جستجوی مقالات مرتبط با کلیدواژه « electronic nose » در نشریات گروه « محیط زیست »

The use of plant-derived compounds is common in medicine and preventive health care, while the scope of use of some substances is steadily increasing. The mint family, with more than 200 genera and 3000 species, is very important economically and medicinally. The mint genus contains 25 to 30 species that grow in different temperate regions of Asia, Europe, Australia and South Africa. There is a great diversity in terms of chemical composition among the species of the mint genus. Peppermint essential oil (Mentha spicata L.) is rich in carvone, which produces the special aroma of mint. The yield of essential oil of Sentha spicata is lower than that of Mentha piperita. Carvone is the main component of Mentha spicata and Mentha longlifolia, while Carvone is absent in Mentha piperita, Mentha aquatic, Mentha arvensis and Mentha pulegium. Peppermint essential oil and extract are used in the pharmaceutical, cosmetic and food industries all over the world. Mentha spicata essential oil and leaves have therapeutic uses and its general properties are analgesic, tonic, stomach tonic, antitussive, anticonvulsant, astringent, analgesic and sedative. Peppermint oil has been used since ancient times for medicinal purposes, mostly to treat headaches, colds and neuralgia. It can also relieve skin irritations and digestive problems and has antispasmodic effects. Although, there is mixed information about the chemical composition of Mentha spicata essential oil, many studies have confirmed carone and limonene as its main components. Carvone is responsible for the smell of peppermint essential oil. The high price of carvone in the market has pushed breeders to improve mint varieties with high carvone. Different chemotypes are characterized by specific odors and biological activities, which indicate different applications in the aromatic and pharmaceutical industries. For example, Europeans enjoy the scent of Carvone. The use of medicinal plants in the food and pharmaceutical industries depends on the amount of biologically active substances and their chemical composition. Changes in the concentration of volatile compounds of mint during drying also depend on several factors, including drying conditions (temperature, air speed), humidity, variety and age of the plant, climate, soil and harvesting method. The drying process and storage conditions of the dried plant can have an adverse effect on the medicinal properties of the essential oil. Drying is one of the efficient methods to preserve agricultural products and maintain food quality. Drying, as an important food preservation technique, is used in the food industry. Drying is required to reduce the water activity of the product to suppress the growth of microorganisms and inhibit chemical reactions to increase the shelf life of the product at room temperature. In addition, drying lightens shipping weight and reduces storage space. Conventional drying methods include hot air drying (HAD), vacuum drying (VD), vacuum freeze drying (VFD), and microwave-hot air alternating drying (MW-HAD). HAD is the most common method that dries food in an oven with a constant flow of hot air. As an optimal approach for drying raw vegetable food, this method has easy operation and low cost, but it requires a long drying time and has low energy consumption.

After the drying process, the essential oil was extracted from the dried product, and for this purpose, a Clonger machine was used using the water distillation method. Distillation with water is a method of extracting essential oils. This method is cheap because it mostly uses water as a solvent. Qualitative GC-MS analysis of the extracted essential oils was performed using an HP 6890 gas chromatograph coupled to an HP 5973 mass-selective detector (Agilent Technologies, Foster City, CA, USA) operating at 70 eV mode. The electronic nose consists of three parts: (1) a sample transport system (2) a detection system consisting of a set of gas sensors with partial characteristics and (3) an odor data processing system. The e-nose instrument can detect the presence of VOCs in various molecular structures with high accuracy and reliability regardless of more or less odor. Samples were analyzed using a portable e-nose, which consists of a multiple gas sensor array, a signal acquisition unit, and pattern recognition software. Essential oil samples (1 mL) were placed in a 10 mL sealed glass vial and equilibrated at 40 °C for 30 min under stirring. Clean ambient air was used as the carrier gas to transport the volatiles in the headspace of the sealed glass vials to the temperature and humidity controlled sensing chamber. The conductivity change in the sensor array is expressed by the normalized response of the sensor. Each measurement cycle lasted 100 seconds, which allows the sensor to reach a steady state, and the data collection interval using a computer was 1 second. Between measurement cycles, the sensor was purged for 200 s with purge gas filtered through activated charcoal to return the sensor signal to baseline. 15 measurements were made for each sample of peppermint essential oil. Data obtained from GC-MS analysis were first processed by in-house MSD Chemstation and structural identification was performed through NIST 2014 library research along withretention index (RI) validation. The dataset consists of pre-processed signals from 9 MOS gas sensors obtained in the e-nose during 120 measurements corresponding to 8 independent samples evaluated with 15 repetitions. The performance of e-nose for evaluating peppermint essential oil samples was evaluated using three supervised statistical methods, namely QDA, MDA and ANN.

