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Food Chem

Title:		Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning
Author(s):		Sharin SN; Sani MSA; Jaafar MA; Yuswan MH; Kassim NK; Manaf YN; Wasoh H; Zaki NNM; Hashim AM;
Address:		"Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. International Institute for Halal Research and Training, Level 3, KICT Building, International Islamic University Malaysia, 53100 Kuala Lumpur, Malaysia. Centre for Marker Discovery and Validation (CMDV), Malaysian Agricultural Research and Development Institute (MARDI), 43400 Seri Kembangan, Selangor, Malaysia. Department of Chemistry, Faculty of Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. Electronic address: amalia@upm.edu.my"
Journal Title:		Food Chem
Year:		2021
Volume:		20210115
Issue:
Page Number:		128654 -
DOI:		10.1016/j.foodchem.2020.128654
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:		"Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted"
Keywords:		Animals Bees/*chemistry Cluster Analysis Discriminant Analysis Honey/*analysis Least-Squares Analysis *Machine Learning Phylogeny Principal Component Analysis Volatile Organic Compounds/*analysis Chemical markers Chemometrics and machine learning Discrimi;
Notes:		"MedlineSharin, Siti Nurhidayah Sani, Muhamad Shirwan Abdullah Jaafar, Mohd Azwan Yuswan, Mohd Hafis Kassim, Nur Kartinee Manaf, Yanty Noorzianna Wasoh, Helmi Zaki, Nor Nadiha Mohd Hashim, Amalia Mohd eng England 2021/01/20 Food Chem. 2021 Jun 1; 346:128654. doi: 10.1016/j.foodchem.2020.128654. Epub 2021 Jan 15"