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Green chromatographic fingerprinting: An environmentally friendly approach for the development of separation methods for fingerprinting complex matrices
Authors:Cristiano Soleo Funari  Renato Lajarim Carneiro  André Marques Andrade  Emily Frances Hilder  Alberto José Cavalheiro
Affiliation:1. Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, University of Tasmania, Hobart, Tasmania, Australia;2. Chemistry Institute, S?o Paulo State University, Araraquara, S?o Paulo, Brazil;3. Department of Chemistry, Federal University of S?o Carlos, S?o Carlos, S?o Paulo, Brazil
Abstract:A chromatographic fingerprint is a comprehensive method that reveals the distinctive pattern of peaks across the chromatogram for a given sample. It is considered an effective strategy to assess the identity and quality of herbal materials, as well as for the control of the quality of their derived products. HPLC is the most employed technique for these purposes and it is used routinely for quality control in industry. Hence, its impact on the environment should not be neglected. This work provides a rational and generic procedure to qualitatively fingerprint complex matrices. Resource‐ and time‐saving experimental designs were selected; an alternative safer organic solvent was tested and a time‐saving and innovative response entitled the green chromatographic fingerprinting response was developed and employed. This procedure was applied in the development of chromatographic fingerprints for extracts of Bauhinia forficata and Casearia sylvestris. Moreover, the response proposed here can be combined with a complementary metric available in the literature to compare methods using different solvents. According to this, the chromatographic fingerprints developed here using ethanol as the organic solvent provided a performance better than that of reference methods in which more harmful acetonitrile or methanol were employed.
Keywords:Bauhinia forficata  Casearia sylvestris  Quality by design  Green liquid chromatography  Metabolite profiling
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