(P06-021-25) Detection of Creatine Adulteration With Cornstarch Using Mid-Infrared Spectroscopy and Chemometric Methods

Objectives: To detect the adulteration of creatine supplements with cornstarch using attenuated total reflection-Fourier transform infrared (ATR-FTIR) combined with chemometric methods. We hypothesize that ATR-FTIR can easily and rapidly detect the adulteration of creatine samples with cornstarch.

Methods: Six commercial brands of creatine supplement were adulterated with commercial cornstarch (10 to 100%, w/w). The spectra were collected in triplicate using the ATR-FTIR Agilent Cary 630 and the MicroLab FTIR software, across a spectral range of 4000-400 cm^-1, with resolution of 4 cm^-1 and using 32 scans in the absorbance mode. Three hundred micrograms of each sample were used, and analysis was performed under controlled conditions of temperature and humidity (22,1 ± 0.2ºC and 38 ± 2.1%, respectively). Chemometric analysis was performed using GNU Octave software. For minimizing the variability unrelated to the component of interest, the Multiplicative Scatter Correction preprocess was appied. A total of 183 samples were divided into training (80%) and test (20%) sets using the Kennard-Stone method, and the classification model was constructed using Partial Least Squares Discriminant Analysis (PLS-DA).

Results: PLS-DA showed 95% precision, 100% sensitivity, and 98% specificity in the training set, with most samples correctly classified, and only two samples classified as false negatives. In the validation set, the model achieved 100% accuracy, precision, sensitivity, and specificity in distinguishing pure from adulterated creatine with cornstarch, demonstrating robust performance that is essential for quality control applications.

Conclusions: ATR-FTIR, combined with chemometric analysis, successfully detected creatine adulteration with cornstarch, suggesting that this method is promising, fast, and efficient for detecting such adulteration in creatine supplements.

Funding Sources: This study was funded by the Research and Support Foundation of Espírito Santo (FAPES), grants # 2022-41989 and # 2023-VHF35.