Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models

Authors

  • Mokhtar Al-Awadhi Department of Information Technology, Faculty of Engineering and Information Technology, Taiz University, Yemen
  • Ratnadeep Deshmukh Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

DOI:

https://doi.org/10.46947/joaasr632024932

Keywords:

Machine Learning, Hyperspectral Imaging, Honey Adulteration, Principal Component Analysis, Regression Models

Abstract

Recently, honey has become a target of falsification using inexpensive artificial sugar syrup. Current methods for detecting honey adulteration are destructive, slow, and expensive. This paper aims to use hyperspectral imaging (HSI) coupled with Machine Learning (ML) techniques to predict and quantify honey adulteration. The honey adulteration prediction approach proposed in this paper comprises two main steps: spatial and spectral dimensionality reduction and adulteration prediction. We used mathematical averaging to reduce spatial features and employed the Principal Component Analysis and Linear Discriminant Analysis algorithms for spectral feature extraction. Five ML regression models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and K-Nearest Neighbor Regression (KNNR), were used for predicting the sugar concentration in honey. We used a public honey HSI dataset to assess the proposed system's performance. Results show that KNNR outperformed other models in quantifying honey adulteration, achieving a coefficient of determination R2 of 0.94 and a Root Mean Squared Error (RMSE) of 5.12. Findings indicate that HSI coupled with ML models can provide a fast and nondestructive prediction of honey adulteration.

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Published

2024-05-30

How to Cite

Mokhtar Al-Awadhi, & Ratnadeep Deshmukh. (2024). Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). https://doi.org/10.46947/joaasr632024932