Conventional Graphical Methods Research Articles

Discussion of “Compressibility Behavior of Soils: A Statistical Approach” by Syed Iftekhar Ahmed and Sumi Siddiqua [Geotechnical and Geological Engineering, doi: 10.1007/s10706-016-9996-7

Ahmed and Siddiqua (2016) introduced a new regression-based relationship in the form of a three-parameter Weibull function, herein referred to as the 3P-W model, for modeling the compressibility behavior (i.e. the void ratio-effective stress relationship) of soils. In this discussion, important drawbacks associated with the newly proposed 3P-W model were outlined. In addition, an attempt was made to extend the practicality of the proposed model by introducing a simple analytical solution to derive regression-based equations for determining the compressibility curve variables (i.e. the recompression index, pre-consolidation pressure and compression index). The proposed analytical concept is primarily intended to replace the current conventional graphical method by providing consistent-error free results. The proposed equations in this discussion accompanied by the original 3P-W model construct a unique regression aided-analytical framework which can be employed for advance numerical simulations.

Application of spreadsheet to estimate infiltration parameters

Summary Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG) solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.

Field performance of a genetic algorithm in the settlement prediction of a thick soft clay deposit in the southern part of the Korean peninsula

Long-term settlement data of a thick soft clay deposit improved by vertical drains frequently shows different field settlement behaviors from laboratory-driven predictions by conventional theories due to the variability and uncertainty of the soil properties, modeling simplifications, and types of prediction methods. This paper presents the field application of a back-analysis method based on a genetic algorithm (GA) to evaluate the performance of a new settlement prediction method compared with conventional graphical settlement prediction methods, such as the hyperbolic method and the Asaoka method. The GA back-analysis method shows better flexibility in modifying surcharging plans and adaptability to multi-layered thick soft soil deposits at the early stages of post-construction settlement. Thus, this new settlement prediction method enables geotechnical engineers to subsequently modify the heights of surcharge fills in accordance with field settlement data monitored in the interim for rapid and cost-effective construction. The comparative results show that the GA back-analysis method is capable of superior field performance in settlement predictions compared with two conventional graphical methods, within a margin of less than 200mm in a thick soft clay deposit with multiple layers under complex loading conditions.

Classification of UO<sub>2</sub> Green Pellet Quality Using Intelligent Techniques

Abstract. Modern production facilities are large and highly complex, and they operate with numerous variables under closed loop control. In the production of green uranium pellets, pellet quality control involves many variables. Therefore, the classification of the quality of pellets is important for improving the efficiency of the production process. Classification of pellet quality using the conventional graphical method has some drawbacks; for example, the scale of the graph affects the accuracy and ease of use. In this paper, intelligent techniques are used to classify the quality of the pressurized water reactors(PWRs) green pellets into three categories according to the guidelines in the quality control manual of the experimental fuel elements laboratory of BATAN. Four features are used for classification, namely, height, volume, weight, density and theoretical density of the pellets. A dataset (150 observations) was collected from one lot of compacted UO2 pellets and was used for training and testing of an ANFIS model. Up to 86.27% of the data can be classified correctly using the ANFIS model. Such performance is comparable to that of artificial neural networks. Thus, this model can be applied effectively for the evaluation and classification of pellet quality.

Chemometric analysis to infer hydro-geochemical processes in a semi-arid region of India

Groundwater exploitation in Punjab has increased in last few decades due to rapid increase in industrialization, population, crop production, and erratic monsoon. In the present study, groundwater samples from 29 locations were collected and analyzed for almost all major anions, cations, and heavy metals. The analyzed parameters formed the attribute database for statistical analysis. The study approach included multivariate statistical analysis of hydro-chemical data to identify hydro-geochemical processes occurring in the study area and its relation to groundwater quality. The principal component analysis produced seven significant factors that explained nearly 77 % of the cumulative variance. Factor 1 explained nearly 22.05 % of dataset with variables loading indicating mineralization of geological component of soil. Trilinear plot and other graphical methods were also used to identify chemical facies of groundwater and geochemical processes occurring in study area. The water type in the study area is of Na/K–Mg–HCO3 type. It was found that the general hydro-geochemistry of groundwater in the study area dominated is by the processes such as carbonate/silicate weathering, ion-exchange, and dissolution. Thus, statistical methods can prove to be an effective tool understanding hydro-geochemistry of a region along with conventional graphical methods.

