Texture and Color Analysis of Lentils and Rice for Instant Meal Using Image Processing Techniques

Abstract

Typical approaches for measuring color and texture properties are mostly timeconsuming. An image-based method was used to evaluate texture and color in lentils and rice subjected to freeze-drying for an instant meal. Cooked and cooked freeze-dried rehydrated lentils and rice were analyzed by scanning electron microscopy. Texture properties were analyzed by texture analyzer and image analysis. Color was performed with a digital camera. Significant differences for color and texture (P < 0.05) were observed for cooked and cooked freeze-dried rehydrated lentils and rice. A linear trend with a linear correlation was applied for mechanical and image features. Results showed that image features such as contrast, correlation, energy and homogeneity calculated from Gray-level co-occurrence matrix had high correlations with mechanical features of hardness, adhesiveness, chewiness and gumminess for lentils, and for rice, mechanical features adhesiveness, gumminess and image features homogeneity and contrast. With this approach to quality, image processing techniques can be a useful tool. PRACTICAL APPLICATIONSDevelopment and innovation of new technologies are necessary, especially in food quality, because most instrumental techniques for measuring quality properties involve a considerable amount of manual work. Freeze-drying is not a new technique; it is expensive but an excellent option for instant meals. Different types of cereal and legumes are necessary to be studied so they can be included in instant meals. Therefore, by having enough knowledge on surface of cereals and legumes among the process, it will be possible by using image processing techniques to estimate quality parameters in cereals and legumes for consumer acceptance.

Quality parameters of freeze-dried peach snack

“…The GLCM shows the probability that a pixel of a particular grey level occurs at a specified direction and distance ( d =1) from its neighbouring pixels. Grey level co-occurrence matrix is represented by Pd, θ ( i , j ) where counts the neighbouring pair pixels with grey values i and j at the distance of d and the direction of θ (16) (Pieniazek and Messina., 2016b; Pieniazek et al , 2015).…”

Thermal processing of rice grains affects the physical properties of their pregelatinised rice flours

“…Both cooking time and temperature did not significantly impact on L *, a * and b * in WRF, LARF and HARF from cooked grain. Pieniazek et al () reported that higher temperature in the cooking process could decrease the L * values of rice and lentil. Cooking generally reduced L * and b * as a function of cooking time but had no effect on a * value.…”

Nutritional and Physicochemical Properties and Safe Consumption of Jackfruit Seeds (Artocarpus heterophyllus Lam.)

“…The color of the jackfruit seeds was analyzed with a Chroma Meter (CR-300; Minolta, Tokyo, Japan) using the Hunter system, which identifies color using three attributes: L* or lightness (white = 100, black = 0), a (red = positive, green = negative) and b (yellow = positive, blue = negative). The color difference (ΔE), measures the distance in color space between two colors [16] and was determined by comparison to a standard white tile with colorimeter values of L* = 94.5, a = −1.0, and b = 0.2 using ( 12):…”