2026-10-12T12:59:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027205 2026-09-29T18:07:30Z category_4 category_146 category_12 category_21 category_272 category_873 category_734 category_931 portal_63 item_type_6 month_year_09_2026

Concentration Determination of Aspirin Deposit Patterns Using Quantitative Image Analysis and Artificial Intelligence Sanam Pudasaini (12406697) Danielle O. Eniola (25138499) Oliver Steinbock (1471618) Beni B. Dangi (1652647) Biochemistry Space Science Cell Biology Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified standard addition method resulting crystallization patterns limited analytical infrastructure highest predictive accuracy falsified pharmaceuticals remains dried patterns correlate based method combined principal component analysis artificial intelligence provides 2 </ sup deep learning models pharmaceutical concentration analysis extracted image features artificial intelligence textural features machine learning based regression work demonstrates trained using structural differences revealed concentration resolution camera quantitative estimation quality assessment major challenge imaged using glass slides evaluated using direct image destructive strategy deposit morphologies dependent clustering cost imaging concentrations ranging concentrations obtained concentration determination ambient conditions acetylsalicylic acid 46 geometric 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. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acsomega.6c05231.s001 https://figshare.com/articles/journal_contribution/Concentration_Determination_of_Aspirin_Deposit_Patterns_Using_Quantitative_Image_Analysis_and_Artificial_Intelligence/34027205 CC BY-NC 4.0