Fuente: PubMed "apis" Comb Chem High Throughput Screen. 2026 May 20. doi: 10.2174/0113862073415336251124162649. Online ahead of print.ABSTRACTINTRODUCTION: Chemometric multivariate techniques are statistical and mathematical methods used in chemistry and related sciences to extract relevant information from complex data sets. These techniques are essential in analyzing multivariate data where multiple variables are measured simultaneously, as is common in spectroscopic, chromatographic, or process-monitoring data.METHOD: The article was prepared by collecting data from various sources like books, journals, websites, databases from e-resources, including Springer, Elsevier, Bentham, Taylor and Francis, etc A literature survey on 150 articles (research and review) on the relevant topic was conducted to conclude our findings. Chemometric multivariate methods offer an efficient and robust solution by utilizing mathematical and statistical techniques to analyze complex data.RESULT: Chemometric methods overcome the limitations of traditional univariate approaches by enabling accurate, precise, and non-destructive analysis without the need for extensive sample separation or prior information about the analytes. In multicomponent formulations, overlapping UV absorption spectra of APIs hinder accurate quantification using conventional methods like absorbance ratio, derivative spectroscopy, or classical least squares without calibration. Discussion - This work highlights the potential of chemometric techniques to improve quality control and streamline pharmaceutical analysis workflows. It is more straightforward in use, cost-effective, necessitates minimal sample handling, and yields trustworthy analytical results compared to alternatives. It is an exceptionally versatile instrument for pharmaceutical analysis across all tiers.CONCLUSION: The efficacies of the proposed methods were demonstrated using UV-Vis spectroscopy and chromatographic data, with results showing high predictive ability and minimal interference from excipients.PMID:42227499 | DOI:10.2174/0113862073415336251124162649