Abstract
AbstractThis paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$
95
%
accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Biochemistry,General Chemistry,Food Science,Biotechnology
Reference41 articles.
1. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 37–49
2. Bansal S, Singh A, Mangal M et al (2017) Food adulteration: sources, health risks, and detection methods. Criti Rev Food Sci Nutr 57(6):1174–1189
3. Bong J, Loomes KM, Schlothauer RC et al (2016) Fluorescence markers in some New Zealand honeys. Food Chem 192:1006–1014. https://doi.org/10.1016/j.foodchem.2015.07.118
4. Canadian Food Inspection Agency (2019) Report: enhanced honey authenticity surveillance (2018–2019). Tech. rep., Canadian Food Inspection Agency, https://www.inspection.gc.ca/about-cfia/science-and-research/our-research-and-publications/report/eng/1557531883418/1557531883647
5. El Masry G, Sun DW (2010) Principles of hyperspectral imaging technology. In: Hyperspectral imaging for food quality analysis and control. Elsevier, p 3–43. https://doi.org/10.1016/b978-0-12-374753-2.10001-2
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