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

被引:0
作者
Alawadhi, Mokhtar [1 ]
Deshmukh, Ratnadeep [1 ,2 ]
机构
[1] Taiz Univ, Fac Engn & Informat Technol, Dept Informat Technol, Taizi, Yemen
[2] Dr Babasaheb Ambedkar Marathwada Univ, Dept Comp Sci & IT, Aurangabad, India
来源
JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH | 2024年 / 6卷 / 03期
关键词
Machine Learning; Hyperspectral Imaging; Honey Adulteration; Principal Compo- nent Analysis; Regression Models 3 rd International Conference ICKE2023; REGRESSION;
D O I
暂无
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:216 / 228
页数:13
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