M. R. Zarezadeh; M. Aboonajmi; M. Ghasemi-Varnamkhasti; F. Azarikia
Abstract
IntroductionExtra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases ...
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IntroductionExtra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc.Materials and MethodsThere are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported.Results and DiscussionHistogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time.ConclusionDue to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).
M. Hajinezhad; S. S. Mohtasebi; M. Ghasemi-Varnamkhasti; M. Aghbashlo
Abstract
Introduction Honey is a supersaturated sugar and viscose solution taken from the nectar of flowers, collected and modified by honeybees. Many producers of honey add some variety of sugars in honey that make difficulties with detection of adulterated and pure honey. Flavor is one of the most important ...
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Introduction Honey is a supersaturated sugar and viscose solution taken from the nectar of flowers, collected and modified by honeybees. Many producers of honey add some variety of sugars in honey that make difficulties with detection of adulterated and pure honey. Flavor is one of the most important parameters in the classification of honey samples and the smell emitted by the honey depending on the different flowers and constituents that could be different. This causes using an electronic nose system to detect honey adulteration. Materials and Methods Honey samples used in this study were lotus honey that was supplied from a market in Karaj city, Alborz province, Iran. Adulterated honey, along with percentages of fraud (by weight) of zero, 20, 35 and 50 percent, was prepared by mixing sugar syrup. Each group of samples, nine times were tested by the electronic nose system. The proposed system, consists of six metal oxide semiconductor sensors, sensor chamber, sample chamber, data acquisition systems, power supply, electric valves, and pumps. Electronic nose is planned for three-phase system baseline correction, the smell of sample injection and cleaning of the sensor and sample chambers with clean air (Oxygen). Responses of the sensors were collected and stored in 420 seconds by a data acquisition system and LabView ver 2012 software. We used fractional method in this study, in order to improve the quality of the information available and to optimize the array output before passing it on to the pattern recognition system. Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and Artificial neural network (ANN) were the methods used for analyzing and recognizing pattern of electronic nose signals. Data processing was carried out using Microsoft Excel, neuralsolution 5 and Unscrambler X 10.3 (CAMO AS, Norway). Results and Discussion PCA Results PCA reduces the complexity of the data-set and is performed with no information on the classification of samples. It is based on the variance of the data-set. For PCA analysis, overall PC1 and PC2 explained 91% of the total variance among Lotus honey samples and the adulterations (PC1=80% and PC2=11%). The results indicate that it is clearly possible to recognize Lotus honey with adulterant using electronic nose systems. LDA Results The LDA method for the detection of adulterated honey samples using leave-one-out validation was estimated. The method is most widely used as a method of classification that maximizes variance between the clusters and minimizes variance of within classes. By applying LDA on the collected data, 100% accurate classification for detecting of honey and their adulterations was obtained. It can also be concluded that this method could recognize adulterated honey samples properly. ANN Results The back propagation multilayer perceptron algorithm was used to classify and to detect honey and adulterated types. Performance evaluations of each designed networks were compared by mean square error (MSE) and correlation coefficient (r).The data were divided to three subsets: 60% was used for training, 20% for testing and the remaining 20% were kept for cross validation.After network training and validation using optimized ANN model, i.e. 6-8-4 structure, success rate for 4 outputs (0, 20, 35 and 50% adulterated levels)were found to be 100%.After detecting adulteration, e-nose system accompanied with ANN can accurately classify honey from honey mixtures with fraud materials. Conclusion An electronic nose based on six metal oxide semiconductor sensors was used to detect adulterated honey samples. Electronic nose system can successfully classify between original honey and the adulterated one by pattern recognition method. The PCA, LDA and ANN techniques and analyzes of the electronic nose were very useful for evaluating the quality of the lotus honey. The results of these methods were used to classify the fraud in Lotus honey. However, there is a need to do more researches on the detection of adulteration in other agricultural and food products by electronic nose system.
Design and Construction
M. Tohri; D. Ghanbarian; O. Taki; M. Ghasemi-Varnamkhasti
Abstract
IntroductionIn recent years due to lack of water resources in our country, planting of bare root seedlings of onion has been welcomed by farmers. Considering the desired high dense planting of Iranian farmers, lack of proper transplanting machine has appeared as the main problem. To overcome this problem, ...
