机器学习辅助的拉曼光谱用于对原油棕油质量进行非破坏性分析
Selorm Yao-Say Solomon Adade1,2,3, Akwasi Akomeah Agyekum4, Xorlali Nunekpeku5
1College of Ocean Food and Biological Engineering, Jimei University, Xiamen, PR China. syadade@gmail.com.
NPJ science of food
|January 14, 2026
概括
机器学习和拉曼光谱学提供了一种快速,非破坏性的方法来评估原油棕油质量. 这种技术可以准确预测过氧化物 (PV) 和 (IV) 的含量,克服了传统测试的局限性.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
背景情况:
- 在全球范围内,评估原油棕油的质量至关重要.
- 传统的方法往往是缓慢的,破坏性的,不适合资源贫困的环境.
- 开发快速,非破坏性的技术对于棕油行业至关重要.
研究的目的:
- 开发和验证一种机器学习辅助的拉曼光谱法,用于对原油棕油进行非破坏性质量评估.
- 为了预测关键的质量参数:过氧化物值 (PV) 和值 (IV).
- 为了识别标志着石油质量的光谱特征.
主要方法:
- 从加纳200个原始棕油样本中收集了拉曼光谱.
- 应用第二衍生预处理来增强光谱特征.
- 开发了12种使用变量选择 (CARS,GA,UVE) 和回归方法 (PLS,SVM,RF) 的预测模型.
- 使用相关系数 (Rp) 和性能与偏差 (RPD) 的比率评估模型性能.
主要成果:
- 基因算法-随机森林 (GA-RF) 模型实现了PV (Rp = 0.9831) 和IV (Rp = 0.9752) 的高预测准确度.
- 该GA-RF模型显示出出色的预测能力,RPD值为7.7397的PV和6.3927的IV.
- 确定了与不和 (1287-1657厘米−1) 和氧化 (1748-1840厘米−1) 相关的关键光谱区域.
结论:
- 机器学习辅助的拉曼光谱学为原油棕油质量评估提供了一种快速而非破坏性的方法.
- 这种方法适用于资源贫困地区,在棕油价值链中具有广泛的应用.
- 该GA-RF模型显示了实时质量监测的巨大潜力.
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