فهرست مطالب saman abdanan

Sugarcane, an important crop in sugar production in the country, is widely produced in Khuzestan province. After harvesting the sugarcane and when delivered to the sugar factory, the cane is shredded. In this study, for measurement of sucrose concentration in shredded cane, the dielectric spectroscopy technique in the frequency range of 0-100 MHz was evaluated using a cylindrical parallel-plate sensor with compaction of the shredded cane. 55 samples of shredded cane were taken from the sugar production line of Salman Farsi factory during the harvesting season of 2021-2022 and their reference properties including sugar concentration with three indicators of Brix using refractometry method, Pol using simple polarimetry method and the sucrose measured using double polarimetry were measured. The samples were subjected to dielectric spectroscopy using the capacitance sensor and the dielectric spectra were analyzed for sugar concentration prediction. Multivariate regression analyses of partial least-sugare regresseion (PLSR), principal component regression (PCR) and support vector regression (SVR) were applied for development of prediction models of sugar concentration variables. Validation of the prediction model of PLSR showed a stronger prediction of sucrose (R2= 0.79, RMSE= 0.89, RPD= 2.22) measured using the double-polarimetry as compared to Pol (R2= 0.76, RMSE= 0.8, RPD= 2.07) and Brix (R2= 0.77, RMSE= 0.89, RPD= 2). This result was explained with higher number of OH groups of sucrose molecule as compared to the other sugars existing in the sugarcane juice and the dominant effect of sucrose on the dielectric characteristics of the juice. With optimization and improvement of the accuracy of this method, a simple and cost-effective instrument can be developed for use in sugar industries.

Determination of sugar concentration of sugar beet in sugar factories is of great importance and is a basis for valuation of the sugar beet as well as assessment of the sugar production process in the factory. In this study with the aim of development of a non-destructive method for measurement of the sugar concentration of sugar beet in terms of Brix, a proximal dielectric sensor was developed and evaluated. This sensor is an open-end coaxial probe consisting of two metallic concentric ring electrodes which is connected to function generator and spectrum analyzer through coaxial cables. One hundred random samples of sugar beet were selected and measured by the dielectric sensor. Dielectric spectra i.e. amplitude as a function of frequency were obtained in the frequency range of 0-150 MHz. The reference Brix was measured with extracting juice from the points of dielectric measurement using a refractometer. The results showed that in the ranges of 30-50 and 120-140 MHz, the dielectric spectra varied noticeably in relation with variations in sugar beet Brix so that the amplitude decreased with increasing the Brix. The partial least square regression (PLSR) method could model the Brix as a function of the dielectric spectra variables with R2= 0.81 and RMSE of 0.72 Brix. The results of the study indicated that the dielectric sensor and measurement method was a simple and reliable method for non-destructive measuring of sugar beet Brix.

Fish quality is affected by terms of handling, maintenance and storage time. These terms make chemical changes in fish and accelerate the deterioration its of tissue and make it dangerous for human body. There are several methods use for assessment of fish freshness, most of them are costly and destructive., Therefore, in this paper a non-destructive machine vision system based on gill and eye color and textural features is proposed. . Accordingly, after segmentation of region of interest in the images (eyes and gills), the color and textural properties of the images were extracted and the most suitable ones were selected using Fisher's selection algorithm and QDA and LDA classification methods were applied. For the QDA classifier , the V_HSV (extracted from the gills), the energy and the contrast (extracted from the fish's eye) and for the LDA classifier, the energy (extracted from the eye), the contrast )extraction from the eye) ,V_HSV (extracted from the gills) ,homogeneity (extracted from the eye) and H_HSV (extracted from the gills) were extracted. The classification accuracy for QDA and LDA were 93% and 96%, respectively.

