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

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.