Abstract

Herbal systems are difficult to study due to their complex chemical composition, even if spectroscopic and chromatographic methods are used to standardize herbal remedies. To address these issues, many advanced analytical methods have been developed to assess the quality of herbal medications, including spectroscopy (nuclear magnetic resonance, infrared, ultraviolet, and inductively coupled plasma spectroscopy techniques, high-performance liquid chromatography, gas chromatography, capillary electrophoresis and extensive approaches along with hybridized chemometric techniques which are used to extract valuable information through various data processing methodologies which are increasingly used in herbal medication authentication. Numerous phytochemical and pharmacological investigations show that one plant can contain hundreds or thousands of chemical constituents. These components create the intended pharmacological effects through various targets and pathways. As a result, the WHO has developed standards for evaluating herbal therapies. Advanced software tools are used in chemometrics, which combines chemistry and statistics to extract relevant information from large chemical datasets. In conclusion, chemometrics software includes spectral analysis, statistical approaches, machine learning, and data visualization. Researchers can gain insights from large chemical datasets using these software programs, improving our understanding of complex systems. This review article describes chemometrics software tools and their use in data analysis, pattern detection, and model construction which are extremely useful for the futuristic approaches towards evaluating herbal medicines.

Key words: Chemometrics, Principal component analysis, PLS_Toolbox , Unscrambler X, SIMCA