Drying is the most suitable method used to preserve the natural products of plants. Choosing a special drying method is one of the important costs in the production and commercialization of medicinal plants. This study determined the effect of different drying methods on the quantity and quality of peppermint essential oil. The results showed that the highest yield of essential oil was in the HAD1A drying method and the lowest yield was related to the sun drying method. Also, the obtained compounds of the essential oil were determined by the GC-MS method, and in the HAD drying method, 18 compounds were determined, and the content of some of them decreased significantly with the increase of the drying temperature. In the dried samples, the main components were Carvone (64.30-7.45%), Limonene (24.21- 6.59%) and Carveol (18.34-1.92%). Also, the aroma characteristics of mint essential oil were evaluated with the help of an E-nose. Three classification algorithms QDA, MDA and ANN were used, and the highest percentage of classification related to QDA and MDA methods was 100%, and the accuracy of the ANN method was also 0.967%. The findings of this study provide a theoretical basis for the development of hot air thin layer drying process for medicinal plants and improving their sensory quality and related products. The future perspective is to continuously improve the in situ drying technique for medicinal plants and develop a suitable monitor system to control the sensory quality of the final products based on the findings of the current study.

Potato is considered one of the most important food sources in the world (4th rank) and studying its various aspects is very important to ensure that the produced product has the necessary qualifications and can satisfy the customer. In the food industry, this product is transformed into various products such as baked potatoes, fried potatoes, potato chips, potato starch, dry fried potatoes, etc.The complexity of food odours makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, expert sensory analysis is costly and requires trained people who can only work for a relatively short period. Problems such as the human subjectivity of the response to smell and the variation between people should also be considered. Hence, there is a need for a tool such as an electronic nose with high sensitivity and correlation with human sensory panel data for specific applications in food control. Due to its easy construction, cheapness and the need for little time for analysis, the electronic nose is becoming an automatic non-destructive method to describe the smell of food.An olfactory machine can recognize the fragrance composition by estimating its concentration or determining some of its intrinsic properties, which the human nose is hardly able to do. In general, the human olfactory system is a five-step process including smelling, receiving the scent, evaluating, detecting and erasing the effect of the scent. The olfactory phenomenon begins with inhaling the intended smell and ends with breathing fresh air to remove the effect of the scent. The human olfactory system, with all its unique capabilities, also has disadvantages that limit its use in quality control processes, including subjectivity, low reproducibility (for example, results depending on time, people's health, analysis before the presence of odour and fatigue is variable), time-consuming, high labour cost, adaptation of people (less sensitivity when exposed to odour for a long time). In addition, it cannot be used to evaluate dangerous odours.Meanwhile, the electronic nose can detect the volatile compounds of potatoes. The electronic nose has been used in extensive research to identify and classify food and agricultural products.
The purpose of this research was to evaluate the ability of the electronic nose using one of the chemometrics methods to detect 5 different potato cultivars.

First, 5 varieties of potato were prepared from the agricultural research centre of Ardabil city. These 5 varieties included Colombo, Milwa, Agria, Esprit and Sante.After preparing the cultivars, first, the samples were placed in a closed container (sample compartment) for 1 day to saturate the space of the container with the aroma and smell of potatoes, and then the sample compartments were used for data collection with the electronic nose.In this research, the electronic nose made in the Biosystems Engineering Department of Mohaghegh Ardabili University was used. In this device, 9 metal oxide semiconductor (MOS) sensors with low power consumption are used, which are listed in Table 1.The sample chamber was connected to the electronic nose device and data collection was done. This data collection was done in such a way that first, clean air was passed through the sensor chamber for 150 seconds to clean the sensors from the presence of odours and other gases. Then, the smell of the sample was sucked from the sample chamber by the pump for 150 seconds and directed to the sensors, and finally, clean air was injected into the sensor chamber for 150 seconds to prepare the device for repetition and subsequent tests. 15 repetitions were considered for each sample.Through the mentioned steps, the output voltage of the sensors was changed due to exposure to gases emitted from the sample (potato smell) and their olfactory responses were collected and recorded by data collection cards, the sensor signals were recorded and stored at 1-second intervals. . A fractional method was used to correct the baseline, in which noise or possible deviations were removed and the responses of the sensors were normalized and dimensionless.
By chemometrics method in this research, it started with principal component analysis (PCA) to discover the output response of the sensors and reduce the dimension of the data.Principal component analysis (PCA) is one of the simplest multivariatemethods and is known as an unsupervised technique for clustering data according to groups. It is usually used to reduce the dimensionality of the data and the best results are obtained when the data are positively or negatively correlated. Another advantage of PCA is that this technique reduces the volume of multidimensional data while removing redundant data without losing important information.