Determination of soil–water characteristic curve variables

Soil–water characteristic curve (SWCC) contains the fundamental information needed for describing the mechanical behavior of unsaturated soil. Some parameters such as air-entry value, slope at the inflection point, residual water content and residual suction are commonly used to describe the SWCC and other associated properties such as shear strength and permeability. Currently these parameters are determined using the graphical method which can be subjective and time consuming. Equations for determining these parameters are proposed and the relationships between SWCC parameters and fitting parameters are discussed in this paper. These equations can be used for computational analyses to replace the conventional graphical method in providing consistent results.

Hydrochemical Characterization of Aquifers Using Sequential Multivariate Analyses and Geographic Information Systems in a Tropical Setting

This study demonstrates yet another innovation in the application of multivariate analysis to the understanding of regional hydrochemical processes in a tropical setting. Sequential Q-mode hierarchical cluster analysis (HCA) and factor analysis were used jointly with geographic information systems (GIS) and conventional graphical methods to determine spatial groundwater types in Ghana, and the spatial variation of the major sources of hydrochemical change in groundwater in Ghana. This study finds that two major groundwater types exist in the country: the Na-Cl groundwater types, which dominate the coastal aquifers where groundwater salinity is high, and Na-K-HCO3 groundwater types, which dominate aquifers farther inland. The major source of variation in hydrochemistry of groundwater in Ghana is mineral weathering and saline water intrusion. Silicate mineral weathering has strong effects in the more inland aquifers whereas saline water intrusion has the greatest effects along the coast. Chemical fertilizer...

An assessment of the origin and variation of groundwater salinity in southeastern Ghana

Groundwater from the major aquifers in southeastern part of Ghana was sampled to determine the main controls on groundwater salinity in the area. This paper uses multivariate statistical methods, conventional graphical methods and stable isotope data to determine spatial relationships among groundwaters from the different hydrogeologic units in the area on the basis of salinity. Q-mode hierarchical cluster analysis (HCA) was used to spatially classify the samples, whilst R-mode factor analysis was used to reduce the dataset into two major principal components representing the sources of variation in the hydrochemistry. Analysis of the major chemical parameters suggests that the principal component responsible for salinity increment in the area is the weathering of minerals in the aquifers. This factor is especially more significant in the upland areas away from the coast. The second factor responsible for salinity in the area is the combined effects of seawater intrusion, and anthropogenic activities. This study finds that four major spatial groundwater groups exist in the area: low salinity, acidic groundwaters which are mainly derived from the Birimian and Togo Series aquifers; low salinity, moderate to neutral pH groundwaters which are mainly from the Voltaian, Buem and Cape Coast granitoids; very high salinity waters which are not suitable for most domestic and irrigation purposes and are mainly from the Keta aquifers; and intermediate salinity groundwaters comprising groundwater from the Keta basin aquifers with minor contributions from the other major terrains. The major water type identified in this study is the Ca–Mg–HCO3 type, which degrades into predominantly Na–Cl–SO4 more saline groundwaters toward the coast. Stable isotope data analyses suggest that groundwater in the Voltaian aquifers is largely of recent meteoric origin. The Birimian and Togo aquifers receive a component of recharge from the tributaries of the Densu and Volta Rivers, after the waters have undergone evaporative enrichment of the heavier isotopes. In the Keta basin, recharge is mainly from precipitation but an observed enrichment of 2H and 18O isotopes is probably due to seawater and evaporative effects since the water table there is very shallow. An analysis of the irrigation quality of groundwater from the six aquifers in the study area using sodium adsorption ratio and electrical conductivity suggests that most of the aquifers supply groundwater of acceptable quality for irrigation. The only exception is the Keta Basin area, where extremely high salinities and SAR values render groundwater from this basin unsuitable for irrigation purposes.

