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ISSN: 2755-6328 | Open Access

Open Access Journal of Pharmaceutical Sciences and Drugs

Volume : 1 Issue : 2

Standardization and Quality Assurance of Herbal Pigments

Hariom Rajput

ABSTRACT
Herbal pigments, derived from various plant sources, are increasingly being recognized as safe and eco-friendly alternatives to synthetic colorants in food, cosmetics, pharmaceuticals, and textiles. These natural pigments, including anthocyanins, carotenoids, betalains, chlorophylls, and curcuminoids, not only impart vibrant colors but also offer therapeutic properties such as antioxidant, anti-inflammatory, and antimicrobial effects. However, the commercialization of herbal pigments faces significant challenges due to variability in raw material quality, extraction inefficiencies, pigment degradation, and a lack of uniform regulatory standards. Standardization of herbal pigments is essential to ensure consistency, stability, and efficacy across production batches. This involves comprehensive characterization of plant materials through botanical identification, physicochemical profiling, and phytochemical quantification using advanced analytical techniques like HPLC, HPTLC, UV-Vi’s spectrophotometry, and FTIR. Quality assurance further encompasses stringent adherence to Good Agricultural and Collection Practices (GACP), Good Manufacturing Practices (GMP), and robust quality control tests for contaminants, heavy metals, and microbial loads. The development of validated analytical methods, stability studies, and eco-friendly extraction technologies has advanced the reliability and scalability of herbal pigments. Additionally, regulatory frameworks provided by WHO, FSSAI, USFDA, and ISO are guiding the establishment of quality benchmarks, though global harmonization remains a challenge. This review emphasizes the need for integrated standardization protocols and quality assurance strategies to enhance the commercial viability and global acceptance of herbal pigments. Future research should focus on molecular-level authentication, sustainable production methods, and the application of AI-driven quality prediction models for industry-wide implementation

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