The scores chart (Figure 1) showed that the variance of the total data is equal to PC-1 (94%) and PC-2 (3%), respectively, and the first two principal components account for 97% of the variance of the total normalized data. When the total variance is higher than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. So it can be concluded that the electronic nose has a good response to the smell of potatoes and its cultivars can be distinguished, which shows the high accuracy of the electronic nose in identifying the smell of different products.With the correlation loadings plot, the relationships between all variables can be shown. The loading diagram (Figure 2) shows the relative role of sensors for each main component. The inner oval represents 50% and the outer oval represents 100% of the total variance of the data. The higher the loading coefficient of a sensor is, the greater the role of that sensor in identification and classification. Therefore, the sensors that are located on the outer circle have a greater role in data classification. According to the figure, it is clear that all the sensors have an important role in identifying the rice variety, including the role of sensors number 1 and 9, which are respectively the same sensors as MQ9 (to detect carbon dioxide and combustible gases) and MQ3 (to detect alcohol, methane, natural gases), it was less than the rest of the sensors, and by removing these two sensors, the cost of making an olfactory device (to distinguish genuine and fake rice) can be reduced and costs can be saved. In this research, an electronic nose with 9 metal oxide sensors was used to identify and distinguish potato cultivars. The Chemometrics PCA method was used for qualitative and quantitative analysis of complex data from the electronic sensor array. PCA was used to reduce the data and with two main components PC1 and PC2, it described 97% of the variance of the data set and provided an initial classification. The electronic nose has the ability to be used as a fast and non-destructive method to identify potato varieties. Using this method will be very useful for consumers, especially restaurants and processing units, in order to choose high-quality cultivars.

Potato is an important vegetable that grows all over the world and is considered an important product in developing and developed countries for the human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, after wheat, rice and corn, it is the fourth product in the food basket of human societies. According to the statistics of the Food and Agriculture Organization of the United Nations, the area under cultivation of this crop in Iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. Traditional methods of determining potato varieties were based more on morphological features, but with the production of new products, there was a need for methods that were faster and more recognizable. In the meantime, the high-performance artificial neural network can be used to classify cultivars. Artificial neural networks can classify and detect cultivars, are flexible, and are used in most agricultural products. Therefore, the olfactory machine can have high efficiency in classifying and distinguishing cultivar, originality and storage time. The olfactory machine is a system that has a different structure and approach from other methods (image processing, neural network, etc.), is flexible and is used in most agricultural products due to the presence of odour in them.With the rapid and rapid advancement of computer technology and sensor technology, the application of the bionic electronic nose, including a semiconductor gas-sensitive sensor and a pattern recognition system as a means of detection, offers a new method for rapid classification and digit recognition. Give. The electronic nose has also introduced a new method for classifying and detecting rough rice in a non-destructive and fast way.Due to a large number of potato varieties and sometimes the lack of familiarity of processing units with its cultivars and also time-consuming and inaccurate in identifying different potato cultivars by experts and farmers, and the importance of identifying potato cultivars and other agricultural products in At every stage of the food industry process, it is necessary to find ways to do this accurately and quickly enough. The aim of this study was to evaluate the ability and accuracy of the electronic nose with the help of an artificial neural network to detect and differentiate several potato cultivars.

First, potatoes in 3 different cultivars (Colombo, Milvana and Sante) were prepared from Ardabil Agricultural Research Center and kept at a temperature of 10-4 ° C. One day after the data were collected, data collection began with an olfactory machine. 3-4 potatoes from each cultivar were placed in the sample container for 1 day to saturate the sample container with the smell. Then the sample chamber was connected to the electronic nasal device and data collection was performed. The data were collected by the olfactory machine in such a way that first clean air was passed through the sensor chamber for 100 seconds to clean the sensors from other odours. The odour (gases emitted from the sample) was then pumped out of the sample chamber by the pump for 100 seconds and directed to the sensors. Finally, clean air was injected into the sensor chamber for 100 seconds to prepare it for further data collection. According to these steps, the output voltage of the sensors was changed due to exposure to various gases (potato odour) and their olfactory response was collected by data collection cards, sensor signals were recorded and stored in the USB gate of the computer at 1-second intervals. A fractional method was used to correct the baseline in which noise or possible deviations were eliminated and the sensor responses were normalized and dimensionless. In the next step, linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify the 3 potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of the variance between class and within the class, and being able to classify two or more groups of samples. ANN and pattern recognition were used to find similarities and differences in the classification of potato cultivars. For this, 1 neuron was considered for the input layer, the hidden layer with the optimal number of neurons will be considered and five output neurons with Depending on the number of output classes the target will be considered. In-network training, logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used. Also, the amount of error was calculated using the mean square error. For learning (70%), testing (15%) and validation (15%) all data were randomly selected. Training data was provided to the network during the training and the network was adjusted according to their error. Validation was used to measure network generalization and completion of training. Data testing had no effect on training and therefore provided an independent measurement of network performance during and after training.

