University of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622Investigating meat and oil quality in chicken nuggets using electronic nose and image processing techniquesInvestigating meat and oil quality in chicken nuggets using electronic nose and image processing techniques1149478410.22059/ijbse.2023.363744.665517FAHafezOzgoliDepartment of Agricultural Machinery Engineering, Faculty of Agriculture, School of Agriculture & Natural Resources, University of Tehran, Karaj, Iran0009-0007-5203-4367Seyed SaeidMohtasebiProfessor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran0000-0002-4031-1095SoleimanHosseinpourAssociate Professor, Department of Agricultural Machinery Engineering, Faculty of Agriculture, School of Agriculture & Natural Resources, University of Tehran, Karaj, Iran0000-0002-0572-3932MohammadHosseinpour-ZarnaqDepartment of Agricultural Machinery Engineering, Faculty of Agriculture, School of Agriculture & Natural Resources, University of Tehran, Karaj, Iran0000-0002-3861-1638Journal Article20230813Chicken nugget is a popular processed form of chicken meat. Nugget’s special flavor and color are interesting for consumers. Healthy meat is an important quality factor of nuggets. In the production process, the nugget is deeply fried for 1 minute at 185 °C. So, oil quality has an important effect on the final quality and healthy aspects. In this research, for the first time, the meat and oil quality used in chicken nuggets was checked using automatic and non-destructive techniques including an electronic nose (E-nose) and machine vision system. Meat health (healthy and spoiled) and consumed oil (healthy and burnt) were considered quality indicators. After frying, the excess oil was removed, and then the samples were subjected to machine vision and electronic nose systems for quality evaluations. In this research, the optimum of important parameters in the E-nose and the required times for sensing and removing the odor were determined. Also, the sensors' response was investigated. Principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural network (ANN) were used to analyze and classify the data. The accuracy results of both machine vision and electronic nose systems were similar and healthy meat and healthy oil classes were detected with up to 90 % accuracy. The evaluation results of the E-nose were similar to the machine vision system. Therefore, the combination of the two methods can be used in the measurement of color and smell characteristics.Chicken nugget is a popular processed form of chicken meat. Nugget’s special flavor and color are interesting for consumers. Healthy meat is an important quality factor of nuggets. In the production process, the nugget is deeply fried for 1 minute at 185 °C. So, oil quality has an important effect on the final quality and healthy aspects. In this research, for the first time, the meat and oil quality used in chicken nuggets was checked using automatic and non-destructive techniques including an electronic nose (E-nose) and machine vision system. Meat health (healthy and spoiled) and consumed oil (healthy and burnt) were considered quality indicators. After frying, the excess oil was removed, and then the samples were subjected to machine vision and electronic nose systems for quality evaluations. In this research, the optimum of important parameters in the E-nose and the required times for sensing and removing the odor were determined. Also, the sensors' response was investigated. Principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural network (ANN) were used to analyze and classify the data. The accuracy results of both machine vision and electronic nose systems were similar and healthy meat and healthy oil classes were detected with up to 90 % accuracy. The evaluation results of the E-nose were similar to the machine vision system. Therefore, the combination of the two methods can be used in the measurement of color and smell characteristics.https://ijbse.ut.ac.ir/article_94784_624be6e34355719afee744791897d442.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622Evaluation of the environmental impacts of two pathways for utilizing chicken meat production waste in Guilan ProvinceEvaluation of the environmental impacts of two pathways for utilizing chicken meat production waste in Guilan Province15319485610.22059/ijbse.2023.364574.665520FAShamsiSoodmand-MoghaddamDepartment of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran,, Iran0000-0003-3008-2564MohammadSharifiDepartment of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran0000-0003-4594-4972MajidKhanaliDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran0000-0001-6133-4862HomaHosseinzadeh-bandbafhaDepartment of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, Tehran