Dual-Stage ultrasound in deep frying of potato chips; effects on the oil absorption and the quality of fried chips.

Potato chips are popular high-consuming ready-to-eat meals in all of the world which specially attract a lot of attention from youth and children. Reducing oil absorption and improving the quality of chips are major undertakings within the industry. This research aimed to find the best ultrasonic bath-based method by investigating the optimal ultrasonic pre-treatment and developing an ultrasound (US) assisted frying system (UAFS) to reduce the oil absorption of potato chips while maintaining an acceptable quality. Through this technique, the potato chips get sonicated during deep frying in hot oil. US-pretreatment at temperatures of 25 °C and 73 °C, along with US-assisted frying, resulted in the minimal amount of oil which may be due to the US creating potential pores during the pre-treatment phase, which then expand further during the subsequent sonication stage. UAFS in combination with US-pretreatment produced more crispy chips due to the fact that the texture of potato slices becomes more porous. UAFS resulted in a decrease in the moisture content of the fried chips attributed to an increase in the effective diffusion coefficient and mass. Pretreating the chips at 73 °C significantly reduce the color change producing brighter product by inactivation of enzymes such as polyphenol oxidase. Finally, the result of TOPSIS optimization based on potato chips properties confirms that US-pretreatment in 73 °C brine followed by frying using UAFS is the best approach. Scanning electron microscope (SEM) images of potato chips also support this issue.

Design and evaluation of an intelligent sorting system for bell pepper using deep convolutional neural networks.

Homogeneity of appearance attributes of bell peppers is essential for consumers and food industries. This research aimed to develop an in-line sorting system using a deep convolutional neural network (DCNN) which is considered the state-of-the-art in the field of machine vision-based classifications, for grading bell peppers into five classes. According to export standards, the crop should be graded based on maturity stage and size. For that, the fully connected layer in the ResNet50 architecture of DCNN was replaced with a developed classifier block, including a global average-pooling layer, dense layers, batch normalization, and dropout layer. The developed model was trained and evaluated through the five-fold cross-validation method. The required processing time to classify each sample in the proposed model was estimated as 4 ms which is fast enough for real-time applications. Accordingly, the DCNN model was integrated with a machine vision-based designed sorting machine. Then, the developed system was evaluated in the in-line phase. The performance parameters in the in-line phase include accuracy, precision, sensitivity, specificity, F1-score, and overall accuracies were 98.7%, 97%, 96.9%, 99%, 96.9%, and 96.9%, respectively. The total rate of sorting the bell pepper was also measured as approximately 3000 sample/h with one sorting line. The proposed sorting system demonstrates a very good capability that allows it to be used in industrial applications. PRACTICAL APPLICATION: A developed intelligent model was integrated with a machine vision-based designed sorting machine for bell peppers. The developed system can sort the crop according to export criteria with an accuracy of 96.9%. The proposed sorting system demonstrated a very good capability that allows it to be used in industrial applications.

Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea.

On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400-700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.

A critical review on intelligent and active packaging in the food industry: Research and development.

The emergence of many new food products on the market with need of consumers to constantly monitor their quality until consuming, in addition to the necessity for reducing food corruption during preservation time, have led to the development of some modern packaging technologies such as intelligent packaging (IP) and active packaging (AP). The benefits of IP are detecting defects, quality monitoring and tracking the packaged food products to control the storage conditions from the production stage to the consumption stage by using various sensors and indicators such as time-temperature indicators (TTIs), gas indicators, humidity sensors, optical, calorimetric and electrochemical biosensors. While, AP helps to increase the shelf-life of products by using absorbing and diffusion systems for various materials like carbon dioxide, oxygen, and ethanol. However, there are some important issues over these emerging technologies including cost, marketability, consumer acceptance, safety and organoleptic quality of the food and emphatically environmental safety concerns. Therefore, future researches should be conducted to solve these problems and to prompt applications of IP and AP in the food industry. This paper reviews the latest innovations in these advanced packaging technologies and their applications in food industry. The IP systems namely indicators, barcoding techniques, radio frequency identification systems, sensors and biosensor are reviewed and then the latest innovations in AP methods including scavengers, diffusion systems and antimicrobial packaging are reviewed in detail.

A GIS-MCDM-based road network planning for tourism development and management in Arasbaran forest, Iran.

It is very important for managers to plan a road network that meets all the requirements for tourism development and management. The aim of this study was to evaluate and modify an existing road network for tourism purposes in the Arasbaran protected area. First, the map layers of effective criteria were prepared in GIS and were standardized by a fuzzy logic approach and finally combined considering their relative importance weights obtained through pair-wise comparison technique. A suitability map was then acquired. After that, 14 different scenarios of road network were designed to access the recreational area using PEGGER extension in ArcView. Then, they were evaluated in terms of technical, environmental, and socio-economic criteria to achieve the optimal-designed road network. Moreover, the existing road network was modified according to the optimal-designed road scenario. Finally, a modified version of the existing road network was proposed for tourism development and management in the Arasbaran region. Regarding the results, the slope criterion with a value of 0.289 was identified as the most important factor in providing a suitability map for road planning. The seventh scenario, with a road density of 3.34 m ha-1 and accessibility (hard) of 64.68%, was chosen as the optimal option to modify the existing road network due to the best performances in terms of minimum costs and environmental impacts on the basis of the highest value per unit length (72.26). According to the assessments and chi-square test comparison, the optimal-designed road network and the proposed road network were identified as better alternatives compared to the existing road. Based on this work, it can be concluded that the combination of GIS-MCDM approaches can properly assist in tourism planning and management.

