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<p>This study presents an analytical comparison of two extraction techniques—pressurized liquid extraction (PLE) and ultrasound-assisted extraction (UAE)—for the recovery of anthocyanins from purple corn using sustainable solvents (<i>o</i>-phosphoric acid, ethanol, and water). Twelve anthocyanins were identified using high-performance liquid chromatography with ultraviolet detection and tandem mass spectrometry, confirming method specificity and selectivity. Both extraction methods were optimized using multivariate experimental designs and validated following U.S. Food and Drug Administration guidelines for analytical methods in food matrices. The protocols demonstrated excellent linearity (coefficient of determination ≥ 0.9992), low detection limits (0.30–1.70&#xa0;mg/kg), and high precision and accuracy (relative standard deviation ≤ 5.4%, recoveries between 97.1 and 101.9%) at 50, 100, and 150&#xa0;mg/kg. To assess their environmental and operational performance, both methods were evaluated using two quantitative tools: AGREEprep, which measures greenness in sample preparation workflows, and the Blue Applicability Grade Index (BAGI), which evaluates practical applicability. PLE achieved a higher sample throughput and lower detection limits, while UAE minimized waste and required less energy. AGREEprep scores (0.73 for PLE, 0.76 for UAE) and BAGI scores (77.5 for PLE, 72.5 for UAE) confirmed both techniques as sustainable and viable for routine analysis. These results demonstrate that both PLE and UAE provide validated, efficient, and environmentally friendly alternatives for anthocyanin extraction from plant-based matrices. The study contributes to the advancement of green sample preparation and highlights the use of structured assessment tools in optimizing analytical workflows for bioactive compound determination in food analysis.</p> Graphical abstract <p></p>

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Green sample preparation for anthocyanin extraction from purple corn: analytical evaluation of pressurized liquid and ultrasound-assisted extraction using sustainable solvents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月11日

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  • Jorge A. Custodio-Mendoza,
  • Alexandra Rangel Silva,
  • Patryk Pokorski,
  • Havva Aktaş,
  • Marcin A. Kurek

摘要

This study presents an analytical comparison of two extraction techniques—pressurized liquid extraction (PLE) and ultrasound-assisted extraction (UAE)—for the recovery of anthocyanins from purple corn using sustainable solvents (o-phosphoric acid, ethanol, and water). Twelve anthocyanins were identified using high-performance liquid chromatography with ultraviolet detection and tandem mass spectrometry, confirming method specificity and selectivity. Both extraction methods were optimized using multivariate experimental designs and validated following U.S. Food and Drug Administration guidelines for analytical methods in food matrices. The protocols demonstrated excellent linearity (coefficient of determination ≥ 0.9992), low detection limits (0.30–1.70 mg/kg), and high precision and accuracy (relative standard deviation ≤ 5.4%, recoveries between 97.1 and 101.9%) at 50, 100, and 150 mg/kg. To assess their environmental and operational performance, both methods were evaluated using two quantitative tools: AGREEprep, which measures greenness in sample preparation workflows, and the Blue Applicability Grade Index (BAGI), which evaluates practical applicability. PLE achieved a higher sample throughput and lower detection limits, while UAE minimized waste and required less energy. AGREEprep scores (0.73 for PLE, 0.76 for UAE) and BAGI scores (77.5 for PLE, 72.5 for UAE) confirmed both techniques as sustainable and viable for routine analysis. These results demonstrate that both PLE and UAE provide validated, efficient, and environmentally friendly alternatives for anthocyanin extraction from plant-based matrices. The study contributes to the advancement of green sample preparation and highlights the use of structured assessment tools in optimizing analytical workflows for bioactive compound determination in food analysis.

Graphical abstract