سیب زمینی

Potato (Solanum tuberosum L.) is a key crop within the Solanaceae family and ranks as the most significant non-cereal crop globally following major staples such as wheat, rice, and corn. Potatoes can reproduce sexually and asexually via tubers, and plant tissue culture is emerging as an effective method for vegetative propagation, addressing the increasing global demand for agricultural products. Ethylene, a critical plant growth regulator, influences various physiological processes including growth and development. During in vitro culture and due to the wounding of explants, ethylene accumulation can lead to abnormal biological responses, with potato seedlings being susceptible. Thus, investigating the effects of ethylene biosynthesis inhibitors such as pyrazinamide (PZA) and AgNO₃ on potato growth in vitro is essential.

In this study, potato seedlings were cultivated in Murashige and Skoog (MS) medium, with concentrations of PZA ranging from 0 to 6 mg L⁻¹ and AgNO₃ at 2 mg L⁻¹. After four weeks, the seedlings were harvested and stored at -70°C for later analysis. The growth parameters measured included fresh weight (FW), dry weight (DW), stem and root lengths, leaf area, and leaf and root number. In addition, biochemical parameters, such as photosynthetic pigment levels, total phenol content (TPC), total reactive oxygen species (ROS), and proline concentration were analyzed. Statistical evaluations were conducted using SPSS and PAST software

The results showed that the 2 mg L⁻¹ PZA treatment led to the highest FW and DW and increased leaf numbers; however, it was also correlated with a lower number of rooted plants. Conversely, treatments with 6 mg L⁻¹ PZA promoted longer stem growth, whereas control plants exhibited the largest leaf area, and AgNO3-treated plants produced the longest roots. The accumulation of H₂O₂ in plants treated with ethylene inhibitors was like controls, but total ROS levels soared by 36% in those treated with 6 mg L⁻¹ PZA compared to controls. This suggests a link between reduced ethylene production, oxidative stress mitigation, and enhanced potato growth. Additionally, total ROS was positively correlated with stem length, but negatively correlated with root length.Plants use several strategies to combat the damaging effects of ROS, such as the production of antioxidant compounds such as phenolics. Although PZA did not significantly alter TPC compared to controls, treatment with AgNO₃ caused a 61% reduction in TPC. Therefore, PZA did not appear to significantly affect phenolics production in the potato seedlings.Proline, another critical antioxidant in plants, was found to accumulate significantly in the leaves of plants treated with 6 mg L⁻¹ PZA, which was more than 2.3 times higher than that in controls. This accumulation correlated positively with ROS levels at higher PZA concentrations but showed an inverse relationship with photosynthetic pigment levels.The PCA revealed the relationships between the measured parameters and the applied elicitors. The samples were categorized into four distinct groups:Control group: This group primarily exhibited higher FW, DW, and longer roots compared to the treated plants.Low PZA dose group: These plants displayed elevated levels of photosynthetic pigments, TPC, and leaf area.Medium PZA dose group: Correlations were observed with an increased number of roots.6 mg L⁻¹ PZA and AgNO₃ group: These samples contained elevated levels of total ROS and proline.

The study concludes that low concentrations of PZA can stimulate growth while inhibiting ethylene production, resulting in fewer growth abnormalities compared to control plants. However, at elevated PZA concentrations, increased ROS levels may lead to oxidative stress, emphasizing the delicate balance in ethylene's role in plant growth and the necessity for further research to optimize conditions for potato cultivation in vitro. The findings contribute to a deeper understanding of how ethylene inhibitors can enhance potato propagation and possibly other crops in controlled agricultural environments.

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and 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. Meanwhile, the high-performance artificial neural network can be used to classify cultivars. An artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified usithe ng LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.In another study that was conducted using neural networks and image processing on 5 sweet potato cultivars, the researchers showed that this method was successful and could classify sweet potato cultivars with 100% accuracy.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching it. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.Several types of research have been conducted on the physical and mechanical properties of agricultural products in Iran and other countries. In a research conducted by Ali Mohammadi and Rasakh to determine some mechanical properties of lime fruit under quasi-static loading, the results showed that the effect of loading speed, loading direction and size of a lime on the breaking force of lime is significant. As the size of the lemon decreases, the breaking force and deformation decrease, and also with increasing loading speed, the braking force increases. In another research conducted by Mohd Nejad and Khosdada, the effect of size, speed and direction of loading on the mechanical properties of lime was investigated and the results showed that the interaction of loading speed and size on fracture energy and toughness and the main effects of size, loading speed and The loading direction is significant on the modulus of elasticity, but none of the effects on the rupture force is significant.

First, 5 different varieties of potatoes (Agria, Spirit, Sante, Marfona and Jelly) were prepared from Ardabil Agricultural Research Center immediately after harvest. After preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done with santam machine.To measure the rupture energy of potato samples, santam device (available in the Biosystem Engineering Department of Mohaghegh Ardabili University) was used. For this purpose, each potato variety was subjected to a compressive force at three loading speed levels (10, 40 and 70 mm/min) with 7 repetitions. Then, using the amount of rupture force and deformation (surface area under the force-deformation curve), the amount of rupture energy was calculated. The data obtained from the experiment were analyzed statistically with Minitab 18 software.

Conclusion:

 The amount of rupture energy in 5 different varieties of potato was obtained using santam device and equation 1. The values obtained for 5 potato cultivars were analyzed using Minitab18 software and the results are given in Table (1).The results of the analysis of variance for the firmness of 5 different potato cultivars were significant at the 1% level and the coefficient of variation was 9.6. In Figure 2, you can see the average results.According to Figure 2, it is clear that the lowest amount of rupture energy is related to the Agria variety and the highest is related to the Jali variety. Also, it can be found that with a loading speed of 10 mm/min, the highest amount of rupture energy is obtained in all figures.In this research, the firmness level for 5 different potato cultivars was calculated using the santam machine available at Mohaghegh Ardabili University and the area under the force-deformation curve. The amount of calculated rupture energy has the ability to be used as a method for the proper selection of different potato cultivars. The use of this method in potato cultivars will be very useful for factories such as chips factories and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and hardness and with the help of different statistical methods to optimize production and The processing of agricultural products can be used in the food industry, which leads to more customer-friendliness and can also reduce agricultural waste.

