اسما کیسالائی

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 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.

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.

Approximately three-quarters of harvested tomatoes are freshly used. Good quality is an important factor in distributing of fresh tomato. Chlorophyll is the green chemicals to provide required food of plants and ensure plant growth and productivity. The main function of chlorophyll is to absorb blue and red lights and perform photosynthesis. In recent years, the tendency to use of prediction methods such as soft computing and artificial intelligence for growth of plans has increased. The main aim of this study was to investigate the relationship between height and chlorophyll content in the leaves of tomato plants using modeling and predicting techniques and compare the accuracy of these methods. In this study, some cultivated plants of tomato were randomly selected for height and SPAD measurements. The results showed the relationship between Chlorophyll content and height of plants was very low (R2 = 0.276). However using the modelling of ANN and ANFIS improved the prediction power up to (R2=0.982 and 0.913), respectively.