Rapid detection and spectroscopic feature analysis of mineral content in camel milk using fourier-transform mid-infrared spectroscopy and traditional machine learning algorithms

文献类型： 外文期刊

第一作者： Li, Yongqing

作者： Li, Yongqing;Fan, Yikai;Gao, Jingyi;Liu, Li;Wang, Haitong;Chu, Chu;Yang, Zhuo;Yang, Guochang;Wen, Peipei;Wang, Dongwei;Zhang, Shujun;Li, Yongqing;Liu, Li;Cao, Lijun;Hu, Bo;Abula, Zunongjiang;Xieermaola, Yeerlan;Zheng, Wenxin

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关键词： Camel milk; Minerals; FT-MIRS; Machine learning; Detection method

期刊名称：FOOD CONTROL （ 影响因子：6.3； 五年影响因子：6.1 ）

ISSN： 0956-7135

年卷期： 2025 年 169 卷

页码：

收录情况： SCI

摘要： Camel milk is rich in nutrients and bioactive factors, with mineral content generally higher than that of cow milk, but there is currently no internationally established, rapid, batch-testing method for the mineral content. This study collected samples of camel milk from 113 locations in Xinjiang, China. For the first time internationally, based on the true mineral values determined by ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) and the extracted mid-infrared spectra data, a quantitative prediction model for 10 key minerals (Ca, Fe, K, Mg, Mn, Na, P, Sr, Zn, and Se) was established using Fourier-Transform Mid-Infrared Spectroscopy (FTMIRS) and the traditional machine learning algorithm Partial Least Squares Regression. The Rt2 of the test set ranged from 0.61 to 0.91, RMSEt ranged from 2.21ug/kg(Se) to 197.08 mg/kg(K) and the RPDt from 1.59 to 3.28. In addition, the distribution, patterns, and correlations of mineral-related characteristic wavenumbers in camel milk were summarized. This study opens a new avenue for the rapid detection of minerals in camel milk and fills the research gap in in using FT-MIRS to detect mineral content in camel milk.

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