Post-harvest technology
Saleh Azari; Esmaeil Mirzaee- Ghaleh; Hekmat Rabbani; Hamed Karami
Abstract
Introduction: Coffee is a common drink which is obtained from the roasted and ground beans of the coffee plant. Coffee beverages are widely consumed as a stimulant, a property largely attributed to the presence of caffeine, which is the most active pharmaceutical ingredient consumed worldwide. When the ...
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Introduction: Coffee is a common drink which is obtained from the roasted and ground beans of the coffee plant. Coffee beverages are widely consumed as a stimulant, a property largely attributed to the presence of caffeine, which is the most active pharmaceutical ingredient consumed worldwide. When the fruit of the coffee plant ripens, the coffee beans are harvested, processed, and finally dried. Dried coffee beans are roasted to different degrees and graded depending on the desired aroma and taste. It is very important to detect natural and unnatural impurities and adulteration in coffee.Materials and Methods: An odor machine system based on eight MOS sensors was used to investigate the effect of bread storage time based on odor characteristics. The designed system includes a data acquisition system, sensors, sensor shield, sample container, power supply, connections, electric valves, air pump, and air filter. The sensor array consisted of 8 MOS sensors, including MQ136, TGS822, MQ9, MQ3, TGS813, TG2620, TG2602, and MQ135, each reacting to specific volatile compounds. These sensors are widely used in olfactory machines because of their high chemical stability, high durability, low response to moisture, and affordable prices. They are the most commonly used sensors in electronic nose systems. Sensors are the main components of an electronic nose system; therefore, it is necessary to select sensors able to detect differences among samples. In this research, the use of electronic nose technology and artificial intelligence was evaluated to detect common adulteration in Arabica coffee (Medium Dark). Robusta coffee samples with weight percentages of 10, 40, 30, 20, and 50% were used for experiments and adulteration. An electronic nose equipped with eight metal oxide sensors was used to carry out experiments related to odor. The data received from 8 sensors was first recorded and stored as raw data. In this research, the fractional method was used to normalize the data. Preprocessed data were used as the input matrix for multivariate analytical methods. The unsupervised multivariate principal component analysis (PCA) method was used to analyze the data. The LDA method was used to reduce classification differences and expand the differences between different groups. The artificial neural networks (ANN) method was used for classification. All calculations and analyses were done using Excel 2016, Unscrambler x10.4, and MATLAB software. Model evaluation criteria are used to evaluate algorithm performance in supervised learning. To analyze the system's performance, common criteria including Specificity, Recall, Precision, Accuracy, Area Under the Curve (AUC), and F-score were used.Results and Discussion: The results of PCA showed that 87% of the total variance of the data was explained by PC1, and 8% by PC2, and the two main components constituted 95% of the total variance of the normalized data. Based on the results, pure Robusta coffee (B) was located on the right side of the PCA diagram and completely separate from other levels of adulteration. Also, pure Arabica coffee (A) was placed in the vicinity of counterfeit samples, and all counterfeit samples showed the same behavior as Arabica coffee, which is very difficult to distinguish. The loading diagram was examined to determine the role of sensors in separating the groups. Based on the loading diagram for coffee adulteration detection, the sensors that had the highest value on the principal component were MQ9, TGS822, and MQ136. Other sensors also showed a high correlation with the smell of the samples. In other words, other sensors could be neglected. The models of artificial neural networks analysis were evaluated by the correct classification rate (CCR), root mean square error (RMSE), and coefficient of determination (R2). According to the results obtained for 7 different coffee groups, the 7-8-8 structure had the best results. This structure has 8 neurons in the input layer (number of sensors), a hidden layer with 8 neurons, and 7 neurons in the output layer (7 groups). The average values of the class obtained from the ANN model for the parameters of accuracy, precision, recall, specificity, area under the curve (AUC), and F-score were equal to 0.984, 0.952, 0.943, 0.990, 0.971, and 0.942, respectively. Also, the ANN method showed higher accuracy than the LDA method.Conclusion: The electronic nose showed that it is a fast and effective tool for detecting adulteration substances in coffee.
