A Machine Learning Approach for Honey Adulteration Detection Using Mineral Element Profiles
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-7892-0_29
Reference18 articles.
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2. Tosun M (2013) Detection of adulteration in honey samples added various sugar syrups with 13C/12C isotope ratio analysis method. Food Chem 138:1629–1632. https://doi.org/10.1016/j.foodchem.2012.11.068
3. Islam MK, Sostaric T, Lim LY, Hammer K, Locher C (2020) Sugar profiling of honeys for authentication and detection of adulterants using high-performance thin layer chromatography. Molecules (Basel, Switzerland) 25. https://doi.org/10.3390/molecules25225289
4. Al-Mahasneh M, Al-U’Datt M, Rababah T, Al-Widyan M, Abu Kaeed A, Al-Mahasneh AJ, Abu-Khalaf N (2021) Classification and prediction of bee honey indirect adulteration using physiochemical properties coupled with k-means clustering and simulated annealing-artificial neural networks (SA-ANNs). J Food Qual. https://doi.org/10.1155/2021/6634598
5. Song X, She S, Xin M, Chen L, Li Y, Heyden YV, Rogers KM, Chen L (2020) Detection of adulteration in Chinese monofloral honey using 1H nuclear magnetic resonance and chemometrics. J Food Compos Anal 86. https://doi.org/10.1016/j.jfca.2019.103390
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1. Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity;ACS Agricultural Science & Technology;2024-07-03
2. Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models;JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH;2024-05-30
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