Quantitative analysis of creatine monohydrate using near-infrared spectroscopy and hyperspectral imaging combined with multi-model fusion and data fusion strategies

IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Meiling Zhu, Weiran Song, Xuan Tang and Xiangzeng Kong
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Abstract

Creatine monohydrate is an important sports nutrition supplement that enhances energy and promotes muscle growth. Recent concerns about the quality and authenticity of creatine monohydrate have highlighted the urgent need for rapid and cost-effective assessment methods. This study presents a new approach for assessing the quality of creatine monohydrate using spectroscopy combined with machine learning. Spectral data of creatine monohydrate samples from 15 brands are acquired using portable near-infrared (NIR) spectroscopy and benchtop hyperspectral imaging (HSI). Machine learning methods are employed to extract high-level features from the spectral data and model the relationship between the data and creatine concentrations. The root mean square error (RMSE) for models based on NIR data ranges from 0.258 to 0.291, whereas those derived from HSI data vary between 0.468 and 0.576. To improve the accuracy and reliability of spectral data analysis, multi-model fusion and data fusion strategies are used to integrate the outputs of different models and data from different sources, respectively. By combining NIR-HSI data fusion with multi-model fusion, the lowest RMSE for creatine quantification is reduced to 0.18. These results demonstrate that spectroscopic techniques coupled with machine learning can provide a rapid and cost-effective solution for assessing the quality and authenticity of creatine monohydrate.

Abstract Image

结合多模型融合和数据融合策略的近红外光谱和高光谱成像定量分析一水肌酸。
一水肌酸是一种重要的运动营养补充剂,可以增强能量,促进肌肉生长。最近对一水肌酸的质量和真实性的关注突出了对快速和具有成本效益的评估方法的迫切需要。本研究提出了一种利用光谱与机器学习相结合来评估一水肌酸质量的新方法。采用便携式近红外(NIR)光谱和台式高光谱成像(HSI)技术获得了15个品牌的一水肌酸样品的光谱数据。采用机器学习方法从光谱数据中提取高级特征,并建立数据与肌酸浓度之间的关系模型。基于近红外数据的模型的均方根误差(RMSE)在0.258 ~ 0.291之间,而基于恒指数据的模型的均方根误差在0.468 ~ 0.576之间。为了提高光谱数据分析的准确性和可靠性,采用多模型融合和数据融合策略,分别对不同模型的输出和不同来源的数据进行融合。通过将NIR-HSI数据融合与多模型融合相结合,肌酸定量的最低RMSE降至0.18。这些结果表明,光谱技术与机器学习相结合,可以为评估一水肌酸的质量和真实性提供一种快速、经济的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
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