LDA and ANN methods were used to detect potato cultivars based on sensor output response. The LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 3 potato cultivars based on the output response of the sensors. Detection results of cultivars equal to 100% were obtained (Figure 1). Also, in the ANN method, 8 sensors were considered in the input layer of 8 neurons according to the output data. Also, 3 layers of neurons were considered for the output layer according to the type of cultivars. Therefore, the 3-6-8 topology had the highest accuracy for detecting potato cultivars, so the RMSE value was 0.008 and the R2 value was 99.8. There was also a very high correlation between predicted and measured data (Figure 2). In this study, a portable olfactory machine system with 8 metal oxide sensors was used to investigate the detection of potato cultivars. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of complex data using an electronic sensor array. LDA and ANN were able to accurately identify and classify different potato cultivars with 100% accuracy. The electronic nose has the potential to be used as a fast and non-destructive method to detect different potato cultivars. Using this method in identifying potato cultivars will be very useful for researchers to select and produce pure cultivars and for farmers to produce a uniform and certified crop.

Walnut is an important economic and commercial product all over the world. The smell can be one of the key factors in determining the ripening time of the fruit and it depends on the content of the chemical compounds of the fruit and its skin. Crunchyness and easy peeling are the main features that affect the level of satisfaction of walnut consumers.The complexity of food odor makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period of time. Problems such as the human subjectivity of the response to smell and the variation between people should also be considered. Hence, there is a need for a tool such as an electronic nose with high sensitivity and correlation with human sensory panel data for specific applications in food control. Due to its easy construction, cheapness and the need for little time for analysis, the electronic nose is becoming an automatic non-destructive method to describe the smell of food.An olfactory machine can recognize the fragrance composition by estimating its concentration or determining some of its intrinsic properties, which the human nose is hardly able to do. In general, the human olfactory system is a five-step process including smelling, receiving the scent, evaluating, detecting and erasing the effect of the scent. The olfactory phenomenon begins with inhaling the intended smell and ends with breathing fresh air to remove the effect of the scent. The human olfactory system, with all its unique capabilities, also has disadvantages that limit its use in quality control processes, including subjectivity, low reproducibility (for example, results depending on time, people's health, analysis before the presence of odor and fatigue is variable), time-consuming, high labor cost, adaptation of people (less sensitivity when exposed to odor for a long time). In addition, it cannot be used to evaluate dangerous odors.The purpose of this research was to evaluate the ability of the electronic nose using chemometrics methods to detect the ripening time of walnuts with the help of its volatile compounds during the ripening period.

In each process of investigation (including 5 courses and intervals were determined as one week), premature walnut samples plus its ripe samples (in the last period) from one of the gardens around Ardebil (located in the village September) It was prepared and with an electronic data nose.In this study, the electronic nose was made in the Biosystem Engineering Department of Mohaghegh Ardebili University. The device uses 9 metal oxide semiconductor sensors (MOS) with low power consumption. The data is that the clean air was first passed through the sensor chamber for 150 seconds to clean the sensors from the smell and other gases. The sample smell was then sucked for 150 seconds by the pump from the sample chamber and directed to the sensors, and finally, clean air was injected into the sensor chamber for 150 seconds to prepare the device for recurring and subsequent tests. 20 repetitions are intended for each sample. During the above steps, the output voltage of the sensors was changed due to exposure to gases emitted from the sample (walnut aroma) and their smell response was collected and recorded by data collection cards.The Chemometrics method in this study will begin with the analysis of the main components (PCA) to discover the sensor output response and reduce the data dimension. The next step is to classify the time of walnut proceedings using artificial neural network analysis (ANN).

