Quantifying Botanical Origin Drift in Artisanal Honey Batches via Automated DNA Barcoding Pipelines

Advances in Food Science and Technology | Vol. 14, No. 8, August 2026 | pp. 049–054

DOI: 10.46882/2026/AFST/000248

Article Type: Original Research Paper

Title: Quantifying Botanical Origin Drift in Artisanal Honey Batches via Automated DNA Barcoding Pipelines

Names of Authors: Elena R. Rostova¹, Kofi Mensah²

Authors’ Affiliations: ¹Department of Food Science, Advanced Research Center, Stockholm, Sweden; ²School of Computing, University of Digital Sciences, Accra, Ghana

Abstract: Abstract: Premium monofloral honey variants suffer from extensive global product mislabeling due to unintentional pollen mixing or fraudulent dilution with polyfloral syrups. This research presents an automated validation framework that uses high-throughput DNA barcoding targeted at the rbcL and matK chloroplast genes to verify honey botanical origins. We collected and extracted residual pollen DNA from 220 commercial artisanal honey samples claiming monofloral origins (including Manuka and Acacia varieties). The sequence data pools were run through a dynamic reference database using a machine learning clustering protocol to determine floral fractions. The diagnostic mapping revealed that 34.2% of the test batches displayed significant botanical origin drift, with secondary pollen inclusion tiers exceeding a 45% volume fraction. The automated analysis pipeline isolated minor trace contamination down to a boundary of 1.5%, establishing high precision. This molecular approach offers code inspectors a scalable method to enforce truth-in-labeling standards.

Keywords: Honey Authentication, DNA Barcoding, Monofloral Verification, Food Fraud, Chloroplast Genes, Clustering Algorithms

Manuscript Timeline: Received: April 02, 2026; Revised: June 10, 2026; Accepted: July 18, 2026; Published: August 14, 2026

Citation: Rostova, E. R., & Mensah, K. (2026). Quantifying Botanical Origin Drift in Artisanal Honey Batches via Automated DNA Barcoding Pipelines. Advances in Food Science and Technology, 14(8), 049–054. DOI: 10.46882/2026/AFST/000248

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