使用一维卷积自编码器和近红外光谱仪检测玉米核中的蛋白质,粉,油和水分含量
Ozcan Cataltas1, Kemal Tutuncu1
1Faculty of Technology, Selcuk University, Konya, Turkey.
PeerJ. Computer science
|June 22, 2023
概括
一种使用卷积自编码器与近红外光谱学的新深度学习方法准确地分析了玉米核的组成. 这种方法减少了光谱特征,改善了蛋白质,水分,油和粉含量的分析.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 对谷物产品进行准确的营养和化学分析对于质量评估至关重要.
- 近红外光谱 (NIRS) 为快速分析提供了优势,但通常依赖于试验和错误进行预处理和模型选择.
- 与NIRS的深度学习集成提供了一种新的方法来克服现有的局限性.
研究的目的:
- 开发和评估一种新的方法,将深度学习与NIRS结合起来,用于分析玉米核的组成.
- 评估一维卷积自编码器 (1D-CAE) 在NIRS中减小光谱数据的有效性.
- 将拟议方法的性能与传统的化学测量技术进行比较.
主要方法:
- 开发了一种一维卷积自编码器 (1D-CAE) 来处理玉米核的光谱数据.
- 1D-CAE模型从高维光谱数据 (700个特征) 中生成了一个低维表示 (32个潜在变量).
- 使用这些潜在变量构建多重线性回归 (MLR) 模型,以预测蛋白质,水分,油脂和粉含量.
主要成果:
- 1D-CAE实现了高的重建精度,平均根-平均平方百分比误差 (RMSPE) 为1.90% (校准) 和2.27% (预测).
- 频谱数据的维度从700个特征显著减少到32个潜在变量.
- 拟议的1D-CAE-MLR方法与部分最小平方回归和主要成分回归相比,表现优越,特别是在特定数据集 (MP5,MP6) 上.
结论:
- 1D-CAE与NIRS的整合为分析玉米核的营养和化学成分提供了有效和高效的方法.
- 这种基于深度学习的方法显著降低了光谱数据的维度,同时保持了高的预测准确性.
- 这些发现表明,使用NIRS.在农产品分析中应用深度学习的有希望的新方向.
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