partial least squares regression

Faba bean (Vicia faba L.) is one of the oldest cultivated legumes, widely grown in temperate regions and at high altitudes in tropical areas. This crop thrives in wet and cool conditions, while hot and dry weather can severely damage its yield. Therefore, the sowing date must be carefully selected to avoid excessive heat, particularly during the flowering period. In field data analysis, the 'environment' refers to any recommended management practice for producers, such as sowing date, planting density, or fertilizer application. GGE biplot (Genotype plus Genotype-by-Environment biplot) and PLSR (Partial Least Squares Regression) can be used to analyze these datasets. The purpose of this study is to evaluate the interaction between variety and sowing date by multiplicative models such as SREG and GGE biplot and to evaluate the quality of varieties using a sensory panel.

The present experiment was carried out in a split plot based on randomized complete block design with three replications in Soamesera, Gilan province. The main factor in this experiment was sowing date (11 November, 11 December, 10 January and 9 February) and the split factor was faba bean varieties (Guilan landrace, Barakat, Feiz, Shadan and Mahta). Evaluation of the effect of sowing date was caried out by analysis of variance, mean comparison, GGE biplot and PLSR methods by GEA-R software. A sensory panel was used to evaluate the quality of taste in the fresh seeds of bean varieties obtained from the first sowing date by Kruskal-Wallis and Mann-Whitney U using SPSS software.

Analysis of variance showed that the effect of variety were significant on all traits including days to emergence, days to pod formation, plant height, green pod length, stems per plant, seeds per pod, pods per plant, hundred seed weight, green pod yield, biological yield and harvest index. The effect of sowing date was significant on all traits except stems per plant. The interaction effect of two factors was also significant on all traits except days to emergence and stems per plant. For traits with significant interaction between two factors, the mean comparison of variety (sub factor) was carried out at each level of sowing date (main factor). Days to pod formation, plant height and pods per plant in all varieties had a downward trend from the first to the fourth sowing date. Feiz had the longest length of green pods and the highest number of seeds per pod in all of sowing dates. Mahta and Shadan with smaller seed size had more pods per plant than other varieties. Shadan and Faiz had more hundred seed weight compared to other varieties in the late sowing dates. The highest seed yield was obtained in Shadan (431.65 g.m-2) on 11 November and the lowest yield was obtained in the Guilan landrace (75.65 g.m-2) on 9 February. With the delay in sowing, the seed yield decreased in Barkat, Feiz and Guilan landrace, but in Mahta and Shadan, with the delay in sowing, the seed yield decreased until the third sowing date, and again increased on the fourth sowing date. GGE biplot analysis showed that Barkat, Feiz and Shadan had the highest; and Mahta and Guilan landrace had the lowest seed yield. Also, Faiz and Guilan landrace had more stable yield in different sowing dates. Based on another view of GGE biplot, the best genotype for first sowing date was Guilan landrace; for second sowing date, Feiz; for third sowing date, Barket; and for fourth sowing date, Shadan and Faiz. In PLSR approach was investigated the relationship between the dependent variable (seed yield, Y) and the climatic explanatory variables (X variables). The results showed that the genotypes had different responses to different sowing dates and also their climatic needs were different, so that Mahta, Shadan and Barkat were more under the influence of temperature factors at the end of the growing season, and Faiz and Guilan landrace were more under the influence of temperature factors at the beginning of the growing season, as well as the amount of precipitation and evaporation in whole period of growth. Wide temperature range (difference between high and low temperature) at the end of the growing season has a negative effect on the yield of Feiz and Guilan landrace, while low temperature at the beginning of the growing season has a positive effect on these genotypes. Evaluation of the quality of taste with a sensory panel in the fresh seeds showed that Feiz had the highest rank of taste among the varieties.

In conclusion, although Guilan landrace and Feiz had more stability of yield in comparison with other varieties, considering the higher yield of Faiz compared to Guilan landrace and also its superiority in the sensory panel, this variety can be considered as a substitute for Guilan landrace faba bean in Guilan province. Also, the results of this study show the significant reaction of the seed yield of all five varieties to the sowing date and the necessity of timely sowing to achieve high yield. According to the results of this study, the best time to sowing broad beans in all five varieties was on the 11 November and 11 December; and in case of delay in sowing, Feiz are more recommendable than other varieties.

Wind erosion and dust phenomenon are considered as one of the important processes of land degradation and a serious challenge in Iran. This phenomenon occurs due to the interaction between weather and terrestrial processes. The purpose of this research is to investigate and analyze the trend of temporal and spatial changes in the number of dusty days in Iran and to investigate the role of climate in its spread.

