使用基于有效波长选择算法的可见近红外高光谱成像来预测类水果中的可溶固体含量
Min-Jee Kim1, Woo-Hyeong Yu2, Doo-Jin Song3
1Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon 24341, Republic of Korea.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
可见和近红外 (VNIR) 超光谱成像准确预测的可溶性固体含量 (SSC). 这种非破坏性方法提高了水果质量评估和收获后管理.
科学领域:
- 农业工程 农业工程
- 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:
- 果的质量是使用外部和内部因素来评估的.
- 非破坏性方法对于有效的收获后评估至关重要.
- 可见和近红外 (VNIR) 超光谱成像提供了快速的质量评估.
研究的目的:
- 评估VNIR高光谱成像,以预测的可溶性固体含量 (SSC).
- 开发和优化类水果中SSC的预测模型.
主要方法:
- 使用了一个VNIR高光谱成像系统 (400-1000nm).
- 在两个方向上从类水果获得了超光谱图像.
- 开发了一种部分最小平方回归 (PLSR) 模型,结合了竞争性适应性重权取样 (CARS) 来进行波长选择和异常值去除.
主要成果:
- 卡斯-PLSR模型实现了约0.75.5的确定系数 (R2).
- 该模型表明根平均平方误差 (RMSE) 约为0.56°Brix.
- 通过频谱预处理,有效的波长选择和异常值检测,优化模型性能.
结论:
- VNIR高光谱成像是一种可行的非破坏性技术,用于预测类SSC.
- 开发的CARS-PLSR模型显示了实时质量评估的潜力.
- 这些发现支持在农业和食品收获后管理系统中的应用.
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