New method can authenticate Philippine stingless bee honey, considered the “next superfood”

Stingless bee honey is gaining recognition as a superfood because of its exceptional health and therapeutic benefits, which rival—or even surpass—those of the world-famous Manuka honey. As a result, some now describe it as a “miracle liquid” and the “next superfood.”

In our study, we developed a method using handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its entomological origin. Machine learning makes food authenticity and traceability more reliable. The method will screen or confirm if the honey from Philippine stingless bees is authentic or collected by bees from flowers. This will ensure food authenticity for the benefit of consumers and strengthen the honey industry and economy in the country.

Honey samples from three different bee species were analyzed, specifically the European honeybee (Apis mellifera), the Philippine giant honeybees (Apis breviligula and Apis dorsata), and the Philippine stingless bee (Tetragonula biroi). Random forest and logistic regression models were used on the hXRF dataset for entomological origin classification.

The optimized random forest model classified entomological origin with 85.2 % (225 out of 264) overall accuracy. The logistic regression model confirmed the entomological origin of Philippine stingless bees with 94.1% accuracy and 100.0% specificity. As such, honey that passes this model’s test is undoubtedly made by Philippine stingless bees, making it an excellent screening tool for authenticating Philippine stingless bee honey.

Authors:

Angel T. Bautista VII (Department of Science and Technology–Philippine Nuclear Research Institute), June Hope D. Aznar (Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila), Remjohn Aron H. Magtaas (Department of Science and Technology–Philippine Nuclear Research Institute), Mary Margareth T. Bauyon (Department of Science and Technology–Philippine Nuclear Research Institute), Andrei Joshua R. Yu (Department of Science and Technology–Philippine Nuclear Research Institute), Joshua Kian G. Balaguer (Department of Science and Technology–Philippine Nuclear Research Institute), Jervee M. Punzalan (Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila | Dodd-Waals Centre for Photonic and Quantum Technologies, Department of Chemistry, University of Otago), Jessica B. Baroga-Barbecho (Bee Program, College of Arts and Sciences, University of the Philippines Los Baños) and Cleofas R. Cervancia (Bee Program, College of Arts and Sciences, University of the Philippines Los Baños)

Read the full paper: https://doi.org/10.1016/j.foodchem.2025.143165

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