University0000-0002-1120-9219Journal Article20230829A chicken meat processing systems are a primary source of valuable materials because of the considerable amount of waste they generate. Among the most effective methods for managing and converting waste into higher-value materials is the adoption of a circular bioeconomy strategy within biorefineries. this study examines two biorefineries that utilize chicken waste. In the initial pathway, chicken meat and sound insulation from chicken feathers are manufactured while the second pathway also involves the production of chicken meat, sound insulation, as well as biodiesel and glycerol from chicken fat waste. In this research, Simapro software and ReCiPe method were used to evaluate the life cycle assessment. The findings from this research indicated that the second pathway offers superior environmental performance when compared to the first pathway. Based on the findings, the second pathway presents a decrease of 21.92% in the overall environmental impact caused by producing chicken meat when compared to the first pathway. Following the first pathway, the production of one ton of chicken meat can leads to damages of 6.83E-03 DALY to human health, 6.81E-05 species.yr to ecosystems, and $101 to resources. Following the second pathway, production leads to a reduction of 23.26% in human health damage, 24.48% in ecosystem damage, and 17.76% in resource depletion. As such, moving toward production along the second pathway, i.e. diverse materials production, represents a promising strategy for managing the existing waste and reducing the environmental damage caused by chicken meat production.A chicken meat processing systems are a primary source of valuable materials because of the considerable amount of waste they generate. Among the most effective methods for managing and converting waste into higher-value materials is the adoption of a circular bioeconomy strategy within biorefineries. this study examines two biorefineries that utilize chicken waste. In the initial pathway, chicken meat and sound insulation from chicken feathers are manufactured while the second pathway also involves the production of chicken meat, sound insulation, as well as biodiesel and glycerol from chicken fat waste. In this research, Simapro software and ReCiPe method were used to evaluate the life cycle assessment. The findings from this research indicated that the second pathway offers superior environmental performance when compared to the first pathway. Based on the findings, the second pathway presents a decrease of 21.92% in the overall environmental impact caused by producing chicken meat when compared to the first pathway. Following the first pathway, the production of one ton of chicken meat can leads to damages of 6.83E-03 DALY to human health, 6.81E-05 species.yr to ecosystems, and $101 to resources. Following the second pathway, production leads to a reduction of 23.26% in human health damage, 24.48% in ecosystem damage, and 17.76% in resource depletion. As such, moving toward production along the second pathway, i.e. diverse materials production, represents a promising strategy for managing the existing waste and reducing the environmental damage caused by chicken meat production.https://ijbse.ut.ac.ir/article_94856_d5d7927d8802a05245c89b9427dae918.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622Diagnosis of disease in tomato paste by Bacillus subtilis bacteria, Penicillium fungi and Aspergillus fungi with the help of electronic noseDiagnosis of disease in tomato paste by Bacillus subtilis bacteria, Penicillium fungi and Aspergillus fungi with the help of electronic nose33479485410.22059/ijbse.2023.361002.665513FASanazSadriyanDepartment of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran0009-0005-4101-0744HosseinJavadikiaDepartment of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran0000-0003-1753-6891NahidAghili NateghDepartment of Agricultural Machinery Engineering, Sonqor Faculty of Agriculture, Razi University, , Kermanshah, Iran0000-0003-4321-620XLeilaNaderlooDepartment of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran0000-0003-1173-3245RouhallahSharifiDepartment of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah, Iran0000-0001-9013-3445Journal Article20230618 Maintaining the quality of tomato paste is very important for processing industry. Bacteria, fungal toxins and molds are factors that can cause food contamination and spoilage. The purpose of this research was to investigate the performance of electronic nose in detecting spoilage in tomato paste and also to investigate the changes of some important physicochemical properties due to spoilage in tomato paste. Bacillus subtilis bacteria and Penicillium and