Blackberry is a perennial woody plant native to warm, temperate, and subtropical regions of Asia, Africa, North America, and southern Europe. Blackberry fruit (Morus Alba Varnigra L.) is a rich source of anthocyanins. Furthermore, it has great many medicinal properties such as an antidiabetic (Asano et al., 2001), antihyperglycemic (Andallu &Varadacharyulu, 2003), antiviral (Du et al., 2003), antioxidative (Kim et al., 1998), hypolipidemic (El-Beshbishy et al., 2006), and neuroprotective (Kang et al., 2006). However, measuring some qualitative and nutrient parameters in this fruit such as anthocyanins, vitamin C and phenol directly has become a major issue (Pace et al, 2013). Therefore, researchers try to predict aforementioned parameters by mathematical models. One of these models is the fractal model which is widely used to study the properties of the images/objects (Welstead, 1999; Zhang, 2007). Recently, many researchers try to develop different methods to classify or predict the agricultural products quality (Langner, 2001). In a research Seng and Mirisaee (2009) designed a machine vision algorithm for classification of fruits (apple, lemon, strawberry and banana) based on color, shape and size. Li and He investigated the application of visible/near infrared spectroscopy (Vis/NIRS) for measuring the acidity of Chinese bayberry. The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138 showed an excellent prediction performance. Therefore, the aim of this study was to predict biochemical parameters (TSS, anthocyanins, browning compounds, total phenols, Ascorbic Acid, pH) of blackberry juice, nondestructively, during maturity process using machine vision and fractal analysis. To develop predictive models and data classification, artificial neural networks (ANN) and k-nearest neighbor (k-NN) were used.
Eighty blackberry fruits from four maturity stages were selected. The fruit samples were placed in airtight polyethylene bags, stored in an ice-filled cooler and transported to the laboratory to keep at cold temperature (4±1◦C).
Fresh fruits were squeezed by a household juicer, and immediately transported to the laboratory. Then, juice images were taken with a digital camera CASIO (Model Exilim EX-ZR700; 16 megapixels, Japan) and stored to the computer.
There are several ways to measure the fractal dimension. In this study, the proposed method by Addison (2005) was used to calculate the fractal dimension.
Feature selection is one of the issues that have been raised in the context of machine learning. In this study, floating search method feature selection was used (Pudil et al., 1994).
k-Nearest Neighbor (k-NN) is one of the simplest methods for information classification. In this study, the Euclidean distance between two points was used to determine the distance between the input data with the training patterns (Mucherino et al., 2009).
To train the neural network, Levenberg–Marquardt training algorithm was used. In this regard, the data were divided randomly into two parts (two-thirds for training (60) and one-third (20) for testing the network). Input parameters were Xa, Xb, X, Y and S and output parameters were TSS, ascorbic acide, acidity, polyphenols, anthocyanins, brown-causing substances and pH. Moreover, in this study, the number of neurons in the hidden layer was selected by trial and error method.
After selecting the best features extracted from the image processing with the highest correlation with chemical parameters (TSS, anthocyanins, total phenols, ascorbic acid, and pH), a machine vision system was designed and built to be able to determine the internal properties of black mulberry juice.
Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan). The anthocyanin content was estimated following the procedure of Holecraft et al., (1998). Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al., (1973) procedure. Titratable acidity was measured according to the Eksi and Turkman, (2011) method. Waterhouse (2002) method was used for measuring the total phenol of juice.
Artificial neural network (ANN) and (k-NN) models were used to predict the changes of anthocyanin (AC), browning compounds, ascorbic acid (AA), total phenols (TP), acidity, TSS and pH in mulberry juice during ripening based on fractal analysis. Two features namely: maximum fractal and fractal curve area were selected from five extracted features and used for training neural network and k-NN classifier

