Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination
Интеграция искусственного интеллекта в устойчивую интеллектуальную аналитическую химию для анализа влияния выбора делителя на эффективность УФ-спектрофотометрического определения комбинации солифенацина и мирабегрона
2026-05-01
SCID: 54.1/nbj5y6cy
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UV spectrophotometryhigh impact amplitude manipulationmirabegronsolifenacin succinatesustainable analytical chemistry
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Abstract (AI)
Abstract This study examines the influence of divisor selection on the efficacy of advanced analytical spectrophotometric methods that integrate artificial intelligence (AI), green-chemistry principles, and white-analytical-chemistry (WAC) frameworks for pharmaceutical investigation. Advanced analytical chemistry, which combines environmental sustainability, analytical practice and computational cleverness, was employed to create innovative spectrophotometric techniques for the concurrent quantification of solifenacin succinate (SOF), and mirabegron (MIR), both utilized in the treatment of overactive bladder. Three divisor approaches were evaluated within complementary smart resolution strategies based on high impact amplitude manipulation method (HIAM) using normalized divisor of MIR, concentration-dependent divisor of MIR at 3.0, 8.0, and 14.0 µg/mL, as well as extracted zero order spectra of MIR obtained by absorbance resolution method (AR). Linearity for SOF was observed from 2.5 to 25.0 µg/mL using first derivative D 1 at 222 nm, while MIR exhibited linearity from 1.5 to 15.0 µg/mL at its maxima 249.0 nm. To assess the robustness and risk, cumulative validation score; CVS, was calculated, serving as instrumental sign for evaluating analytical reliability and method performance. We introduce Sustainable & Smart Analytical Chemistry (SSAC), conjoining Green Analytical Chemistry (GAC), WAC, and AI to develop analytical methods that are efficient, environmentally responsible, and consistent with the multiple Sustainable Development Goals; SDGs. The Sustainability of Analytical Methods Index (SAMI) was applied to evaluate the method’s holistic alignment with the 17 SDGs. Using the Multi-Color Assessment Tool (MA), the method’s greenness, realism, presentation, and novelty were evaluated, demonstrating its sustainability and global impact.
Key Findings
1
A cumulative validation score was introduced as an indicator of analytical robustness, reliability, and method performance.
2
Solifenacin showed linear response from 2.5–25.0 µg/mL using first-derivative measurements at 222 nm, while mirabegron was linear from 1.5–15.0 µg/mL at 249.0 nm.
3
The proposed Sustainable and Smart Analytical Chemistry framework combines green chemistry, white analytical chemistry, and AI, with SAMI and the Multi-Color Assessment Tool used to assess sustainability and alignment with the 17 SDGs.
4
The study evaluates how divisor selection affects AI-integrated UV-spectrophotometric determination of solifenacin succinate and mirabegron.
5
Three divisor strategies were compared within HIAM-based resolution methods: normalized mirabegrin divisor, concentration-dependent divisors, and extracted zero-order mirabegron spectra.
Research Object
solifenacin succinate and mirabegron combination analyzed by UV-spectrophotometric methods
Research Subject
the influence of divisor selection on the efficiency, robustness, sustainability, and analytical performance of AI-integrated spectrophotometric quantification
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2026-05-01
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