Detection and classification of honey samples using FTIR spectroscopy and multivariate statistical analysis

Bahreini, Maryam; Nabizadeh, Reyhaneh; Ragerdi Kashani, Nastaran

doi:10.47176/ijpr.22.3.11388

Detection and classification of honey samples using FTIR spectroscopy and multivariate statistical analysis

Document Type : Original Article

Authors

Maryam Bahreini

Reyhaneh Nabizadeh

Nastaran Ragerdi Kashani

School of Physics, Iran University of Science and Technology, Tehran, Iran

https://doi.org/10.47176/ijpr.22.3.11388

Abstract

Honey is a sweet and natural food product produced by bees which is mainly composed of sugar. Honey is also a rich source of amino acids, vitamins, minerals and other biologically active compounds. These properties lead to the widespread use of honey and increase the demand for honey around the world. Therefore, it is so important to make sure that the honey is genuine or counterfeit. In this study, FTIR spectra of 6 honey samples of forty-herb honey and Zirofen honey in 10%, 30% and 50% water concentrations and 2 glucose syrup with 10% and 30% concentrations were acquired and analysed using multivariate statistical analysis. The classification results indicate a proper distinction between genuine honey and counterfeit sample. The aim of this study was to provide a cheap, fast and accurate classification method for honey authentication and shows that FTIR method combined by multivariate statistical analysis is a useful tool for testing the authenticity of honey

Keywords

Fourier transform infrared spectroscopy

honey

multivariate statistical analysis

20.1001.1.16826957.1401.22.3.25.9

Subjects

Optics and Photonics

Z Farhadian and M Honarvar, Iran Food Sciences and Industries 15, 74 (2018) 263 (in Persian).
M L Al, et al., Food chemistry 112, 4 (2009) 863.
M Al Mamary, A Al Meeri, and M Al Habori, Nutrition Research 22, 9 (2002) 1041.
A Puścion Jakubik, M H Borawska, and K Socha, Foods 9, 8 (2020) 1028.
A Aljadi and M Kamaruddin, Food Chemistry 85, 4 (2004) 513.
L da C Azeredo, et al., Food Chemistry 80, 2 (2003) 249.
M Bahreini, et al., Talanta 204 (2019) 826.
A Safi, M Bahreini, and S Tavassoli, Optics and Spectroscopy 120, 3 (2016) 367.
M Bahreini, Journal of Lasers in Medical Sciences 6, 2 (2015) 51.
A Seifalinezhad, et al., Journal of Lasers in Medical Sciences 10, 1 (2019) 64.
M Darbani and M Zahmatkesh, “Optical spectroscopy and applications”, Malek Ashtar University of Technology 1 (2019) 120 (in Persian).
J Head, J Kinyanjui, and M Talbott, Pittcon 2015 (2015) 2220.
S Samarghandian, T Farkhondeh, and F Samini, Pharmacognosy Research 9, 2 (2017) 121.
D S Moore, W I Notz, and W Notz, “Statistics: Concepts and controversies” Macmillan (2006).
R Safari and K Habibpour, A comprehensive guide to using SPSS in surveyresearch (quantitative data analysis) 6 (2018) 200 (in Persian).
K Ruoff and S Bogdanov, Apiacta 38, 38 (2004) 317.
M Sahlan, et al., Veterinary World 12, 8 (2019) 1304.
O Anjos, et al., Food Chemistry 169 (2015)