Nutritional quality of food during storage has become increasingly an important problem. The loss of some nutrients such as ascorbic acid (vitamin C) might be a critical factor for the shelf life of some products as citrus juice concentrates, since vitamin C content of citrus juices undergoes destruction during storage (Plaza et al., 2011a). Ascorbic acid is an important component of our nutrition and used as additive in many foods because of its antioxidant capacity. Thus, it increases quality and technological properties of food as well as nutritional value (Larisch et al., 1998). However, ascorbic acid is an unstable compound and even under minor desirable conditions it decomposes easily. Degradation of ascorbic acid proceeds both aerobic and anaerobic pathways and depends upon many factors such as oxygen, heat, light, storage temperature and storage time. Oxidation of ascorbic acid occurs mainly during the processing of citrus juices, whereas, anaerobic degradation of ascorbic acid mainly appears during storage which is especially observed in thermally preserved citrus juices (Lee & Coates, 1999). It was reported that several decomposition reactive products occur via the degradation of vitamin C and these compounds may combine with amino acids, thus result in formation of brown pigments (Wibowo et al., 2015). In recent years, several nondestructive methods such as computer vision, spectroscopy, ultrasonic have been developed to objectively evaluate different agricultural materials (Abdanan Mehdizadeh et al., 2014; Wang and Paliwal, 2007). However, due to the physical properties of fruit, machine vision has not been discussed much in the literature (Fernanzed-Vazquez et al., 2011). One disadvantage of using spectroscopic methods is that these methods require expensive equipment and also carrying these instruments are difficult. On the contrary, the combining of a digital camera and its image processing software that replaces the traditional measuring instruments have been widely used to provide a cheaper and versatile form to measure some internal quality of many foods. Therefore, the goal of this research is to determine the best features of surface texture (entropy, homogeneity, contrast, correlation and prominence) in order to predict quality factors (pH, acidity, soluble solids and ascorbic acid) of citrus juice.
Orange, sour lemon, sour orange and tangerine fruit were obtained from one of local marker in Ahvaz, Iran. All samples were washed and the juice was extracted using a Pars-Khazar rotary extractor. The citrus juice, (sour orange, orange, lemon and tangerine) immediately after pasteurization process, were kept at a temperature of refrigerators (4º C) for 60 days in darkness. After taking images of the citrus juice, pH, acidity, ascorbic acid and soluble solids were measured on days 0, 20, 40 and 60.
Physicochemical analysis: The pH of samples was determined with a pH meter (Methrohm, 827 pH lab, Switzerland). The soluble solids content of concentrates was determined as o Bx using a refractometer (Atago Co, Ltd. Carnation, WA). Total titrable acidity was assessed by titration with sodium hydroxide (0.1 N) and expressed as % citric acid (Kimball, 1999). Ascorbic acid was determined using 2,6-dichlorophenolindophenol by visual titration (Kabasakalis, 2000).
Imaging and color analysis: Samples were placed under the camera (Canon PowerShot SX60 HS, Japan) of a computer vision system at the distance of 300 mm inside a black box with the size of 100 ×100 ×100 cm3. The samples were illuminated using four fluorescent lamps at the angle of 45o in relation with the sample.
After taking images, color images were transformed to L*a*b* color space. The L* parameter (luminosity) is an attribute by which a surface emits more or less light and can take values between 0 (absolute black) to 100 (absolute white). The parameters a* and b* represent the chromaticity, where a* defines the red-green component (red for positive values and green for negative values) and the b* parameter defines the yellow-blue component (yellow for positive values and blue for negative values) (Quevedo et al., 2009a). Following color transformation, the well-known textural parameter called the Gray-Level Co-Occurrence Matrix (GLCM function) was applied to the images and six features through Eq. 1-6 were extracted (Table 1).
Color changes during storage in three color channels L*,a*,b* showed that the variation of channel L* could illustrate deterioration of citrus juice better than other channels. In the Figure 1, a gallery of four selected images (taken at different times in the experiment) corresponding to one sour orange sample and their corresponding surface intensity (based on L* value) are showed.
The results of statistical analysis depicted that acidity and ascorbic acid, in four citrus juices, significantly (P