Assessing water quality using water quality index and multivariate analysis

Groundwater is increasingly gaining significance as the main solution to the water supply problems in Ghana, especially in the rural areas. This study was conducted to determine which factors play significant roles in the hydrochemistry of groundwater from the southern Voltaian formation. Conventional graphical and multivariate statistical methods were used. The study reveals three main factors controlling the hydrochemistry. Silicate mineral weathering and reverse cation exchange are the most important processes affecting the hydrochemistry of groundwater at this part of the formation. Interpolation maps created from factor scores suggest that these processes are the most pervasive, recording high scores almost everywhere in the study area. Carbonate mineral weathering is the second most important process in the hydrochemistry. This study finds that carbonate mineral weathering in the area is probably facilitated by carbonic acid rather than sulfuric acid. Chemicals from agricultural activities constitute the third most important process influencing groundwater quality in the area. Eighty samples were used to calculate water quality indices, WQI, which were in turn used to classify groundwater from the study area. Over 98% of the samples fall within the “Excellent” and “Good” categories, suggesting that groundwater from the southern Voltaian formation is generally acceptable for drinking purposes.

Analysis of groundwater quality using water quality index and conventional graphical methods: the Volta region, Ghana

Conventional graphical and statistical methods were used with water quality indices to characterize the hydrochemistry of groundwater from the northern part of the Volta region of Ghana. The objective was to determine the processes that affect the hydrochemistry and the variation of these processes in space among the three main geological terrains: the Buem formation, Voltaian System and the Togo series that underlie the area, and to determine the suitability of groundwater from the area for drinking purposes. The Q-mode cluster analysis reveals three main water groups. The groups established from the Q-mode HCA appear to indicate different degrees of weathering which could further indicate varying levels of fracturing aquifer hydraulic properties. R-mode HCA and factor analysis (using varimax rotation and Kaiser Criterion) were then applied to determine the significant sources of variation in the hydrochemistry. This study finds that groundwater hydrochemistry in the area is controlled by the weathering of silicate and carbonate minerals, as well as the chemistry of infiltrating precipitation. Mineral activity diagrams for the CaO–Na2O–Al2O3–SiO2–H2O and CaO–MgO–Al2O3–SiO2–H2O systems plotted for the area indicate stability in the smectite field and attribute hydrochemistry to the weathering of silicate minerals. Silicate mineral weathering and the effects of precipitation appear to be pervasive among all the three main geological terrains, whereas carbonate weathering is localized among the Voltaian aquifers. Cation exchange does not appear to play a significant role in the hydrochemistry but mild Water quality indices (WQI) were calculated for the samples using the concentrations of Na+, Ca2+, Mg2+, Cl−, NO3 −, F−, and EC at the various sample locations. The WQI values indicate that groundwater from the study area is of excellent quality for drinking purposes. WQI values from groundwater samples are averagely higher than samples taken from surface water sources in the area. This implies that geology has had an impact on the WQI of groundwater in the area.

Nixtamalized Instant Flour from Corn (Zea mays L.) Meal: Optimization of Nixtamalization Conditions

ABSTRACTThe present investigation provides a new method for the nixtamalization process wherein corn endosperm fractions (corn meal) are treated in an alkaline solution that yields quality masa or instant masa flour like traditional nixtamalization process (alkaline cooking of corn with lime). The objective of this work was to determine the best combination of nixtamalization process variables for producing nixtamalized instant flour (NIF) from corn meal. Nixtamalization conditions were selected from factorial combinations of process variables including nixtamalization time (NT 8–22 min) and cooking temperature (CT 78–88°C). A central composite rotable experimental design was chosen. Lime concentration was 1% (10 g of Ca(OH)2/L of water) and ratio of corn meal to cooking medium was 1:4. At the end of each cooking, each treatment was steeped for 5 hr at room temperature (25°C). Nixtamalized corn meal was dried (55°C/12 hr) and milled to pass through 80 U.S. mesh to obtain NIF. Response surface methodology (RSM) was applied as an optimization technique over four response variables: masa firmness (MF), masa adhesiveness (MA), tortilla cutting force (CF), and tortilla tensile strength (TS). Predictive models for response variables were developed as a function of process variables. Conventional graphic methods were applied to obtain response variable values similar to the control (MASECA). Contour plots of each response variable applied superposition surface methodology to obtain a contour plot for observation and for selecting the best combination of nixtamalization time (NT 15 min) and cooking temperature (CT 83°C) for producing an optimized NIF from corn meal. Values of MF, MA, CF, and TS obtained from the predictive models were compared with those derived from experimental tests; a close agreement (coefficient of variance < 10%) between both values was observed.

