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On-The-Go VIS + SW − NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard

机译:实时VIS + SW-NIR光谱仪可作为葡萄园内葡萄成分的可靠监测工具

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摘要

Visible-Short Wave Near Infrared (VIS + SW − NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW − NIR spectrometer, operating in the 570–990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters’ prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R2cv) and prediction (R2P) of 0.92 and 0.95 for TSS, R2cv = 0.75, and R2p = 0.79 for anthocyanins, and R2cv = 0.42 and R2p = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard.
机译:可见-短波近红外(VIS + SW-NIR)光谱是打破精密葡萄栽培中的下一个障碍的真正替代方法,从而可以可靠地监视葡萄园内的葡萄成分,从而促进决策过程,以处理葡萄的质量分选和例如,收获计划。使用VIS + SW-NIR光谱仪,以570-990 nm光谱范围运行,并以5 km / h的速度移动平台,对葡萄簇进行实时光谱测量。在树冠东侧2017年葡萄成熟期间的四个日期进行了光谱测量,该光谱在集束关闭时已部分脱叶。在整个测量季节中,共使用标准的湿化学参考方法对144个实验块进行了监测,采样并对其果实中的总可溶性固形物(TSS),花色苷和总多酚浓度进行了分析。偏最小二乘(PLS)回归被用作训练葡萄成分参数的预测模型的算法。最佳的交叉验证和外部验证(预测)模型得出的交叉验证(R 2 cv)和预测(R 2 P)的确定系数分别为0.92和0.95 TSS,花色苷的R 2 cv = 0.75,R 2 p = 0.79,R 2 cv = 0.42和R 2 p = 0.43(对于多酚总量)。使用该技术针对不同日期生成的葡萄园变异性图说明了监视时空动态以及在商业葡萄园中葡萄成熟过程中总可溶性固形物,花色苷和总多酚分布的能力。

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