Concentration Determination of Aspirin Deposit Patterns Using Quantitative Image Analysis and Artificial Intelligence

Abstract The rapid detection of substandard or falsified pharmaceuticals remains a major challenge, particularly in regions with limited analytical infrastructure. In this study, a low-cost imaging-based method combined with machine learning was investigated for quantitative estimation of aspirin (acetylsalicylic acid) concentration through analysis of dried sessile droplet patterns. Aspirin solutions at concentrations ranging from 6% to 100% (saturation) were deposited on glass slides and allowed to evaporate under ambient conditions. The resulting crystallization patterns were imaged using a high-resolution camera, and 46 geometric and textural features were extracted from each image. Principal component analysis (PCA) revealed concentration-dependent clustering of deposit morphologies, indicating that structural differences in the dried patterns correlate with aspirin concentration. Regression models including Random Forest and XGBoost were trained using the extracted image features, while deep learning models (ResNet-50 and EfficientNet-B3) were applied for direct image-based regression. Amongst the models, EfficientNet-B3 achieved the highest predictive accuracy with an R2 = 0.99, while feature-based models also demonstrated strong predictive performance with R2 = 0.98. This approach was further evaluated using a commercial aspirin tablet sample, and results were compared with concentrations obtained through more conventional UV–visible spectroscopy using the standard addition method. This work demonstrates that droplet deposition imaging combined with artificial intelligence provides a promising, rapid, and non-destructive strategy for pharmaceutical concentration analysis and quality assessment.