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IntroductionIn recent years due to lack of water resources in our country, planting of bare root seedlings of onion has been welcomed by farmers. Considering the desired high dense planting of Iranian farmers, lack of proper transplanting machine has appeared as the main problem. To overcome this problem, some researchers tested a few methods, but none of them reached to complete successfully. As the one of last efforts, Taki and Asadi (2012) developed a semi-automatic transplanting machine with 9 planting units. This machine requires to 9 men to separate and single out a bunch of seedlings. Usage of this machine is very time-consuming and labor intensive. In Iran, transplanting of bare root seedlings is practically performed by hand with a density of 700-800 thousand plants at hectare. The main purpose of this study was designed, manufacture, and evaluation of an automatic metering device that with the separation and singulars of bare root seedlings of onion could get a high density planting.Materials and MethodsFig. 1 shows the main employed idea of this research for separation and single out a bunch of seedlings.As shown in Fig. 1, the metering device consisted of two carrying and separating belts with different teeth forms. Placing seedling bunches between the two belts, the belts move at different speeds in opposite directions and separate seedlings from their bunch.For proper design of metering device system, measurement of some physical properties were necessary. The obtained information was used to select two belts form. A belt with flexible plastic teethes with a height of 6 mm and the distance of 4mm was selected as separator while for carrier, two types of belts were selected: the first was the same as a separator and the second was made of metal teethes. Based on the average thickness of seedling bunch and some pre-tests, the horizontal angle of separator belt determined as α=20 degrees. Theoretical calculations were done to computatingof the needed force of the system. In this section, seedlings were modeled as some solid cylinders with a length of 200 and a diameter of 10 mm. In the mentioned system, it was necessary that the speed of separator belt is more than the speed of carrier belt. Thus, ratio of two linear velocities ( ) of 1.67 and 2.32 were considered for evaluation of the system. For evaluation of manufactured metering device, the effects of three factors, i.e., carrier belt type, ratio of linear velocities of the belts, and number of seedlings in a bunch (n = 30 and n =60), on qualitative planting parameters were studied in a factorial experiment based on completely randomized design with three replications. The studied qualitative planting parameters were miss index, consumed seedlings, miss length, quality of feed index, multiple index, mean, and damaged seedlings.Results and DiscussionThe results of analysis of variance showed that, except of belt type, effects of the two studied factors and all interactions are statistically non-significant on consumed seedlings and miss length indexes. The results indicated significant differences between miss index (P<0.01), multiple index (P<0.05), and mean (P<0.05) as affected by belt type. None of the studied variable had a significant effect on damaged seedlings. Interactions of belt type and ratio of linear velocities significantly affected the quality of feed index (P < 0.01). An increase in ratio of linear velocities in plastic toothed belt lead to decrease of mean and miss indexes, whereas in case of metal toothed belt there is no significant effect on this two indexes. The results also showed that increase of linear velocities for the two types of carrier belt lead to increase of consumed seedlings and decrease of miss length. At the two ratios of linear velocities, miss length in metal toothed is less than plastic toothed belt. ConclusionsCommercial transplanting machines are not suitable for dense planting of onion. In this research an automatic metering device for separation and singularize of bare root seedlings of onion was manufactured and evaluated. The results indicated that the carrier belt with long and rigid teeth, having an angle of attack, could separate seedlings more efficiently. The results also showed a 80 percent increased in uniformity of plant seedlings distances is reachable using the metering system.
A. Sanaeifar; S. S. Mohtasebi; M. Ghasemi-Varnamkhasti; H. Ahmadi
Abstract
Aroma is one of the most important sensory properties of fruits and is particularly sensitive to the changes in fruit compounds. Gases involved in aroma of fruits are produced from the metabolic activities during ripening, harvest, post-harvest and storage stages. Therefore, the emitted aroma of fruits ...
