فهرست مطالب a. moghimi

A novel, simple, sensitive and effective method has been developed for removal of Mo(II) on the superparamagnetic adsorbent as Fe@Mg-Al LDH adsorbent. Spectrometry was used to track the effects of Fe@Mg-Al LDH dose, pH extraction, and contact duration on Mo(II) removal from sample water. This research also looked at adsorption isotherm models for the sorption of Mo(II). The magnetic Fe@Mg-Al LDH adsorbent Mo(II) adsorption capability was 29.74 mg/g respectively. The Freundlich isotherm model fits the removal of Mo(II) utilizing magnetic Fe@Mg-Al LDH adsorbent. This approach has an outstanding linear range of 0.5-26 mg/L for Mo2+ ions. The limits of detection (LODs) for Mo2+ ions adsorption by magnetic Fe@Mg-Al LDH adsorbent were reached by utilizing a signal-to-noise ratio of 3 (LOD= 2.35 μg/L). These findings show that magnetic Fe@Mg-Al LDH adsorbent is an effective adsorbent for extracting Mo(II) from different sample waters.

Layered double hydroxide (LDH) has recently attracted the attention of chemists in the use of these materials as organic and inorganic composites. LDHs have relatively weak intra-layer bonds and so have a high ability to capture organic and inorganic ions. In this study, the superparamagnetic adsorbent as Fe@Mg-Al LDH was synthesized by different methods with two-step for the removal of heavy metal ions from water samples. An easy, practical, economical, and replicable method was introduced to remove water contaminants, including heavy ions from aquatic environments. For better separation, the ethylenediaminetetraacetic acid ligand was used, forming a complex with Hg2+ ions to create suitable conditions for the removal of these ions. This was treated with ethylenediaminetetraacetic acid ligand in another solvent-free procedure for the formation of a Fe@Mg-Al LDH. Cold vapor atomic absorption spectrometry (CVAAS) was utilized for determination of Hg2+. The effects of solution pH solution, elution conditions on pre-concentration of trace Hg2+ were studied and the effect of interfering ions was also investigated. Preconcentration factor was 100 .The method was successfully applied to the recovery of Hg2+ in different type of water samples.Hg2+ ions was studied by floating in aqueous environments with this superparamagnetic adsorbent due to effective factors such as pH, the amount of superparamagnetic adsorbent, contact time, sample temperature, volume, and ligand concentration.

A UV-Vis spectrophotometric method for the determination of eriochrome black T (EBT) after adsorption of synthesis 2-aminopyridine/graphene oxide nano-plates has been developed. The nanocomposite was used to remove eriochrome black T (EBT) from the aqueous solution. The effects of pH, adsorption time, initial concentration of EBT, dose, and temperatures were studied. Applying a facile and accessible sample preparation method is highly important before the experimental analysis process. In the current study, a technique was introduced for preconcentration and analysis of trace amounts of EBT in water samples. The important parameters in the analytical procedure of EBT were optimized such as the pH of extraction, amount and type of elution solvent, time of extraction, and the effect of another ion. Analytical parameters such as the concentration factor, the limit of detection (LOD) of the technique, and relative standard deviation (RSD %) were achieved as 25, 15.0 μg L-1, and 2.7%, respectively.

Biomass is a crucial component of the carbon cycle; thus, accurate evaluation is essential to manage the forest and understand its role in climate change. Biomass estimation also supports the international reduced emission from deforestation and forest degradation (REDD), including cases such as deforestation reduction, sustainable management of forests, protection and enhancement of forest carbon reserves). Today, using remote sensing techniques with the help of field data has revolutionized the estimation of forest biomass. Forest biomass estimation can be based on the processing of remote sensing data obtained from active sensors (for example, lidar and radar) and passive sensors (for example, optical sensors). In most previous studies, mainly vegetation indices (such as Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Soil-Adjusted Vegetation Index (SAVI)) have been used to estimate biomass. Using terrain data, the amount of biomass is estimated using allometric equations, and the required pre-processing is done on optical and radar images. The attributes obtained from the scattering matrix and the ratios of the components of the scattering matrix and the attributes obtained from H/α decomposition are extracted from the radar image and the attributes of vegetation, soil and water are extracted from the optical image. Results and discussion  In this study, in order to improve the accuracy of estimating the biomass of forest areas, the features extracted from the optical images of Sentinel-2 sensor and Sentinel-1 radar data as well as field data of Noor forest areas, Mazandaran province, whose forest cover type, Carpinus betulus and Quercus Castaneifolia and also includes rare species such as Populus Caspica Bornm trees. In this study, we used the genetic optimization method in four classes of mixed vegetation, natural forest, degraded forest, and forest reserves were studied. In this regard, multivariate linear regression and support vector regression have been used to model between ground data and radar and optical features. Genetic algorithm (GA) is one of the most common evolutionary algorithms. This method finds potential solutions to optimize problems at the right time, especially when the search space is very wide. Also, a genetic algorithm has been used during the modeling process using multivariate linear regression to select the optimal features extracted from radar and optical images. Evaluation of the results showed that the use of the multivariate regression method led to more accurate results than the support vector regression method in the study area. Also, evaluation of the results showed that using features selected by a genetic algorithm led to an accurate R2 of 0.78, 0.87, 0.68, and 0.79 for first to fourth vegetation classes, respectively. Therefore, the results showed that the efficiency of the genetic algorithm in feature selection for biomass estimation from satellite images using the multivariate regression method.