Aspergillus fungi were used to spoil tomato paste. Sampling for samples infected with bacteria was carried out in 4-hour intervals for 24 hours, and for samples infected with fungi, it was taken daily for one week. . Quadratic Discretion Analysis (QDA), Artificial Neural Network (ANN), Supppport Vector Regression (SVR), were the methods used to achieve this goal. The QDA method showed a good performance in the classification of bacteria and fungi and was able to detect bacterial growth and spoilage of tomato paste in 6 sampling times with 100% accuracy. Classification accuracy with the help of neural network for bacteria based on sampling time was 86.7% and for samples infected with fungi based on the type of fungus was 90%. The best prediction of the physicochemical properties of the sample infected with bacteria and fungi by the ANN model related to the properties of sediment weight percentage and acidity, respectively, and in the SVR model, it related to the properties of pH and acidity, respectively. Maintaining the quality of tomato paste is very important for processing industry. Bacteria, fungal toxins and molds are factors that can cause food contamination and spoilage. The purpose of this research was to investigate the performance of electronic nose in detecting spoilage in tomato paste and also to investigate the changes of some important physicochemical properties due to spoilage in tomato paste. Bacillus subtilis bacteria and Penicillium and Aspergillus fungi were used to spoil tomato paste. Sampling for samples infected with bacteria was carried out in 4-hour intervals for 24 hours, and for samples infected with fungi, it was taken daily for one week. . Quadratic Discretion Analysis (QDA), Artificial Neural Network (ANN), Supppport Vector Regression (SVR), were the methods used to achieve this goal. The QDA method showed a good performance in the classification of bacteria and fungi and was able to detect bacterial growth and spoilage of tomato paste in 6 sampling times with 100% accuracy. Classification accuracy with the help of neural network for bacteria based on sampling time was 86.7% and for samples infected with fungi based on the type of fungus was 90%. The best prediction of the physicochemical properties of the sample infected with bacteria and fungi by the ANN model related to the properties of sediment weight percentage and acidity, respectively, and in the SVR model, it related to the properties of pH and acidity, respectively.https://ijbse.ut.ac.ir/article_94854_e0501dd4a84df989ef1664ea7eb22adb.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622Prediction of output moisture content of dill from hot-air conveyor belt dryer using machine visionPrediction of output moisture content of dill from hot-air conveyor belt dryer using machine vision49669521010.22059/ijbse.2023.367648.665529FAHawinAlipanahiDepartment of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran0009-0007-4300-4801NasserBehroozi-KhazaeiDepartment of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.0000-0001-6095-2343KavehMollazadeDepartment of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran0000-0001-7379-839XHosainDarvishiDepartment of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran0000-0003-1496-7565Journal Article20231107Predicting the output moisture content of product from the conveyor belt hot air dryer for controlling the drying process is one of important parameters. Therefore, in this research, a conveyor belt dryer with a hot air flow equipped with a machine vision system was developed. Dryer also consists of air temperature and conveyor belt speed controlling section, lighting and imaging system. The control sections for air temperature and conveyor belt speed include SSR relays and a programmed algorithm in MATLAB software environment. The machine vision section comprises three cameras placed at the beginning, middle, and end of the conveyor belt. In this study, experiments were conducted at two temperature levels of 50 and 60 °C and three levels of conveyor belt speed for each treatment. Then, using the developed image processing algorithm in MATLAB, the changes in shrinkage were extracted and analyzed. Finally, the out moisture content of the product from dryer was modeled using the ANN. The results of this study indicated that the out moisture content and shrinkage of the dried product are dependent on temperature of dryer and speed of the conveyor belt. Specifically, with an increase in temperature and a decrease in conveyor belt speed, the degree of shrinkage increases Finally, results revealed that the ANN with 4-12-1 structure had best prediction performance with 1.06e-6, 1.24e-6, 9.46e-7 of RMSE and 0.9999, 0.9999, 0.9999 of R, respectively for training, validation and