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In the  Bacillusamyloliquefaciens α-amylase (BAA), the loop (residues from 177-185; region І) is the constructive part of the cage responsible for attachment to calcium. It has two more amino acid residues than the α-amylase from Bacillus licheniformis (BLA). Arg176 in this region makes an ionic interaction with Glu126 from region ІІ (residues 118-131) but this interaction is lost in BLA due to substitution of R176Q and E126V. It is the common feature of α-amylases that calcium ion is required for their thermal stability. The present work quantitatively estimates the effect of ionic interaction on the overall stability of the enzyme. To clarify the functional and structural significance of corresponding salt bridge, first an automated homology model of the mutant enzyme (∆E126) was built by the Swiss-Model Protein Modeling Server.  Bacillus amyloliquefaciens α-amylase (3BH4.pdb) was used as the template and examined by GETAREA and WHAT IF programs, then Glu126 was deleted (∆E126) by site-directed mutagenesis and the thermostability was examined for the wild-type and mutant enzymes. Modeling results showed that deletion of salt bridge affected on the hydrophobic and hydrophilic residues orientation of two discussed regions (Ι, ΙΙ). The mutant enzyme also exhibited lower thermostability relative to the wild-type enzyme. Thus, it may be suggested that salt bridge could affect on accessible surface area of the discussed regions, decrease water diffusion,  prevent diffusion of cations and improve the thermostability of the whole protein.

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It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features, color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloe vera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features.

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In the present study, in the first step, the effect of Aloe vera gel (75% v/v) coating containing different concentrations of hemp seed oil (1-5% v/v) on some physicochemical properties of cherry tomatoes during storage at room temperature was investigated. The results revealed the ability of hemp seed oil to improve the physicochemical properties of cherry tomatoes during storage, although no significant difference was observed between 3 and 5% levels of hemp seed oil (p> 0.05). Slope change in the ripening index trend occurred for A. vera gel (75% v/v) coated sample on day 12 and for A. vera gel containing 3% hemp seed oil coated sample on day 16. Using an image processing system, the changes of the coated samples were evaluated based on the color statistical and color texture features extracted from the images and were graded through different procedures. The results showed that the principal component analysis (PCA) and artificial neural network (ANN) methods were able to divide the cherry tomatoes into intact and blemished grades which the ANN method was graded samples using color texture features with higher accuracy (97.41%). The adaptive neuro-fuzzy inference system (ANFIS) method had higher diagnostic power than the other two methods and was able to grade the samples into three grades including intact, grade 2 and unusable with accuracy of 98.96%.

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Cherry tomato as a climacteric product has a short postharvest life and so it is necessary to use methods such as coating to increase its shelf life. Therefore, the aim of the current study was to investigate the effect of fresh Aloe vera gel edible coating concentration (25, 50, 75 and 100% v/v) and storage temperature (5, 12 and 25 °C) on postharvest quality characteristics of cherry tomatoes such as weight loss, titratable acidity, total soluble solids and sensorial properties stored for 24 days and also to study the kinetics of their changes. During the storage period, the weight loss, total soluble solids of all samples increased and the titratable acidity decreased. The use of A. vera gel considerably controlled the quality characteristics changes while the increase in temperature intensified the changes in the studied characteristics. In addition, the use of A. vera gel and low storage temperature reduced the incidence of decay and increased the score of sensory properties. Kinetic studies showed that the changes in weight loss and total soluble solids followed the first-order model and the titratable acidity followed the fractional conversion model. The constant rate of postharvest characteristics changes under different conditions followed Arrhenius equation. Based on the findings of the current study, it seems that applying fresh A. vera gel with 75% concentration and storage temperature of 5 °C improves the postharvest qualitative and sensorial characteristics of cherry tomatoes during 24 days of storage.