Automated method development in HPLC for the quantitative determination of catechins in tea

Applications | 2018 | Thermo Fisher ScientificInstrumentation
HPLC
Industries
Food & Agriculture
Manufacturer
Thermo Fisher Scientific

Importance of the Topic


Tea is one of the most widely consumed beverages worldwide, and its health benefits have been linked to catechins, a class of polyphenolic antioxidants. Reliable and rapid quantification of catechins is essential for quality control in the food and beverage industry, consumer safety, and research into bioactive compounds.

Objectives and Study Overview


The primary aim was to implement a software-based workflow for automated HPLC method development to separate and quantify seven major tea catechins (catechin, epicatechin, epigallocatechin, epigallocatechin gallate, gallocatechin, gallocatechin gallate, epicatechin gallate), plus caffeine, gallic acid and theobromine. Following screening and optimization, the method was applied to white, green and black tea infusions to determine catechin content per cup.

Methodology and Instrumentation


The workflow combined ChromSwordAuto 5 software modules (Scout and Developer) with a Thermo Scientific UltiMate 3000 RS HPLC system. Key instrumentation and conditions included:
  • UltiMate 3000 RS system: LPG-3400RS pump, WPS-3000TRS autosampler, TCC-3000RS column compartment, DAD-3000RS detector (280 nm)
  • Columns screened: Hypersil GOLD aQ, Acclaim Vanquish Polar Advantage II, Accucore Polar Premium, Accucore Phenyl-X (100 × 3 mm, 2.6 µm)
  • Mobile phase screening: four aqueous eluents (0.1% acetic acid, 0.1% formic acid, 20 mM ammonium acetate pH 3.8, 20 mM ammonium formate pH 3.8) with acetonitrile or methanol
  • Automated tasks: column and eluent scouting, rapid optimization (69 conditions tested), fine optimization and sample profiling using real tea matrix

Key Results and Discussion


Following rapid optimization, the Accucore Phenyl-X column with 20 mM ammonium acetate (pH 3.8) and acetonitrile yielded the best resolution and shortest run time (9.4 min). Fine gradient adjustment separated all analytes and critical matrix interferences within 12 min, improving peak purity from 0.897 to 0.959 for epigallocatechin. Calibration was linear (1–100 µg/mL, R² 0.9984–0.9995); limits of detection ranged 0.1–2.5 µg/mL.

Benefits and Practical Applications


  • Automated software-driven development reduced total analyst time to ~24.5 h and instrument time to ~50.5 h, enabling method readiness within 5 days.
  • The Accucore Phenyl-X phase provided robust separation of aromatic catechins and compatibility with highly aqueous mobile phases.
  • Quantitative analysis of tea infusions delivered catechin contents of 107 mg per cup for green tea, 53 mg for white tea, and 4 mg for black tea, supporting nutritional assessment and product standardization.

Future Trends and Potential Applications


Integration of artificial intelligence and machine learning could further accelerate method development and adapt protocols to novel matrices. Expanding automated workflows to tandem mass spectrometry or ultrahigh-pressure systems may enhance sensitivity and broaden compound coverage in complex botanical samples.

Conclusion


The study validates a fully automated HPLC method development strategy using ChromSwordAuto and UltiMate 3000 instrumentation for efficient, reproducible quantification of tea catechins. This approach minimizes operator intervention, ensures high separation quality, and is well suited for routine quality control and research laboratories.

Reference


  • Zuo Y, Chen H, Deng Y. Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector. Talanta. 2002;57(2):307–316.
  • Neves MF, Trombin VG, Lopes FF, Kalaki R, Milan P. World consumption of beverages. In: The Orange Juice Business. Wageningen Academic Publishers; 2011. p. 255–265.
  • Mohan M, Jeevanandan G, Raja SM. The role of green tea in oral health – A review. Asian J Pharm Clin Res. 2018;11(4):1–5.

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