使用高光谱成像与复制分配策略增强的堆叠合体学习模型相结合,预测Baijiu的芳香成分的组成
Yuexiang Huang1, Jianping Tian1, Xinjun Hu2
1School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
概括
这项研究开发了一种超光谱成像 (HSI) 方法,采用堆叠集体学习 (SEL) 模型,以准确测量大豆-芳香类型Baijiu (SSAB) 中的和酸芳香化合物. 这些发现为Baijiu质量分析提供了一种新的,非破坏性的方法.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 和酸性化合物显著影响Baijiu的香味特征,有助于果味,花或烤的音色.
- 准确量化这些化合物对于Baijiu的质量控制和感官分析至关重要.
研究的目的:
- 量化检测大豆中的和酸含量,以非破坏性的方式.
- 开发和验证使用高光谱成像 (HSI) 和堆叠集体学习 (SEL) 的强大的分析模型.
主要方法:
- 利用高光谱成像 (HSI) 技术来获取光谱数据.
- 采用堆叠集体学习 (SEL) 模型,结合复制分配策略 (RAS) 来解决数据不平衡.
- 开发并优化了一个随机森林 (RF) -RAS-SEL模型用于预测.
主要成果:
- 该RF-RAS-SEL模型在预测含量方面取得了很高的准确性 (Rp2 = 0.9803,RMSEP = 0.3314 mg/L).
- 该模型还在预测酸含量方面表现出色 (Rp2 = 0.9914,RMSEP = 0.4565 mg/L).
- 证明了关键芳香化合物的非破坏性定量检测的可行性.
结论:
- 超光谱成像 (HSI) 与SEL相结合,为分析SSAB中的和酸含量提供了准确而非破坏性的方法.
- 这种方法为Baijiu的质量评估和香味分析提供了一种新而有效的工具.
- 开发的RF-RAS-SEL模型显示了在Baijiu工业中工业应用的巨大潜力.
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