In this research, after receiving hourly and daily statistics of dust events (through 148 synoptic and climatology stations) and parameters of precipitation, evaporation, temperature as key climatic elements (from 171 stations) in 22 provinces located in arid and semi-arid regions of Iran, The spread of dust was investigated and the correlation between climate and dust was determined. After calculating the number of dust days in Iran, the information was called in the GIS environment and a map of the spatial changes of the parameter of the number of dust days in the three decades of 1987-1996, 1997-2006, 2007-2016 was prepared. In the following, the spatial analysis of this parameter and its expansion over three decades were investigated. Next, the climate parameter that had the highest correlation with the number of dusty days was selected and after preparing a map of its spatial changes in the GIS software environment, the spatial compatibility of the desired parameter with the expansion of the number of dusty days was investigated. In the next step, after performing the homogeneity test on the dust occurrences, the partial square regression model was used to determine the contribution of the three mentioned climatic elements as independent variables and the number of dust days as a dependent variable.

The results showed that the number of dusty days and the area of its occurrence have increased in Iran, so that in the last decade under study (2007-2016) compared to the previous two decades, the dustiest event occurred in Iran. This increase has been more intense especially in the western and southwestern regions of the country, which were also affected by extra-local dust events. These dust events have spread to the central regions of the country with greater frequency. The homogeneity test in many provinces showed a common change point (year 2007-2008) in the number of dusty days, which could be related to the beginning of severe droughts and climate changes from this year in Iran. The study of climatic parameters (temperature, precipitation and evaporation) on changes in the number of dust days in Iran showed that during recent years, temperature had the highest significant correlation level (0.58) with the occurrence of dust. The temperature gradient map in the last decade (2007-2016) compared to the previous decade (1997-2006) also showed the increasing trend of temperature, especially in the western and southwestern regions as well as the southeastern regions of Iran. Modeling the effect of climatic parameters of temperature, precipitation and evaporation on the parameter of the number of dusty days showed that the contribution of the studied climatic parameters to the occurrence of dusty days is about 33% and other effects can be related to things such as human factors or soil characteristics, type of vegetation, solar radiation, etc.

In general, the results of examining the trend of changes in the number of dust days and climatic indicators for nearly 3 decades indicate a significant increase in the number of dust days in most parts of Iran and the conditions for this increase have been provided and if the trend of changing these indicators in the coming years is according to the current trend, the risk of environmental problems in the country can be expected to increase.

 Ultrafiltration is one of the most common membrane processes in the dairy industry, especially for condensing and separating milk components. Using this process, several products can be produced, including milk concentrate used for cheese production, low-lactose dairy products, milk protein concentrate, and serum proteins for dietary supplements. The efficiency and cost of a membrane process depend on the percentage of rejection of the soluble components. Therefore, the use of concentrated milk made by ultrafiltration in the production of various dairy products depends on the efficiency of the membrane process and the changes in milk components during this process. On the one hand, the physicochemical properties of camel milk are different from those of cow milk, especially in terms of type and amount of protein. Because significant differences exist between the physicochemical properties of camel and cow milk, likely, the membrane processing conditions and the physicochemical properties of their products will be different completely. Although many studies have been conducted on the efficacy of the ultrafiltration processing of cow milk, there is no information about the efficacy of camel milk ultrafiltration, and most of the research done regarding optimizing is based on classical algorithms, Therefore, in this study, the effects of transmembrane pressure and temperature on the solutes rejection (protein, lactose, ash, and total solids) during camel milk ultrafiltration process were investigated, Then, these properties were optimized using particle swarm algorithm. Also, because the performance of the particle swarm algorithm is highly dependent on related parameters such as the number of iterations, the number of particles, accelerate constant, inertia weight, and velocity of the particles, so before optimization, the effect of these parameters on optimal responses were examined by partial least squares regression (PLS).

 In this study, a pilot crossflow ultrafiltration system was used. A UF membrane (Model 3838 HFK-131, Koch membrane systems, Inc., USA) made of polysulfone amid (PSA) with MWCO of 20 kDa was applied. Camel milk was purchased from a local market in Mashhad and for camel skim milk production, its fat was separated by a pilot plant milk fat separator in the Food Research Complex, Ferdowsi University of Mashhad. The weight percentages of protein, fat, lactose, ash, and total solids of UF permeate samples were measured by ISO 8968-1:2014, ISO 1211: 2010, ISO 26462/IDF 214:2010, ISO 5544:2008, and ISO 6731:2010 at two replications, respectively. the process treatments were performed in the form of a central composite design (CCD) (5 replications at the central point) for two independent variables at three levels so that the total number of 13 treatments was obtained. The data were modeled using the statistical software of Design Expert (version 11) based on the response surface methodology and each of the response variables in the form of a regression model was presented as a function of independent variables.

