使用可见和NIR高光谱成像和机器学习来非破坏性检测中的营养含量
Kai Wu1,2, Zilin Zhang1,2, Xiuhan He1,2
1College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, 030801, China.
Scientific reports
|February 19, 2025
概括
精确检测的检测方法
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:
- 对的营养成分分析对农业和食品工业至关重要.
- 需要非破坏性,快速和准确的方法来分析.
研究的目的:
- 利用可见近红外 (VIS-NIR) 超光谱数据开发和验证用于检测中的原蛋白,素和原脂的模型.
- 为了确定最佳的波长变量,以准确预测营养成分.
主要方法:
- 使用可见近红外 (VIS-NIR) 超光谱成像来收集的光谱数据.
- 使用了变量选择算法,包括竞争性适应性重权取样 (CARS),引导软收缩 (BOSS) 和代保留信息变量 (IRIV).
- 为了营养成分预测,建立了部分最小平方 (PLS),逆向传播 (BP) 和极端学习机器 (ELM) 模型.
主要成果:
- 对原蛋白 (41个变量),素 (38个变量) 和原油脂肪 (22个变量) 确定了最佳波长集.
- 在CARS-IRIV-PLS,BOSS-IRIV-PLS和BOSS-IRIV-ELM模型中,分别证明了它们适合检测原蛋白,宁和原脂.
- 这些模型实现了预测性绩效指标 (Rp2,RMSEp,RPDp),表明了营养成分的有效估计.
结论:
- 与先进的变量选择和建模技术相结合的VIS-NIR高光谱技术,可以对进行准确和快速的营养分析.
- 这些开发的模型为食品营养评估和产业的质量控制提供了有价值的工具.
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