testing data.Predicting the output moisture content of product from the conveyor belt hot air dryer for controlling the drying process is one of important parameters. Therefore, in this research, a conveyor belt dryer with a hot air flow equipped with a machine vision system was developed. Dryer also consists of air temperature and conveyor belt speed controlling section, lighting and imaging system. The control sections for air temperature and conveyor belt speed include SSR relays and a programmed algorithm in MATLAB software environment. The machine vision section comprises three cameras placed at the beginning, middle, and end of the conveyor belt. In this study, experiments were conducted at two temperature levels of 50 and 60 °C and three levels of conveyor belt speed for each treatment. Then, using the developed image processing algorithm in MATLAB, the changes in shrinkage were extracted and analyzed. Finally, the out moisture content of the product from dryer was modeled using the ANN. The results of this study indicated that the out moisture content and shrinkage of the dried product are dependent on temperature of dryer and speed of the conveyor belt. Specifically, with an increase in temperature and a decrease in conveyor belt speed, the degree of shrinkage increases Finally, results revealed that the ANN with 4-12-1 structure had best prediction performance with 1.06e-6, 1.24e-6, 9.46e-7 of RMSE and 0.9999, 0.9999, 0.9999 of R, respectively for training, validation and testing data.https://ijbse.ut.ac.ir/article_95210_14e613272979fa4a99e01542f8c981aa.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622Technical and economic evaluation of three dry bean harvesting methods in Markazi ProvinceTechnical and economic evaluation of three dry bean harvesting methods in Markazi Province67809618510.22059/ijbse.2024.370418.665533FAMahmoodSafariScientific board member / Agriculture Engineering Research Institute(AERI)0000-0002-5588-1189AbolfazlHedayatiporMember of scientific of Agricultural Engineering Research Department, Agricultural Research and Natural Resources Center of Markazi0000-0003-2808-1618SaeedZarifneshatAgricultural engineering department, Khorasan Razavi Agricultural and natural resources research and education center, AREEO, Mashhad, Iran0000-0002-9787-0787HoomanSharifnasabMember of scientific board of Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran0000-0001-9521-9686Journal Article20231231 Bean (Phaseolus vulgaris) is one of the legumes that has high consumption in the world that about 50% of it directly is consumed by humans. Currently, the harvest of this crop is done by hand with high labor cost and after harvesting, it leaves in the field. When the humidity of the seeds reached about 12% (on the wet basis) and the pods became dry, they are transferred to the threshing. In this research, harvesting methods of different red dry been were evaluated in terms of technical and economic terms in Markazi province (Khomein region). The treatments was included: the cutter bar equipped with pick up, rooting machine, conventional harvesting machine, and the manual method in the form of a randomized block design with three replications. The results of the research showed that the use of the cutter bar mechanism along with the pickup unit for harvesting of the dry bean crop did not suitable. There was a significant difference between the other experimental treatments in terms of field capacity, field efficiency and harvesting losses. In the rooting machine, the conventional harvesting machine and the manual method, the theoretical capacity was 0.63, 0.37 and 0.15 hectares per hour, effective field capacity was 0.56, 0.28 and 0.11 hectares per hour, field efficiency was 87.9, 76.59 and 76.01 percent and harvesting losses was 1.48, 3.35 and 7.07 percent respectively. Economically, the difference between the methods was not significant at the 5% level, and the benefit-cost ratio was 2.48, 2.61, and 2.26, respectively. According to these results, the use of a rooting machine was recommended in terms of technical and economic point of view. Bean (Phaseolus vulgaris) is one of the legumes that has high consumption in the world that about 50% of it directly is consumed by humans. Currently, the harvest of this crop is done by hand with high labor cost and after harvesting, it leaves in the field. When the humidity of the seeds reached about 12% (on the wet basis) and the pods became dry, they are transferred to the threshing. In this research, harvesting methods of different red dry been were evaluated in terms of technical and economic terms in Markazi province (Khomein region). The treatments was included: the cutter bar equipped with pick up, rooting machine, conventional harvesting