نشریه فناوری های جدید در صنعت غذا
سال نهم شماره 2 (پیاپی 34، زمستان 1400)

Quality assessment and fruits’ sorting are important activities in postharvest process that are attracting notices interestingly because of increasing demand for healthy products with better quality. In the recent decades, various non-invasive and non-destructive techniques for fruits and vegetables evaluations have been employed. Among these methods, hyperspectral imaging as a non-destructive, fast and ecofriendly technique is getting researchers’ attraction increasingly in order to crops properties assessment. Regarding this fact that consumption of organic acids and consequent changes pH is considered as one of the important qualitative parameters of apple fruits, this study aimed to investigate the effect of shelf life time on pH value of Red Delicious apples during 60 days. Hyperspectral reflecting imaging in range of 400-1000 nm has been applied while the pH of samples was measured destructively. After discarding noises using principal component analysis (PCA) analysis, to improve spectrum, different primary pre-processing had been applied and their effects were investigated. The suitable model was obtained via Partial Least Square method (PLS). Important wavelengths were selected based on regression coefficient of the best model includes large absolute values of weighted regression coefficients (BW) and using various techniques were modeled. Concerning the PLS analysis, the best results were obtained through smoothing Savitzky-Golay pre-processing with mean square root error (RMSE) of 0.02 and 0.018 and coefficient of determination (R2) both 0.980 for calibration and validation data, respectively. According to regression coefficient of the best model, 9 wavelengths were determined as the best. In modeling by efficient wavelength, artificial neural network (ANN) gave the best result. Hence, it was obvious that non-destructive method of hyperspectral imaging was capable to predict pH values of apple fruits with high accuracy during the shelf life.

Every year, a large part of the Produced strawberries is losses due to postharvest corruption. On the other hand, the demand for Biodegradable packaging materials has increased sharply due to environmental concerns about the use of petroleum polymers. Therefore, biopolymer-based packaging materials have a high potential for replacement with petroleum plastics. Research has also shown that plant essential oils can control postharvest diseases of agricultural products. However, their use is associated with difficulties due to physical and chemical instabilities odors and taste in the product. One of the best way to solve this problems is to include essential oils in the packaging film. Therefore, the present study aimed to investigate the effect of biodegradable film based on Zein containing Zataria multiflora essential oil (with concentrations of 0, 5, 10 and 20% w/w) as an active packaging to increase the Postharvest shelf life of strawberry fruit. The effect of films on maintaining the quality properties of strawberries during 15 days of storage at 4°C was evaluated and the amount of changes in weight, firmness, soluble solids, pH, treatable acidity, vitamin C and microbial contamination of strawberry samples during this period were evaluated. The results showed that during the storage, the quality of the samples decreased but the decreasing trend in packaged samples was much less than unpackaged samples. Also, the addition of essential oil to the polymer film improved the quality properties of strawberries, and with increasing the concentration of essential oil, the quality of strawberry samples decreased. Therefore, it seems that it is possible to greatly reduce the waste after harvest of strawberries by using the biodegradable film of saddle containing Zataria multiflora essential oil as an active packaging.

Camel meat can be a suitable alternative for other red meat types in human nutrition, due to its low cholesterol and low-fat content and the appropriate protein content. This research aims to investigate and evaluate the fat content and freshness of camel meat using machine vision technology as a non-destructive method. Therefore, using image processing as a non-destructive method and Soxhlet device as a destructive method, the amount of fat content was predicted, and also the freshness was classified for camel meat. In the image processing section, 108 textual features and 39 color features were extracted in the RGB, HSV, HIS, and CIElab color spaces. Moreover, to predict the freshness and quality of meat, feed-forward back propagation artificial neural networks with one and two hidden layers, a various number of neurons, and threshold functions were used. Also, according to the regression diagram of fat content obtained from the destructive method (fat content obtained from Soxhlet device) with fat content obtained from non-destructive method (machine vision), the coefficient of determination and accuracy between them achieved 0.841. The results of the evaluation of the neural networks showed that the best desirable network for classification based on freshness is a one-hidden layer network with topology 147-3-1, tangent-sigmoid transfer function at hidden layer and purelin transfer function at output layer (R2= 0.996), and also to prete of fat content the best network is two-hidden layer network with linear, log-sigmoid, log-sigmoid transfer function at first hidden layer, second hidden layer and output layer respectively (R2= 0.99). Therefore, the results of this study show that the proposed system with the help of machine vision technology can predict the freshness and fat content of camel meat with acceptable accuracy.