Introduction:

 Potato with scientific name Solanum tuberosum. L is a plant that is cultivated as an important crop in all countries and is known as a source of carbohydrates, proteins, and vitamins in the human diet. This is a native product of South America and its origin is from Peru. After wheat, rice and corn, potato is the fourth product in people's food basket, which in Iran sometimes takes the place of rice and takes second place, which shows its importance in meeting people's food needs. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvested product from this area was about 32.5 million tons. 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.Agria, Sante, Arinda, Marfona, Jelli, Born, Satina, Milva, Banba, Fontane, Ramos and Esprit varieties are among the most common potato varieties in Iran. Due to the increasing expectations for food products with high quality and safety standards, it is necessary to accurately, quickly and purposefully determine the characteristics of food products. In the apple-potato product, quality assessment after the harvest stage is necessary to provide a reliable and uniform product to the market, because potatoes, like many other products, have uneven quality and handling during the harvest stage. - Be At the same time, the safety and desirability of food play an important role in the food industry and are directly related to people's health. In addition, a huge part of the potatoes used in the processing industry is stored, so considering the importance of this food item and the demand of the people throughout the year, it is possible to meet the needs of the applicants only through long-term storage with optimal conditions was responsible. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin color and no sprouting. Storage conditions after harvesting can cause changes in the chemical composition and quality of the product.The nutritional and chemical composition of potatoes differs from each other depending on the variety, storage period, growing season, soil type and pre-harvest nutrition. In general, potatoes contain 70-80% water and 16-24% starch and contain small amounts (less than 4%) of protein, fat, anthocyanins, minerals, etc.Storage conditions after harvesting can cause many changes in the chemical composition of potato tubers and as a result, change the quality characteristics of the final product. Sugar and starch are the main components that are affected by metabolism after harvesting in the potato tuber and may ultimately affect their texture, sensory and cooking properties.The quality of potatoes and, consequently, the quality of processed products, significantly depends on the variety and environmental conditions, both during the growing season and during the storage period.Although the quality of raw potatoes is determined primarily by the size, shape, color and attractiveness of the tuber, its quality is mainly determined by examining the quality of the final product. The quality of processed potato products is evaluated in terms of color, flavor and texture, and most of their quality depends on the quality of raw potatoes.By analyzing the relationship between the color of chips, dry solids, sucrose, reducing sugar, ascorbic acid, protein and storage temperature data, Meza showed that dry solids, reducing sugar and sucrose in determining the color of fresh potato chips and reducing sugar, tuber temperature and sucrose content are very important in determining the color of stored tuber chips, and the relative importance of each of these parameters changes with the variety and age of potato tubers.

different varieties of potato were prepared from Arallo Agricultural Research Center (Ardebil Province) immediately after harvest. Then, data collection was done from different samples and cultivars (measurement of carbohydrate content) as explained below.The carbohydrate content of the samples was extracted using the equipment available in the central laboratory of Mohaghegh Ardabili University. This process was carried out by the Schlegel method, in which carbohydrates were extracted using 95% ethanol based on the sulfuric acid method in each sample. The amount of light absorption of each sample was obtained from a nano-spectrophotometer device (Nanodrop) with a volume of 1000 microliters (Figure 1) using a cuvette (made by Termo scientific company from the USA) and the amount of extracted carbohydrates were obtained based on micrograms per millilitre from the standard curve.Glucose was used to prepare the standard curve. Serial dilution of glucose was prepared and color development at 490 nm was controlled for different concentrations of glucose and one millilitre of distilled water was used as a blank. This standard curve was used to calculate the concentration of total carbohydrates in the samples. The standard curve had a coefficient of determination of 0.9955.For each sample, data collection was done in three repetitions and the amount of absorption wavelength and then the amount of carbohydrate was calculated.

In order to obtain the number of carbohydrates, the number of the absorption wavelength was placed in the relationship obtained from the standard curve, and the number of carbohydrates was obtained in micrograms per millilitre. The results of the analysis of the variance of the effect of cultivar on potato carbohydrate content can be seen in Table 1. According to the analysis of the variance table, the effect of variety on potato carbohydrate content was significant at the 1% probability level.As you can see, Sante and Esprit cultivars have more carbohydrates than other cultivars. Also, the carbohydrate content of the Marfona variety was the lowest.According to the data and results of the research, it was observed that the amount of carbohydrates in different potato cultivars is different, and Sante and Esprit cultivars had more carbohydrates at the time of harvest. Also, according to the resulting graphs, it was observed that the amount of carbohydrates of the Marfona cultivar is lower than other cultivars. It is recommended to choose a more suitable variety according to the type of consumption and the importance of quality characteristics for consumption and processing, according to the storage conditions and time.

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 with the scientific name (Solanum tuberosum. L.) is a plant that is cultivated as an important product in all countries and is known as a source of carbohydrates, proteins, and vitamins in the human diet. This is a native product of South America and its origin is from Peru. After wheat, rice and corn, potato is the fourth product in people's food basket, which in Iran sometimes takes the place of rice and takes second place, which shows its importance in meeting people's food needs. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvested product from this area was about 32.5 million tons. 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.Since the expectations for food products with appropriate quality and safety standards have increased, it seems necessary to determine the characteristics of food products. In the meantime, in the potato product, quality evaluation after the harvest stage is necessary to provide a reliable and uniform product to the market, because potatoes, like many other products, have uneven quality and handling in It is the harvesting stage. At the same time, the safety and desirability of food play an important role in the food industry and are directly related to people's health. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin color and no sprouting. Stored potatoes may suffer from sweetening, rotting, water loss and sprout growth during storage. Storage conditions after harvesting can cause changes in the chemical composition and quality of the product. Therefore, the management of potato tubers in all stages of production and storage is very important.The quality of this product and its processed products depends on the variety and environmental conditions (both during the growing season and during the storage period). By analyzing the relationship between chips color, dry solids, sucrose, reducing sugar, ascorbic acid, protein and storage temperature data, Meza showed that dry solids, reducing sugar and sucrose in determining the color of fresh potato chips and reducing sugar, tuber temperature and sucrose content are very important in determining the color of stored tuber chips and the relative importance of each of these parameters changes with the type of tuber variety and storage.The amount of potato sugar significantly depends on the variety and storage temperature and it happens quickly in cold weather. In potato tubers during the storage period, starch is gradually hydrolyzed and turned into sugar (glucose). In unripe tubers and potatoes that are stored for a long time at low temperatures, there are more amounts of glucose, this feature is considered an anti-quality feature for the potato product in the industry, why? The increase of regenerating sugars causes the produced chips to turn brown and bitter. Storing potatoes for more than 7 months can cause ageing or old sweetness, and storage at a temperature of fewer than 10 degrees Celsius can cause sweetness caused by cold. Although potato storage at low temperatures can have beneficial results such as reducing respiration rate, reducing physiological ageing, inhibiting germination, reducing evaporative water loss and reducing microbial pathogens. But sugars accumulate when the balance between starch degradation and breakdown is not established and there is carbohydrate respiration. Therefore, potatoes that are kept at a lower temperature have a lot of sugar. Researchers reported that when potatoes are stored at zero degrees Celsius, there will be a complete stop in th accumulation of sugar.