The scores chart (Figure 2) showed the total variance of the total data to PC-1 (98%) and PC-2 (1%), respectively, and the first two main components make up 99%of the total variance of normal data. When the total variance is above 90 %, it means that the first two PCSs are sufficient to explain the total variance. So it can be concluded that E-nose has a good response to peach smell and can be distinguished from peach figures, which indicates the high accuracy of the electronic nose in identifying the smell of different products. These results are highly compatible with the results obtained by XU et al., In a study conducted on class 6 rice digits, the PCA method was 99.5% accurate. The artificial neural network method was also used to identify and differentiate peaches based on the output of the sensors. The results of the diagnosis of walnut proceedings were obtained by 99% (Figure 3), which was the same as the PCA method.Aimin Li and colleagues, using an electronic nose with GC-MS tests, identified Chinese maca (MacA) at macroscopic and microscopic levels, concluding that there was a direct relationship between the Maca smell and chemical compounds (LI ET AL, 2019). Min Yee Lim and colleagues also achieved good results with the PCA method (Lim et al, 2020). They used the electronic nose to grade the quality of the Chinese commercial mum and were able to classify their quality with 94.3% accuracy, with the results of their PCA method in accordance with our research results. Arun Jana et al. (Jana et al, 2011) also used the olfactory machine with Ann, PCA and LDA to detect aromatic and non-aromatic rice, with the accuracy of the results used for the methods used, respectively: 93%, 96.5% and 80%. The results of our research were far more accurate than this study, which could be due to the presence of different volatile compounds in grape leaves.In this study, an olfactory machine with 9 metal oxide sensors was used to handle walnuts using their smell. Chemometrics, including PCA and ANN, were used for qualitative and quantitative data analysis of electronic sensor arrays. PCA was used to reduce data and, with two main components of PC1 and PC2, described 99% of the variance of the data set and provided an initial classification, as well as the artificial neural network capable of identifying and accurately classifying grape figures with grape cultivars the accuracy was 99%. The olfactory machine has the ability to use and operate as a rapid and non -destructive way to detect walnuts from their smell. Using this method will be very useful in identifying proper harvesting time for gardeners and manufacturers, especially processing units and food industries.

Peach, as an edible fruit with an acceptable economic advantage, is mainly produced in the Mediterranean region and Central Asia and consumed all over the world. Flavor is one of the key factors in fruit quality, and it largely depends on the content of soluble sugars and organic acids. Sweetness, which is determined by the level of soluble sugars, is one of the main characteristics that affect consumer satisfaction. In the mature peach fruit, sucrose constitutes more than 54% of the total soluble sugars, which are mainly stored in the vacuole and occupy up to 90% of the total cell. However, the underlying mechanisms of sugar accumulation in peach fruit remain largely unknown.The complexity of food odor makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period of time. Problems such as the human subjectivity of the response to smell and the variation between people should also be considered. Hence, there is a need for a tool such as an electronic nose with high sensitivity and correlation with human sensory panel data for specific applications in food control. Due to its easy construction, cheapness and the need for little time for analysis, the electronic nose is becoming an automatic non-destructive method to describe the smell of food.An olfactory machine can recognize the fragrance composition by estimating its concentration or determining some of its intrinsic properties, which the human nose is hardly able to do. In general, the human olfactory system is a five-step process including smelling, receiving the scent, evaluating, detecting and erasing the effect of the scent. The olfactory phenomenon begins with inhaling the intended smell and ends with breathing fresh air to remove the effect of the scent. The human olfactory system, with all its unique capabilities, also has disadvantages that limit its use in quality control processes, including subjectivity, low reproducibility (for example, results depending on time, people's health, analysis before the presence of odor and fatigue is variable), time-consuming, high labor cost, adaptation of people (less sensitivity when exposed to odor for a long time). In addition, it cannot be used to evaluate dangerous odors.The purpose of this research was to evaluate the ability and accuracy of the electronic nose using chemometrics methods to detect and differentiate peach cultivars using their volatile compounds.

First, 5 varieties of peaches were prepared. After preparing different varieties of peaches, first, the samples were placed in a closed container (sample compartment) for 1 day to saturate the space of the container with the aroma and smell of peach fruit, and then the sample compartments were used for data collection with an odor machine.In this research, the electronic nose made in the Biosystems Engineering Department of Mohaghegh Ardabili University was used. In this device, 9 metal oxide semiconductor (MOS) sensors with low power consumption are used, which are listed in Table1.The sample chamber was connected to the electronic nose device and data collection was done. This data collection was done in such a way that first, clean air was passed through the sensor chamber for 100 seconds to clean the sensors from thepresence of odors and other gases. Then the smell of the sample was sucked from the sample chamber by the pump for 100 seconds and directed to the sensors, and finally, clean air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 repetitions were considered for each sample.The chemometrics method in this research, started with principal component analysis (PCA) to discover the output response of the sensors and reduce the dimension of the data. In the next step, linear discriminant analysis (LDA) was used to classify 5 peach cultivars.Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data according to groups. It is usually used to reduce the dimensionality of the data and the best results are obtained when the data are highly correlated, positively or negatively.