A simple method has been developed for the preconcentration of chromium (VI) based on the adsorption of its modified by2-aminopyridine/graphene oxide nano-plates.A novel and selective method has been developed for the fast determination of trace amounts of chromium (VI) ions in water samples .The procedure is based on the selective formation of chromium (VI) ions using modified 2-aminopyridine/graphene oxide nano-plates at different pH values followed by elution with organic eluents and determination by atomic absorption spectrometry The preconcentration factor was 20. The limit of detection of the proposed method is 9.7 ng mL-1. The maximum sorption capacity of the sorbent under the optimum conditions has been found to be 5mg of chromium (VI) per gram of sorbent. The relative standard deviation under the optimum conditions was 2.5% (n = 10). Accuracy and application of the method was estimated by using the test samples of natural and synthetic water spiked with different amounts of chromium (VI) ion.

Trace amounts of Cu (II) and Pb (II) in the wastewater sample were removed with a new nano biosorbent based on cross-linked chitosan magnetic beads modified with methionine-glutaraldehyde Schiff's base (MG-Chi/Fe3O4) in wastewater samples. Fourier transform infrared spectra (FTIR), X-ray diffraction (XRD) analysis, and scanning electron microscope (SEM) have been applied to characterize nano biosorbent. Various parameters affecting the uptake behavior of the metal ions were examined and optimized, including pH, adsorbent dose, contact time, and initial concentration. Freundlich and Langmuir isotherm models were applied to estimate adsorption mode. The equilibrium data were well fitted with the Langmuir isotherm model. The maximum adsorption capacities for Cu (II) and Pb (II) were 172.4 and 175.4 mg g-1, respectively. The adsorption kinetics are better consistent with the pseudo-second-order model. It was concluded that this method was successfully applied for the simultaneous removal of Cu (II) and Pb (II) in industrial wastewater samples.

Pistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues is crucial for making decisions about topical garden management. Since it is not possible to diagnose psylla disease even after the onset of symptoms with the help of color images by drones, hyperspectral and multispectral sensors are needed. The main purpose of this study was to extract spectral bands suitable for distinguishing healthy leaves from psylla leaves. For this purpose, in this paper, a new method for selecting sensitive spectral properties from hyperspectral data with the high spectral resolution is presented. The intelligent selection of sensitive bands is a convenient way to build multispectral sensors for a specific application (in this article, the diagnosis of psylla leaves). Knowledge of disease-sensitive wavelengths can also help researchers analyze multispectral and hyperspectral aerial images captured by satellites or drones.

A total number of 160 healthy and diseased leaves were scanned in 64 spectral bands between 400-1100 nm with 10 nm spectral resolution. A random forest algorithm was used to identify the importance of features in classifying the dataset into diseased and healthy leaves. After computing the importance of the features, a clustering algorithm was developed to cluster the most important features into six clusters such that the center of clusters was 50 nm apart. To transfer the hyperspectral dataset into a multispectral dataset, the reflectance was averaged in spectral bands within ±15 nm of each cluster center and achieved six broad multispectral bands. Afterwards a support vector machine algorithm was utilized to classify the diseased and healthy leaves using both hyperspectral and multispectral datasets.

