Today, human’s specific attention to food quality has led to the development of fast, easy and non-destructive methods to assess the quality of foodstuffs. Meat is one of the most important foods and its freshness is considered as the most important qualitative feature. Therefore, checking its quality for consumption has great value worthwhile. The main objective of the present study is to investigate the possibility of using electronic nose and artificial neural network methods for detecting the freshness of chicken meat during storage in a refrigerator at 4 ºC. In the used neural network system, the input layer consists of 10 neurons based on the number of sensors and the output layer includes 3 neurons related to classes of different freshness classes of chicken meat. Different classifier networks were designed and after investigation of different network structures; the best structure of the network was obtained with a hidden layer and 6 neurons in that layer. Finally, the optimal network with a general structure of 10-6-3 was created to detect the freshness of chicken meat during different days of storage. The used statistical indices to assess the classifier to evaluate the freshness of chicken meat including accuracy, precision, sensitivity, specificity and area under the curve factors. The values of these indices for classification using selected characteristics are 95.77, 94.7, 92.18, 95.95, and 94.1 respectively. Therefore, given that the main objective of the present study was to develop and implementation of an intelligent diagnosis system of chicken meat freshness using an electronic nose system. The acceptable obtained results of the present study indicate that the proposed applied system based on the electronic nose system and artificial neural networks methods as a smart and reliable method can online classification of chicken meat as fast, easy, economical, non-destructive and with appropriate accuracy.

Honey is a delicious sweet viscous fluid, produced by bees through sucking of the nectar of flowers. Honey is in total a highly concentrated water-soluble compound of sugars which due to its containing of fermenting material, is helpful in food exchanges, and as an aid to digestion. It is also of a high order of standing among the foods. Flavor is one of the determinant parameters in honey for quality distinction and classification. Gases that are involved in honey smell are resulted from flower’s pollen scents collected by bees. It is also dependent upon the processes in the bee’s body to convert pollen to honey. So the emitted smell by the honey depends on flowers varieties and can naturally be different. These factors led the authors to design an apparatus of olfactory system based on Metal Oxide Sensors (MOS) to distinct and classify different floral origins of honeys. Ten samples from each of the seven different floral origins of honey comprising a total of 70 samples (at the rate of 5 g in each sample) were placed in sterile petri dishes and then tested. Experiments were conducted in three stages: baseline correction, injection of sample gas odor and cleansing of the sensor. The fractional data preprocessing procedure was employed for normalization and to prevent over fitting, as well as reducing the input data. Principal Component Analyze (PCA), Linear Discriminant Analyze (LDA) and Artificial Neural Network (ANN) were methods to classify and analyze the extracted features obtained from the signals of the olfactory system apparatus. To classify the floral origin of the honey using the OLFACTION apparatus, the results indicated 97% of variance by PCA, as well as 87.3% and 88.5% accuracy classification by LDA and ANN, respectively. As a conclusion, it was found that the apparatus, namely the electronic nose could provide the proper information for the classification of different floral origin honeys.

Introduction: One the most important discussions of the world community is the importance and the role of edible oils in the nutrition and physical health of individuals, especially in the prevention of cardiovascular disease. One of these oils, used in cooking, is cow ghee. Cow ghee should be free of vegetable oil, animal fat, mineral oils, flavored additives and any other external ingredients. It is hard to find a technique that can easily and reliably measure the quality of the oil. So far, no special MACHINE or system has been designed or built to distinguish the pure cow ghee from the adulterated ones. Electronic nose is a new method that has recently been considered by researchers in agriculture especially in the field of food quality. Because of high ability of e-nose system, in this research, this system was used for the detection of pure cow gee from the adulterants ones. Materials and Methods: An olfactory MACHINE system based on eight MOS sensors was designed to detect pure cow ghee from the adulterated with various proportions of vegetable oil and animal fat. Designed system includes data acquisition system, sensors, sensors chamber, sample box, power supply, connections, electric valves, air pump and air filter. The sensor array was consisted of the 8 MOS sensors that each of them react 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. To prepare samples with different percentages of adulteration, animal body fat and refined vegetable oils were added to pure cow ghee. In order to carry out the experiments, the sample was placed in sample box and in the baseline correction step (200 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 (120 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 was prepared for the next test. The signals obtained from the sensors were recorded and then pre-processed. Results and Discussion: PCA and QDA analysis were used for detection the differences between pure cow ghee and adulterated ones. The data obtained from the signals processing with fractional method were used as input of PCA. The PCA results showed that the total variance between pure cow ghee and mixture of cow ghee with animal's fat was 97%. Also score plot of cow’ s ghee and its mixture with vegetable oil showed the total variance of 96% between different samples. Sensors are the main components of an electronic nose system therefore it is necessary to select the best sensors to detect differences between samples. The loading plot was obtained to show the role of sensors in e-nose system and demonstrates that the selected sensors have a high degree of complementarity. Based on confusion matrix obtained from QDA analysis, pure samples were detected from vegetable oil and animal fat samples with correct classification rate of 95. 24 and 97. 15, respectively. Conclusions: An eight-sensory olfactory MACHINE system (MOS) was designed to detect pure cow ghee from the presence of vegetable oil and animal fat oil. In PCA analysis, the variance between samples was 97% and 98%, respectively. According to the results the radar graph of PCA analysis, it can be concluded that the sensors No 2 (TGS822), 3(MQ136), 4(MQ9) and 8(TGS2620) have the highest and sensor 6 (MQ135) has the lowest ability in classification. The MQ135 sensor reacts to the detection of ammonia, benzene, and sulfide. In other words these gases did not play important role in separating of cow ghee from other mixed oils.