The scores chart (Figure 2) showed that the total variance of the data is equal to PC-1 (89%) and PC-2 (7%), respectively, and the first two principal components account for 96% of the total variance of the normalized data. When the total variance is higher than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. So it can be concluded that the e-Nose has a good response to the smell of peaches and it is possible to distinguish peach cultivars, which shows the high accuracy of the electronic nose in identifying the smell of different products.The LDA method was also used to identify and distinguish peach cultivars based on the output response of the sensors. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize the resolution between classes. Therefore, this method was used to detect 5 varieties of peach based on the output response of the sensors. The results of the identification of cultivars were equal to 90% (Figure 3).
In this research, an olfactory machine with 9 metal oxide sensors was used to identify and differentiate peach cultivars using their scent. Chemometrics methods including PCA and LDA were used for qualitative and quantitative analysis of complex data from the electronic sensor arrays. PCA was used for data reduction and with two principal components PC1 and PC2, it described 96% of the variance of the data set and provided an initial classification, while LDA was able to accurately identify and classify grape cultivars. It became 90%. The scent machine has the ability to be used and exploited as a quick and non-destructive method to identify peach cultivars based on their smell. The use of this method in identifying peach cultivars will be very useful for consumers, especially processing units and food industries, in order to choose suitable cultivars.

Pepper (Capsicum annuum L.) is one of the most consumed vegetables in the world, containing a large amount of vitamins C and A, as well as minerals. Therefore, the consumption of about 60 to 80 g of pepper per day can provide 100 and 25% of the recommended daily amount of vitamin C and A, respectively. In addition, this horticultural product contains considerable levels of other health-promoting substances with antioxidant activity, including carotenoids, flavonoids, and other polyphenols.The quality of fresh pepper depends primarily on consumer acceptance, which is determined primarily by color, pungency, and aroma. Aroma plays an essential role in determining the sensory characteristics of these products. Volatile organic compounds (VOCs) are generally associated with the taste and aroma of foods and are important factors in assessing consumer acceptance or rejection. Consequently, food quality, originality, purity, and origin can be evaluated by determining VOC.Because it is important to distinguish hot peppers from sweet ones, we used an electronic nose to determine food quality in this study. Research has shown that the electronic nose is able to discriminate between products.

The variety used in this study was Padrón, a very popular species in Spain. The peppers can be harvested when they reach a length of 2.5 to 4 cm. One fruit out of 20 has a spicy flavor, while the rest has a mild taste. The green fruits showed no signs of ripening or discoloration and remained completely green.The peppers weighed an average of 12 ± 2 g when fresh. The weights for the sweet and spicy varieties were determined by weighing 30 fruits each. The fruits to be examined were evaluated by electronic nose.In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. This device uses 9 low-power metal oxide (MOS) semiconductor sensors.The sample chamber was connected to the electronic nose and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 100 seconds to clear the sensors of odors and other gases. The sample odor was then sucked out of the sample chamber by the pump for 100 seconds and directed to the sensors, and finally fresh air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 replicates were considered for each sample.The study began with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. In the next step, Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) were used to classify 2 group of pepper. Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data by groups. It is usually used to reduce the size of the data and the best results are obtained when the data are positively or negatively correlated with each other.Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) are the most common monitored technique for separating samples into predetermined categories. This technique selects independent data variables to differentiate the sample that is to follow the normal distribution. The QDA and MDA are based on linear classification functions in which intergroup variance is maximized and intragroup variance is minimized.

Principal component analysis diagram shows the total variance of the data equal to PC-1 (90%) and PC-2 (6%), respectively, and the first two principal components constitute 96% of the total variance of the normalized data. When the total variance is above 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. two group of pepper are well differentiated by PCA method. Therefore, it can be concluded that e-Nose has a good response to the smell of 2 group of pepper and they can be distinguished from each other, which shows the high accuracy of the electronic nose in detecting the smell of different products.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 2 group of pepper based on the output response of sensors. The results of detection of cultivars were equal to 100%.The electronic nose has the ability to be used and exploited as a fast and non-destructive method to distinguish sweet and hot pepper from leaf odor. Using this method in identifying sweet and hot pepper will be very useful for consumers, especially processing units and food industries in order to select appropriate cultivars. Since the detection of pepper using an electronic nose has not yet been researched, the promising results of this study can be widely applied in the sorting industry.