First, 5 different varieties of fresh potatoes (Spirit, Agria, Sante, Jelli and Marfona) were prepared from Ardabil Agricultural Research Center (Arallo District). It should be noted that these potatoes were prepared immediately after harvesting so that there are no changes in the amount of sugar due to the time interval after harvesting.The amount of sugar in each sample was measured in three replicates using a liquid refractometer model BPTR100 (Middle East Control System Company, brand name Prisma Tech, made in Iran) available at Mohaghegh Ardabili University (Figure 1). For this, first, some water was taken from the samples and after pouring it into a microtube, it was placed inside a refrigerated centrifuge (top speed) of the LISA France model, and after rotating at a speed of 1800 rpm for 2 minutes, the impurities at the bottom settled and separated the pure liquid (pure potato juice). After reaching the ambient temperature, the said liquid was placed on the refractometer and its sugar content was read in terms of Brix.

The results of the analysis of the variance of cultivar effect on potato sugar content are shown in Table 1. According to the analysis of the variance table, the effect of variety on potato sugar level was significant at 1% probability level. You can see the changes in the amount of sugar of different potato cultivars in Figure 2. The difference in the amount of sugar in different cultivars is due to the difference in their starch hydrolysis (the main compound of potato-potato tubers) which occurs as a result of the respiration of the product, and it is in this way that the higher the amount of starch in If one variety is less, that variety has less sugar, and it is important to note that the chemical composition depends on the potato variety, soil, climate, and agricultural factors. In general, it can be said that potatoes with more sugar are suitable for the chips industry, and potatoes with medium sugar are suitable for frying. According to Figure 2, the highest amount of sugar is related to the Esprit variety and the lowest amount is related to the Jelly variety. The reason for the difference in the amount of sugar between different cultivars is mainly related to the type of soil, fertilizer and poison used. According to the data and the results of the research, it was observed that the amount of sugar in different varieties of potato is different, in the meantime, the jelly variety generally has a lower amount of sugar at the time of harvesting, and the variety of Esprit has the highest amount of sugar at the time of harvesting. Was. It is recommended to choose a more suitable variety according to the conditions according to the type of consumption and the importance of quality characteristics for consumption and processing, of course, physical characteristics are also involved in this relationship, which should be taken into consideration.

Potato with the scientific name (Solanum tuberosum. L.) is a plant that is cultivated as an important crop in all countries and is known as a source of carbohydrates, proteins, and vitamins in the human diet. This is a native product of South America and its origin is from Peru. 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.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 apple-potato product, quality assessment after the harvest stage is necessary to provide a reliable and uniform product to the market, because potatoes, like many other products, have uneven quality and processing during the harvest stage. - Be At the same time, the safety and desirability of food play an important role in the food industry and are directly related to people's health. In addition, a huge part of potatoes used in the processing industry is stored, so considering the importance of this food item and the demand of the people throughout the year, it is possible to meet the needs of the applicants only through long-term storage with optimal conditions was responsible. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin colour and no sprouting.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. 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 havebeen able to meet the above conditions and have been justified in terms of technical and industrial aspects.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 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, changes in acidity and SSC of potato were investigated over time.

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 to display and store the acquired spectra in the Spectra Wiz software through the USB port. To create 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 to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process.To measure SSC, liquid refractometer model BPTR100 (Middle East System Control Company, brand name Prisma Tech, made in Iran) available at Mohaghegh Ardabili University is used. For this, first, some water is taken from the samples and after pouring it into the microtube, we allow it to reach the ambient temperature, and then it is placed on the refractometer and the amount of sugar is read in terms of Brix.For this purpose, a laboratory pH meter, which is also called a pH meter, was used. The pH meter is actually a potentiometer consisting of an ion-selective glass electrode that selectively responds to the activity of hydrogen ions in the solution and measures the potential difference between the external solution (sample) and the internal solution (reference electrode solution). The pH-sensitive part is made of a special thin glass membrane that is at the bottom of the electrode.

In this research, in order to estimate the amount of acidity and SSC of potato-potato and the amount of wavelength absorption in 5 different periods of storage (two-week periods), reflectance spectroscopy was performed in the wavelength range of 400 to 1100 nm. After removing the noises by PCA analysis, to improve the spectrum, different pre-processings were applied and their effects were investigated. The appropriate model was determined using the partial least squares (PLS) method. Important wavelengths were selected based on the regression coefficient of the best model. Based on PLS analysis, the best results were obtained with Savitzky-Golay smoothing preprocessing. As a result, it seems that the non-destructive method of ultraspectral imaging was able to detect the amount of SSC in potatoes, but no acceptable result was obtained in the case of acidity.

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.

Potato is an important vegetable that grows all over the world and is considered as 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, and 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. Meanwhile, the high-performance artificial neural network can be used to classify cultivars. An artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified use the ng LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.In another study that was conducted using neural networks and image processing on 5 sweet potato cultivars, the researchers showed that this method was successful and could classify sweet potato cultivars with 100% accuracy.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching it. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.Several types of research have been conducted on the physical and mechanical properties of agricultural products in Iran and other countries. In a research conducted by Ali Mohammadi and Rasakh to determine some mechanical properties of lime fruit under quasi-static loading, the results showed that the effect of loading speed, loading direction and size of a lime on the breaking force of lime is significant. As the size of the lemon decreases, the breaking force and deformation decrease, and also with increasing loading speed, the braking force increases. In another research conducted by Mohd Nejad and Khosdada, the effect of size, speed and direction of loading on the mechanical properties of lime was investigated and the results showed that the interaction of loading speed and size on fracture energy and toughness and the main effects of size, loading speed and The loading direction is significant on the modulus of elasticity, but none of the effects on the rupture force is significant.

