FARMATSEVTIK TAHLILDA "GREEN CHEMISTRY" TAMOYILLARI ASOSIDA HPLC METODLARINI SUN'IY INTELLEKT (AI) ALGORITMLARI YORDAMIDA OPTIMALLASHTIRISH
DOI:
https://doi.org/10.5281/zenodo.21448926Ключевые слова:
Yashil xromatografiya, Sun'iy intellekt, HPLC, Mashinali o‘qitish, Ekologik barqarorlik, Metod optimallashtirish, Dori vositalari tahlili.Аннотация
Ushbu tezisda farmatsevtika sanoatida sifat nazorati (QC) laboratoriyalarining atrof-muhitga salbiy ta'sirini kamaytirish maqsadida, yuqori samarali suyuqlik xromatografiyasi (HPLC) usullarini "Yashil kimyo" (Green Chemistry) mezonlari asosida optimallashtirish masalasi ko‘rib chiqilgan. An'anaviy sinov-xato (trial-and-error) yondashuvlarining vaqt va zaharli erituvchilar sarfi bo'yicha cheklovlarini bartaraf etish uchun sun'iy intellekt (AI) va mashinali o‘qitish (Machine Learning) algoritmlarini integratsiya qilish metodologiyasi taklif etilgan. Sun'iy neyron tarmoqlari (ANN) yordamida zaharli asetonitril erituvchisini ekologik xavfsiz etanol va suv aralashmalari bilan almashtirish, mobil faza oqim tezligini kamaytirish va xromatografik ajralish sifatini maksimal darajada saqlab qolish imkoniyatlari ilmiy asoslangan.Библиографические ссылки
Gaida, M., et al. (2025). Green Analytical Chemistry: Principles and current algorithms for liquid chromatography optimization. Trends in Analytical Chemistry, 172, 117-128.
Płotka-Wasylka, J., et al. (2024). The AGREE—Analytical Greenness Metric: Approach and tools. Analytical Chemistry, 92(14), 10076–10082.
Pravin, S., & Kumar, A. (2026). Machine learning and artificial neural networks in pharmaceutical method development. Journal of Pharmaceutical and Biomedical Analysis, 238, 115-124.
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2026-07-20
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Yusupova, K. (2026). FARMATSEVTIK TAHLILDA "GREEN CHEMISTRY" TAMOYILLARI ASOSIDA HPLC METODLARINI SUN’IY INTELLEKT (AI) ALGORITMLARI YORDAMIDA OPTIMALLASHTIRISH. Наука и инновации, 4(66), 85-85. https://doi.org/10.5281/zenodo.21448926
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