# Hybrid sampling chemometric strategy for accurate and sustainable quantification of co-administered β-lactam antibiotics

## TL;DR

This study introduces a new chemometric framework combining sampling strategies and modeling to accurately and sustainably quantify two antibiotics, aztreonam and meropenem, using UV–Vis spectroscopy.

## Contribution

The novel hybrid sampling and chemometric modeling approach improves accuracy and sustainability for quantifying co-administered β-lactam antibiotics.

## Key findings

- ANN models with Sobol Sequences sampling reduced RMSE by up to 39.9% for meropenem compared to standard PLS.
- GA-ICOMP-PLS improved prediction performance by 35.1% to 63.6% over conventional PLS for both antibiotics.
- The method achieved high green metrics (SDS 0.84, AGREE 85.0), indicating strong sustainability and eco-friendliness.

## Abstract

Reliable chemometric models require representative and well-distributed calibration and validation datasets.

The current study introduces a novel framework integrating diverse sampling strategies with multivariate modeling for the simultaneous UV–Vis quantification of aztreonam (AZM) and meropenem (MPM).

Three sampling techniques, including Monte Carlo (MC), Latin Hypercube Sampling (LHS), and Sobol Sequences (SS), were systematically evaluated in combination with Partial Least Squares (PLS), Genetic Algorithm–Assisted PLS (GA-PLS), and Artificial Neural Networks (ANN). Response surface strategy was followed for each sampling technique to assess their space coverage and points’ distribution across the experimental domain. Moreover, a hybrid variable-selection approach, namely, Genetic Algorithm Information Complexity–Partial Least Squares (GA-ICOMP-PLS), was also introduced to optimize PLS model variables. Different validation techniques, including nested cross-validation, Y-randomization tests, and noise-profiling, have been followed to ensure the models’ reliability.

The ANN model trained with the SS technique achieved the highest predictive accuracy, reducing RMSE by 2.6% for AZM and 39.9% for MPM compared to standard PLS. However, LHS-GA-PLS delivered low prediction errors (22.8% AZM and 5.7% MPM), while MC-PLS showed lower consistency due to non-uniform sampling. GA-ICOMP-PLS further improved prediction performance for both analytes, with error reduction ranging from 35.1% to 63.6% compared to conventional PLS. Rigorous validation testing confirmed unbiased predictions and model resilience under realistic analytical conditions.

This is the first study to integrate structured sampling with chemometric modeling for β-lactam analysis. The approach improved model robustness, generalization, and sustainability, achieving high green metrics of SDS (Safety Data Sheets), AGREE (Analytical Greenness metric), and BAGI (Balanced Analytical Greenness Index) values of 0.84 and 85.0, respectively. The developed method represents a reliable, efficient, and eco-friendly strategy for pharmaceutical quality control and environmental monitoring.

The online version contains supplementary material available at 10.1186/s13065-025-01695-9.