machine, and the manual method in the form of a randomized block design with three replications. The results of the research showed that the use of the cutter bar mechanism along with the pickup unit for harvesting of the dry bean crop did not suitable. There was a significant difference between the other experimental treatments in terms of field capacity, field efficiency and harvesting losses. In the rooting machine, the conventional harvesting machine and the manual method, the theoretical capacity was 0.63, 0.37 and 0.15 hectares per hour, effective field capacity was 0.56, 0.28 and 0.11 hectares per hour, field efficiency was 87.9, 76.59 and 76.01 percent and harvesting losses was 1.48, 3.35 and 7.07 percent respectively. Economically, the difference between the methods was not significant at the 5% level, and the benefit-cost ratio was 2.48, 2.61, and 2.26, respectively. According to these results, the use of a rooting machine was recommended in terms of technical and economic point of view.https://ijbse.ut.ac.ir/article_96185_21a6ad9904b1e040b518d8b431f51696.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354220230622The effect of forced air pre-cooling on dill shelf lifeThe effect of forced air pre-cooling on dill shelf life81959618410.22059/ijbse.2024.369139.665532FAمریمRavaghiAssistant Professor, Agricultural Engineering Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ahvaz, Iran.0000-0002-2461-0896AzarakhshAzizi. Research Instructor, Agricultural Engineering Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ahvaz, Iran0000-0002-5201-6843LeilaBehbahaniResearcher, Agricultural Engineering Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ahvaz, Iran0000-0002-4242-2768Journal Article20231204Proper management of post-harvest systems is one of the most crucial steps in reducing agricultural losses and waste. This study was aimed to find the effect of forced air pre-cooling of dill using a window air conditioner on its shelf life. For this purpose, a metal frame with dimensions of 2×1×1 m was covered with a polyethylene foam sheet and a window air conditioner (after removing its thermostat) equipped with a controller was connected to one end of this chamber. In the next step, bunches of dill without packaging and the cardboard packaging with polyethylene liner were placed in three different conditions, including without pre-cooling and keeping at ambient temperature, with pre-cooling and keeping at ambient temperature, with pre-cooling and keeping under refrigeration. The results showed that during 4 days of storage, bunches of dill kept at ambient temperature had more total loss (50-60%) but after that, packaging caused severe loss (100%) due to moisture accumulation. If cold storage and packaging was ignored after pre-cooling, total loss would increase swiftly. For this reason pre-cooled dill with proper packaging and refrigeration showed the least amount of total loss (8.95±1.03%), physiological water loss (5.16±1.78%), pH (6.16±0.16), a*(-20.29±1.45) and the highest amount of moisture content (87.60±3.76), hardness (764±50 N), ascorbic acid (2.74±0.05 mg. g-1 of dill dry weight basis), total acceptance (4.1±0.65), L* (43.44±6.39), and b* (20.09±4.31).Proper management of post-harvest systems is one of the most crucial steps in reducing agricultural losses and waste. This study was aimed to find the effect of forced air pre-cooling of dill using a window air conditioner on its shelf life. For this purpose, a metal frame with dimensions of 2×1×1 m was covered with a polyethylene foam sheet and a window air conditioner (after removing its thermostat) equipped with a controller was connected to one end of this chamber. In the next step, bunches of dill without packaging and the cardboard packaging with polyethylene liner were placed in three different conditions, including without pre-cooling and keeping at ambient temperature, with pre-cooling and keeping at ambient temperature, with pre-cooling and keeping under refrigeration. The results showed that during 4 days of storage, bunches of dill kept at ambient temperature had more total loss (50-60%) but after that, packaging caused severe loss (100%) due to moisture accumulation. If cold storage and packaging was ignored after pre-cooling, total loss would increase swiftly. For this reason pre-cooled dill with proper packaging and refrigeration showed the least amount of total loss (8.95±1.03%), physiological water loss (5.16±1.78%), pH (6.16±0.16), a*(-20.29±1.45) and the highest amount of moisture content (87.60±3.76), hardness (764±50 N), ascorbic acid (2.74±0.05 mg. g-1 of dill dry weight basis), total acceptance (4.1±0.65), L* (43.44±6.39), and b* (20.09±4.31).https://ijbse.ut.ac.ir/article_96184_57fcde0e1e570f34863aa8c7b9d9f5d7.pdf