Green simultaneous quantification of levodopa, carbidopa, and benserazide in anti-Parkinson's tablets by ATR-FTIR spectroscopy combined with hurdle modelling and machine learning

Manh Huy Nguyen; Thanh Dam Nguyen; Hồng Anh Dương; Hung Viet Pham

doi:10.1039/d6ra02243j

January 1, 2026RSC AdvancesOpen Access

Green simultaneous quantification of levodopa, carbidopa, and benserazide in anti-Parkinson's tablets by ATR-FTIR spectroscopy combined with hurdle modelling and machine learning

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Key Points

This study aims to develop a green method for analyzing levodopa, carbidopa, and benserazide in anti-Parkinson's tablets using ATR-FTIR and hurdle modelling.
Utilized a two-stage ATR-FTIR/hurdle machine learning workflow for analysis.
Implemented calibration transfer for predicting commercial samples' composition.
Achieved simultaneous quantification with high accuracy.
Calibration transfer significantly improved prediction accuracy of commercial samples.

Abstract

A two-stage ATR-FTIR/hurdle ML workflow enabled green simultaneous analysis of levodopa, carbidopa, and benserazide in Parkinson's tablets, while calibration transfer improved prediction accuracy for commercial samples.

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Cite This Study

Nguyen et al. (2026) studied this question.

synapsesocial.com/papers/6a0d5064f03e14405aa9c277 https://doi.org/10.1039/d6ra02243j

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Green simultaneous quantification of levodopa, carbidopa, and benserazide in anti-Parkinson's tablets by ATR-FTIR spectroscopy combined with hurdle modelling and machine learning

Key Points

Abstract

Discussion

Cite This Study

Green simultaneous quantification of levodopa, carbidopa, and benserazide in anti-Parkinson's tablets by ATR-FTIR spectroscopy combined with hurdle modelling and machine learning

Key Points

Abstract

Discussion

Cite This Study

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