Lee Suan Chua* Pages 233 - 240 ( 8 )
Hydroxymethylfurfural (HMF) is one of the important parameters to determine the quality and freshness of honey relating to thermal treatment and storage condition, respectively. Many studies have been extensively carried out to investigate the effect of heating temperature and duration on the formation of HMF. These data were collected from literature, and critically analyzed using statistical techniques including multivariate data analysis and neural network modelling. It was found that the formation of HMF follows zero order kinetics at low temperature (30-40°C), but first order kinetics at high temperature (90-100°C). No significant trend was found for the data between 50-80°C. A three dimensional bubble plot shows that the effect of heating duration is higher than temperature, especially at high temperature (90-100°C). An artificial neural network was also trained by the functions of log sigmoid and pure linear transfer using previously reported data on HMF content in relation with heating temperature and time as inputs in model development. The network was proven to show the goodness of the fit with high correlation coefficient, R2 0.9163 and low root mean square error, RMSE 0.0019 to the experimental data generated from the present study using Tualang honey heated at 90°C for 0-5 hours. The thermal treatment on Tualang honey have also shown to have moderate change on the chemical profile which was generated from liquid chromatography tandem mass spectrometry. The first two principal components have shown to have 33-38 % of the total variance in the chemical profile based on principal component analysis.
Hydroxymethylfurfural, honey, thermal treatment, multivariate data analysis, artificial neural network, heating time.
Institute of Bioproduct Development, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor