使用全传输Vis-NIR光谱和组合算法检测番茄中可溶性固体的含量
Letian Cai1, Yizhi Zhang1, Zhonglei Cai1
1Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
这项研究表明,可见和近红外 (Vis-NIR) 光谱学可以准确预测番茄溶性固体含量 (SSC). 将波长选择与智能算法相结合,显著提高了这一关键风味指标的预测准确性.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 溶性固体含量 (SSC) 对番茄风味至关重要,但传统的测量方法具有破坏性和耗时性.
- 开发快速,非破坏性的SSC评估方法对于番茄行业至关重要.
研究的目的:
- 调查可见和近红外 (Vis-NIR) 全透射光谱学对预测番茄SSC的有效性.
- 评估数据采集导向,光谱预处理和波长选择对SSC回归模型的影响.
主要方法:
- 使用全透射率Vis-NIR光谱法与多位数据采集 (Z1和Z2方向).
- 应用了四种光谱预处理技术和三种波长选择方法.
- 开发了部分最小平方回归 (PLSR) 模型用于SSC预测.
主要成果:
- 使用来自Z2方向的光谱和预处理数据的PLSR模型实现了良好的预测 (R = 0.877,RMSEP = 0.417%).
- 波长选择方法的组合 (向后变量选择 - 部分最小方形和模拟化) 将波长减少到20个,并提高了预测准确性 (R = 0.912,RMSEP = 0.354%).
结论:
- 完全传输的Vis-NIR光谱对于非破坏性预测西红SSC.有效.
- 将先进的波长选择与智能算法相结合,为番茄的快速和准确的SSC评估提供了一个有希望的方法.
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