The center of clusters were 468 nm, 598 nm, 710 nm, 791 nm, 858 nm, and 1023 nm, which were calculated by taking the average of all the members assigned to the individual clusters. These are the most informative spectral bands to distinguish the pistachio leaves infected by Psylla from the healthy leaves. The F1-score was 90.91 when the hyperspectral dataset (all bands) was used, while the F1-score was 88.69 for the multispectral dataset. The subtle difference between the F1-scores indicates that the proposed pipeline in this study was able to select appropriately the sensitive bands while retaining all relevant information.

The importance of spectral bands in the visible and near-infrared region (between 400 and 1100 nm) was obtained to identify pistachio tree leaves infected with psylla disease. Based on the importance of spectral properties and using a clustering algorithm, six wavelengths were obtained as the best wavelengths for classifying healthy and diseased pistachio leaves. Then, by averaging the wavelengths at a distance of 15 nm from these six centers, the hyperspectral data (64 bands) became multispectral (6 bands). Since the correlation between the wavelengths in the near-infrared region was very high (more than 95%), out of the three selected wavelengths in the near-infrared region (710, 791, and 1023), only the 710-nm wavelength, which was closer to the visible region, was selected. The results of classification of infected and diseased leaves using hyperspectral and multispectral data showed that the degree of classification accuracy decreases by about 2% and if only 4 bands are used, the degree of accuracy decreases by about 3%.The results of this study revealed that the proposed framework could be used for selecting the most informative spectral bands and accordingly develop custom-designed multispectral sensors for disease detection in pistachio. In addition, we could reduce the dimensionality of the hyperspectral datasets and avoid the issues related to the curse of dimensionalitylity.

A flame atomic absorption spectrophotometric (FAAS) method for the determination of trace Co(II) after adsorption of synthesis 2-aminopyridine/graphene oxide nano-plates has been developed. Applying a facile and accessible sample preparation method is highly important before the experimental analysis process. In the current study, a technique was introduced for preconcentration and analysis of trace amounts of Co(II) ions in water samples. The important parameters in the analytical procedure of Co(II) ions were optimized such as the pH of extraction, amount and type of elution solvent, time of extraction, and the effect of another ion. Analytical parameters such as the concentration factor, the limit of detection (LOD) of the technique, and relative standard deviation (RSD %) were achieved as 20, 8.0 μg L-1, and 2.4%, respectively.

A flame atomic absorption spectrophotometric (FAAS) method for the determination oftrace Co(II) after adsorption of synthesis modify Multi walled carbon nanotubes carboxylate has beendeveloped. Applying a facile and accessible sample preparation method is highly important before theexperimental analysis process. In the current study, a technique was introduced for preconcentrationand analysis of trace amounts of Co(II) ions in water samples. The important parameters in the analyticalprocedure of Co(II) ions were optimized such as the pH of extraction, amount and type of elution solvent,time of extraction, and the effect of another ion. Analytical parameters such as the concentration factor,the limit of detection (LOD) of the technique, and relative standard deviation (RSD %) were achieved as20, 9.0 μg L-1, and 2.7%, respectively.

A flame atomic absorption spectrophotometric (FAAS) method for the determination oftrace Co(II) after adsorption of synthesis modify Multi walled carbon nanotubes carboxylate has beendeveloped. Applying a facile and accessible sample preparation method is highly important before theexperimental analysis process. In the current study, a technique was introduced for preconcentrationand analysis of trace amounts of Co(II) ions in water samples. The important parameters in the analyticalprocedure of Co(II) ions were optimized such as the pH of extraction, amount and type of elution solvent,time of extraction, and the effect of another ion. Analytical parameters such as the concentration factor,the limit of detection (LOD) of the technique, and relative standard deviation (RSD %) were achieved as20, 9.0 μg L-1, and 2.7%, respectively.

The training data selection is an important and operative step in the classification and change detection procedure from remote sensing images, which needs to be provided with high sensitivity. These samples are often determined by the human factor, which is a time-consuming process and prone to high error. Old maps can be a valuable source of information for selecting and preparing training samples. If these samples are accurately refined, they can speed up, facilitate and also increase the accuracy of the change detection process. The main innovation of the present paper is the diligence in the sampling process, which has been made imaginable by proposing a model based on the chi-squared statistical test and k-means clustering. This method, while using Chi-square statistical test, tries to select pure training samples, by selecting samples that are close to the centers of internal clusters in each class with multiple k-means clustering that takes into account the internal spectral diversity of classes. In this method, the spectral and the first and second-order of co-occurrence matrix are extracted and used in the support vector machine (SVM) classification process. Furthermore, the feature selection and SVM parameters have been optimized by the genetic algorithm to more improve the classification and change detection accuracy. For implementation, bitemporal satellite images at 2011 and 2015 and land use map of 2009 related to the Shiraz has been employed. Using the proposed method led to update the thematic maps of the study area with an overall accuracy of 98.72% and 94.57%, and a from-to change map. Experimental results showed that the refinement process of the training samples improves the results of the 2011 image classification (increasing the kappa coefficient from 65% to 87% and increasing the overall accuracy from 73% to 91%) as well as the 2015 image (increasing the overall accuracy from 69% to 86.32% and Kappa coefficient has been increased from 59% to 80.48%).

