Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination (R²) using the test set. The R² values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.

The aim of the present study is investigating the effect of some biological binders such as microbial transglutaminase "MTGase" and non biological binders such as textured soy protein "TSP", soy protein concentrate "SPC" and sodium alginate on the texture of meat patties using the texture profile analysis (TPA) comparing with sensory evaluation. The results indicated that the patties samples containing SPC and MTGase had the highest values of hardness (3.65 N), chewiness (2.70) and number of chewings (25.30). Also, the results of sensory evaluation showed that the patties sample containing SPC and the MTGase was more acceptable than the other samples. It can be concluded that using plant protein binders such as SPC and cross- linking enzyme "MTGase" enhanced the texture of meat patties. Also,the texture profile analysis can be used successfully for measuring some characters in meat products.