Detection of Low-Level Adulteration of Hungarian Honey Using near Infrared Spectroscopy

Author:

Bodor Zsanett1ORCID,Majadi Mariem2,Benedek Csilla1ORCID,Zaukuu John-Lewis3ORCID,Veresné Bálint Márta1,Csajbókné Csobod Éva1,Kovacs Zoltan2ORCID

Affiliation:

1. Department of Dietetics and Nutrition, Faculty of Health Sciences, Semmelweis University, 17 Vas Street, H-1088 Budapest, Hungary

2. Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14–16 Somlói Street, H-1118 Budapest, Hungary

3. Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi AK-039-5028, Ghana

Abstract

Honey adulteration is a worldwide problem; however, its detection is a challenge for researchers and authorities. There are numerous ways of honey counterfeiting; amongst them, direct adulteration is one of the most common methods. Correlative techniques, such as near-infrared spectroscopy (NIRS), are useful tools in the detection of honey adulteration; however, this method has not been applied to Hungarian honeys. The aim of this research was to investigate the performance of NIRS for the detection of sugar syrup addition to Hungarian honeys at lower concentration levels (<10% w/w). Acacia, rape, forest, sunflower, and linden honeys were mixed with high-fructose-content sugar syrup, rice syrup, or self-made glucose fructose syrup in 3%, 5%, and 10% w/w. NIRS analysis was performed in the spectral range of 950–1650 nm. Principal component analysis was coupled with linear discriminant analysis and partial least square regression models were built for the classification and prediction of adulteration levels, respectively. Our results showed that the performance of NIRS highly depends on both type of syrup and honey. PCA-LDA models provided the 100% correct classification of control in the case of all the models, while PLSR results could predict the added sugar syrup content in the case of rice and F40 syrup models, obtaining >2.2 RPDCV value.

Funder

Hungarian University of Agriculture and Life Sciences

Doctoral School of Food Science of MATE

Publisher

MDPI AG

Subject

Physical and Theoretical Chemistry,Analytical Chemistry

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