Impact of harvesting seasons on physicochemical properties and volatile compound profiles of Malaysian stingless bee honey analysed using chemometrics and support vector machine.

Stingless bee honey's nutritional value is gaining attention, but the impact of harvesting seasons, specifically the rainy (September 2018) and dry (February 2019) seasons in Malaysia on the honey's physicochemical properties and volatile compounds remains insufficiently explored. This research revealed marginal differences in the physicochemical properties between seasons. However, through individual bee species and cumulative data analysis, honey samples were effectively differentiated based on harvesting seasons. A set of seventeen volatile compounds were identified as potential chemical markers for distinguishing H. bakeri, G. thoracica, and T. binghami honey between rainy and dry seasons. For cumulative data, four significant markers were proposed. These discrimination methods and chemical markers can serve as valuable references in distinguishing stingless bee honey, whether its entomological origin is specified or not between rainy and dry seasons.

A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia.

This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.

Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning.

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.