First, potatoes in 5 different varieties (Agria, Esprit, Sante, Marfona and Jelli) were prepared at Ardabil Agricultural Research Center and stored at 4-10 degrees Celsius. One day after preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done. The data collection included mechanical properties.To determine the toughness of the samples, the santam machine available in the mechanical properties laboratory of the biosystem engineering department of Mohaghegh Ardabili University was used. Each potato variety was subjected to a compressive force at three loading speed levels of 10, 40 and 70 mm/min and with 7 repetitions. Then, using the amount of braking force, deformation and sample volume, the toughness was calculated according to equation (1).
These experiments were carried out in 5 storage periods (at 2-week intervals).

The toughness of different cultivars showed different behavior during the storage period so no changes were observed in the Marfona cultivar for toughness during the storage period, and in the Sante cultivar, the toughness level was almost the same at the beginning and end of the period and only in the middle of the storage period the value There was a slight increase. But in the case of Agria, Sprit and Jali cultivars, it should be said that the changes in toughness do not follow a specific trend and are unpredictable. Also, according to Figure 3, it is quite clear that in all figures, the lower the loading speed, the greater the toughness obtained, and the reason for this is that at a lower loading speed, the breaking force occurs in high values. Falls, and as a result, according to relation 1, the toughness value also increases.According to Figure 3, during the storage period, the two varieties of Jelli and Marfona (especially the Marfona variety) did not change much in terms of toughness and considering this issue, it is recommended to use these two varieties for some purposes, including frying.In this research, firmness was calculated for 5 different varieties of potatoes in 5 storage periods using the santam machine available at Mohaghegh Ardabili University and with the help of equation 1. The results showed that Jali and Marfona cultivars maintained their firmness during the storage period, and hence they are recommended for uses such as chips.

Sweet potato grows as a strong plant all over the world and is a product compatible with drought, temperature, and low fertile soils. Potatoes are high in starch, vitamins, minerals, and non-mineral salts such as calcium, phosphorus, iron and low in calories. This product is widely consumed fresh, boiled, etc. due to its functions for various reasons, such as improving immunity and preventing cancer, and its consumption is due to the abundance of nutrients such as carbohydrates, dietary fiber, minerals and other health-promoting compounds such as beta-carotene, vitamin C, phenolic acids, etc. are on the rise.Conventional evaluation methods for the internal quality of potatoes are mostly destructive and inefficient. In the practical production of potatoes, the quality evaluation system must have good accuracy, high speed, and low cost. Such goals can be achieved using modern techniques such as spectroscopy and electronic nose, as they do not require sample preparation, are non-destructive, efficient, fast, accurate, pollution-free, and inexpensive.Organic acids (OAs) are organic acidic compounds containing carboxyl groups that are widely present in organisms. Organic acids in fruits mainly include citric acid, malic acid, tartaric acid, and succinic acid. The traditional method for detecting OA concentrations is ion chromatography in the laboratory. Ion chromatographic testing requires standard solutions as a reference, also requires the use of chemical reagents, and organic acids must be measured separately. This is a tedious operation that wastes a lot of time. Therefore, a rapid detection technology is needed and preferred as an alternative.Near-infrared spectroscopy is a type of rapid detection technology that extracts spectral information from a sample through the difference between radiated light and reflected light. NIR technology has the advantages of fast performance, no use of chemical reagents and is also able to detect multiple components simultaneously. Spectral signals can be further amplified by the combined use of the Fourier transform technique. Fourier transform near-infrared spectroscopy has been widely used in the fields of food science, agricultural informatics, environmental monitoring, biomedicine, and pharmacy.Based on the simplicity of PLS regression, nonlinear methods are investigated to improve the PLS algorithm by embedding nonlinear core functions. This method plots the data before PLS scoring in a high-dimensional feature space, and the data converted in the new space characterize the samples. In this study, a neural network as a core function is designed to optimize PLS in the quantitative NIR analysis of OA concentrations in potato samples. A three-layer lattice with an adjustable number of neural nodes is designed to extract spectral feature variables to optimize the PLS core model.

Potato samples were harvested and 248 of healthy size and almost the same size were selected. The samples were transferred to the laboratory 24 hours after picking and stored at room temperature for 2 days. In the next 5 days, about 50 glands per day were selected and their OA concentration and FT-NIR spectrum were identified. Each potato sample was divided into two parts, half of which were used to detect the OA concentration and the other half to measure the NIR spectrum. The FT-NIR spectrum was measured using a PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA. Temperature and humidity were kept constant at 25 ° C and 47% during the spectrum study.PLS kernel is an improved PLS method to deal with the nonlinear problem of spectral data. Raw data is mapped by a special nonlinear core function in high-resolution image space, so the original PLS linear algorithm can be used to discover the relationship between feature data and sample analysis. In short, this method can be done in two consecutive steps of mapping and regression.In modern studies, a neural network is a good tool for operating dynamic data, as it is flexibly taught by automatically fitting its link weights to the data-based model. A three-layer neural network was constructed in this study as a new nucleus for PLS output in the quantitative NIR analysis of potato OA concentrations.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.

Core PLS regression was applied to create FT-NIR calibration models to quantify OA concentrations in potato samples. The proposed network architecture was used as a new kernel conversion function to select attribute variables. The network was created connected with an input layer, a hidden layer, and an output layer.All 3114 wave number variables were transferred to the input layer. The same number of input nodes were generated to accept the data, and then perceptron units were applied, converting the data into a hidden layer. In the case of using a data-driven learning mechanism, the number of hidden nodes varies from 10 to 200 with step 10. Each Nh value was tested to screen for the best latent structure. Perceptron calculations converted the hidden data into an output layer, and a total of 20 output neurons were generated in the output layer to reduce the dimensions. These output variables were mostly used for PLS regression.In general, neural perceptron units were adjusted with their link weights, which automatically matched the data. 20 output variables were delivered to the softmax MLR predictor. Predictive errors were used for 50 rounds of error-feedback repetition optimization on link weights. Figure 3 shows that the RMSEV gradually shrinks with more repetitions and gradually decreases for each Nh number. This phenomenon means that the initial feedback and error replication mechanism can optimize machine learning for the network kernel. Duplicate optimized network link weights were used to serve the network architecture as a core evaluation function to optimize PLS regression. . The most optimal network structure was constructed with 130 hidden nodes and 20 output nodes.Then, the optimal network structure constructed with 130 hidden nodes and 20 output nodes is used as the core function for PLS regression. Hidden PLS variables were selected by network search mode. We tested PLS regression models with f = 1, 2… 20 based on the optimal network core. The results of model training for validation samples are shown in Figure 4. The optimal number of latent variables was determined as f = 8. The results of the network core model prediction and common cores are listed in Table 2.According to the principle of sample division introduced, PLS core models were quantified for FT-NIR analysis of potato OA concentration based on calibration samples and optimized by validation samples. The PLS model of the selected optimal network core should then be evaluated by 64 experimental samples that were unique to the model training process. Spectral data of the experimental samples were entered into the core of the optimal network with 130 hidden nodes and 20 output nodes.Table 2 shows that for PLS kernel regression, the proposed network kernel performs better than conventional kernels, regardless of the model training process or in the model evaluation process. Therefore, using neural network architecture to optimize the PLS regression kernel is a practical idea. FT-NIR calibration models have clearly improved compatibility by the adjustable network core.

