Investigation of the availability of image processing as an alternative method to spectrophotometry for prediction of HMF content in honey for different temperatures

Abstract

This study aims to determine the hydroxymethylfurfural (HMF) content in the honey samples which are processed at different temperatures by using a spectrophotometric method and to investigate the suitability of image processing method as an alternative method to spectrophotometry. Honey samples were subjected to conventional heating by using a conventional oven at 75°C, 100°C, 125°C and 150°C for 20 min. The HMF content of the samples were determined by using spectrophotometric method and the images of heated honey samples in tubes were captured by a professional camera simultaneously to compare results obtained from spectrophotometry and image processing. The results showed a correlation (y = 18.54x-1.224, R 2 = 0.987) between the data obtained by image processing and spectrophotometric analysis. Thus, it was concluded that, image processing can be used as an alternative method for determining the amount of HMF content in honey for different temperatures. Practical applicationsWith the development of image processing technologies, there is a great potential for food safety and food preservation. This study will bring about improvement in the field of food quality and preservation analysis for food manufacturers. Moreover, this application can be used in food industry for a quick quality determination for mass production.

Estimating Hydroxymethyfurfural (HMF) Concentration Via Modified Seliwanoff Test Using Artificial Neural Network (ANN)

“…These results show that the HMF content can be determined with higher accuracy applying ANN to data compared to the calibration curve method based on linear statistics. Erbakan and coauthors [63] determined the HMF content with 0.987 correlation coefficient in the honey by spectrophotometry and image processing. HMF content was determined by Chua [64] using statistical techniques including multivariate data analysis and neural network modelling with correlation coefficient, R 2 0.9163.…”

Determination of shrinkage characteristics of pumpkin discs during convection, microwave, and combined drying using computer‐aided technique: Image processing application

“…Image processing is now used in a variety of fields, including medicine and biology, geography, archeological remains detection, physics (spectrometers, electron microscope images), space sciences (satellites), industrial applications (process and production supervision), consumer electronics, and food production processes. Studies on the application of image processing in the food industry, such as determining the amount of adulteration, estimating color, and classifying vegetables and fruits, determining morphological changes such as shrinkage after drying are on the rise (Chang et al, 2022; Erbakan et al, 2021; Fermo et al, 2021; Gonzatti et al, 2022; Hu et al, 2016; Jagtap et al, 2019; Kheiralipour & Pormah, 2017; Pedreschi et al, 2011; Ropelewska & Szwejda‐Grzybowska, 2021; Sajjacholapunt, 2022; Solak & Altinişik, 2018). In a research conducted by Yadollahinia et al, it was used an image processing system that could measure the continued shrinkage of potato slices during drying with high precision in short intervals to investigate the effect of temperature on shrinkage and moisture changes during convective potato slice drying.…”