Introduction: Extra 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 Methods: There 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 Discussion: Histogram 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. Conclusions: Due 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).

Adulteration in milk and other dairy products is not only a serious threat to human health but it also leads to economic losses in the dairy industry. Utilization of materials that reduce microbial load is a common adulteration. In this study, a MACHINE OLFACTION (electronic nose) based on 8 Metal Oxide Semiconductor (MOS) sensors were fabricated, developed, and its capability of formalin detection in raw milk investigated. Feature vector was then extracted from the sensors’ response and used as the inputs to lay the pattern of recognition models. Based on the obtained results, Principal Component Analysis (PCA) with two first PCs (PC1 and PC2) could describe 93% of variance within the data. In the sensor array, MQ4, FIS, TGS822, and TGS2620 sensors presented the highest loading coefficient values whilst TGS2602 devoted the lowest loading one. Linear Discriminant Analysis (LDA) revealed the classification accuracy as 80.1%. Support Vector MACHINE (SVM) with three order multinomial kernel function showing the training and validation accuracy values as 100% and 90.91%, respectively. Also, the full success rate was obtained for the overall classification, using Artificial Neural Network.

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 shelflife.

The dried products quality is influenced by the drying methods and different temperatures, especially on their smell and aroma. The electrohydrodynamic (EHD) method as a non-thermal drying method has an increasing effect on the rate of evaporation of product moisture content in ambient temperature and pressure, and preserve quality in dried food products. The objective of this study was to classify the quality of dried dates at three different air velocity by different drying conditions of electrohydrodynamic (EHD in 25 and 35 ° C), hot air (HA at 60 ° C) and hybrid drying (EHD-HA at 60 ° C) techniques based on odor using a multi-sensory olfactory MACHINE. The results showed that dried date fruit quality base on its odor was classified to three classes (1: EHD at 25° C, 2: EHD at 35° C and 3: HA and EHD-HA at 60 ° C). The response of metal oxide semiconductor sensors in EHD method at 25 ° C was higher than the others which show the aroma of dried date fruit in class 1 is conserved more better than the other classes. Finally, the effective sensors were determined to recognize the odor of date fruit.

Medicinal plants are widely used in many fields around the world. Medicinal plants are an integral part of daily life and the increase in demand for these plants leads to fraud and reduced quality in the final product. Therefore, authentication is vitally important for consumers. Mint is valuable because of its medicinal properties and its essential oil, which is used in the food, pharmaceutical, cosmetic and health industries. In this research, the use of electronic nose to detect the age of mint plant by the odor emitted from its leaves was investigated. The main branches of mint plant with different ages of one to five years were taken from the distance of 4 to 5 cm from the ground. An electronic nose equipped with 8 metal oxide sensors was used to carry out experiments. To analyze the data, PCA, LDA and QDA analyzes were used. PCA results showed that 95% of the total variance of the data was explained by PC1 and 4% by PC2, and the two principal components expressed accounted for 99% of the variance of the normalized data. Also, the QDA method was 100% accurate in determining the age of the mint plant. The electronic nose has shown that it is a fast and effective tool to detect the quality parameters of plants and agricultural products.