Novel Anthocyanin-Based Colorimetric Assay for the Rapid, Sensitive, and Quantitative Detection of Helicobacter pylori

• Published in: Analytical chemistry (Washington) Vol. 93; no. 15; pp. 6246 - 6253
• Main Authors: Celik, Cagla, Can Sezgin, Gulten, Kocabas, Umut Gorkem, Gursoy, Sebnem, Ildiz, Nilay, Tan, Weihong, Ocsoy, Ismail
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 20.04.2021
• Subjects: Anthocyanins; Brassica; Brassica oleracea; Chemistry; Colorimetry; Environmental effects; Filter paper; Helicobacter pylori; Image processing; Reaction time; Urease
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.1c00663

Several different diagnostic tests have been reported for rapid, sensitive, and economical detection of bacterial pathogens, but most lack widespread and practical use in the clinic. In this study, we used anthocyanins from red cabbage (Brassica oleracea) as a natural pH indicator and, for the first time, incorporated this agent into a simple, rapid, and economical colorimetric strategy for the detection of Helicobacter pylori (H. pylori) (RCE@test). We prepared two sets of RCE@test solutions (test 1 is purple, and test 2 is blue) in different forms, including liquid, adsorbed filter paper, and agar, and investigated the performance of each RCE@test as a function of the test volume, H. pylori concentration, and reaction time. To elucidate the effect of the pathophysiological environment on these RCE@tests, H. pylori in an artificial gastric fluid was also detected. The 10 and 1 CFU/mL H. pylori suspensions were detected in 15 min and 3 h, respectively, and the limit of detection was determined down to 1 CFU/mL. We experimentally demonstrated the advantages of the RCE@test for detection of H. pylori by comparing it to a commercially available rapid urease test, the “CLO test (Campylobacter-like organism test)”. In addition to colorimetric detection by the naked eyes, RGB (Red Green Blue) and Delta-E analysis in image-processing software was run to quantitatively monitor changes of color in the RCE@test using a smartphone application. Finally, we propose that this test provides simple, effective, rapid, and inexpensive detection and that it can be easily implemented for clinical use.