Researchers have developed rapid quantification models to detect melamine adulteration in sports nutrition supplements using benchtop and portable near-infrared (NIR) spectroscopy instruments. This study highlights the efficiency of these methods in ensuring the safety and quality of sports supplements.
Protein powder supplement for sports nutrition © DELstudio - stock.adobe.com
In the quest for enhanced athletic performance and better health, sports nutrition supplements (SNS) have become indispensable. These products, ranging from protein powders to amino acids, cater not only to elite athletes but also to a growing number of fitness enthusiasts and casual gym-goers. However, the burgeoning market for SNS has also opened doors to fraudulent practices, particularly adulteration with harmful substances like melamine to falsely boost protein content. A study, conducted by researchers from Ss Cyril and Methodius University in Skopje in the Republic of North Macedonia, addresses the urgent need for rapid, effective, and sensitive methods to detect such adulteration (1).
Research Overview
Melamine, a nitrogen-rich chemical compound commonly used in the production of plastics, laminates, and resins, is toxic and not intended for human consumption. Melamine has been notoriously used to adulterate food and dietary supplements, leading to severe health hazards, including nephrotoxicity and bladder cancer. Melamine in the human diet poses significant health risks, primarily causing severe kidney damage and kidney stones due to the formation of insoluble crystals in the kidneys. It can also lead to bladder issues and potentially increase the risk of bladder cancer. Prolonged exposure has been linked to reproductive problems, and while human data is limited, animal studies suggest significant adverse effects. The acute toxicity of melamine, particularly in infants and children, underscores the critical need for stringent food safety measures to prevent contamination (2).
Read More: Melamine Detection in Foods
Traditional methods for detecting protein content, such as Kjeldahl and Dumas tests, fail to distinguish melamine from actual protein, necessitating more advanced techniques. The Kjeldahl and Dumas tests are traditional methods for determining protein content in food and other products by measuring the total nitrogen present. However, these tests cannot distinguish between natural proteins and melamine because they only quantify the overall nitrogen levels without identifying the source. Melamine, a nitrogen-rich compound, artificially inflates the nitrogen content, leading to falsely elevated protein values in these assays. Consequently, these methods are inadequate for detecting melamine adulteration, as they are unable to differentiate between nitrogen derived from true proteins and that from harmful additives like melamine (1).
The research team, including Kristina Shutevska, Ana Marija Bajatovska, Liljana Anastasova, Zoran Zhivikj, and Marija Zafirova Gjorgievska, explored the use of near-infrared (NIR) spectroscopy coupled with multivariate data processing to develop rapid quantification models for melamine detection. Their study compared the performance of benchtop and portable NIR instruments, employing a stepwise approach involving orthogonal projections to latent structures discriminant analysis (OPLS-DA) and partial least squares (PLS) analysis (1).
Key Findings
The benchtop NIR instrument demonstrated excellent discrimination among different SNS matrices, with high statistical indicators (R2Y = 0.964, Q2 = 0.933). The portable NIR device, despite its narrower spectral range and lower resolution, showed comparable discrimination capability (R2Y = 0.966, Q2 = 0.931). Both instruments provided reliable quantitative models for melamine estimation, with reasonable errors. Calibration errors were reported as RMSEE: 0.3–2.4% for modeling estimation, and RMSEP: 0.98–2.99% for prediction.
However, higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, likely due to protein agglomeration affecting sample homogeneity. Despite these challenges, the models proved suitable for predicting melamine content in various SNS products (1).
Methodology
The study involved rigorous testing using both NIR instruments on the same samples to eliminate sampling variance. A high-performance liquid chromatography (HPLC) method was used as a reference check for quantifying melamine, ensuring the reliability of the NIR-based models (1).
Conclusion
This innovative study successfully developed rapid quantification models for detecting melamine adulteration in sports nutrition supplements. The benchtop and portable NIR instruments, despite some limitations, demonstrated strong potential for on-site assessment and routine quality control. By providing a faster and more accessible means of ensuring product safety, these methods represent a significant advancement in the fight against food fraud (1).
References
(1) Shutevska, K.;vBajatovska, A. M.; Anastasova, L.; Zhivikj, Z.; Gjorgievska, M. Z.; Spasikj, S.; Ivanovska, T. P.; Makreski, P.; and Geskovski, N. Rapid Quantification Models for Assessing Melamine Adulteration in Sport Nutrition Supplements via Benchtop and portable NIRS instruments. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2024, 317, 124370. DOI: 10.1016/j.saa.2024.124370
(2) Skinner, C. G.; Thomas, J. D.; Osterloh, J. D. Melamine Toxicity. J. Med. Toxicol. 2010, 6, 50–55. DOI: 10.1007/s13181-010-0038-1
Best of the Week: AI, Rapid Food Analysis, Agriculture Analysis, and Soil Property Prediction
September 6th 2024Top articles published this week include a review article on the latest research in agriculture analysis, a peer-reviewed article on near-infrared (NIR) spectroscopy, and an interview about using fluorescence spectroscopy in cheese ripening.
AI-Powered Spectroscopy Faces Hurdles in Rapid Food Analysis
September 4th 2024A recent study reveals on the challenges and limitations of AI-driven spectroscopy methods for rapid food analysis. Despite the promise of these technologies, issues like small sample sizes, misuse of advanced modeling techniques, and validation problems hinder their effectiveness. The authors suggest guidelines for improving accuracy and reliability in both research and industrial settings.
Non-Linear Memory-Based Learning Advances Soil Property Prediction Using vis-NIR Spectral Data
September 3rd 2024Researchers from Zhejiang University have developed a new non-linear memory-based learning (N-MBL) model that enhances the prediction accuracy of soil properties using visible near-infrared (vis-NIR) spectroscopy. By comparing N-MBL with traditional machine learning and local modeling methods, the study reveals its superior performance, particularly in predicting soil organic matter and total nitrogen.
Measuring Muscle Oxidation in Athletes with Near-Infrared Spectroscopy
September 2nd 2024A recent article authored by scientists from the Institute of Sport and Preventive Medicine, part of the University of Saarland (Saarbrücken, Germany), discusses their investigation of the absolute and relative test-retest reliability of the Moxy Monitor, as well as their investigations into side differences of oxygen saturation at the vastus lateralis muscle of both legs in male cyclists.