进步米 ((Chenopodium 米将d.) 的时间. 使用高光谱成像进行质量评估
Xiaojiang Wang1, Junying Han1, Chengzhong Liu1
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730000, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 20, 2025
概括
光谱融合和特征增强显著改善了菜 (Chenopodium quinoa Willd.) 的非破坏性质量评估. 该方法使用先进的光谱分析和可解释的AI准确预测蛋白质,粉和含量.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
背景情况:
- 昆是一种营养丰富的食物,需要使用非破坏性质量评估方法.
- 精确测量蛋白质,粉和含量对于子质量至关重要.
研究的目的:
- 通过使用光谱技术,提高昆的主要质量属性的预测准确度.
- 为了验证光谱融合和特征增强,以改善麦的质量评估.
- 开发一种可解释的人工智能模型来评估麦的质量.
主要方法:
- 结合的可见近红外 (VIS-NIR) 和近红外 (NIR) 光谱数据.
- 使用光谱特征选择算法 (随机青,SPA,CARS) 进行频段选择.
- 集成可解释的人工智能 (Shapley增量扩展 - SHAP) 与预测模型.
主要成果:
- 显著改善了蛋白质 (RP2=0.9561),粉 (RP2=0.9692) 和总 (RP2=0.9420) 的预测模型.
- 光谱特征增强加强了特征带,提高了模型性能.
- SHAP值证实了开发的AI模型的有效性和可解释性.
结论:
- 频谱信息融合和特征增强为非破坏性昆质量评估提供了强大的方法.
- 集成的人工智能框架提供了可靠和可解释的洞察力,对昆的质量属性.
- 这种方法有助于检测华的关键质量参数.
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