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. J. Mahmoodi; M. Azadbakht
Abstract
Nowadays, the dielectric properties of food and biological products have become a valuable parameter in foodstuff engineering and coating technology, covering a remarkable spectral domain from 10-6 to 1012. In the present study, 27 completely healthy pears were selected and subjected to quasi-static ...
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Nowadays, the dielectric properties of food and biological products have become a valuable parameter in foodstuff engineering and coating technology, covering a remarkable spectral domain from 10-6 to 1012. In the present study, 27 completely healthy pears were selected and subjected to quasi-static and dynamic loading. The storage period was ten days. In this study, the qualitative characteristics and their relationship with changes in dielectric coefficient were investigated. At the end of the storage period, the fruits’ dielectric coefficient values and their qualitative characteristics were measured. The measurements were carried out for a capacitor plates’ distance interval of 11 cm, 10 V input voltage and 60 kHz input voltage frequency. According to the results, in the dynamic loading mode of 400 N, the highest dielectric coefficient with a value of 5.2989 was obtained. In dynamic loading mode of 400 N, the qualitative property had the minimum value. The antioxidant, phenol content, Vitamin C content and firmness were 33.925%, 14.523 mg/100g, 5.7 mg/100g and 5.5333 g, respectively. The results of the study indicated that increasing the loading force on the pear reduces all qualitative indicators for all loading modes and an increase in dielectric coefficients of the products was observed.
H. Taghizadeh; M. Ziyaei Hajipirlu; V. Khederli; B. Shamsi
Abstract
Introduction Discovering and understanding customer needs and expectations are considered as important factors on customer satisfaction and play vital role to maintain the current activity among its competitors, proceeding and obtaining customer satisfaction which are critical factors to design a successful ...
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Introduction Discovering and understanding customer needs and expectations are considered as important factors on customer satisfaction and play vital role to maintain the current activity among its competitors, proceeding and obtaining customer satisfaction which are critical factors to design a successful production; thus the successful organizations must meet their needs containing the quality of the products or services to customers. Quality Function Deployment (QFD) is a technique for studying demands and needs of customers which is going to give more emphasis to the customer's interests in this way. The QFD method in general implemented various tools and methods for reaching qualitative goals; but the most important and the main tool of this method is the house of quality diagrams. The Analytic Hierarchy Process (AHP) is a famous and common MADM method based on pair wise comparisons used for determining the priority of understudied factors in various studies until now. With considering effectiveness of QFD method to explicating customer's demands and obtaining customer satisfaction, generally, the researchers followed this question's suite and scientific answer: how can QFD explicate real demands and requirements of customers from tractor final production and what is the prioritization of these demands and requirements in view of customers. Accordingly, the aim of this study was to identify and prioritize the customer requirements of Massey Ferguson (MF 285) tractor production in Iran tractor manufacturing company with t- student statistical test, AHP and QFD methods. Materials and Methods Research method was descriptive and statistical population included all of the tractor customers of Tractor Manufacturing Company in Iran from March 2011 to March 2015. The statistical sample size was 171 which are determined with Cochran index. Moreover, 20 experts' opinion has been considered for determining product's technical requirements. Literature and theoretical bases of this study have been collected with research paper tab and the research data has been collected through four researcher-made questionnaires and interview tools. The questionnaire Type 1, used for determining the most important demands and needs of customers based on five choices Likert scale. The questionnaire Type 2 was for gathering data requirements to hierarchical AHP method, and the questionnaire Type 3 was for doing some evaluation about organization's present situation related with competitor's situation based on customer's demands and needs; and the questionnaire Type 4 had been implemented for finding technical requirement weights respect to customer's demands and needs. The reliability of the type 1, 3 and 4 questionnaires determined by Chronbach's Alpha method; after gathering required data for mentioned statistical test, these questionnaires' reliability rates are obtained: 0.768, 0.784 and 0.793, respectively. As well as, the validity of the questionnaires has been examined with content validity method. In this research, for analyzing the gathered data, while taking into account the different stages of QFD method, t- student statistical test was used for identifing the needs and demands of customers, and AHP was used for determining the priority of needs and demands of customers. Results and Discussion The results of one sample t-test for identifying the customer’s most important demands and needs showed that the factors such as: producing low price tractor, the quality of used auto-parts, sustainability and long-lasting the final production (production life and durability), comfort and peace during work, creating operator cabin, easy access to spare components, the amount of fuel consumption, warranty and maintenance, easy access to official repair stations, technical experts and suitable auto-parts, fast respond of brake system during braking and smooth moving identified the important demands and needs of customers. The results of AHP method for determining primary priorities of perceived customers needs and demands revealed as follows respectively: quality of parts, warranty and maintenance, low price, fuel consumption, comfort and peace, life and durability, smooth moving, fast respond of brakes, creating operator cabin and easy access to spare components. Finally, the main demands and related technical requirements have been identified and prioritized with QFD method; the Final results of customer demands and needs by QFD method revealed this prioritization: Quality of Parts, Warranty And Maintenance, low price, Fuel Consumption, Peace and Comfort, Life and Durability, Smooth Movement and lower engine knocking, Fast Respond of Braking System, Creating Operator Room and Easy Access to Components. Conclusion Without any doubt it is obvious that the obtaining customer satisfaction is the most important strategic tool for having successful and highly developed industry in this era. Knowing the customer demands and needs can lead the organization to enhance competitive advantages. This research showed that how could use structured QFD method for identifying prioritization of tractor customer demands and needs for maintain their satisfaction, and identifying importance of each demands, considering the production techniques.