GAP: A graphical environment for matrix visualization and cluster analysis

GAP is a Java-designed exploratory data analysis (EDA) software for matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects. Various matrix permutation algorithms and clustering methods with validation indices are implemented for extracting embedded information. GAP has a friendly graphical user interface for easy handling of data and proximity matrices. It is more powerful and effective than conventional graphical methods when dimension reduction techniques fail or when data is of ordinal, binary, and nominal type.

A hybrid numerical method to calculate the sag of composite conductors

A modification to the numerical sag method (NSM) for predicting conductor sag is presented that accurately predicts the observed bilinear sag behavior of composite conductors. The modified method, called the hybrid sag method (HSM), uses the stress–strain curves of conductors to calculate the initial load distribution between the components of the composite conductors. In this study, the HSM is used to predict sag of conductors with conventional designs. The HSM predictions are compared with those obtained using a conventional graphical sag method as well as NSM. The HSM shows superior accuracy over NSM and virtually the same accuracy as the graphical method for predicting sag for composite conductors operated under specific conditions. The HSM predictions of sag are validated by comparisons with experimental measurements.

Determining Hydraulic Characteristics of Production Wells using Genetic Algorithm

Proper well management requires the determination of characteristic hydraulic parameters of production wells such as well loss coefficient (C) and aquifer loss coefficient (B), which are conventionally determined by the graphical analysis ofstep-drawdowntest data. However, in the present study, the efficacy of a non-conventional optimization technique called Genetic Algorithm (GA), which ensures near-optimal or optimal solutions, is assessedin determining well parameters from step-drawdown test data. Computer programs were developed to optimize the well parametersby GA technique for two cases: (i) optimization of ‘B’ and ‘C’ only, and (ii) optimization of ‘B’, ‘C’ and ‘p’ (exponent) as well as to evaluate the well condition. The reliability and robustness of the developed computer programs were tested usingnine sets of published and unpublished step-drawdown data from varying hydrogeologic conditions. The well parameters obtained by the GA technique were compared with those obtained by the conventional graphical method in terms of root mean square error(RMSE) and visual inspection. It was revealed that the GA technique yielded more reliable well parameters with significantlylow values of RMSE for almost all the datasets, especially in caseof three-variable optimization. The optimal values of the parameters‘B’, ‘C’ and ‘p’ for the nine datasets were found to range from 0.382 to 2.292 min m-2, 0.091 to 3.262, and 1.8 to 3.6, respectively. Because of a wide variation of ‘p’, the GA techniqueresulted in considerably different but dependable and robust well parameters as well as well specific capacity and well efficiency compared to the graphical method. The condition of three wells was found to be ‘good’, one well ‘bad’ and that of the remaining five wells ‘satisfactory’. The performance evaluation of the developed GA code indicated that a proper selection of generation number and population size is essential to ensure efficient optimization. Furthermore,a sensitivity analysis of the obtained optimal parameters demonstrated that the GA technique resulted in a unique set ofthe parameters for all the nine datasets. It is concluded thatthe GA technique is an effective and reliable numerical tool for determining the characteristic hydraulic parameters of production wells.

Solid state fermentation process for producing chickpea (Cicer arietinum L) tempeh flour. Physicochemical and nutritional characteristics of the product

AbstractSolid state fermentation (SSF) represents a technological alternative for processing a great variety of legumes and/or cereals to improve their nutritional quality and to obtain edible products with palatable sensorial characteristics. The objectives of this work were (1) to determine the best combination of SSF process variables (fermentation temperature FT/fermentation time Ft) for producing chickpea tempeh flour and (2) to characterise the physicochemical and nutritional properties of the product. Response surface methodology was applied as optimisation technique over three response variables: in vitro protein digestibility (PD), true protein (TP) and water absorption index (WAI). A central composite experimental design with two factors and five levels was used. The process variables FT and Ft had variation levels of 31–36 °C and 48–72 h respectively. Rhizopus oligosporus (1 × 109 spores l−1 in distilled water) was used as starter. Prediction models for response variables were developed as a function of process variables. A conventional graphical method was applied to obtain maximum PD, TP and WAI. Contour plots of each of the response variables were superimposed to obtain a contour plot for observation and selection of the best combination of FT (34.9 °C) and Ft (51.3 h) for producing of chickpea tempeh, which was dried (52 °C, 24 h) and milled to pass through an 80‐US mesh (0.180 mm) screen to obtain optimised chickpea tempeh flour. This flour had higher (p ≤ 0.05) TP (25.7 vs 19.7% dry matter (DM)), total colour difference (30.3 vs 16.7), WAI (4.18 vs 2.15 kg gel kg−1 DM), available lysine (42.7 vs 30.4 g kg−1 protein) and PD (83.2 vs 72.2%) and lower lipid content (2.6 vs 6.1% DM), phytic acid (1.1 vs 10.85 g kg−1 DM), tannins (2.65 vs 21.95 g catechin kg−1 DM) and pH (5.9 vs 6.3) than raw chickpea flour. Copyright © 2004 Society of Chemical Industry