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Aroma is one of the most important sensory properties of fruits and is particularly sensitive to the changes in fruit compounds. Gases involved in aroma of fruits are produced from the metabolic activities during ripening, harvest, post-harvest and storage stages. Therefore, the emitted aroma of fruits changes during the shelf-life period. The electronic nose (machine olfaction) would simulate the human sense of smell to identify and realize the complex aromas by using an array of chemical sensors. In this research, a low cost electronic nose based on six metal oxide semiconductor (MOS) sensors were designed, developed and implemented and its ability for monitoring changes in aroma fingerprint during ripening of banana was studied. The main components are used in the e-nose system include sampling system, an array of gas sensors, data acquisition system and an appropriate pattern recognition algorithm. Linear Discriminant Analysis (LDA) technique was used for classification of the extracted features of e-nose signals. Based on the results, the classification accuracy of 97/3% was obtained. Results showed the high ability of e-nose for distinguishing between the stages of ripening. It is concluded that the system can be considered as a nondestructive tool for quality control during banana shelf-life.
D. Ghanbarian; M. Shirvani; M. Ghasemi-Varnamkhasti; H. Golestanian
Abstract
Unfortunately despite the great ranking of Iran for apple production around the world, the export potential is not suitable. It seems that one of the major causes of poor quality for Iranian apple varieties is bruising damage of this product. Therefore, in this study, some factors affecting the apple ...
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Unfortunately despite the great ranking of Iran for apple production around the world, the export potential is not suitable. It seems that one of the major causes of poor quality for Iranian apple varieties is bruising damage of this product. Therefore, in this study, some factors affecting the apple bruising were addressed. For this purpose, factorial experiment in a completely randomized design with 72 treatments, including variety factor in three levels (Golden Delicious, Red Delicious and Granny Smith), type of padding surface in four levels (Cardboard on plastic, wood, Rubber on steel and apple) and the drop height in six levels (5, 15, 25, 35, 45 and 55 cm) with four replications were considered. Moreover, the maximum allowable drop heights of apples along with bruising volume estimation models were studied. Analysis of variance (ANOVA) showed that bruising area and volume were significantly affected by all experimental parameters at the 1% level. The comparison test revealed that Granny Smith has tougher tissues and is less prone to vulnerability. Based on the results of this study, the maximum allowable drop heights for the Red Delicious, Golden Delicious and Granny Smith varieties were found to be 12, 15 and 20 cm, respectively. In addition, the effect of apple variety on the dependent parameters was significant. Based on the findings of this study, the bruising due to the impact of apple and apple was lower for the moving apples compared with the stationary apples.
M. Ghasemi-Varnamkhasti; S. M. Hashemi; S. A. Hashemi
Abstract
As one of the most important conditions in sustainable agriculture, optimization of energy consumption in agriculture is necessary in order to reduce the production cost and saving non renewable resources as well as reduction of air pollutants. In this regard, this study was conducted in Saman region, ...
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As one of the most important conditions in sustainable agriculture, optimization of energy consumption in agriculture is necessary in order to reduce the production cost and saving non renewable resources as well as reduction of air pollutants. In this regard, this study was conducted in Saman region, Chaharmahal va Bakhtiari province. A linear programming based on Data Envelopment Analysis (DEA) was used for optimization of energy consumption in peach production in order to increase the technical efficiency. By performing a linear regression analysis, some inputs including animal fertilizer, pesticide, human labor and machinery had no significant influence on product yield, while some other inputs including fuel, electricity, water and chemical fertilizer showed a significant effect on the product yield. Therefore, the latter inputs and the product yield were considered as the inputs and output, respectively. Selecting the BCC model (efficiency to variable scale model of input nature) and using DEA Solver software, efficient and inefficient farmers were determined. The efficient farmers had the technical efficiency of unit (one) and the inefficient farmers had this value within 0.47-0.94. Also, the technical efficiency of inefficient farmers was computed as 0.74. This means that using 74% of the inputs and keeping the current yield, the inefficient farmers can approach to the efficiency limit. Total technical efficiency of all farmers was found to be 0.82. Based on the results, the maximum value of inefficiency belonged to electricity energy with 65.32%.
