Thin-layer chromatography image segmentation for toxicological studies
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Lytvynenko Volodymyr I., Olszewski Serge, Smolarz Andrzej, Kobernik Serhii , Zaets Eva, Demchenko Violetta, Dontsova Dariia, Kumargazhanova Saule |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 1 - 12 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024 |
| Skrócona nazwa konferencji: | SPIE-IEEE-PSP 2024 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 27 czerwca 2024 do 29 czerwca 2024 |
| Miasto konferencji: | Lublin |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The solution to TLC (Thin-Layer Chromatography) image segmentation in organochlorine pesticides (OCPs) toxicology research is crucial for scientific advancement and industrial applications. OCPs are found in minute concentrations in biological and environmental samples, necessitating precise segmentation for detection. Accurate segmentation is essential due to the potential multiple OCPs in samples, ensuring reliable component separation. Automated segmentation minimizes subjective errors inherent in manual methods, maintaining accuracy in toxicological studies. Standardized, automated processes ensure reproducible results, accelerating analysis for large-scale studies like environmental monitoring. Rapid TLC image segmentation reduces turnaround time critical for time-sensitive studies such as environmental assessments. Integration with analytical techniques like chromatography enhances comprehensive OCP analysis. Challenges include sample complexity with overlapping chemicals on TLC plates, and low OCP concentrations necessitating sensitive detection amidst noise and artifacts. Sophisticated algorithms are required for accurate analysis, sensitive to trace OCP signals and robust against experimental variations. Developing machine learning algorithms involves creating diverse training samples, a resource-intensive but essential task for reliable segmentation. This paper reviews segmentation methods, emphasizing threshold segmentation for its simplicity, speed, and accuracy. Algorithms for signal correction and peak detection enhance clarity, with trapezoidal integration accurately measuring peak areas. Developed in R, the program processes chromatographic images, providing tools for analysis, method refinement, and validation. These advancements are pivotal for ensuring the accuracy, efficiency, and reliability of OCP toxicology studies, crucial for environmental protection, public health, and regulatory compliance. |