Nixtamalized Flour From Quality Protein Maize (Zea mays L). Optimization of Alkaline Processing

Quality of maize proteins is poor, they are deficient in the essential amino acids lysine and tryptophan. Recently, in Mexico were successfully developed nutritionally improved 26 new hybrids and cultivars called quality protein maize (QPM) which contain greater amounts of lysine and tryptophan. Alkaline cooking of maize with lime (nixtamalization) is the first step for producing several maize products (masa, tortillas, flours, snacks). Processors adjust nixtamalization variables based on experience. The objective of this work was to determine the best combination of nixtamalization process variables for producing nixtamalized maize flour (NMF) from QPM V-537 variety. Nixtamalization conditions were selected from factorial combinations of process variables: nixtamalization time (NT, 20-85 min), lime concentration (LC, 3.3-6.7 g Ca(OH)2/l, in distilled water), and steep time (ST, 8-16 hours). Nixtamalization temperature and ratio of grain to cooking medium were 85 degrees C and 1:3 (w/v), respectively. At the end of each cooking treatment the steeping started for the required time. Steeping was finished by draining the cooking liquor (nejayote). Nixtamal (alkaline-cooked maize kernels) was washed with running tap water. Wet nixtamal was dried (24 hours, 55 degrees C) and milled to pass through 80-US mesh screen to obtain NMF. Response surface methodology (RSM) was applied as optimization technique, over four response variables: In vitro protein digestibility (PD), total color difference (deltaE), water absorption index (WAI), and pH. Predictive models for response variables were developed as a function of process variables. Conventional graphical method was applied to obtain maximum PD, WAI and minimum deltaE, pH. Contour plots of each of the response variables were utilized applying superposition surface methodology, to obtain three contour plots for observation and selection of best combination of NT (31 min), LC (5.4 g Ca(OH)2/l), and ST (8.1 hours) for producing optimized NMF from QPM.

Quality protein maize ( Zea mays L.) tempeh flour through solid state fermentation process

Solid state fermentation (SSF) process represents a technological alternative for a great variety of legumes and cereals, or combination of them, to improve their nutritional quality and to obtain edible products with palatable sensorial characteristics. The objectives of this work were (1) to determine the best combination of SSF process variables (temperature/time) for production of quality protein maize ( Zea mays L.) (QPM) tempeh flour, and (2) to characterize physico-chemical properties and nutritionally quality of optimized QPM tempeh flour. Response surface methodology (RSM) was applied as optimization technique, over three response variables: In vitro protein digestibility ( PD), true protein ( TD) and water absorption index ( WAI). A central composite experimental design with two factors [fermentation temperature ( FT), fermentation time ( Ft)] and five levels (2 factorial, 2 axial, 1 central) was used. The process variables, FT and Ft, had variation levels of 31–36°C and 48–72 h, respectively. Spores from Rhizopus oligosporus were suspended in distilled water (1×10 6 spores/ml) and used as starter. Predictive models for response variables were developed as a function of process variables. Conventional graphical method was applied to obtain maximum PD, TP and WAI. Contour plots of each of the response variables were utilized, applying superposition surface methodology, to obtain a contour plot for observation and selection of the best combination of FT (35.4°C) and Ft (54.6 h) for production of QPM tempeh, which was dried (52°C, 24 h) and milled to pass through a 80-US mesh (0.180 mm) screen to obtain optimized QPM tempeh flour. This flour had higher ( p⩽0.05) true protein (13.1 vs 9.1 g/100 g, DM), total colour difference (21.7 vs 9.2), WAI (2.9 vs 1.25 g gel/g dry flour), available lysine (5.67 vs 4.2 g lysine/100 g protein) and PD (83.6 vs 78.5%, DM), and lower ( p⩽0.05) lipid content (4.3 vs 6.1 g/100 g, DM), phytic acid (0.8 vs 10.1 mg phytic acid/g, DM), and pH (4.5 vs 6.12) than raw QPM flour.

