Detecting the ingredients of sports drinks has essential application value in protecting consumer health and safety and ensuring the production quality of enterprises. With the rapid expansion of the sports drink market, consumers are increasingly concerned about the safety and authenticity of beverage ingredients. Therefore, there is an urgent need for an efficient detection method to meet market demand and protect consumer rights. This study aims to develop a Raman spectroscopy technique based on the combination of Variational Mode Decomposition (VMD) and Partial Least Squares Regression (PLSR) algorithms for rapid and accurate detection of ingredients in sports drinks. Firstly, spectral data of various sports drink samples were obtained through Raman spectroscopy technology. Raman spectroscopy is often affected by noise, baseline drift, and other interference factors, leading to a decrease in the accuracy of analysis results. To solve this problem, this article introduces the VMD algorithm to decompose complex signals into multiple modalities, effectively extracting essential features from the signal and reducing noise interference in the analysis results. The PLSR algorithm was used to model the spectral features processed by VMD, and the predictive ability and stability of the model were evaluated through cross-validation and external validation. The R² and RMSE of the test set were 0.9759 and 0.0024, respectively, and the R² in the low-concentration application testing stage was 0.8901. The results indicate that the VMD-PLSR method has significantly better accuracy and robustness in component detection than traditional regression analysis methods. In summary, the VMD-PLSR algorithm and Raman spectroscopy-based method for detecting ingredients in sports drinks have demonstrated outstanding potential for application, providing an efficient and reliable technical means for quality control and ingredient analysis of sports drinks. Future work will focus on optimizing algorithm parameters and expanding the application of this method in detecting other food and beverage ingredients further to improve the efficiency and accuracy of food safety detection.