高光谱成像
梨
采后
瘀伤
光谱成像
偏最小二乘回归
模式识别(心理学)
遥感
人工智能
数学
环境科学
园艺
计算机科学
统计
地质学
生物
医学
外科
作者
Yiting Li,Sicong You,Shasha Wu,Mengyao Wang,Song Jin,Weijie Lan,Kang Tu,Leiqing Pan
标识
DOI:10.1016/j.postharvbio.2023.112668
摘要
This work aimed to compare the discrimination ability and detection of limit on the different early implicit bruised pears based on visible and short-wave near-infrared (400–1000 nm) hyperspectral imaging (Vis-SWNIR-HSI) features and spectral information. The changes of physical characteristics were much more sensitive than chemical parameters for bruised pears while the postharvest storage. The pear bruising changes can be addressed to the spectral variations at 416–486 nm, 516–646 nm, 939–954 nm, and 973 nm. The partial least squares discrimination models (PLS-DA) based on selected HSI spectral variables can satisfactorily identify the bruised pear area about 63.32 ± 9.02 mm2, with the discrimination accuracy of 90%, but not available of using imaging features. Further, PLS-DA models based on the fusion of selected HSI spectral variables and imaging features reached the discrimination rate of 98.8% between bruised and non-bruised pears, and provided the discrimination rate of 95% to successfully identify the early bruised pears with bruise area of 72.18 ± 7.32 mm2 after 1 day of storage. Consequently, the combination of hyperspectral imaging features and spectral variables had the best ability to detect early implicit bruised 'Korla' fragrant pears during postharvest storage.
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