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枇杷含水率分布图可视化及新鲜度分级
农业监测
2026/09/21
摘要：在枇杷果实的储存过程中，水分含量（（MC）和新鲜度会发生显著变化，因此它们是衡量枇杷果实品质的重要指标。 传统的水分含量和新鲜度检测方法通常具有破坏性且耗时较长，且无法提供整个枇杷果实的水分含量分布图。在本研究 中，我们探讨了高光谱成像技术（（ HSI）在快速、无损预测枇杷水分含量以及可视化其水分分布图方面的应用。此外，我 们还利用偏最小二乘判别分析（（ PLS-DA）、多项式逻辑回归（（ MLogR）和反向传播神经网络（（ BPNN）分类模型，建立了 不同储存时间下枇杷新鲜度等级的判别模型。水分含量得到了良好的预测，其中最佳残差预测偏差（RPD）值为 2.40。 通过最佳预测模型，我们成功可视化了枇杷果实的水分含量分布图。此外，竞争性自适应重加权采样（（CARS）结合BPNN 模型在判别性能上表现出色，校正集和预测集的准确率分别达到了95.56%和93.33%。我们的研究结果表明，高光谱成像 技术是一种有前景的快速无损检测方法，可用于测定枇杷果实的水分含量和新鲜度。
关键词：枇杷果实；水分含量；分布图；新鲜度；分类；高光谱成像；无损检测
期刊：Journal of Food Composition and Analysis
DOI：https://doi.org/10.1016/j.jfca.2024.106265
设备：GaiaField
Visualisation of moisture content distribution maps and classiffcation of freshness level of loquats
Abstract：Moisture content (MC) and freshness undergo evident changes during the storage of loquat fruits and thus are important indices of loquat fruit quality. Conventional methods for assessing MC and freshness are typically destructive and time-consuming, and cannot provide distribution maps of MC in the whole loquats. In this study, the use of hyperspectral imaging (HSI) was investigated for rapid and non-destructive prediction of MC in loquats, and for the visualisation of their MC distribution maps. Furthermore, freshness level discrimination models for loquats at different storage times were established using partial least squares discriminant analysis (PLS-DA), multinomial logistic regression (MLogR), and back-propagation neural network (BPNN) classiffcation models. MC was well-predicted, with the best residual predictive deviation (RPD) of 2.40. Using the best prediction model, the distribution maps of MC in loquats were visualised. Additionally, the competitive adaptive reweighted sampling (CARS)-BPNN model obtained superior discrimination performance, with 95.56 % and 93.33 % accuracy for the calibration and prediction sets. Our results show that HSI is a promising fast and non-destructive method for determining MC and freshness of loquats.
Keywords：Loquat fruit; Moisture content; Distribution maps; Freshness; Classiffcation; Hyperspectral imaging; Non-destructive