 The rejection of total solids and protein of the tested samples varied in the range of 45.4-51.03% and 94.09-97.51%, respectively. It means that in each TMP and T, more than 45% of the total solids and 94% of the protein of camel milk were kept by the membrane. The results also showed that none of the linear, quadratic and interactive effects of TMP and T on the total solids and protein rejections were not significant. According to the results, the RL reduced with increasing T. Increasing the TMP also led to a reduction at high T and an increase in RL rate of the samples at lover T. Also, the effect of TMP on RA showed a non-linear trend, so that TMP at high T led to an increase, and at low T, it led to a reduction in the RA of the samples.

 The optimization results with the particle swarm algorithm showed that this algorithm has a high convergence speed and by recognizing and analyzing its parameters, the optimal conditions can be easily found. The optimum ultrafiltration conditions in this study with the lowest RL and RA were determined as 80 kPa TMP and 29.85 ͦ C T.

The rapid, accurate, and low-cost determination of soil properties has particularly important for land planning and management. The objective of this study was to evaluate the Vis-NIR spectral reflectance of soils, as a rapid, cost-effective, and non-destructive technique, for estimating some soil properties [sand, silt, clay, pH, and calcium carbonate equivalent (CCE)] by partial least-square regression (PLSR) and bagging-PLSR methods. For this purpose, a total of 220 composite soil samples were collected from 0-20 cm depth in Ghorveh Plain, Kurdistan province, in September 2019. The selected soil properties were measured by standard laboratory methods. The proximal spectral reflectance of soil samples was also measured within the 350-2500 nm range (Vis-NIR) using a handheld spectroradiometer. Different pre-processing methods were assessed after recording the spectra. The results indicated that the R2 values for the PLSR method ranged from 0.58 to 0.76, while the bagging-PLSR produced R2 values between 0.59 and 0.74. The RMSE values obtained for sand, silt, clay, CCE, and pH were 17.43, 7.65, 7.83, 7.94, and 0.66, respectively for the PLSR, and 16.66, 7.63, 8.13, 7.71, and 0.45 for the bagging-PLSR. Based on the ratio of prediction to deviation (RPD) values, the bagging-PLSR model achieved the best performance in predicting sand and CCE. However, for clay and pH prediction, the PLSR model was the most accurate. Both the PLSR and bagging-PLSR models yielded identical predictions for silt content, with an RPD value of 1.53. Overall, the results showed that PLSR and bagging-PLSR models have acceptable accuracy for estimating the proposed properties of the soils.

Nowadays, due to the evaluation and high costs of maintenance and repair of sugarcane harvesting machines, it is necessary to monitor sugarcane harvester hydraulic oil using a faster and non-destructive method to determine contamination and TAN index. In this research, the ability of the visible spectroscopy method to non-destructively measure and predict the water content and TAN index in harvester Austoft 7000 hydraulic oil samples at different operating hours was investigated. For this purpose, spectra were taken from the samples in the spectral region of 400-780 nm. Multivariate Partial Least Squares (PLS) regression models were developed based on reference measurements and pre-processed spectra information by combining different pre-processing (Moving Average, Savitzky-Golay, Standard normal variate and First Derivative) methods to measure and to predict the water content and TAN index of hydraulic oil.  The results showed that the visible spectroscopy method could be used for quick and non-destructive measurement of water content and TAN index at different operating hours of harvester Austoft 7000 hydraulic oil. The best prediction results of water content in hydraulic oil were obtained with PLS model based on moving average (MA) preprocessing method (rcv=0.96, RMSECV=1.86, rp=0.89 and RMSEP=3.18), which had excellent accuracy (SDR=3.12). On the other hand, the PLS model based on the combination of moving average preprocessing and standard normal distribution (MA+SNV) was able to predict the TAN index with excellent accuracy (SDR=3.1) (rcv=0.94, RMSECV=0.007, rp=0.89 and RMSEP= 0.010). Therefore, the application of visible spectroscopy technology in agriculture and industries can be recommended for rapid monitoring of hydraulic oil quality and with the aim of controlling pollution.