Besides introducing the triple-speed coaxial magnetic gear, the paper studies the influence of the geometric structure on torque and tangential and radial flux density. As rectangular and trapezoidal structures are more welcomed in most projects thanks to their ease of use, the present paper also introduces triple-speed coaxial magnetic gears with rectangular and trapezoidal structures. Then these gears are compared with crescent-shaped magnetic gear using the finite element method. The comparison shows that rectangular magnetic gears are not much different from crescent-shaped gears in terms of the flux density on the edges and corners. However, flux density distribution is more desirable in the former as the air gap between the middle rotor and modulators is variable, and less oscillation is experienced considering tangent and radial flux density distributions. Nonetheless, the trapezoidal structure presents unsatisfactory performance in comparison to the crescent-shaped structure in terms of flux density distribution and torque. An experimental crescent-shaped magnetic gear was built. The obtained results prove the validity of the use of this structure in triple-speed coaxial magnetic gears and the high performance of it compared to other structures used in magnetic gears.

One of the economic challenges facing developing countries is the cost of tackling air pollution and improving its quality. On the other hand, in addition to cost, the health of people in the community and existing diseases are also directly related to air quality. Therefore, the study and analysis of changes in air pollutants, including nitrogen dioxide, carbon monoxide, and ozone can provide valuable information for experts to analyze air quality. The presence of high-resolution spatial sensors to study a variety of applications has enabled experts in this field to study most environmental phenomena. In the present study, temporal and spatial changes of air pollutants were determined using Sentinel-5 satellite data in April 2016, simultaneously with the release of Covid-19 virus for Iran, and compared with the values of the same period in 1998. The spread of Covid-19 virus during this period had a variety of consequences that led to a reduction in factory activity as well as a decrease in vehicle traffic. Therefore, the present study seeks to investigate the effect of these factors by analyzing the temporal changes and spatial distribution of pollutants in the interval between these two times. The results confirm the improvement of air quality in this period compared to the previous year, April 1998. According to the results, the concentration of nitrogen dioxide in the entire barley column and tropospheric nitrogen dioxide , which is directly related to transportation and human activities, has decreased.

Change detection is one of the most important processes in photogrammetry and remote sensing, in which occurred changes in a same geographical area are identified over time. Forests are one of the national assets of any country that has a vital role in climate change, groundwater formation and prevention of floods and soil erosion. Thus, an accurate change detection method should be exploited to monitor and maintain forest regions. In this paper, an efficient unsupervised change detection method is proposed for this purpose. Here, two bitemporal sattelite images, acquired at the forest areas of Unitted stasted and Australia are employed  to evaluate the proposed change detection method. In the first step of the proposed approach, discrete wavelet transform was used to generate an efficient change index by fusing of two difference images derived by NDVI and GNDVI vegetation indices. Anisotropic diffusion filtering was then applied to obtain robust change index in which noises was reduced while change regions was highlited. In the next step, the generated index was segmented into changed and unchanged classes using an improved k-means algorithm. Finally, improved MRF model initialed with the initial change map is employed to generate final change map. The proposed MRF model include two novel improvements in the main energy function, resulting in preserveing changed region details. The proposed improved MRF contained superiority of 0.49% and 0.61% compared to traditional MRF in datasets 1 and 2, respectively. The proposed MRF also outperformed Otsu, PCA-kmeans, GAFCM and GMMMRF methods, so that reduced the total error rate by an average of 0.93% and 5.31% in data sets 1 and 2, respectively. In general, the proposed method has a high capability for accurate identifying changes for vegetated areas.