Instant flour from quality protein maize ( Zea mays L). Optimization of extrusion process

Traditional nixtamalization process for producing instant flours is highly time and energy consuming; in addition, it presents problems of high liquid waste discharges (3–10 L H 2O/kg maize). Extrusion represents an alternative technological for producing instant flours and does not generate effluents. The objective of this work was to determine the best combination of extrusion process variables for the production of instant flour from quality protein maize (QPM) ( Zea mays L) V537 variety. Prior to extrusion, the maize kernels were broken to obtain grits (1−2 mm) which were mixed with lime and water to reach a moisture content of 28 g/100 g. The single screw extruder operation conditions were selected from a factorial combination of process variables: extrusion temperature ( ET, 70–100°C), lime concentration ( LC, 0.1–0.3 g/100 g maize) and screw velocity ( SV, 30–80 rpm). A central composite experimental design with five variation levels was chosen. Response surface methodology was applied as optimization technique, over four response variables: in vitro protein digestibility ( PD), total color difference (Δ E), water absorption index ( WAI) and pH. Predictive models for response variables were developed as a function of process variables. The conventional graphical method was applied to obtain maximum PD, WAI, pH and minimum Δ E. Contour plots of each of the response variables were utilized applying superposition surface methodology, to obtain three contour plots for observation and selection of a superior (optimum) combination of ET (79.4°C), LC (0.24 g/100 g maize) and SV (73.5 rpm) to obtain optimized extruded maize flour (EMF) from QPM with a single screw extruder. Optimized EMF had similar physico-chemical and functional characteristics than commercial nixtamalized maize flours (NMF).

Optimisation of extrusion process to transform hardened chickpeas (Cicer arietinum L) into a useful product

AbstractThe objective of this work was to determine the optimum conditions for the production of extruded flour from hardened chickpeas (Cicer arietinum L). Hardened chickpeas were produced using accelerated storage at 33–35 °C and 76% relative humidity for 160 days. Hardened chickpeas were broken, dehulled and softened with a salt solution (2.5 g l−1 NaCl + 7.5 g l−1 NaHCO3 in distilled water) prior to extrusion. The single‐screw extruder operation conditions were selected from factorial combinations of process variables: extrusion temperature (ET, 130–160 °C), feed moisture content (FMC, 200–300 g H2O kg−1, wet basis) and screw velocity (SV, 150–200 rpm). Response surface methodology was applied as optimisation technique over four response variables: in vitro protein digestibility (PD), total colour difference (ΔE), water absorption index (WAI) and water solubility index (WSI). Predictive models for response variables were developed as a function of process variables. The conventional graphical method was applied to obtain maximum PD and WAI and minimum ΔE and WSI. Contour plots of each of the response variables were utilised, applying superposition surface methodology to obtain three contour plots for observation and selection of the best (optimum) combination of ET (150.5 °C), FMC (265 g H2O kg−1) and SV (190.5 rpm) for the production of extruded flour from hardened chickpeas with a single‐screw extruder. Extrusion significantly improved the in vitro protein digestibility (21.7%) and protein efficiency ratio (36.9%) of hardened chickpeas.© 2002 Society of Chemical Industry

Statistical evaluation of geochemical parameter distribution in a ground water system contaminated with petroleum hydrocarbons.

A shallow-depth ground water area was investigated to identify the dominant processes governing the distribution of hydrocarbon contaminants and hydrogeochemical parameters. The ground water in the study site has been highly contaminated with petroleum hydrocarbons. A preliminary pump-and-treatment remediation technology was applied for 4 yr at the site. Multivariate analyses were applied to hydrogeochemical data obtained before and after the rainy season. The pump-and-treatment application, indigenous biodegradation, and mixing by precipitation recharge are the major factors or events involved in the distribution of geochemical parameters of the ground water in the study area. Site-specific artificial pavement also played an important role in the evolution of the ground water chemistry. A conventional graphical analysis method (Piper plot) of major ions did not effectively reveal these effects. In this study, we demonstrate the usefulness of multivariate analysis (factor and cluster analyses) using biodegradation indicator parameters, as well as major cations and anions, for the study of the ground water system in the hydrocarbon-contaminated site.