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使用VIS-NIR光谱和人工神经网络开发和预测储存的枣子的非破坏性质量指数 (Qi)

Mahmoud G Elamshity¹, Abdullah M Alhamdan¹

¹Chair of Dates Industry and Technology, Department of Agricultural Engineering, College of Food and Agricultural Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Foods (Basel, Switzerland)

|September 13, 2025

概括

这项研究引入了一种非破坏性方法,使用可见近红外光谱和人工神经网络来预测枣果在储存期间的质量. 最好的质量在-18°C下保持,使用特定的包装.

科学领域:

农业科学农业科学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

由于各种因素,日期水果在储存期间质量下降.
有效的质量评估需要非破坏性方法.
了解储存对物理化学特征的影响至关重要.

研究的目的:

开发一种非破坏性的方法来评估和预测在储存期间的枣子质量.
使用可见近红外 (VIS-NIR) 光谱和人工神经网络 (ANN) 建模一个复合质量指数 (Qi).
评估储存条件 (温度,包装) 对日果品质的影响.

主要方法:

从Sukkary和Khlass日品种收集的光谱数据 (410-990nm) 在不同的温度和包装下存储了12个月.
测量了关键的物理化学特征 (水分,pH,硬度,TSS,密度,颜色,微生物负载) 以开发一个复合质量指数 (Qi).
应用了部分最小平方回归 (PLSR) 和人工神经网络 (ANN) 来进行建模,使用第二导数转换预处理的光谱数据.

主要成果:

在复合质量指数 (Qi) 方面,ANN模型实现了高预测准确性 (R2高达0.944),表现优于PLSR.
鉴于压缩半刚性塑料容器 (PSSPC) 的最佳存储条件是-18°C.
与线性模型相比,二级 Qi 模型更适合于非线性降解模式.

结论:

开发的VIS-NIR光谱和ANN系统提供了一种可靠的,非侵入性的实时枣果质量监测方法.
这种方法可以帮助自动化决策,用于收获后管理,包装选择和保质期预测.
在PSSPC中以-18°C的温度储存显著提高了日果的质量保留,并最大限度地减少了微生物破坏.

关键词:

一个年龄,一个年龄.这是VIS-NIR.枣子果实果实果实这是微生物的微生物.建模建模模型建模模型物理和机械性能物理和机械性能.质量指数质量指数储存储存储存储存储存是一个

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