In this study, the doping effect of TiO2 on carboxylated multi-walled carbon nanotube (FMWCNT) on adsorption of Indigo carmine dye, an acidic dye, form aqueous enviroment has been Investigated. TiO2 was doped on FMWCNT surface and it was checked by fourier-transform infrared spectroscopy (FTIR), x-ray diffraction (XRD) and scanning electron microscope (SEM). The dye adsorption experiments were carried out by using a batch procedure. The effects of contact time, pH, dye concentration, adsorbent dose and temperature on adsorption of indigo carmine by TiO2 doped and undoped adsorbents (FMWCNT /TiO2 and FMWCNT) were evaluated. Pseudo first order, pseudo second order and intra-particle kinetic models were used to describe adsorption kinetics. Furthermore, Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherms were used to describe equilibrium adsorption of the indigo carmine dye on adsorbents. The obtained data were then analyzed using linear regression method. Experimental results have shown that acidic pH (3.0), favored the adsorption for both adsorbents. The dye adsorption equilibrium was attained after 90 minute of contact time. The capacity of adsorption of indigo carmine increases with doping of TiO2 on FMWCNT. Pseudo second order kinetic model is best fitted with experimental data for both adsorbents. The equilibrium data of dye adsorption on both adsorbents are best fitted with Langmuir isotherm at 25, 35 and 45 ºC. Thermodynamic study of dye adsorption on FMWCNT and Fmwcnt /TiO2 adsorbents showed that the adsorption process is exothermic and chemisorption.

A novel, simple, sensitive and effective method has been developed for preconcentration of the trace Pb (II) ions in aqueous Samples by CNT carboxyl which in order to improve the performance of extraction and preconcentration was used. Amounting of the ions density in solution was performed by the method of flame atomic absorption spectrometry. The effective parameters on extraction such as: effect of pH, the amount and eluent solvent, time of extraction and effect of other cation were optimized. Concentrate factor was found 30 and LOD method of limit of detection was found to 0.17µg.L-1. The relative standard deviation is 1.70% (RSD%).

Harmaline is one of the beta carboline of Peganumharmal. Harmaline and other carbolines are inverse agonists for benzodiazepine receptors with many inverse effects of benzodiazepines including anxiety and CNS excitement. The aim of this study is the effect of harmaline on seizure- induced by amygdala kindling in rats.

in this experimental study, eighteen kindled wistar rats (3 groups) were injected intraperitoneally with saline and on the following day, with Harmaline (50, 25, 12.5 mg/kg). After discharge duration (ADD), stage 5 duration (S5D), stage 2 latency (S2L) and stage 4 latency (S4L) of seizures were recorded and compared with those of the saline groups.

Harmaline reduced S2L and S4L in 3 groups, and increased ADD and S5D not significantly but increased ADD only in group 15 mg/kg significantly (P<0.05).

The obtained results revealed that harmaline has proconvulsant effect and maybe via stimulation of excitatory pathways or blocking of inhibitory pathways, or any other excitatory mechanisms cause increase in neuron activity and firing.

vitamin K2 involves one of the vitamin K derivatives which is synthesized by intestinal bacteria. A great number of studies have demonstrated the presence of high concentration of vitamin K2 as well as its important functions in the brain. Vitamin K2 is capable to inhibit many inflammatory mediators in different diseases. Therefore, this study intended to investigate the effect of vitamin K2 at the level of spinal cord in inflammatory paw edema induced by formalin.

In this research study, male Wistar rats (200-250g), except those of the control group, after recovery from cannulation surgery for intrathecal administration (i.t.), were classified into four groups including sham (DMSO), 2µg/10µl vitamin K2, 10µg/10µl vitamin K2 and 20µg/10µl vitamin K2. In each group, inflammatory edema volume caused by sub plantar injection of formalin (0.05 ml, 2.5%) was measured after one hour using plethysmometric method.

The study results indicated that I.t injection of vitamin K2 reduced formalin induced inflammatory paw edema significantly (P<0.01). In addition, in comparison with the other concentrations, the lowest concentration of vitamin K2 (2µg/10µl) demonstrated the highest decrease in inflammatory paw edema volume induced by formalin (P<0.05).

Vitamin K2 can reduce the edema volume induced by formalin, probably by diminishing as well as inhibiting the activity of COX and synthesis of prostaglandins. Moreover, this effect on edema can be exerted via reducing free radicals such as ROS and NO followed by decreasing the release of inflammatory and pro-inflammatory mediators in the spinal cord and thus in the peripheral.

