کمومتریکس

Nectarine is a plant that is cultivated as an important commercial product in some countries and is known as an important source of sugar and vitamins in the human diet. Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the nectarine product, quality evaluation after the harvesting stage is necessary to provide a reliable and uniform product to the market. The purpose of this study is to identify and classify nectarines by extracting characteristics from the response patterns of the spectrometer and using chemometrics methods. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties. The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive manner, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality. With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now essential. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, the variety of nectarine fruit was identified.

For this study, 5 different nectarine cultivars were prepared from the gardens of Moghan city (Ardebil province) and were tested and data collected. A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, and portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process. The data obtained from spectral imaging may be affected by the scattering of light by the detector with sample change, sample size change, surface roughness in the sample, the noise created due to the increase in temperature of the device and many other factors, and unwanted information affect the accuracy of calibration models. Therefore, to achieve stable, accurate and reliable calibration models, data pre-processing is needed (Rossel, 2008). In this research, Savitzky-Golay smoothing, first and second derivatives, baseline, standard normal distribution, and incremental scatter correction were applied to the data. The use of non-destructive methods based on spectroscopy in the full range of wavelengths requires spending time and very high costs, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible value. Chemometrics uses multivariate statistics to extract useful information from complex analytical data. The chemometrics used in this study started with principal component analysis (PCA) to explore the output response of the sensors and reduce the dimensionality of the data. In the next step, linear diagnostic analysis (LDA) was also used to classify 5 varieties of Shail. (PCA) is one of the most common statistical data reduction methods. This method is an unsupervised technique used to explore and reduce the dimensionality of a dataset. The analysis itself involves the determination of variable components, which are linear combinations of many investigated characteristics. In this research, in order to construct the LDA model, the data were randomly divided into two parts: 70% of the samples were used for training and cross-validation, and the rest of the data were used for independent validation.

Based on the results of the PCA analysis presented in Figure 2, the first principal component (PC-1) describes 72% and the second principal component (PC-2) 13% of the variance of the tested samples. As a result, the first two principal components together express 85% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or socalled outliers The LDA method is a supervised method that is used to find the most distinct eigenvectors and maximizes the between-class and intra-class variance ratios and is capable of classifying two or more groups of samples. The LDA method was used to identify the nectarine cultivars based on the output response of the spectrometer. Unlike the PCA method, the LDA method can extract the resulting information to optimize the resolution between classes. Therefore, this method was used to detect 5 nectarine cultivars based on the output response of the spectrometer. The results of the identification of figures equal to 100% were obtained.

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.

Walnut is an important economic and commercial product all over the world. The reflected light spectrum 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 the reflectance spectrum of food 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. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties.The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented so far for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive way, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality.With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now necessary. Because manual methods do not have automaticcontrol, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, the ripening time of walnuts was investigated using spectrometry and chemometrics methods.

In each treatment period (in total 5 periods were considered and the intervals of periods were determined as one week), unripe walnut samples in addition to ripe samples (in the last period) were taken from one of the orchards around Ardabil (located in Shahrivar village) was prepared, tested and data collected.A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process, which is shown in Figure 1.

The average absorption spectra of Vis/NIR absorption spectra for different treatments in the range of 680-970 nm are presented in Figure 1.Environmental factors (light and heat) as well as the spectrometer's expression quality cause disturbances in the initial and final wavelengths of the spectra, so some of these wavelengths are removed from the data set. And as it is clear in Figure 1, the samples had an almost similar trend; this may be affected by the colour of the samples. According to Figure 1, there are two distinct peaks for the spectra and it is that the peaks appeared around the wavelength of 680 and 970 nm. It can also be seen in Figure 1 that the amount of absorption of ripe walnuts is higher compared to other periods, which can be due to the difference in the content and texture of the product.Based on the PCA analysis results presented in Figure 2, the first principal component (PC-1) describes 94% and the second principal component (PC-2) describes 5% of the variance of the tested samples. As a result, the first two principal components together express 99% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or so-called outliers.The values of R2 and RMSE for calibration and validation sets of different regression models (PLS) with raw and processed data are presented in Figure 3, which is equal to 0.98. The results show that the spectra are able to detect the ripening time of walnuts with high accuracy. Khodabakhshian et al investigated the potential of visible and infrared spectroscopy to classify the ripening stage and predict the quality traits of pomegranate varieties including SSC and TA. Among the methods of centring, Savitzky-Golay smoothing, median filter, standard normal variable, incremental spread correction (MSC) and differentiation with first derivative and second derivative, the use of incremental spread correction (MSC) has the highest accuracy in identifying pomegranate quality parameters. followed Zhang and colleagues (Zhang et al, 2018) in estimating the SSC of red Fuji apple using near-infrared spectroscopy to reduce noises using the functions of additive scatter correction (MSC) and standard normal distribution (SNV) and reported that the additive scatter correction method (MSC) compared to the standard normal distribution (SNV) will result in a more accurate estimate of the SSC value. Kim et al. estimated the SSC of oriental melon using near-infrared spectroscopy among different pre-processing methods including Savitzky-Golay smoothing, normalization with maximum and minimum, stable normalization, standardization, stable normal variable, distribution Standard normal (SNV) and incremental spread correction (MSC) reported that the best result was obtained with a standard normal distribution (SNV). Although considering the different nature of the samples, measurement methods and equipment, and other conditions affecting the spectral properties of the product, it is better not to compare the data obtained from different researches with each other.

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.