Annual herbaceous rice, standing, rooted, shallow, strong, and white, belongs to the Oryza family, belonging to the Oryzeae family. Rice is the staple food of about 2.5 billion people, which is about 20 percent of the energy needed, and provides protein for 15 percent of the world's population. In general, tropical and subtropical countries Burma, Thailand, Vietnam, Laos, Indonesia, Philippines, Pakistan, India, USA, Japan, Italy, Egypt, China, Brazil, Cuba, Mexico, and Australia are the main rice producers in the world. Among them, Sadri, Tarom, and Hashemi cultivars are among the best and most high-quality rice cultivars native to Iran, and the most productive cultivars of this country can be Caspian, Speedroad, Sahel, Kadous, Shafaq, Darfak, Gohar and Neda pointed out. Accurate determination and evaluation of odor in rice require identification of substances affecting odor in parallel with the development of methods for determining their amount. More than 3 decades have passed since the beginning of studies related to recognizing the creative and effective factors in rice aroma. Much research has been done in the field of using more efficient and faster methods in identifying rice volatiles and identifying the main causes. Of the more than 100 known compounds in rice, a few are effective in creating its aroma and aroma. In the meantime, the electronic nose can detect volatile compounds in rice. The electronic nose has been used in extensive research to identify and classify food and agricultural products. Pandan leaf aroma of rice is a special feature and is used to differentiate the quality of rice. Quality determines whether it has a certain percentage of cleanliness and purity or not. Aromatic rice is usually preferred by consumers due to its good quality, which includes delicacy, shape, colour, aroma, taste, and consumers use aromatic rice for celebrations and occasions due to high demand and use good quality. The quality of aromatic rice is influenced by various factors such as cultivation location, climatic conditions, genetic activities and post-harvest. Important issues in the rice industry include quality control, incorrect labelling, grading and fraud in different types of rice. For this reason, the rice industry uses standard grades based on market criteria to identify grain. Due to these factors, quality control and fraud are the main issues that are wrong labelling and grading are the main problems. The use of human expert panels is the most common technique used to evaluate the quality of aromatic rice. They distinguish rice based on its aroma. With the rapid and rapid advancement of computer technology and sensor technology, the application of the bionic electronic nose, including a semiconductor gas-sensitive sensor and a pattern recognition system as a means of detection, offers a new method for rapid classification and digit recognition. Give. The electronic nose has also introduced a new method for classifying and detecting rough rice in a non-destructive and fast way. The aim of this study was to evaluate the ability and accuracy of the electronic noses using one of the chemometrics methods to distinguish pure rice cultivars from 3 gross cultivars.

First, 4 rice cultivars were prepared from the Iranian Rice Research Center located in Rasht. These 4 cultivars included 1 high-quality rice cultivar named Hashemi and 3 substandard rice cultivars named Neda, Khazar, and Sahel. Therefore, in the experiments, one genuine rice cultivar (Hashemi) and three non-genuine or counterfeit cultivars (mixture of Caspian, Neda, and Sahel cultivars with Hashemi cultivars) were prepared, so that the counterfeit cultivars each contained 80% of Hashemi cultivars and 20% of substandard cultivars. After preparing and mixing the cultivars, first, the samples were placed in a closed container (sample container) for 1 day to saturate the container with the aroma of rice, then the sample containers were used for data collection with an electronic nose. Were located. In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. In this device, 9 low-power metal oxide (MOS) semiconductor sensors are used, which are given in Table 1 of the sensor specifications. The sample chamber was connected to the electronic nasal device and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 150 seconds to clear the sensors of odours and other gases. The sample odor was then sucked out of the sample chamber by the pump for 150 seconds and directed to the sensors, and finally, fresh air was injected into the sensor chamber for 150 seconds to prepare the device for repetition and subsequent tests. 22 replicates were considered for each sample. The study started with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data by groups. It is usually used to reduce the size of the data and the best results are obtained when the data are positively or negatively correlated. Another advantage of PCA is that this technique reduces the size of multidimensional data while eliminating additional data without losing important information.

The scores diagram (Figure 1) showed the total variance of the data equal to PC-1 (99%) and PC-2 (0%), respectively, and the first two principal components constitute 99% of the total variance of the normalized data. When the total variance is greater than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. According to the shape of Hashemi's main cultivar (a) on the left side of the chart and 3 fake cultivars (b, c, and d) are visible, which are well separated by the PCA method. Therefore, it can be concluded that e-Nose has a good response to rice odor and it is possible to distinguish between original and counterfeit rice cultivars, which shows the high accuracy of electronic nose in detecting the smell of different products. The correlation loadings plot diagram can show the relationships between all variables. The loading diagram (Figure 2) shows the relative role of the sensors for each principal component. The inner ellipse represents 50% and the outer ellipse represents 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, sensors mounted on the outer circle have a greater role in data classification. According to the figure, it is clear that all sensors have played an important role in identifying rice cultivars, including the role of sensors No. 1 and 9, which are the same sensors MQ9 (to detect carbon dioxide, combustible gases) and MQ3 (to detect). Alcohol, methane, natural gases) were slightly less than the other sensors, which can be reduced by removing these two sensors to reduce the cost of making the olfactory device (to detect genuine and counterfeit rice) and save costs. In this study, an electronic nose with 9 metal oxide sensors was used to identify and distinguish between original and counterfeit rice cultivars. PCA chemometrics method for qualitative and quantitative analysis of complex data, an electronic sensor array was used. PCA was used to reduce the data and with 99 main components PC1 and PC2, it described 99% of the variance of the data set and provided a preliminary classification. The electronic nose has the ability to be used and exploited as a fast and non-destructive method to detect genuine and counterfeit rice cultivars. Using this method in identifying rice cultivars will be very useful for consumers, especially in restaurants and halls, in order to select pure and high-quality cultivars.

