14 citations · 36 across the 5 of their papers we have counts for
5 papers
Honey Classification using Hyperspectral Imaging and Machine Learning
Mokhtar A. Al-Awadhi, Ratnadeep R. Deshmukh
In this paper, we propose a machine learning-based method for automatically classifying honey botanical origins. Dataset preparation, feature extraction, and classification are the…
Detection of Adulteration in Coconut Milk using Infrared Spectroscopy and Machine Learning
Mokhtar A. Al-Awadhi, Ratnadeep R. Deshmukh
In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: p…
Honey Adulteration Detection using Hyperspectral Imaging and Machine Learning
Mokhtar A. Al-Awadhi, Ratnadeep R. Deshmukh
This paper aims to develop a machine learning-based system for automatically detecting honey adulteration with sugar syrup, based on honey hyperspectral imaging data. First, the fl…
A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles
Mokhtar A. Al-Awadhi, Ratnadeep R. Deshmukh
This paper aims to develop a Machine Learning (ML)-based system for detecting honey adulteration utilizing honey mineral element profiles. The proposed system comprises two phases:…
Classification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning
Mokhtar Al-Awadhi, Ratnadeep Deshmukh
This paper proposes a machine learning-based approach for identifying honey floral and geographical sources using mineral element profiles. The proposed method comprises two steps:…