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, and 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. As expectations for food products with high quality and safety standards increase, it is necessary to determine their characteristics accurately, quickly, and purposefully. In the potato crop, quality evaluation, after harvest and isolation, is very important to provide a reliable and uniform product to the market, because the potato, like many other crops, has the non-uniform quality and care during the harvest stage. While the quality of raw potatoes is primarily determined by the size, shape, color, and attractiveness of the tuber, the quality of potatoes is generally determined by examining the quality of the final product. The quality of processed products is examined in terms of color, flavor, and texture. The quality of most processed products stems from the quality of raw potatoes. Uniformity in size, shape, and composition is essential for optimal quality. During storage, processing, or cooking, potatoes are exposed to a variety of phenomena that affect the final quality of the product. For consumers, the main quality characteristics of potatoes are color, size, and texture. However, quality assessment for industrial potato processing includes various parameters such as dry matter content, starch content and characteristics, shelf life after storage (storage), and after processing. The type of cultivar, physical and chemical composition, and post-harvest storage are important factors that can affect the cooking characteristics of potatoes and potato crops. Quality assessment of agricultural products includes two main methods, quality rating systems based on the apparent properties of agricultural products and quality rating systems based on internal quality assessment, which has gained prominence in recent years. In the meantime, several methods have been developed so far for non-destructive quality classification of agricultural products based on the evaluation of their internal properties, only some of which have been able to meet the above conditions and are technically and industrially justified. To be. In the meantime, spectroscopy can have high efficiency in determining the quality of figures. Spectroscopy is a system that has a different structure and approach from other methods (image processing, neural network, etc.) and can classify and determine the quality of the digit.

First, 3 different potato cultivars were prepared from Ardabil Agricultural Research Center. After preparing the data, data were collected to determine the amount of sugar (SSC) and at the same time, the samples were tested with a spectrometer to determine the wavelengths of the samples. The glucose level of each sample was measured in 18 replications using an SBR-62T ocular refractometer. To do this, the water of the samples was placed on a refractometer at ambient temperature and its sugar level was read in terms of Brix. A PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA was used to obtain the spectrum of the samples. This ultra-small, lightweight, portable spectrophotometer has a 1nm sprayer-type single-diffuser and a linear silicon CCD array detector with 2048 pixels, which has a range of 250-150 nm (Vis / NIR) cover. There is also the ability to connect fiber optics to the PS-100 spectroradiometer and transfer data to a computer for the purpose of displaying and storing the acquired spectra in the Spectra Wiz software via the USB port. The data obtained from spectral imaging may be affected by the scattering of light by the detector by changing the sample, changing the sample size, surface roughness in the sample, noise caused by the temperature of the device and many other factors, and unwanted information Affect the accuracy of calibration models. Therefore, data processing is required to achieve stable, accurate, and reliable calibration models. The application of non-destructive methods based on spectroscopy in the full range of wavelengths requires a lot of time and money, 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. The partial least squares (PLS) regression method seems ideal in this regard. In this study, in order to build the models, the data were randomly divided into two parts: 80% of the samples were used for cross-training and cross-validation and the rest of the data were used for independent validation.

Mean absorption spectra Vis / NIR absorption spectra for different treatments in the range of 1000-500 nm are shown in Figure 1. Environmental factors (light and heat) as well as the quality of spectrometer expression cause perturbations in the initial and final wavelengths of the spectra, so part of these wavelengths are removed from the data set and as shown in Figure 1, the samples had a roughly similar pattern; This may be due to the color of the samples. According to Figure 1, there are two well-defined peaks for the spectra, and it appears that for the Colombo and Sante cultivars the peaks appeared at around 480 and 1000 nm and for the Milwa cultivar at around 540 and 950 nm. Figure 1 also shows that the absorption rate of the Milwa cultivar is higher than the other two cultivars, which can be due to differences in the number of different substances such as sugar or SSC. Based on the analysis (PCA) results presented in Figure 2, the first principal component (PC-1) describes 67% and the second principal component (PC-3) describes 27% of the variance of the samples tested. As a result, the first two principal components together represent 94% of the data. Due to the fact that the relationship between the properties of different samples during the tests, for various reasons such as technical problems of equipment, data collection, incorrect sampling, etc. in some samples is inappropriate or to correct. To be out. The values of R2 and RMSE for the calibration and validation sets of different regression models (PLS) with raw and processed data are presented in Figure 3, which is equal to 1.

Viral diseases are among the factors limiting potato production in the world. Potato virus Y (PVY; genus Potyvirus, family Potyviridae) is one of the most important viruses infecting potato production. In order to detect the virus, plants with viral symptoms (135 samples) such as: mosaic, dwarf, wavy leaf margin, chlorotic spots and leaf malformation were collected from potato fields of different regions of Lorestan province. Infected plants extract was mechanically inoculated on indicator plants: Nicotiana glutinosa, N. occidentalis and N. debneyi. Ten days after inoculation, symptoms including local yellowing, vein clearing, mottling and mosaic on N. occidentalis, mottling and local necrotic on N. glutinosa were observed. Total RNA was extracted from positive indicator plants and cDNA synthesized with specific primers related to CP and NIb gene using RT-PCR. DNA fragments with the expected size of 1115 bp were obtained and PVY contamination was confirmed. Phylogenetic analysis showed that the studied isolate in this research had 98.74 to 99.48% genetic similarity with other recorded sequences in NCBI. The nucleotide sequence of PVY Lorestan isolate was aligned and compared with the related 17 isolates from NCBI. Results of phylogenetic analyses showed that PVY isolates were grouped in two clusters where the PVY Lorestan isolate was placed in cluster ΙI with isolates from Germany which had belonged to Wilga strain. The determined nucleotide sequence in this study was implemented in NCBI with Accession No. MT655949. This is the first report of PVY contamination of potato fields in Lorestan province using the molecular tests.