Post-harvest technology
Hassan Masoudi; Seyed Mahmood Asadi; Gholamreza Akbarizadeh
Abstract
Introduction: In sugar factories, control of sugar crystals growth in the granulation stages is very important to produce sugar grains with a special and required size. Machine vision systems can determine the size of sugar crystals. The main challenge of image processing systems is the lack of capable ...
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Introduction: In sugar factories, control of sugar crystals growth in the granulation stages is very important to produce sugar grains with a special and required size. Machine vision systems can determine the size of sugar crystals. The main challenge of image processing systems is the lack of capable algorithms to separate contact and overlap crystals accurately. So far, various algorithms have been developed to detect crystals and remove their overlapping. However, these methods have not been able to fully detect and separate the overlap of crystals. The purpose of this study was to provide an appropriate image processing algorithm for determining the size of crystals in sugar baking solution (massecuite), which gives us the characteristics of size and shape for the particles in the baking pot instantly to evaluate and improve the quality of the final product.Materials and Methods: The massecuite samples were provided from Debal Khozaei Sugarcane Agro-industrial Company, Ahvaz, Iran. After preparation of the sugar crystals samples under lam and lamer (microscope slides), a digital camera with two Megapixel resolution, attached to a ZS9 Olympus microscope, was used for image capturing. Then, using MATLAB image processing toolbox, the color image (RGB) transferred to grey scale. A mixture of structural operations such as erosion and expansion with spatial filters including median filter were used to remove the image noises. The function of histogram local adjustment was used to improve image contrast. Three methods of segmentation including convexity, fuzzy clustering, and multiplicative intrinsic component optimization (MICO), along with their combination were used to segment the image of massecuite crystals. A reference image was used to determine accuracy of the image processing algorithms. To do this, the massecuite crystals image was manually segmented by Image J software. All segmentation algorithms were applied on the reference image, and seven geometrical parameters, including the mean aperture (MA), coefficient of variation (CV), and standard deviation (SD) were calculated for all the sugar particles in the image. Finally, the percent of MA measurement error was calculated for each sugar crystal to find the best algorithm.Results and Discussion: In manual segmentation, the number of sugar crystals in the selected image was 26. In the manually segmented image, the average of MA, SD and CV for sugar grains in the image were 0.422 mm, 0.157 mm and 37.18% respectively. The relatively large CV of the calculated geometrical parameters indicated the non-uniformity of the sugar particles size inside the massecuite. The convexity method was able to perform well in some areas of the image, and in some other areas, it could not detect the contact between the crystals. The value of the SD and CV of all the geometric parameters determined by the convexity method were greater than the reference values determined by the manual segmentation. This indicates the weaker performance of this method in determining the sugar crystals size compared to the manual method. The values of SD and CV of all geometric parameters determined by the combined fuzzy-convexity method were greater than the reference values, but lower than the values of the convexity method alone. So, the combination of the fuzzy clustering method with the convexity method improved the segmentation performance of crystal images. The SD and CV values of all geometric parameters determined by the combined MICO-convexity method were greater than the reference values, but lower than the values of convexity and fuzzy-convexity methods. This point shows the better performance of the combined MICO-convexity method in segmenting the images of sugar crystals compared to the other two methods. The average of MA, SD and CV for sugar crystals in the image were 0.382 mm, 0.150 mm and 39.23% respectively and had no significant difference with the reference method values in 5% probability level. The mean error of MA determined by the combined MICO-convexity algorithm was 13.24% and Pearson correlation factor was 0.88. As a result, the combined MICO-convexity method was proposed to determine the size of sugar crystals in massecuite.Conclusion: After applying different algorithms on the selected image of sugar crystals in massecuite, it was found that the combined MICO-convexity method can separate sugar crystals well. Also, the CV obtained for this image segmentation algorithm was not much different from the CV of the manual reference method, so this algorithm can be used in the image processing system of the massecuite crystals.
Post-harvest technology
Esmaeil Mirzaee- Ghaleh; Fardin Aayri Samlhe; Amir Hossein Afkari Sayyah
Abstract
Introduction: As the most important source of calories and protein, bread has a special role and importance in the nutrition of the country, and its cheapness has caused it to replace other food items in the diet in recent years. The increase in bread consumption in the low-income and vulnerable groups ...