Meat quality evaluation based on computer vision technique: A review.

Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.

Optimization of rendering process of poultry by-products with batch cooker model monitored by electronic nose.

The best available controlled technology for transforming the disposal of animal by-products and mortalities is rendering. Two aspects of rendering process are mentioned in this research; product quality and emissions. A model of batch cooker with temperature, pressure and agitator speed controllers was designed and developed in order to optimize the process and to investigate the effect of changes in rendering conditions on quality of poultry by-product meal and also on pollutant emissions. An electronic nose system was designed and built based on metal oxide semiconductor sensors to monitor the gases emitted from batch cooker model. Also, GC-MS was used to identify the emitted components. In order to optimize the rendering process, response surface methodology was performed on temperature, cooking time and agitator speed variables. Results showed that the temperature of 140 °C (internal pressure equivalent to about 3.2 bar), the cooking time of 45 min and the agitator speed of 20 rpm optimized the process of batch cooking to maximize the percentage of protein and minimize the percentage of fat, moisture content, energy consumption and emission of pollutants. By GC-MS analysis, about 100 compounds include hydrocarbons, volatile fatty acids, sulfur-containing compounds, alcohols, ketones, aldehydes, and furans were observed in the emission of a batch cooker model. The major groups were organic acids and amides. Principle component analysis showed the most suitable sensors for detecting unpleasant odors from rendering plants.

Real-time color change monitoring of apple slices using image processing during intermittent microwave convective drying.

An intermittent microwave convective drying method combined with a real-time computer vision technique was employed to detect the effect of drying parameters on color properties of apple slices. The experiments were performed at air temperature of 40 to 80℃, air velocities of 1-2 m/s, microwave powers of 200-600 W, and pulse ratios (PRs) of 2-6. Drying rate and drying time varied from 0.014 to 0.000001 min-1 and 27 to 244 min, respectively. The normalized lightness values had ascending and descending parabolic trends with decrease in product moisture content. With descending dimensionless moisture content, redness, yellowness, color change, hue angle, and chroma were enlarged. The normalized redness values changed from -4 to 3. Models relating drying parameters with drying time, drying rate, and lightness were obtained and found to be significant (P < 0.01). Results indicated that microwave power and PRs had more influence on lightness and color change than other parameters.

Optimization of intermittent microwave-convective drying using response surface methodology.

In this study, response surface methodology was used for optimization of intermittent microwave-convective air drying (IMWC) parameters with employing desirability function. Optimization factors were air temperature (40-80°C), air velocity (1-2 m/sec), pulse ratio) PR ((2-6), and microwave power (200-600 W) while responses were rehydration ratio, bulk density, total phenol content (TPC), color change, and energy consumption. Minimum color change, bulk density, energy consumption, maximum rehydration ratio, and TPC were assumed as criteria for optimizing drying conditions of apple slices in IMWC. The optimum values of process variables were 1.78 m/sec air velocity, 40°C air temperature, PR 4.48, and 600 W microwave power that characterized by maximum desirability function (0.792) using Design expert 8.0. The air temperature and microwave power had significant effect on total responses, but the role of air velocity can be ignored. Generally, the results indicated that it was possible to obtain a higher desirability value if the microwave power and temperature, respectively, increase and decrease.

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

A comparative study of dried apple using hot air, intermittent and continuous microwave: evaluation of kinetic parameters and physicochemical quality attributes.

In the study, the effectiveness of intermittent (IMWD) and continuous (CMWD) microwave drying and hot air drying (HAD) treatments on apple slices were compared in terms of drying kinetics (moisture diffusivity and activation energy) and critical physicochemical quality attributes (color change, rehydration ratio, bulk density, and total phenol content (TPC) of the final dried product. The temperature, microwave power, air velocity, and pulse ratio (PR) applied in the experiments were 40-80°C, 200-600 W, 0.5-2 m/s, and 2-6, respectively. Results showed that IMWD and CMWD more effective than HAD in kinetic parameters and physicochemical quality attributes. Also, results indicated CMWD had the lowest and highest drying time and effective diffusivity. The exponential model for estimating IMWD activation energy, considering absolute power (1/P) and pulse ratio were also represented. The color change in apple slices dried by HAD showed the highest change.

Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing.

BACKGROUND: This paper presents a versatile way for estimating antioxidant activity and anthocyanin content at different ripening stages of sweet cherry by combining image processing and two artificial intelligence (AI) techniques. In comparison with common time-consuming laboratory methods for determining these important attributes, this new way is economical and much faster. The accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models was studied to estimate the outputs. Sensitivity analysis and principal component analysis were used with ANN and ANFIS respectively to specify the most effective attributes on outputs. RESULTS: Among the designed ANNs, two hidden layer networks with 11-14-9-1 and 11-6-20-1 architectures had the highest correlation coefficients and lowest error values for modeling antioxidant activity (R = 0.93) and anthocyanin content (R = 0.98) respectively. ANFIS models with triangular and two-term Gaussian membership functions gave the best results for antioxidant activity (R = 0.87) and anthocyanin content (R = 0.90) respectively. CONCLUSION: Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes.