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The diversity and abundance of quality characteristics of agricultural products, has been the main reason for the development of non-destructive methods. Machine vision and artificial intelligence are powerful techniques for diagnosing most physical, mechanical and chemical properties of agricultural products. Before export fruits are classified by shape, volume and weight. Ranking fruit through taste (sweet or tart) non-destructively plays an important role in marketing, choice power and its application. In this research, it was detect the taste of Thompson orange while combining artificial intelligence (AI) and visual machine technique. A closed circuit digital installed in special frame, under specific height and light was used to take picture from samples vertically. Also, an algorithm (program) based on AI was developed to diagnose the variety and taste of Thompson orange through apparent characteristics in Matlab software. The results showed that the success rate of taste determination for Thompson orange using ANFIS and ANN-GA (Artificial Neural Network-Genetic Algorithm) was 96.67 and 90.0% respectively.  

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Amylase improves the texture and sensory properties of bulky bread by degrading starch and producing dextrin in order to faster metabolism by bakery yeast. This study investigates the effect of thermostable α-Amylase 0, 1.9, 2.9 (U/ml), extracted from Bacillus safensis, and fermentation time at 35, 40 and 45 minutes on the quality of bulky bread baked in oven at 210°C for 20 min.­­ The results of our study showed that adding filtered soup containing   1.9 (U/ml) and fermentation for 40 minutes  was more acceptable than other samples in terms of volume, hardness, cohesiveness and overall acceptance, but adding more amounts of amylase enzyme at ­2.9 (U/ml) level did not yield good results in terms of texture and sensory properties of bulky bread.

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The aim of this study was to isolate thermostable amylase producing bacteria from starch-rich wastewater of one of ­the canning factories and then molecular identification of these isolates. In addition, the thermostable amylase extracted from the bacterium was investigated for the optimum temperature and pH of enzyme activity. In this study, 14 heat-resistant microbial isolates were isolated from wastewater and only two isolates had amylase activity.  Molecular identification of isolates based on amplification of 16S rDNA gene by B27F and U1492R primers and then sequencing of PCR product confirmed the presence of Bacillus pumilus and Bacillus safensis. The results showed that the optimum temperature and pH of amylase activity were 60°C and 7, ­respectively, and Bacillus safensis 7.67 (U/ml) had more amylase activity than of Bacillus pumilus 6.33 (U/ml).  

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The present study aims to identify the dimensions and components of the youth entrepreneurship training. Accordingly, the research method is applied based on the purpose and the qualitative research design. The research community consists of experts and youth entrepreneurship. Twenty-two individuals are selected as sample size using targeted sampling while data are collected using semi-deep interviews. To ensure the validity of the tool, the survey method is used by members (interviewers) and triangulation of data sources. The data analysis method is coded as open, axial and selective coding. The results show that the dimensions of youth entrepreneurship development include entrepreneurial attitudes, entrepreneurship education training and entrepreneurship soft skills. Also, the findings show that entrepreneurial attitude includes spirit, education, and entrepreneurship knowledge training, including specialized and management knowledge as well as soft skills of entrepreneurship, including teamwork, creativity and risk taking.