高光谱成像
保质期
采后
偏最小二乘回归
成熟度(心理)
数学
近红外光谱
数据集
支持向量机
园艺
遥感
相关系数
食品科学
化学
人工智能
统计
计算机科学
生物
地理
发展心理学
神经科学
心理学
作者
Yuanyuan Shao,Shengheng Ji,Guantao Xuan,Kaili Wang,Liqiang Xu,Jing Shao
标识
DOI:10.1016/j.postharvbio.2024.112773
摘要
Visible and near infrared (Vis-NIR) hyperspectral imaging was used for soluble solids content (SSC) monitoring and shelf life analysis of winter jujube at different maturity stages. Relationships between SSC of mid-ripe and ripe fruit and their spectral data were investigated using support vector regression (SVR) and partial least squares regression (PLSR) models. The best determination coefficient and residual predictive deviation of the external validation set were 0.837 and 2.47 for mid-ripe winter jujube, and 0.806 and 2.28 for ripe winter jujube, respectively. This implied that the more effective prediction performances for SSC emerged from SVR model using the effective wavelengths screened by successive projections algorithm (SPA). A significant correlation between the spatial distribution of SSC and maturity and shelf life was found in the prediction maps. Furthermore, shelf life was analyzed using a library for support vector machines (LIBSVM) with an accuracy of 89% and 91% for mid-ripe and ripe fruit in the external validation set, respectively. These results indicate the great potential of hyperspectral imaging for quality monitoring and shelf life analysis of postharvest winter jujube.
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