近红外光谱和机器学习用于快速预测瓶的质量
Xiao Guo1,2, Hongyu Huang3, Haiyan Wang2
1College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China.
Foods (Basel, Switzerland)
|July 29, 2025
概括
接近红外光谱 (NIRS) 与机器学习相结合,可以准确预测瓶的质量. 这种快速方法模拟了蛋白质和自由氨基酸 (FAA) 含量,克服了传统的限制.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 瓶的质量取决于蛋白质和氨基酸含量.
- 测量这些参数的传统方法是复杂的,耗时的,昂贵的.
研究的目的:
- 开发一种快速而精确的方法来预测瓶中的蛋白质和自由氨基酸 (FAA) 含量.
- 使用近红外光谱学 (NIRS) 结合机器学习算法进行质量评估.
主要方法:
- 传统方法测量了蛋白质和FAA含量.
- 采集和处理近红外光谱数据使用MSC和SNV.
- 特性重要性选择确定了与蛋白质和FAA相关的关键光谱带.
- 为了预测,开发了包括SVR,RR,RFR和FCNN在内的机器学习模型.
主要成果:
- 斜坡回归 (RR) 显示了最佳的性能.
- RR实现了高的确定系数 (R2):蛋白质为0.96和FAA为0.77.
- 记录了蛋白质的0.23和FAA的0.5的低根平均平方误差 (RMSE) 值.
结论:
- 成功开发了一种精确和快速的预测模型,用于关键的瓶品质指数.
- 与机器学习相结合,NIRS为传统质量评估方法提供了有效的替代方案.
- 这种方法可以通过更快的质量控制来使农业行业显著受益.
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