Tea is one of the most popular beverages in the world and contains several important essential micro nutrients, that are beneficial to human health. The contamination of tea leaves by heavy metals may pose serious problems to human health, because they are not biodegradable and remain in the environment and might enter the food chain. In this study, the concentration of heavy metals; Cd and Pb was measured by atomic absorption spectrometry (GFAAS) according to AOAC method of analysis. Ten samples of black tea cultivated in Iran were compared with 10 samples of imported black tea in 2011. The results of analysis showed that the mean level of Pb was 497.5 ± 175 ppb for Iranian and 293.3 ± 269.5 ppb for imported black tea. However, the value for Cd was 45 ±17ppb for Iranian and 9.14 ±7 ppb for imported black tea. The average contents of detectable heavy metals were significantly (P<0.05) higher in Iranian black tea. Cadmium and Lead concentrations in the sampled brands of black tea were lower in comparison with their upper limits (0.2 and 2 ppm for Cd and Pb, respectively), therefore, it might be concluded that there are not any health problems concerned with tea consumption, for these two elements

Kefir is fermented milk that has a unique flavor because of acetaldehyde, diacetyl, acetoin and ethanol. The aim of this study was to evaluate flavor and aroma compounds present in kefir. Two types of starter (Christian Hansen and Sacco) and three levels of temperatures (30, 33 and 35oC) were used for the preparation of kefir samples. The samples were analyzed for acetaldehyde, diacetyl, acetoin and ethanol by GC equipped with FID detector. Levels of acetaldehyde, diacetyl, acetoin and ethanol were increased with raising the temperature and decreased during cold storage from the first day until the 14th day. pH was decreased and acidity was increased in all samples during cold storage. After organoleptic evaluation, the prepared sample by Sacco at 35oC was known as the best sample and then the prepared samples by Sacco at 33 and 30oC were recognized as the favorable samples.

Binders and plasticizers containing energetic groups such as nitro (-NO2), fluorodinitro (FC(NO2)2-), difluoroamino (-NF2), azide (-N3) and nitrate (-ONO2) have been developed and used in formulation of energetic materials. In this research paper, the synthesis of a high N-component energetic material possessing both energetic functional groups ONO2 and N3 in its molecular structure has been investigated. This compound is Azidodeoxycellulose nitrate(II). To reach the target molecule, nitration of azidodeoxycellulose(I) was performed using acetic anhydride, 98% HNO3 at 0◦C in CH2Cl2 Solvent. The presence of azido groups and the enhancement of nitrogen percent were confirmed by IR spectroscopy and elemental analysis. The heat of explosion and the volume of the obtained gas were investigated by calorimetric and gasometric methods respectively.

High reactivity of Azido group and the interest of energetic materials research centers in azido polymers in recent years due to energy increase and high generation of gas have led to the investigation and production of Azidodeoxycellulose. This compound is made up of nitrocellulose using an easy method and a new catalyst and has propellant application. In this article, methods of preparing nitrocellulose containing less than 10% nitrogen as intermediate (I) and Azidodeoxycellulos(II)have been discussed. The data related to the presence of azido group, the enhancement of nitrogen percentage confirmed by IR spectroscopy and elemental analysis. Heat of explosion, the volume of gas produced, and the thermal decomposition behavior of the product have also been studied.

The assessment of ripeness is a major part of quality evaluation and depends on several factors such as soluble solid content (SSC), acidity and firmness. Most of the methods THAT measure these qualities are destructive. So it is essential to develop an efficient and nondestructive method for measuring internal attributes of fruit. The objective of this study was to investigate visible and near infrared (Vis/NIR) spectroscopy method for prediction of SSC and pH of kiwi fruit. Near infrared spectroscopy is a fast and a nondestructive analytical technique. One of the main advantages of NIR spectroscopy is that IT allows several constituents to be measured at the same time. Transmittance determinations in the 400-1000 nm range were carried out on samples which WERE separated randomly into two groups: first group for making calibration models and second one for quality predictions. Different data preprocessing and spectra treatments such as standard normal variate transformation (SNV), multiplicative scatter correction (MSC), median filter and derivative were used to eliminate noise. Then calibration models were developed by using principal component analysis (PCA) and partial least squares (PLS). Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. The correlation coefficient and root mean square errors of prediction to soluble solids content and pH were 0.93, 0.952 and 0.259˚Brix, 0.236, respectively. The results indicated the feasibility of Vis/NIR transmittance spectral analysis to predict SSC and pH of kiwi fruit in a nondestructive way.