Cation exchange capacity (CEC) is one of the most important soil chemical properties that plays an important role in soil fertility. However, standard laboratory methods for measuring CEC are difficult, costly, and time-consuming. The aim of this study was to use new methods such as 1) Pedotransfer Functions (PTF) based on the basic soil properties using Multiple Linear Regression (MLR), 2) soil spectroscopy (Vis–NIR, 400 – 2500 nm) using Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), for estimating soil CEC. Also, from the regression coefficient analysis, key wavelengths were introduced. For this purpose, CEC was measured using the sodium acetate method for 72 soil samples collected, and spectral reflection of soil samples was determined using spectroscopy. These methods are made from a calibration set (70% of data) and evaluated with a validation set (30% of data). The results showed that Vis - NIR method performed better than PTF. In this study, wavelength ranges around 566, 854, 1354, 1418, 1906, 2071, 2203, 2319, and 2341 nm were investigated as the key wavelengths for estimation of CEC. Furthermore, the results of prediction models showed that SVR has a better performance than PLSR. This study proved that Vis-NIR is a promising method for soil CEC estimation.

In recent years, with increasing public awareness about the harmful effect of fat consumption, demand for low-fat dairy products has increased significantly. On the other hand, fat reduction reduces the rheological and sensory properties of food, such as taste, flavor, texture and mouth feel. Therefore, it is difficult to produce a low-fat product with the same characteristics of high-fat product. In order to imitate different functions of fat in low-fat foods, such as consistency of the product, mouth feel, color, flavor and rheological properties should be considered. Fat substitutes are macromolecules that are used to provide all or part of the fat functions in a food product and produce less calorie intake than fat. Konjac is a neutral and polysaccharide hydrocolloid that is used as a gelling agent in traditional Asian foods due to its high ability in absorbing water. In addition, it is one of the most viscous dietary fiber. The use of konjac in western countries is expanding as a component in functional foods. Konjac can therefore be used as an appropriate substitute for fat in dairy products such as cream, which in addition to reducing the amount of fat can maintain and even improve the physical and other properties of the product. The purpose of this study was to produce low-fat cream with good nutritional value, variety in formulation, as well as lower prices.

Fresh cream of 30% fat and pasteurized milk were purchased from Razavi Dairy Company and konjac gum was provided from Food Chem (China). First, the gum was mixed with the pasteurized milk at 45°C. Then the mixture was added to the cream of 30% fat to reach the fat content of 18– 25%. The final mixture, after homogenization in the different specified values of pressure, was pasteurized at 85°c for 15 min, and then packed in polyethylene bottles and kept in the refrigerator (4ºC) until theday of experiment. The effect of amounts of Kanjac gum (0.2- 0.6), the amount of fat (18- 25%) and homogenization pressure (100- 200 bar) on the sensory, color, and rheological characteristics of low-fat cream were investigated

The results of evaluating the characteristics measured using the response surface methodology indicated that the hardness, consistency, adhesiveness, b*, sensory score of texture, sensory score of aroma and overall acceptance of samples significantly increased with an increasing homogenization pressure. Also increasing fat content caused an increasing the adhesiveness, L*, b*, sensory score of taste, sensory score of aroma and total acceptance of samples. Increasing the konjac gum content also increased hardness, consistency, adhesiveness, and sensory score of texture. The distribution of responses in the principal component also showed that the instrumental measures like hardness and consistency were close to the sensory score of texture of the samples. This proximity of these attributes indicated that the properties measured by the panelists were approximately equivalent to the parameters which were measured by instrumental methods. Also, the results of the correlation coefficient between sensory and instrumental measurements showed that the highest correlation between hardness and sensory score of texture (a strong positive linear relationship (0.774), and between the consistency and sensory score of texture (a strong positive linear relationship (0.760)). Rheological and sensory properties in products such as cream, play an important role in the processing and marketability of the product. In cream, these characteristics are mainly influenced by the method of production, the amount of fat, and potential additives used. The results of this study showed that konjac gum as a hydrocolloid can be used as a proper substitute for fat in the cream, and with suitable sensory, color and rheological properties. The results of this study about the correlation between sensory properties and instrumental specification showed that the properties measured by the panelists are approximately equivalent to the parameters measured by the instrumental methods. Therefore, the results of instrumental methods in most cases can well predict the sensory properties of the cream and then can be replaced it.

Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and digital mapping of soil and on the other hand, soils are temporally and spatially variable, thus distinguish zoning and their monitoring with traditional sampling methods and laboratory analysis is very costly and time consuming. As a result, the development of methods for analyzing the soil and for required information has become very important. Visible and near infrared spectroscopy (VIS-NIR) is widely used to estimate soil physical properties and estimate soil texture. The present study aims to predict soil texture using spectral measurements and artificial neural network models and partial least squares regression.

The study area in southeastern Iran is approximately 70 km from Kerman. In the study area, based on the hypercube technique, 115 profiles were identified and then horizons were sampled. In this way, for each point of study, the necessary information, including the location of the profile on the ground, the type of geomorphic unit and the type of materiel, were recorded and taken from the horizons of each profile. In all soil samples, after drying and passing through 2 mm soil, the soil texture was measured by hypercube. Spectral radiometer was used to measure the spectral reflection of soil samples. The soil samples were air dried and sieved and then placed in a petri dish with an approximate diameter of 10 cm and transferred to the dark room for spectral analysis. Each specimen was tested four times (for each 90 degree sequential rotation) to remove the effects of a change in the radiation geometry. Soil samples were scanned, and absolute reflections at a spectral range of 2500-350 nm yielded 2150 spectral data points (SDPs) per soil sample with a spectral resolution of one nanometer. Finally, to construct a suitable model for forecasting the percentage of clay, sand, and silt, the least squares model was used with the number of factors 1 to 10 by Artificial Neural Network (ANN) modeling using JMP software Work.

The reflectance spectrum of the visible range - near infrared - was measured for specimens. Since preprocessing of spectral data has an effective role in improving the calibration, in order to perform spectral preprocessing, two first nodes of the first and the end of the spectra were first removed in the range of 350-400 and 2450-2500 nm. In addition, the interruption due to the change in the detector in the range of 900 to 1000 nm was also eliminated. Types of preprocessing methods were performed on spectral data. Then, using partial least squares regression analysis, the best model was produced when the first derivative was fitted to reflection values. The explanation coefficients for this low and unacceptable model were obtained. Therefore, using partial least squares regression analysis, the best wavelengths were selected to predict the percentage of clay, sand, soil, and extracted from the model. Then it was used as input in the neural network model. To determine the best combination, root error index and error coefficient were used. The results of artificial neural network showed that the number of neurons 9.8 and 10 had the best composition for predicting clay, sand and soil silt. The root-squared error results for clay, sand, and soil silt were 3.42, 6.94, and 4.383 respectively. Also, the results of the explanatory factor were 0.84, 0.83 and 0.81, respectively. After obtaining the optimal structure in the artificial neural network training phase described above, the trained network has been tested on the test data to determine the accuracy of this model to predict clay, sand and silt of surface soil. The root-squared error results for clay, sand and silt components were obtained at 5.54.9.14 and 7.01. Also, the results of the explanatory factor were 0.76.0.70 and 0.73 respectively. The best result of the prediction for partial least squares regression was obtained for the sand sample. The results indicate that the neural network performance is better than partial least squares regression, which is consistent with Mouazenet. al (2010) and also ViscarraRossel R. et. al (2009). Acceptable performance of the artificial-neural network can be attributed to the ability of this model for non-linear behavior of soil texture in visible spectroscopy. In this study, specific wavelengths, which Ben Finder et al. (2003) obtained in the study on the soils of Israel, were used. This conclusion confirms that various types of soil can be modeled using specific wavelengths. The advantage of this study is that, when using the artificial neural network, no pre-processing of reflection data is required before applying the model. Since the relationship between the percentage of soil particles (clay and gravel) and the reflection of the soil is not linear, the neural network method is very useful for analyzing the relationship between soils. Finally, the map of clay, sand and silt and map of soil texture was prepared by artificial neural network method in GIS environment.

The results of this study showed that the neural-dynamic network has a better performance than partial least squares regression. Calibration models designed and used in this study can be transported for use with other soils. When the partial least squares regression model was implemented, it had a very low accuracy (R2 ~ 0.1-0.3); on the contrary, the neural network-based method had high accuracy and less error. Note that although neural-dynamic modeling estimates higher precision results from soil texture, both approaches depend on wavelength selections, and so wavelengths should be selected before using any of the two models. To be finally, a meaningful relationship between the selected wavelengths and the percentage of clay, sand and silt in the present study indicates that soil texture is not only possible but also reliable by reflection spectroscopy.

Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the soil on a large scale. One of these methods is the use of surface soil spectrometry. In this method, the choice of calibration method significantly affects the accuracy of measuring the characteristics of the surface. In this study, the performance of two regression techniques, namely, partial least-squares regression (PLSR), principal component regression (PCR) were compared to identify the best method to assess sand, silt and clay. For this purpose, 50 soil samples from Tehran province were collected and used as a data set for Calibration and Validation. Soil samples with different moisture levels (oven dry, 5, 10, 15 and 20 w/w) were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The spectra were subjected to three pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), Normalization with SG smoothing (Normal-SG). The R2 results from cross-validation indicated that the PLSR model had a better performance than PCR. Normal + SG pre-processing method for clay loam texture and SG method for sandy clay loam texture showed better estimation of measured properties. The amount of R2 for clay was 0.74, 0.81, 0.97 and 0.87, respectively, in moisture content of oven dry, 5, 15 and 20% in   clay loam texture And 0.95 and 0.61 at oven dry and 5% levels in sandy clay loam. Silt was better predicted by R2 0.67 in moisture content of 5% in clay loam texture and R2 0.97 in moisture content of 20% at sandy clay loam texture. Sand was also predicted (R2= 0.86 and 0.72) in moisture content of 5 and 10% in clay loam texture.

A broad area of saline and semi-saline lands of Khuzestan province have changed into centers susceptible to dust production due to eroded wind and lack of surface coating and low soil resistance. The objective of this study was to model the soil salinity of sensitive areas to dust production in Khuzestan Provenience usin spectrometry method of visible and near-infrared wavelengths (2500-350 nm). The least square multivariate regression model, artificial neural network and random forest model were used to estimate soil salinity. The main soil spectrum was determined using the FieldSpect machine. Also, preprocessing methods including Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normalization method (SNV), and the continuum remove method (CR) were used to eliminate the noise and to increase the accuracy of the multivariate model. The results showed that the combined model partial least squares-artificial neural network model with assessment criteria (RPDcal = 3.40-2.65) has high accuracy for salinity estimation. In contrast, the combined model of least squares - random forest showed the lowest accuracy (RPDcal = 0.85-1.98). Preprocess of the main spectrum in two models (neural network and partial least squares regression) increased the relative accuracy of the model; while in the random forest model, preprocess reduced the accuracy of the model compared to the main spectrum. The ranges of 1800, 1900, 2000, 2300 and 1500 nm were recognized as "the key wavelengths" impressed by soil salinity. The key wavelengths can be used in remote sensing studies and mapping of soil salinity in areas sensitive to dust production in Khuzestan province.

Landscape is one of the main factors influencing hydrological processes of the watershed. Changes in structure and spatial pattern of land use play important role in surface runoff and sediment yield. Determining the relationship between landscape patterns and hydrological processes can be used as an indicator of watershed soil erosion and sediment yield. Therefore, due to the problems in field measurement of sediment yield, its estimation using landscape properties and land use pattern is an appropriate alternative for current estimation methods. The purpose of this research is to determine the relationship between watershed sediment yield and landscape metrics in the selected sub-watersheds of Golestan Province. To this end, suspended sediment concentration data for all hydrometric stations of the studied province were obtained from the relevant resources and appropriate sub-watersheds were selected. Then, using the land use map of Golestan Province, 15 landscape metrics related to sediment yield were determined for different land uses by Fragstats 4.2 software. In order to determine the relationship between watershed sediment yield and landscape metrics, a partial least squares regression was used which combines the methods of principal component analysis and multiple linear regression. The relative importance of landscape metrics was determined through examining the values of Variable Importance for the Projection (VIP) and Regression Coefficients (RCs). The results of this study indicated that the watershed sediment yield is densely associated with land use patterns. The main indices in reducing sediment yield were the Largest Patch Index (LPI), the average of the nearest neighbor distance (ENN-MN) and the average of perimeter-area ratio (PARA –MN) with values of VIPs of 1.296, 1.184 and 1.747,  and regression coefficients of -0.014, -0.039, and -0.002, respectively. The main indices in incrising sediment yield were Landscape Shape Index (LSI) and mean patch size (AREA-MN) with regression coefficients of 0.020 and 0.017, respectively. The landscape characteristics in watersheds could account for as much as 71% of the variation in sediment yield of watershed. The results of study showed that the landscape characteristics can be used for watershed sediment yield modeling.