Echium amoenum is an herbaceous Iranian herb, two or many years old from the Boraginaceae family. Most Echium amoenum varieties have medicinal properties that can be promoted through scientific activity and industrial production of diverse products. In this study, the effects of carrier agents of Maltodextrin and Arabic gum (0(control), 20, 25, 40, 50%) in drying by foam-mat freeze-dried method impact on hygroscopicity, degree of caking, wettability, dispersibility, solubility, water absorption index, total phenolic compounds, antioxidant activity and level of total anthocyanins compounds was investigated. By increasing the concentration of carrier agents, degree of caking, dispersibility and water absorption index 13, 28, and 34 % increased respectively. Hygroscopicity was reduced 2% compared to the control sample. Anthocyanins, antioxidant activity and total phenolic compounds decreased compared to the control sample. According to the results of sensory evaluation, the best treatment in terms of general acceptance, aroma, and taste was contained 25% maltodextrin and 25% Arabic gum together, and contained 0.005 mg/100ml anthocyanins compounds, 9.4 % antioxidant activity and 37 mg/100ml total phenolic compounds.

Recently, the preparation of Pickering emulsion has attracted particular attention due to its wide range of applications in various industries, including the food and pharmaceutical industries. The present study aimed to prepare a Pickering emulsion stabilized with Chitosan nanoparticles (CSNPs) and investigate the effect of nanoparticle concentration and oil content on physical stability. First, the nanoparticles were prepared by the ionic gelation technique. Then, morphology, particle size distribution, wettability, Fourier-transform infrared (FTIR) spectra of nanoparticles were determined. FSEM results revealed that the nanoparticles are predominantly spherical, with an average size of 69 ±1nm. Also, the results of the FTIR spectrum analysis and wettability confirmed the formation of nanoparticles. In the second stage, emulsions were prepared at different concentrations of CSNPs (0.45, 0.6, and 0.75% w/v) and the ratio of oil to the aqueous phase (10, 20, and 30%). The results revealed that nanoparticle concentration and oil content had a significant effect on emulsion physical stability. In a fixed oil volume fraction, physical stability increases with the increase of nanoparticles' concentration. In contrast, the oil content showed different effects on the properties of emulsions prepared with different concentrations of nanoparticles.

The ability to recognize color, texture, and shape in image processing technology has led to the development of machine vision systems in various fields of agriculture, conversion industries, and industry. The existence of impurities and seeds with an unsuitable appearance in chickpeas and the direct effect of the appearance quality of the product on its marketability, shows the need for grading this product. The aim of the present study was to distinguish impurities and chickpeas with an inappropriate appearance from chickpeas with a suitable appearance by developing a machine vision system. Totally 270 images including 54 samples of chickpeas with suitable appearance and 36 samples of each type of chickpeas with inappropriate appearance (wrinkled, green, brown, and split) and foreign materials (stone and stem) were prepared. After preparing the sample images, the pre-processing and feature extraction steps were performed automatically and different color, texture and shape properties were extracted by developing and using an image processing algorithm. An algorithm was developed to select efficient features from the extracted features. Efficient features were classified by the artificial neural network method with total accuracy of 91.9%. The detection accuracy for desirable, wrinkled, cotyledon, immature, and brown chickpea and stem and stone impurities was equal to 98.1, 83.3, 100.0, 91.7, 97.2, 77.8, and 97.2% respectively. Using the developed system, the chickpea product can be graded with high accuracy and low cost, so that after separating the impurities, the desirable and undesirable chickpeas can be separated and sent to the market for different uses.