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Introduction: As the most important source of calories and protein, bread has a special role and importance in the nutrition of the country, and its cheapness has caused it to replace other food items in the diet in recent years. The increase in bread consumption in the low-income and vulnerable groups has been more intense due to the low volume of supply of other food products and the excessive and continuous increase in the price of other alternative products. Flat bread has the highest consumption statistics among other breads in Iran. One of their types is Barbari bread, which is the second most consumed bread after lavash bread in Iran. Therefore, the health and quality of consumed Barbari bread is of particular importance. For this purpose, this study was conducted with the aim of the effect of storage time of Barbari bread based on the characteristic of smell using the olfactory machine system based on eight metal oxide semiconductor sensors.Materials and Methods: An odor machine system based on eight MOS sensors was carried out in order to investigate the effect of bread storage time based on odor characteristics. Designed system includes data acquisition system, sensors, shield of sensors, sample container, power supply, connections, electric valves, air pump and air filter. The sensor array was consisted of the 8 MOS sensors that each one reacts to specific volatile compounds. These sensors are widely used in olfactory machines because of their high chemical stability, high durability, low response to moisture and affordable prices. These are the most commonly used sensors in electronic nose system. Sensors are the main components of an electronic nose system therefor it is necessary to select the able sensors to detect differences among samples. In order to carry out the test, the sample was placed in sample container and in the baseline correction step (150 seconds), clean air was passed through the sensors to transmit the response of sensor array to steady state. At the injection step (180 seconds), the sample headspace was transmitted and passed through sensors chamber. Output voltage of each sensor depends on the type of sensor and its sensitivity. At the cleaning step (150 seconds) the clean air was passed through sensors to get the sensor array responsive to a stable state. Also, at this step the pump removed the odor remaining inside the sample container and system is prepared for the next test. The signals obtained from the sensors were recorded and then pre-processed.Results and Discussion: The olfactory machine system based on eight metal oxide semiconductor (MOS) sensors was investigated with the PCA pattern recognition method due to the storage time of Barbari bread at four different temperatures. The data obtained from the signals processing with fractional method were used as input of PCA. The results of principal component analysis with two components PC1 and PC2 are 95, 90, 86 and 85%, respectively, for Barbari bread that is stored at room temperature (in the table), refrigerator temperature (4°C), room temperature (in foil) And the temperature of the freezer was placed, it showed. The results obtained from the QDA analysis to determine the quality of Barbari bread at 4 °C for 9 days, at room temperature (in foil) and (on the table) for 5 days and Barbari bread at the freezer temperature of the refrigerator (-18 °C) for 15 days of storage with classification accuracy of 98.52, 96, 100 and 97.35% respectively. The results of LDA analysis for the signals obtained from the olfactory machine, in the classification of the duration of storage of Barbari bread at refrigerator temperature, room temperature (in the table), room temperature (in foil) and refrigerator freezer temperature, respectively, with classification accuracy of 79.26 and 85.33, 78.67 and 75.22 percent were obtained. Also, according to the output obtained from the loading linear graphs and the radar graph, the smell of Barbari bread has the most and the least effect on the MQ9 sensor and the TGS813 sensor, respectivelyConclusion: An olfactory machine system based on eight metal oxide semiconductor (MOS) sensors was investigated for the Barbari bread time retention effect at four different temperatures. The results of principal component analysis with two components PC1 and PC2, for Barbari bread at room temperature (the table), refrigerator temperature, room temperature (in foil) and It showed that they were exposed to freezing temperatures. The results obtained from QDA analysis to detect the quality of Barbari bread at 4°C in room temperature (in foil) and (in the table) and refrigerator freezer temperature respectively. The results of LDA analysis for the classification of Barbari bread at refrigerator temperature, room temperature (in the table), room temperature (in foil) and freezer temperature of the refrigerator. The was obtained. Also, according to the output obtained from the loading linear graphs and the radar graph, the smell of Barbari bread has the most and the least effect on the MQ9 sensor and the TGS813 sensor, respectively.