Soil organic matter is considered as an indicator of soil quality and its fertility due to its impact on the physical, chemical and biological properties of soil. The purpose of this study was to investigate the effect of moisture on soil spectral behavior in order to estimate the amount of organic matter using visible-near infrared spectroscopy. Also, the performance of different preprocessing methods was compared in modeling. For this purpose, 50 soil samples from a surface layer (0-30 cm) were randomly collected from some cities in Tehran province. The samples were air-dried and passed through a 2 mm sieve. The amount of organic matter and soil texture were measured in the laboratory. To measure spectral reflectance, soil samples are dry in the oven and they were hydrated with levels of 5, 10, 15 and 20 wt.% (Grams of water / g of soil). Then, Soil samples were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The results of the cross-evaluation indicated that SNV pre-processing method was better in predicting organic carbon content of soil. The best result for the validation group was obtained in dry oven moisture with R2 and RMSE values of 0.83 and 0.422 respectively. Also, R2 and RMSE were 0.75 and 0.543, respectively, at 5% moisture content, 0.70, 0.553 at 10% moisture, 0.605, 0.558 in moisture content of 15%.

Soil spectroscopy in the visible and near infrared (Vis-NIR) range has widely been used as a rapid, cost-effective, and non-destructive technique to predict soil properties. Since little data is available about soil properties determined by using this technique, the present research was carried out to evaluate the efficiency of Vis-NIR spectroscopy to estimate several soil properties in Bardsir area, Kerman Province. About 150 complex surface soil samples were collected from four different land uses from depth of 0-20 cm. Soil organic carbon, equivalent calcium carbonate, pH, and the amount of silt, clay and sand particles were measured by routine laboratory methods. Reflectance spectra were obtained from air-dried samples under controlled laboratory conditions using an ASD FieldSpec Pro spectroradiometer in 350-2500 nm wavelength range. Partial least squares regression was used for calibration of spectral and laboratory data using cross validation. Coefficient of variation for organic carbon, equivalent calcium carbonate, sand, silt, clay, and pH values were 0.68, 0.62, 0.64, 0.66, 0.3, and 0.01, respectively. Based on RPD values (Ratio of Prediction to Deviation), the precision of the prediction model for sand and silt contents was quite suitable, and for organic carbon and equivalent calcium carbonate it was suitable. [H1] However, the predictions of the model for clay content and pH were poor.Furthermore, standard normal variate (SNV) was the best pre-processing method to predict organic carbon, whereas, first derivative with SG smoothing (FD-SG) showed better estimation for carbonate, sand, and silt. Consequently, Vis-NIR spectroscopy is capable of predicting several soil properties at the same time. As the model accuracy is acceptable, it has the potential to substitute conventional laboratory analyses of selected soil properties.