Potato with the scientific name Solanum tuberosum. L is a plant that is cultivated as an important crop in all countries and is known in the human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and originally from Peru. Potato is the fourth crop in the food basket of the people after wheat, rice and corn, which sometimes replaces rice in Iran and is in the second place, which shows its importance in meeting the nutritional needs of the people. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvest from this area was about 5.32 million tons. In the food industry, this product is converted into a variety of products such as baked potatoes, fried potatoes, potato chips, potato starch, dried fries and so on. Due to the increasing expectations for food products with high quality and safety standards, accurate, fast and purposeful determination of food characteristics is essential. In the potato crop, quality evaluation after the harvest stage seems necessary to provide a reliable and uniform product to the market, because the potato, like many other products, has a non-uniform quality and care during the harvest stage. - Be. In addition, food safety and desirability play an important role in the food industry and is directly related to people's health. In addition, a large part of the potatoes used in the processing industry, potatoes are stored, so given the importance of this food and the demand of the people throughout the year, only through optimal and long-term storage can meet the needs of applicants. Was responsive. Potatoes for the processing industry must have some requirements such as low sugar content, dry matter and high specific gravity, high antioxidants, light skin color and no germination. Stored potatoes may be sweetened, rotted, dehydrated, and sprouted during storage. Post-harvest storage conditions can cause changes in chemical composition and product quality. Therefore, the management of potato tubers is very important in all stages of production and storage. Potato changes in storage depend on the variety, storage conditions. Although potato storage is very necessary for domestic and industrial use, due to its chemical and physical changes in the warehouse, it should be said that the characteristics of high quality potatoes in commercial exchanges of this product include more than 70 to 80% of tubers. Light-colored, uniform, firm, no bruising, no scaling, no cracks, no insect damage, rot and greening. Post-harvest storage conditions can cause many changes in the chemical composition of the potato tuber, resulting in changes in the quality characteristics of the final product. Sugar and starch are the main components that are affected by postharvest metabolism in the potato tuber and may ultimately affect their texture, sensory and cooking properties. The quality of potatoes and, consequently, the quality of processed products, depends significantly on the cultivar and environmental conditions, both during the growing season and during storage.

First, potatoes were prepared in 5 different cultivars and stored at 4-10 ° C. Data collection included measuring sugar and carbohydrate levels during storage.The sugar content of each sample was measured in three replications using a liquid refractometer available at Mohaghegh Ardabili University. To do this, first some water was taken from the samples and after pouring into the microtube, it was placed in a refrigerated centrifuge, and after rotating at a speed of 1800 rpm for 2 minutes, the impurities were deposited and the pure liquid was separated. After reaching ambient temperature, the liquid was placed on a refractometer and its sugar level was read in terms of brix.The amount of carbohydrates in the samples was extracted using the equipment available in the central laboratory of Mohaghegh Ardabili University. This operation was performed by the Skigel method. Glucose was used to prepare the standard curve. Consecutive dilution of glucose Preparation and color development at 490 nm for different concentrations of glucose were controlled and one ml of distilled water was used as a blank. This standard curve was used to calculate the total concentration of carbohydrates in the samples.For each sample, sampling was performed in three replications and the amount of absorption wavelength was obtained, then the amount of carbohydrates was calculated by placing the wavelength in Equation (1).

According to the analysis of variance table, the interaction effect of cultivar and storage period on potato sugar content was significant at 1% probability level. You can see the changes in the sugar content of potato cultivars along the storage valley in Figure 4. According to Figure 4, the highest amount of sugar is related to Sprite cultivar and the lowest amount is related to Jali cultivar. Meanwhile, the sugar content of Agria and Jeli cultivars was the same at the time of harvest. Also, after 1 month from the time of potato harvest, the sugar content of Agria and Marfona cultivars were equal and this equality continued until the end of storage period. The reason for the difference in sugar content between different cultivars is mainly related to the type of soil, fertilizer and toxin used. According to the chart, the amount of sugar in all 5 potato cultivars during the storage period first decreases and then with increasing storage period, the amount of sugar also increases.According to the analysis of variance table, the interaction of cultivar and storage period on the amount of potato carbohydrates was also significant at the level of 1% probability. Carbohydrate variations of potato cultivars along the storage valley are shown in Figure 5. According to Figure 5, the highest amount of carbohydrates is related to Sante cultivar and the lowest is related to Marfona cultivar. Also, at the end of the storage period, Marfona and Agria cultivars had the same amount of carbohydrates. As you can see, the amount of potato carbohydrates has decreased over time and with increasing storage time. Among these cultivars, the carbohydrate content of Marfona and Agria cultivars was higher than other cultivars. Also, carbohydrate changes

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and 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. Meanwhile, high-performance artificial neural network can be used to classify cultivars. Artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified using LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.

First, 5 different potato cultivars were prepared from Ardabil Agricultural Research Center and kept at a temperature of 4-10 ° C. One day later, 21 samples of each potato cultivar were prepared using a cutting cylinder and then data were collected. To determine the toughness of the samples, the Centam device available in Mohaghegh Ardabili University was used. Each potato cultivar was subjected to compressive force at three levels of loading speed of 10, 40 and 70 mm / min with 7 repetitions. Then the amount of toughness was calculated according to Equation (1). Then linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of 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, the logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used (Figure 4), and the error value 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. All of the calculations and matrix classification were performed using MATLAB R2018a and X 10.4 Unscrambler software.Toughness in 5 different potato cultivars was obtained using Centam machine and Equation 1. The values obtained for the toughness of 5 potato cultivars were analyzed using Mstatc software. The results of analysis of variance were significant for the toughness of 5 different potato cultivars at the level of 1% and its coefficient of variation was 2.28. LDA and ANN methods were used to detect potato cultivars based on the values calculated for toughness. Detection results of cultivars using LDA were equal to 70.48% (Figure 6). Also, the accuracy of ANN method according to the perturbation matrix was equal to 72.4% (Figure 7).

In this study, the amount of toughness for 5 different potato cultivars was calculated using Centam machine available in Mohaghegh Ardabili University with the help of Equation 1. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of data to identify and classify potato cultivars. Thus, LDA and ANN were able to identify and accurately classify different potato cultivars with an accuracy of over 70%. The obtained toughness has the ability to be used as a method to distinguish different potato cultivars. The use of this method in identifying potato cultivars will be very useful for factories such as chips factory and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and stiffness with the help of chemometrics methods to optimize production and The processing of agricultural products should be used in the food industry, which has led to more customer friendliness and, in addition, can reduce agricultural waste.

