Quantification of browning in apples using colour and textural features by image analysis

This study analyses the effect of browning through image analysis based on colour and textural features in fresh-cut apple slices.A computer vision system(CVS)was developed for image acquisition,which consisted of a digital camera and a florescent lamp source for illumination with a contrasting background.The CVS was calibrated using standard colour values and a model was developed by artificial neural network technique.Three varieties of apples such as Honey crisp,Granny Smith,and Golden Delicious were used for the analysis.The apples were freshly cut and subjected to image acquisition.Normalized colour features(L*,browning index,hue,and colour change)and textural features(entropy,contrast,and homogeneity)were analysed from the acquired images.The varieties Honey Crisp and Granny Smith did undergo browning within 120 min,whereas Golden delicious did not brown significantly.The study concluded that colour and textural features were important decision features for detecting browning in apples through image analysis.

Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages

Colour and moisture content are two most important attributes of the commercial food product.Estimation of moisture content is very important to know the storability of the food product.It also relates to the process of drying in a fruit or vegetable.Extra drying and shrinkage deteriorates the quality of food product.The goal of the experiment was to examine the changes in RGB values of an apple during drying at different temperatures.In this study,emphasis was given on how the colour changes when there is a significant change in the moisture content of the apple.Three randomly chosen varieties of apples were sliced to 8 mm thickness and dried in vacuum oven at 60°C,70°C,and 80°C.The loss of moisture was recorded for every 30 min interval and corresponding digital images were taken to determine the change in RGB value.The images that were captured during the study was analysed in MATLAB image analysis computer software.The analysis of moisture content and average colour share with respect to time showed that average colour share value decreases with time at all three temperatures.More than 50 per cent of variation in moisture content was explained by average colour share.There is a significant linear relationship between moisture content and colour changes in RGB and can be used to predict the moisture content of apple during drying process.