Flat breads are the main dietary staple in many Middle Eastern and North African countries. In today’s demanding world, the use of bread improvers has become an indispensable part of enhancing the quality of bakery products. Bread improvers are technically sophisticated blends of functional ingredients, which if formulated correctly, will enhance the development of dough structure, facilitate trouble-free production, and provide the desired result of consistent products having optimal quality at the lowest possible cost. Among the functional food additives, polyols have been increasingly used to improve the quality and shelf life of bread. Polyols have been used successfully to extend the shelf life of ready to eat bread used by the military, Barbari bread fortified with soy flour, as well as flour tortillas. Emulsifiers, a subset of surfactants, have been widely used by the baking industry. The function of surfactants, as crumb softening agents, is closely related to their interaction or complex formation with starch, particularly the linear amylose fraction, to retard bread staling. Surfactants, such as SSL and DATEM that are the most efficient in breadmaking are able to form lamellar mesophases or gel structures in water. It has been shown that for polar lipids the lamellar liquid-crystalline phase is the most effective form in which this component can be added to bread dough in order to improve loaf volume. Mechanical properties of dough play an important role in baking industries. In a few cases, it can be desirable, for example, to fix some ingredients on bakery products. The dough is expected not to stick to metal surfaces and to show a good resistance to vibration and mechanical shock to minimize detrimental effects such as process breakdown, production loss and contamination of the equipment. Thus, the present study was designed: (a) to examine the effects of different emulsifiers (sodium stearoyl-2-lactylate and diacetyl tartaric acid esters of monoglycerides) and polyol (propylene glycol) on Barbari flat bread performance when used singly and in combination at different levels; (b) to obtain the relationship between mechanical and rheological properties of dough and quality, shelf life, sensory and image parameters of bread using the asymmetric methods (response surface methodology and partial least squares regression) and symmetric methods (coefficients of determination and Principal components analysis) and (c) to determine the optimum formulations for Barbari flat bread improver.
Commercial Triticum aestivum wheat flour (locally named Setareh) was procured from the AceeArd Co., Khorasan, Iran. Propylene glycol (PG) was purchased from J.T. Baker Chemical Company (Phillipsburg, NJ). Sodium stearoyl-2-lactylate (SSL) and diacetyl tartaric acid esters of monoglycerides (DATEMs) were provided by Vista Tejarat Company (Tehran, Iran). Moisture, ash, fat, protein, wet gluten and falling number were determined according to standard AACC methods. Three replications were taken for each characteristic. Gel samples were prepared using sodium stearoyl-2-lactylate (SSL), diacetyl tartaric acid esters of monoglyceride (DATEM) and propylene glycol (PG) in the range of 0-0.5 g/100g. Bread making was carried out according to the method of Maleki et al. (1981). Quality analysis of fresh bread samples was carried out by measuring specific volume. Sensory attributes of bread were selected according to the Iranian flat bread evaluation method, including bread form and shape; upper surface property; bottom surface property; cavity and porosity; firmness and softness of texture; chewiness; odor, flavor and taste and overall quality score. Staling phenomenon and its changes were evaluated by penetration test after 1 hour, 3, 5 and 7 days. The average values of L*, a*and b* colors describing the outer crust regions were obtained. The crumb grain features were the mean cell area, porosity, and cell density. A central composite design was constructed using the software Design Expert Version and was used for sampling of different combination of gel constituents. In addition, Lack of fit, coefficients of determination (R2), adj-R2, coefficient of variation (CV) and significant probabilities were calculated to check the model adequacy. Multivariate correlation matrix, coefficients of determination and principal components analysis were performed by using Minitab 15 software.
The characteristics of the flour are in the range of typical values of medium strong flour, suitable for Iranian Barbari flat bread. The results showed dough adhesiveness was increased by SSL addition but it was decreased by quadratic effect of SSL like as resilience. All of three components to the gel formula caused decrement in the hardness, gumminess and chewiness. But the interaction of them influenced on them. On the other hand, although cohesiveness increased by addition of all components but influenced significantly by interaction of them. Dough mechanical properties were furthermore correlated with dough farinographic aspects, quality, sensory parameters, image processing and shelf life of bread. It was proved that principal components analysis (PCA) is able to extract relevant information and offer an easy and promising approach for the interpretation of dough mechanical properties and its correlation with other properties and dough and bread. Partial least squares regression (PLSR) models were applied to determine the relationships between dough mechanical properties and dough farinographic properties, bread quality, sensory, image processing and shelf life aspects. Results from statistical analysis suggest that the mechanical parameters of dough are capable in predicting more than 50% of dough and bread properties

The aims of this study were to determine the distribution of cadmium and lead in topsoil in Bahar city of Hamadan and to evaluate pollution of these metals in agricultural lands which cultivated potato. Therefore, systematic sampling was used, soil samples were taken from depth 0-45 cm. Visible and near-infrared (VIS-NIR) spectroscopy was provided as a quick and accurate method in 370 -2000 nm range. The reflectance spectrum of 95 soil samples were collected to estimate the concentration of cadmium and lead. To reduce noise on soil spectra, MSC, SNV and derivative preprocessing methods were used. The results showed contamination usually occurs due to combinations of sewage irrigation, it accumulates in the topsoil. Prediction of heavy metals were estimated by partial least squares regression and artificial neural network (R2PLSR = 0/90, R2BPNN = 0/95).

Leaf area index (LAI) is a key variable in primary production and carbon cycling in ecosystems. It is used as an important predictor to explain the processes of forest ecology, forest management, and remote sensing studies. Most of the remote sensing instruments such as LAI-2000 and Fisheye photography are based on three-dimensional space and they consider the geometry of the crown to estimate LAI. The aim of this study was to investigate the relationship between spectral behaviour of Quercus persica and Pistacia atlantica with two-dimensional and three-dimensional LAI. To estimate LAI, a box (0.5× 0.5× 0.5 meters) was placed in the four directions of the crown and all the leaves were harvested. In situ spectral measurements of leaves were done with ASD Fieldspec spectroradiometer. The results of partial least squares regression to model LAI form spectral data of Quercus persica showed maximum regression coefficient at visible and near infrared wavelengths for both LAI3D and LAI2D. The coefficient of determination (R2) between the measured and estimated LAI2D and LAI3D values for Quercus persica was 0.16 and 0.23 respectively, and for Pistacia atlantica was 0.15 and to 0.42, respectively. Generally, LAI3D showed better relationship with spectral reflectance for both species.