Potato tubers are one of the most important sources of nutrition in most countries. This product is the world's fourth important food crop after wheat, rice and maize because of its higher yield potential along with high nutritive value.Soluble solids content (SSC) is one of the quality parameters of potatoes and it play an important role in quality and commercial value of potato. The amount of SSC in potatoes varies greatly depending on the cultivars, growing environment and storage conditions.Today, the development of fast, non-destructive, accurate and online technique identification methods to determine product quality is strongly felt. One method for this purpose is Near-infrared spectroscopy. The high speed and accuracy of near-infrared spectroscopy to determine of vegetables and fruits quality in high tonnage, has led to the use of this method in many grading and quality control systems.Shao, et al., Using reflection Near-infrared spectroscopy for investigating the qualitative properties of tomatoes, including firmness, SSC and treatable acidity, and predicted these properties to be non-destructive with a high correlation coefficient. A study was conducted by Saad, et al. to evaluate the non-destructive quality of stored tomatoes using vis/NIR absorption spectroscopy in wavelength range from 350 to 1050 nm. In another study, the main compounds of potatoes were determined by near-infrared spectroscopy by Ainara Lopez. The sugar content of potato tubers was determined using visible and near infrared spectroscopy by Ji Yu Chen et al.Khodabakhshian et al., using reflection Near-infrared spectroscopy for classify the maturity stage and to predict the quality attributes of pomegranate, Principal component analysis was used to distinguish among different maturities and several preprocessing methods were utilized including centering, smoothing (Savitzky–Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). The results of this study concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method.The aim of this study was to investigate the possibility of using near-infrared spectroscopy (vis/NIR) in estimating changes of SSC in three potato cultivars during storage under different storage conditions.

Three potato cultivars, including SANTE, MARABEL and GRANA, which were different in terms of yield, crop quality and shelf life, were prepared from a farm near the University of Onsekiz Mart Çanakkale in Turkey and were stored under different storage conditions. Required values of each cultivar was stored under three different conditions including 4 ̊C temperature with relative humidity of 90%, 7 ̊C temperature with relative humidity of 80-90% and 22-28 ̊C variable temperature with relative humidity of 70-90%, for 65 days.Spectroscopy was performed using a multi-purpose analyzer (MPA) FT-NIR spectrometer in the range of 800-2500 nm.To measure the SSC in the samples, 7 samples from each potato cultivar were randomly selected and kept in the laboratory for 2 hours at 22 ̊C before analysis. After peeling the potatoes, the juice of the samples was extracted by using a common juicer and then filtered through filter paper to remove any remaining pulps. The amount of SSC in the samples was measured by using a digital refractometer at 25 ̊C as a ̊Brix.In order to remove the outlier data, principal component analysis (PCA) was used before any processing on the data. Moving averages, Multiple Gaussian Fitting Regressions, Median filter, Savitzky-Golay smoothing, normalization, Multiplicative Scatter Correction (MSC) and Standard Normal Variety Transformation (SNV) were applied to the data and compared.Partial least squares regression (PLS) models for all Pre-processed data were extracted and the statistical indicators include correlation coefficient (R2) and Root Mean Square Error (RMSE) were used to find the best model. To extract the models, the data were randomly divided into two parts: 80% of the data was used for training and cross-validation and the rest of the data was used for independent validation.To select the effective wavelengths and reduce the wavelengths to a limited number, the regression coefficients of the best calibration model obtained from (PLS) model were used.In order to find the best fitting model for the relationship between effective wavelengths and SSC changes of potatoes during storage, Partial least squares regression (PLS), Principal Component Regression (PCR), Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were employed.This research were performed with factorial design on a completely randomized design (CRD) base include cultivar in three levels (SANTE, MARABEL and GRANA), storage temperature in three levels (4, 7 and 20 ̊C) and storage time in four levels (20, 34 , 46 and 62 days) with five replications. The statistical analysis was performed using the Unscrambler X 10.4 statistical software.

According to results, the amount of SSC was increases during storage. This increasing trend during storage can be attributed to the increase in sugar content during the ripening process and the hydrolysis of starch to maintain the physiological activities of the crop in the postharvest period.The results also show that the raw and all Pre-processed spectral data are able to predict SSC with acceptable accuracy. The best results were obtained from the Median filter Pre-processed model with RMSEC = 0.410, R2c = 0.875, RMSECV = 0.420, R2CV = 0.867.Based on the regression coefficients of best the (PLS) model, 12 wavelengths in the range of 1400 to 2500 nm were identified as effective wavelengths. Based on distribution of main overtone bond, the discriminated of potato samples according to SSC quality index can be attributed second overtones of C-H, CH2 and CH3 in the wavelength range from 1400 to 1500 nm, the first overtones of C-H, CH2 and CH3 in the wavelength range from 1500 to 2100 nm and composition overton of these bonds in the wavelength range from 2200 to 2500 nm which all this overtone are present in the structure of hydrocarbons such monomeric sugars (glucose and xylose) and oligosaccharides (starch).According to the results, all models are able to predict SSC of potatoes during storage based on effective wavelengths with acceptable accuracy but among these models, the Artificial Neural Network (ANN) model with RMSEC = 0.215, R2c = 0.974, RMSECV = 0.927, R2CV = 0.280 has the highest accuracy to prediction of SSC of potatoes during storage.

Low frequency of transformation and, more importantly, the occurrence of soma clonal variation at very high rates have considered disadvantages of Potato In vitro culture. Two Phytohormones, auxins (NAA, 2, 4-D), and cytokines (BAP, ZR) with concentrations were used to develop an efficient regeneration protocol for three genotypes of Potato.

The explants cultured on MS-medium supplemented with BAP 1 mg.l-1; NAA 0.1 mg.l-1; BAP 2 mg.l-1; NAA 0.2 mg.l-1; BAP 3 mg.l-1; NAA 0.3 mg.l-1; ZR 1 mg.l-1; 2.4-D 0.1 mg.l-1; ZR 2 mg.l-1; 2.4-D 0.2 mg.l-1; ZR 3 mg.l-1; 2.4-D 0.3 mg.l-1.

The results showed that the highest percentage and number of root formation and shoot regeneration obtained in the medium included BAP 3 mg.l-1; NAA 0.3 mg.l-1 or ZR 3 mg.l-1; 2.4-D 0.3 mg.l-1 respectively. In this study, 0.3 mg.l-1 of both NAA and 2, 4-D were able to induce the most effective root regeneration.

