Chen Shanshan, Zhang Fangfang, Ning Jifeng, Liu Xu, Zhang Zhenwen, Yang Shuqin
College of Information Engineering, Northwest A&F University, Yangling 712100, China.
College of Enology, Northwest A&F University, Yangling 712100, China.
Food Chem. 2015 Apr 1;172:788-93. doi: 10.1016/j.foodchem.2014.09.119. Epub 2014 Sep 28.
The aim of this study was to demonstrate the capability of hyperspectral imaging in predicting anthocyanin content changes in wine grapes during ripening. One hundred twenty groups of Cabernet Sauvignon grapes were collected periodically after veraison. The hyperspectral images were recorded by a hyperspectral imaging system with a spectral range from 900 to 1700 nm. The anthocyanin content was measured by the pH differential method. A quantitative model was developed using partial least squares regression (PLSR) or support vector regression (SVR) for calculating the anthocyanin content. The best model was obtained using SVR, yielding a coefficient of validation (P-R(2)) of 0.9414 and a root mean square error of prediction (RMSEP) of 0.0046, higher than the PLSR model, which had a P-R(2) of 0.8407 and a RMSEP of 0.0129. Therefore, hyperspectral imaging can be a fast and non-destructive method for predicting the anthocyanin content of wine grapes during ripening.
本研究的目的是证明高光谱成像在预测酿酒葡萄成熟过程中花青素含量变化方面的能力。在转色期后定期收集120组赤霞珠葡萄。通过光谱范围为900至1700nm的高光谱成像系统记录高光谱图像。采用pH差值法测定花青素含量。使用偏最小二乘回归（PLSR）或支持向量回归（SVR）建立了用于计算花青素含量的定量模型。使用SVR获得了最佳模型，验证系数（P-R(2)）为0.9414，预测均方根误差（RMSEP）为0.0046，高于PLSR模型，PLSR模型的P-R(2)为0.8407，RMSEP为0.0129。因此，高光谱成像可以成为一种快速且无损的方法，用于预测酿酒葡萄成熟过程中的花青素含量。
Zotero 插件