Doctorado en ciencia aplicada, Universidad Antonio Nariño, Bogotá, Colombia.
Facultad de ingeniería agronómica, Universidad del Tolima, Ibagué, Colombia.
J Sci Food Agric. 2021 Feb;101(3):897-906. doi: 10.1002/jsfa.10697. Epub 2020 Aug 27.
'Hass' avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non-destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400-1000 nm.
No correlation between fruit weight and color with the percentage of DM was found in the study area. Cross-validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross-validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R = 0.9, a root-mean-square error of 2.6 g kg DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2.
The results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry.
哈斯鳄梨因其感官特性而越来越受欢迎，因此需要开发新技术来保证出口质量。鳄梨果实不会在树上成熟，其成熟度的目视分类并不准确。最常用的果实成熟度指标是干物质（DM）的百分比。本研究旨在研究一种使用高光谱图像对 400-1000nm 光谱范围内果实的 DM 百分比进行非破坏性预测的方法。
在研究区域内，未发现果实重量和颜色与 DM 百分比之间存在相关性。比较了不同数据源的交叉验证效率，包括光谱提取区域（中心、从花梗到基部的线和整个果实）和一个或两个果实面的平均值。比较了四个线性回归模型。选择使用支持向量机回归的整个果实和每个果实两侧平均值的数据进行预测测试。根据交叉验证的概念，选择了五组校准和测试数据进行优化校准。最佳测试预测集的 R = 0.9，DM 为 2.6g/kg 的均方根误差为 2.6g/kg，皮尔逊相关系数为 0.95，预测与偏差比为 3.2。
研究结果表明，高光谱图像可以对出口水果进行分类，并做出收获决策。© 2020 化学工业协会。