The three potato genotypes were statistically different in their leaf regeneration efficiency. The Odyssey genotype showed a weak response to leaf regeneration.

L-asparaginase is an enzyme which hydrolyze the amino acid asparagine into aspartic acid and ammonia. L-asparaginase used for leukemia and lymphoma treatment is produced mainly from bacteria such as Escherichia coli and Erwinia chrysanthemi and search continues to find new sources for this enzyme. In this study, isolation of bacteria producing L-asparaginase from wastewater of potato processing was performed.The wastewater containing all the soluble compounds in potato juice, such as significant amounts of asparagine amino acids. Bacterial isolation and screening were performed on nitrogen agar and M9 media. Two isolates, called PW4 and PW5, were able to produce ammonium in the M9 medium. The results of molecular identification and biochemical tests showed that these isolates belong to the genus Escherichia and Enterococcus, respectively. Quantitative and qualitative measurements of the enzyme production of these isolates were carried out in different conditions. The highest enzyme activity of 10 IUml-1 was seen in PW4 isolate growth in the in a medium containing glutamine. The maximum amount of enzyme activity of PW5 isolate, 2.33IUml-1 was observed as a result of bacterial culture in a culture medium containing asparagine. The enzymes produced by both isolates did not show glutaminase activity. The maximum activity of the enzyme produced by PW4 isolate was evaluated at 37 ° C and enzyme activity decreased at lower and higher temperatures. These results suggest that this enzyme has a potential to be further studies for use in drug design .

This experiment had been carried out to evaluate the potential of mycorrhiza fungus on growth rate and performance of plantlets derived minituber in water deficiency condition. The test was conducted in a factorial experiment based on completely randomized design with four replications. The factors included four level irrigation regimes (100%, 85%, 75% and 65% of water field capacity), inoculation of potato minituber by mycorrhiza sp. Glomus intraradices and non-inoculated ones. Growth indices such as stem length and diameter, internode number and length, stem and root fresh weight, leaf area and chlorophyll content were measured. Analysis of variance showed that the main effect of mycorrhiza and water irrigation regimes had significant effect by probability of 1% α level on stem length, leaf area, chlorophyll content, colonization percentage, fresh and dry stem weight, fresh and dry root weight and number of minituber. Interaction effect of two factors was significant only in leaf area, chlorophyll content and dry root weight. By mean comparisons demonstrated that the highest internode and stem length, fresh and dry stem weight, fresh root weight, leaf area and chlorophyll content were accomplished in mycorrhizal inoculation with 85% FC providing water. Totally, the results demonstrated that mycorrhization of minituber in two lower water available levels produce plantlets that had same situations as compared with non-inoculated of 100% FC providing water.

The potato (Solanum tuberosum L.) is one of the most important agricultural plants in the world. It is propagated predominantly by asexual methods. The traditional methods for asexual propagation of the plant faced important problems. Therefore, the seed tubers can be replaced by micro tubers produced by tissue culture. The aim of this study is search about effect of different concentrations of CaCL2 and MgSO4 in media culture In vitro in histological - cellular variations of the microtubers. In this study solid and liquid MS media were used to prepare sterilized seedlings and micro tuberization.The concentrations of 0, 1/2, 1, 3/2 and 2 times more than standard concentrations of the mentioned compounds in MS medium were used in separate induction media . Induction was done in the alternating photoperiod. In order to the developmental and anatomical studies cross sections of microtubers was prepared and studied by light microscopy.The number of cell rows, the dimensions of the cells and the starch content of the parenchymal tissues of microtuber were analyzed. the results showed significant variations in histological features of the microtubers developed in media containing different concentrations of macronutrients. CaCL2 with concentrations of 0.5 to 1 time more than standard concentration in MS medium yielded maximum number of cell rows and maximum starch granules content. However, the presence of a minimum amount of magnesium is essential for the formation of the micro tubers. But there is no statistically significant effect on cellular changes of microtubers and starch content.

Salinity is an abiotic stress that seriously constrains agricultural production including potatoes in most regions of the world. This study was carried out to investigate the effect of salicylic acid, on growth properties and biochemical characteristics of in vitro cultureof Solanum tuberosum cv. Agria under salinity stress. The study was carried out at a completely random design with eight replications in the Department of Horticulture of University of Tabriz. Variables under study included salinity stress at two levels (0 and 70 mM/L sodium chloride) and salicylic acid at four levels (0, 1, 10, and 100 mM/L). Results showed that salicylic acid significantly mitigated the effects of salinity. Therefore, the highest plantlet length was recorded under salinity along with 10 mM salicylic acid treatment showing the positive effect of the treatment on mitigating the influences of salinity. However, the concentrations of salicylic acid higher than 10mM/L not only had no effect on plants, but also negatively influenced the effects of salinity stress. Also, antioxidant enzyme activity of the plantlets had a considerable increase at all concentrations of salicylic acid and levels of salinity. Moreover, the study suggested that Solanum tuberosum cv. Agria was relatively sensitive to salinity stress and all characteristics of the plants under study were influenced with salinity. Also, application of salicylic acid caused improvement in the growth and biochemical properties of the plants under study increasing their tolerance against salinity stress.

In cold regions, potatoes are planted in spring. Challenges regarding potato cultivation in the areas facing spring frost, early fall cold and summer heat which has been investigated in this study. This search a factorial experiment in a randomized complete block design was conducted with three replications in 2015 and 2016 in Gandoman regian, Chaharmahal Bakhtiari Province. The first factor was three cultivar: Jelly, Fontaine and Bouren And the second factor, was combination of different treatments in six levels of calcium and jasmonic acid (at a concentration of 5 mM) incuding: 1- calcium jasmonic acid before the stage of rhizome production (T1); 2- calcium jasmonic acid after the stage of rhizome (T2); 3- jasmonic acid before the stage of rhizome production (T3); 4- jasmonic acid in after the stage of rhizome production (T4); 5- calcium from calcium nitrate source (T5); 6- control (no application of both factors (T6) ) were used. According to the results, both factors jasmonic acid and calcium had a positive impact on the examined cultivars, So that the Jelly had a better response rate than the other varieties. Amongst treatments, T1 in all traits except non-photochemical quenching had the highest average in all three cultivars. According to the results, it can be concluded that Jasmonic acid and calcium, reduced the adverse effects of thermal stress and nutrient deficiencies in both before and after stage of the rhizome production. In addition, higher positive effects obsorved when jasmonic acid and calcium compounds, were applied before the stage of the rhizome production.