Qualitative Analysis of UV-Absorbents in Cosmetics Based on UV-Vis Spectrum
Applications | 2019 | ShimadzuInstrumentation
Organic acids play crucial roles as flavor compounds, biochemical intermediates, and industrial feedstocks. Rapid and selective determination of these acids is vital in food quality control, bio-production processes, and microbial fermentation studies, where timely feedback supports optimal process management and product consistency.
This study aimed to develop and demonstrate a high-speed HPLC method for monitoring key organic acids during yogurt fermentation. Using a Shim-pack Fast-OA ion-exclusion column with post-column pH buffering and conductivity detection, the work focused on reducing analysis time while maintaining accuracy for five representative organic acids: phosphoric, citric, malic, lactic, and acetic acids.
The separation employed ion-exclusion chromatography on two Shim-pack Fast-OA columns in series (100 mm × 7.8 mm, with guard) at 30 °C. The mobile phase consisted of 5 mmol/L p-toluenesulfonic acid with 20 mmol/L Bis-Tris and 0.1 mmol/L EDTA, delivered at 0.8 mL/min. Detection was achieved via a conductivity detector (CDD-10Avp) with post-column pH buffering. Sample preparation for yogurt involved extraction with p-toluenesulfonic acid solution, chloroform-mediated protein and lipid removal, centrifugation, filtration (0.2 μm), and 10 μL injection.
Standard mixtures of the five organic acids were fully eluted within 10 minutes, demonstrating effective separation tied to their pKa values. Calibration curves showed excellent linearity (r² > 0.9999) across 10–1000 mg/L and repeatability with RSD < 1.5% at 10 mg/L. Recovery tests in yogurt yielded rates between 96% and 118%. Time-course monitoring revealed lactic acid production onset at about 3.5 hours, confirming the method’s suitability for real-time fermentation tracking.
The approach offers rapid (12-minute) analysis cycles and total sample-to-result times around 20 minutes, enabling frequent measurements for dynamic process control. Its high selectivity against matrix interferences makes it valuable for food quality assurance, biofuel and pharmaceutical fermentation monitoring, and microbial community studies.
Further integration with automated sampling and data analytics could facilitate fully inline monitoring in industrial fermenters. Expansion to other biofluids and complex matrices is anticipated, supported by advances in column design and detector sensitivity. Coupling with emerging techniques such as microfluidics and mass spectrometry may broaden the scope of rapid organic acid profiling.
The Shim-pack Fast-OA based HPLC method provides a fast, accurate, and robust solution for quantifying organic acids in fermentation matrices. Its performance supports enhanced process understanding and control, with potential for broad adoption across food, biotechnology, and environmental analytics.
No specific literature references were provided in the source document for inclusion.
HPLC
IndustriesFood & Agriculture
ManufacturerShimadzu
Summary
Significance of the Topic
Organic acids play crucial roles as flavor compounds, biochemical intermediates, and industrial feedstocks. Rapid and selective determination of these acids is vital in food quality control, bio-production processes, and microbial fermentation studies, where timely feedback supports optimal process management and product consistency.
Objectives and Study Overview
This study aimed to develop and demonstrate a high-speed HPLC method for monitoring key organic acids during yogurt fermentation. Using a Shim-pack Fast-OA ion-exclusion column with post-column pH buffering and conductivity detection, the work focused on reducing analysis time while maintaining accuracy for five representative organic acids: phosphoric, citric, malic, lactic, and acetic acids.
Methodology and Instrumentation
The separation employed ion-exclusion chromatography on two Shim-pack Fast-OA columns in series (100 mm × 7.8 mm, with guard) at 30 °C. The mobile phase consisted of 5 mmol/L p-toluenesulfonic acid with 20 mmol/L Bis-Tris and 0.1 mmol/L EDTA, delivered at 0.8 mL/min. Detection was achieved via a conductivity detector (CDD-10Avp) with post-column pH buffering. Sample preparation for yogurt involved extraction with p-toluenesulfonic acid solution, chloroform-mediated protein and lipid removal, centrifugation, filtration (0.2 μm), and 10 μL injection.
Main Results and Discussion
Standard mixtures of the five organic acids were fully eluted within 10 minutes, demonstrating effective separation tied to their pKa values. Calibration curves showed excellent linearity (r² > 0.9999) across 10–1000 mg/L and repeatability with RSD < 1.5% at 10 mg/L. Recovery tests in yogurt yielded rates between 96% and 118%. Time-course monitoring revealed lactic acid production onset at about 3.5 hours, confirming the method’s suitability for real-time fermentation tracking.
Benefits and Practical Applications
The approach offers rapid (12-minute) analysis cycles and total sample-to-result times around 20 minutes, enabling frequent measurements for dynamic process control. Its high selectivity against matrix interferences makes it valuable for food quality assurance, biofuel and pharmaceutical fermentation monitoring, and microbial community studies.
Future Trends and Applications
Further integration with automated sampling and data analytics could facilitate fully inline monitoring in industrial fermenters. Expansion to other biofluids and complex matrices is anticipated, supported by advances in column design and detector sensitivity. Coupling with emerging techniques such as microfluidics and mass spectrometry may broaden the scope of rapid organic acid profiling.
Conclusion
The Shim-pack Fast-OA based HPLC method provides a fast, accurate, and robust solution for quantifying organic acids in fermentation matrices. Its performance supports enhanced process understanding and control, with potential for broad adoption across food, biotechnology, and environmental analytics.
References
No specific literature references were provided in the source document for inclusion.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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