indicators for assessing ripeness. Near-infrared hyperspectral images during ripening have been explored to achieve an effective method for predicting phenolics contents. Principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) models were built, respectively. The results show that SVR behaves globally better than PLSR and PCR, except in predicting tannins content of seeds. For the best prediction results, the squared correlation coefficient and root mean square error reached 0.8960 and 0.1069g/L (+)-catechin equivalents (CE), respectively, for tannins in skins, 0.9065 and 0.1776 (g/L CE) for total iron-reactive phenolics (TIRP) in skins, 0.8789 and 0.1442 (g/L M3G) for anthocyanins in skins, 0.9243 and 0.2401 (g/L CE) for tannins in seeds, and 0.8790 and 0.5190 (g/L CE) for TIRP in seeds. Our results indicated that NIR hyperspectral imaging has good prospects for evaluation of phenolics in wine grapes.

译文

酿酒葡萄中的酚类含量是评估成熟度的关键指标。已经探索了成熟期间的近红外高光谱图像,以实现预测酚含量的有效方法。分别建立了主成分回归(PCR),偏最小二乘回归(PLSR)和支持向量回归(SVR)模型。结果表明,除了预测种子中单宁含量外,SVR的总体表现优于PLSR和PCR。为了获得最佳的预测结果,皮肤中单宁的平方相关系数和均方根误差分别达到0.8960和0.1069g / L()-儿茶素当量(CE),总铁分别为0.9065和0.1776(g / L CE) -皮肤中的反应性酚醛(TIRP),皮肤中的花色苷为0.8789和0.1442(g / L M3G),种子中的单宁酸为0.9243和0.2401(g / L CE),TIRP为0.8790和0.5190(g / L CE)种子。我们的结果表明,NIR高光谱成像在评估酿酒葡萄中的酚类物质方面具有良好的前景。

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