.Color is the first quality attribute of food evaluated by consumers, and is therefore an important quality component of food which influences consumer’s choice and preferences (Maguire, 1994). Color measurement of food products has been used as an indirect measure of other quality attributes such as flavor and contents of pigments because it is simpler, faster and correlates well with other physicochemical properties. Therefore, rapid and objective measurement of food color is required in quality control for the commercial grading of products (Trusell et al., 2005). Among different color spaces, L*a*b* color space is the most practical system used for measuring of color in food due to the uniform distribution of colors in this system as well its high similarity to human perception of color. All of the commercial L*a*b* colorimeters generally measure small, non- representative areas (Pathare et al., 2013) while the RGB digital cameras obtain information in pixels. Therefore, this research establishes a computational solution which allows acquiring of digital images in L*a*b* color units for each pixel from the digital RGB image (Fernandez-Vazquez et al., 2011). In recent years, computer vision has been used to objectively measure the color of different foods since they provide some obvious advantages over a conventional colorimeter, namely, the possibility of analyzing of each pixel of the entire surface of the food, and quantifying surface characteristics and defects (Mendoza & Aguilera, 2004). The color of many foods has been measured using computer vision techniques (Pedreschi et al., 2011; Lang et al., 2012). A computational technique with a combination of a digital camera, image processing software has been used to provide a less expensive and more versatile way to measure the color of many foods than traditional color-measuring instruments. This study used four models to carry out the RGB to L*a*b* transformation: linear, quadratic, support vector regression and neural network. This article presents the details of each model, their performance, and their advantages and disadvantages. The purpose of this work was to find a model (and estimate its parameters) for obtaining L*a*b* color measurements from RGB measurements.
The images used in this work were taken with the following image acquisition system (Samsung, SM-N9005 color digital camera with 13 Mega pixels of resolution ,Fig.1). The camera was placed vertically at a distance of 60 cm from the samples and the angle between the axis of the lens and the sources of illumination was approximately °45. Illumination was achieved with 4 natural daylight 150 W lights.
Fig. 1. Schematic diagram of image acquisition system.
In order to calibrate the digital color system, the color values of 42 color charts were measured. Each color chart was divided into 24 regions. In each region, the L*a*b* color values were measured using a Minolta colorimeter. Additionally, a RGB digital image was taken of each chart, and the R, G and B color values of the corresponding regions were measured using a Matlab program which computes the mean values for each color value in each region according to the 24 masks. In this study four models for the RGB to L*a*b* transformation namely: linear, quadratic, artificial neural network (ANN), support vector regression (SVR) have been used.
In the evaluation of the performance of the models, the support vector regression and neural network model stands out with an error of only 0.88 and 2.37, respectively. Leon et al. (2004) investigated some models for the RGB to L*a*b* conversion. In the evaluation of the performance of the models, the neural network model showed an error of only 0.93%. In another research Yagzi et al. (2009) measured the L*a*b* values of atlantic salmon fillets subjected to different electron beam doses (0, 1, 1.5, 2 and 3 kGy) using a Minolta CR-200 Chroma Meter and a machine vision system. For both Minolta and machine vision the L* value increased and the a* and b* values decreased with increasing irradiation dose. However, the machine vision system showed significantly higher readings for L*, a*, b* values than the Minolta colorimeter. According to the construction of these models, the correlation between measured and predicted color is well established; therefore, based on the promising obtained results from Computer vision, it is possible to find a L*a*b* color measuring system that is appropriate for an accurate, exacting and detailed characterization of a food item based on a color digital camera. In order to show the capability of the proposed method, the color of an orange was measured using both a Minolta colorimeter and the studied approach. The colorimeter measurement was obtained by averaging 6 measurements in 6 different places of the surface of the orange, whereas the measurement using the digital color image was estimated by averaging all pixels of the surface image. The results are summarized in Fig. 2.
b* a* L* Measurement 35.49 28.32 58.98 Minolta colorimeter
37.35 27.30 61.20 Machine Vision (SVR)
30.60 30.19 60.18 Machine Vision (ANN)
Fig. 2. Estimate of L*a*b* values of an orange

The potential of Vis-IR (400–1100 nm) transmittance method to assess the internal quality (freshness) of intact chicken egg during storage at a temperature of 307 oC and 25% 4 relative humidity was investigated. One hundred chicken egg samples were used for measuring its freshness and spectral collection during egg storage times (up to 30 days). Two correlation models between Haugh unit (HU) and the yolk coefficient (YC) versus the storage time were developed with the correlation coefficients of 0.86, 0.96, respectively. The latter equations showed in that aforementioned conditions egg quality decreased dramatically. Furthermore, the method has developed to acquire the wavelength features based on genetic algorithm (GA). It can solve the problem of the effective information extraction from the high-dimensional data matrix. Regression equations were developed by raw and according to the wavelength features were acquired, respectively basing on different preprocessing (SNV, MSC, FFT, as well as 1st and 2nd derivations). The result indicated that regression equations based on GA and preprocessing SNV and MSC could classify egg’s spectra in three groups while by 1st derivation preprocessing, each daily spectrum could allocate each of them into separated groups.

Therefore، the objective of this study is to test the feasibility of UV-IR transmission spectroscopy for the non-destructive assessment of the freshness of an individual egg as an alternative for traditional inspection methods. To obtain a considerable variation in freshness، groups consisting of 10 eggs were stored (30 7 oC and 25% 4 RH) for 30 days. Data obtained from non-destructive spectral measurements were compared with three destructive freshness parameters including Haugh units (HU)، albumen and yolk pH as well as yolk coefficient (YC(. Partial least squares regression (PLSR) and principal component regression (PCR) models are built in order to predict HU، pH and YC based on the transmission spectra. The correlation coefficients between the predicted value and the measured value are 0. 88، 0. 86 and 0. 78 for HU، YC and pH of the albumen، respectively، in the PLSR method and 0. 64، 0. 62 and 0. 41 for HU، YC and pH of the albumen، respectively، in the PCR method. These results show that the light transmission spectrum of an egg provides better quantitative information about egg freshness based on PLSR method. Effective information concerning egg freshness is restricted to the wavelength interval between 510 and 820 nm.