Grape is a creeping plant that has ivy in front of some of its leaves. France, Italy and Germany are among the most important grape producing countries in Europe, and Iran is one of the most important centers for grape production and cultivation in the world due to its favorable geographical and climatic conditions. Grape fruit is divided into two types, seeded and seedless, each of which is found in different colors of red, yellow, black and almost green. In areas where the maximum temperature is not more than 40 degrees Celsius and the minimum temperature is not less than 15 degrees Celsius below zero, grape fruit grows better. Grapes are made from raisins, jellies, raisins, jams, vinegar and juice, and various products are made from grape seeds. This product is a good source of potassium, fiber and a variety of vitamins and other minerals. Is. According to available reports, there are about 800 to 1000 grape cultivars in Iran, and some of these cultivars are of great economic importance, especially for fresh consumption and preparation of raisins. In Iran, edible grapes are of the genus Winifra, and in addition, there are two types of Labrosca grapes, which are scattered in the north of the country, and wild grapes of the subspecies Westeris in the northern forests and wetlands of the Zagros Mountains. Grapes are widely distributed in terms of climate and have recently been cultivated in temperate and tropical regions in all parts of the world. By recognizing grape cultivars before fruit growth, it is an effective step in determining the purpose and use of the harvest product, in the meantime, the type of grape cultivar can be identified using new post-harvest technologies. One of these methods is to use an electronic nose to identify volatile compounds in grape leaves and to identify its cultivar. Electronic nose has been used in extensive research to identify and classify food and agricultural products.

First, 3 varieties of grape leaves were obtained from vineyards located in Bonab city of West Azerbaijan province. These 3 cultivars were: Jovini, Aq Shaliq and Qara Shaliq. 200 grams of each of these leaves were prepared. After preparing leaves from different grape cultivars, first the samples were placed in a closed container (sample container) for 1 day to saturate the container space with the aroma of grape leaves, then the sample containers were used for data collection with the case of the electronic nose.In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. This device uses 9 low-power metal oxide (MOS) semiconductor sensors.The sample chamber was connected to the electronic nose and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 100 seconds to clear the sensors of odors and other gases. The sample odor was then sucked out of the sample chamber by the pump for 100 seconds and directed to the sensors, and finally fresh air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 replicates were considered for each sample.The study began with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. In the next step, linear detection analysis (LDA) and support vector machine (SVM) were used to classify 3 grape cultivars. Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data by groups. It is usually used to reduce the size of the data and the best results are obtained when the data are positively or negatively correlated with each other.Linear Detection Analysis (LDA) is the most common monitored technique for separating samples into predetermined categories. This technique selects independent data variables to differentiate the sample that is to follow the normal distribution. The LDA is based on linear classification functions in which intergroup variance is maximized and intragroup variance is minimized.

The scores diagram (Figure 2) shows the total variance of the data equal to PC-1 (82%) and PC-2 (11%), respectively, and the first two principal components constitute 93% of the total variance of the normalized data. When the total variance is above 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. Grape cultivars are well differentiated by PCA method. Therefore, it can be concluded that e-Nose has a good response to the smell of grape leaves and grape cultivars can be distinguished from each other, which shows the high accuracy of the electronic nose in detecting the smell of different products. The correlation loadings plot diagram can show the relationships between all variables. The loading diagram (Figure 3) shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors TGS2620, TGS822 and TGS813 have played an important role in identifying grape cultivars from their leaf aroma.LDA and SVM methods were used to identify and differentiate grape cultivars based on the output response of sensors. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 3 grape cultivars based on the output response of sensors. The results of detection of cultivars were equal to 100% and also the accuracy of SVM method for detection of 3 grape cultivars was equal to 83.33% (Figures 4 and 5).In this study, an electronic nose with 9 metal oxide sensors was used to identify and differentiate grape cultivars using their leaf aroma. Chemometrics methods including PCA, LDA and SVM were used for qualitative and quantitative analysis of complex data using electronic sensor array. The electronic nose has the ability to be used and exploited as a fast and non-destructive method to distinguish grape cultivars from leaf odor. Using this method in identifying grape cultivars will be very useful for consumers, especially processing units and food industries in order to select appropriate cultivars.