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对乳粉改的Vis-NIR光谱分辨分析的光谱分离度方法

Lu Yuan¹, Xianghui Chen², Yongqi Huang²

¹Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|June 10, 2023

概括

检测奶粉 adulteration 是一个挑战. 可见和近红外 (Vis-NIR) 光谱与k-最近邻居 (kNN) 在识别假奶粉方面实现了100%的准确性,为质量控制提供了一种新方法.

科学领域:

分析化学分析化学
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:

奶粉造,特别是将普通奶粉添加到高端品牌中,由于样本之间的高度相似性,这是一个重大挑战.
准确的检测方法对于确保消费者安全和保持乳制品行业市场完整性至关重要.

研究的目的:

开发一种高精度的分辨分析模型,用可见和近红外 (Vis-NIR) 光谱检测高端奶粉中的造物质.
提出和验证一种新的波长选择方法,分离度优先组合-kNN (SDPC-kNN),以优化光谱分析.

主要方法:

使用可见和近红外 (Vis-NIR) 光谱学从纯牛奶粉和造牛奶粉样本中收集光谱数据.
频谱数据通过使用标准的正常变量转换和诺里斯衍生过器进行了预处理.
开发并应用了一种新的波长选择方法,分离度优先组合-kNN (SDPC-kNN) 和其变体SDPC-波长逐步淘汰-kNN (SDPC-WSP-kNN).

主要成果:

开发的模型在不同频谱区域的预测 (RARp) 和验证 (RARv) 中实现了高的识别准确率.
在长NIR区域 (1100-2498nm) 的单波长kNN模型达到100%的RARp,在1174nm的最佳模型达到97.4%的RARv.
在可见和短NIR区域的SDPC-WSP-kNN模型也表现出色,RARp高达100%,RARv高达96.1%.

结论:

与少波长kNN相结合的Vis-NIR光谱学提供了一种可行且高度准确的乳粉改检测方法.
拟议的SDPC方法是一种有效且计算效率高的波长选择技术,适用于各种光谱差异分析.
少数波长的光谱图为为特定的光谱区域开发专门的小型化光谱仪提供了宝贵的参考.

关键词:

粉末牛奶的改歧视性分离剂微型化的波长优化优化分离度优先组合分离度优先组合分离度的频谱分离度的频谱.可见光和近红外光谱学.波长逐步逐步淘汰的逐步淘汰方式

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