Home About us Contact
|
Home About us Contact | ||
|
|
||
Root Mean Square Error (root + mean_square_error)
Selected AbstractsModelling of air drying of Hac,haliloglu-type apricotsJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 2 2006Hakan Okyay Menges Abstract In this study a laboratory dryer was used for the thin layer drying of sulfured and non-sulfured apricots. The moisture ratio values throughout the drying process were calculated by 14 different mathematical models, namely Newton, Page, modified Page, modified Page-II, Henderson and Pabis, logarithmic, two-term, two-term exponential, Wang and Singh, Thompson, diffusion approximation, modified Henderson and Papis, Verma et al. and Midilli et al. models. Root mean square error, reduced chi-square, mean bias error, adjusted R -square and modelling efficiency were used as statistical parameters to determine the most suitable model among them. According to the results, the Page model was chosen to explain the thin layer drying behaviour of sulfured and non-sulfured apricots. The effects of drying air temperature (T) and velocity (V) on the constants and coefficients of the best moisture ratio model were determined by multiple regression analysis. The moisture ratio (MR) could be predicted by the Page model equation MR = exp(,ktn) with constants and coefficients k = 0.470893 + 0.078775V and n = 0.017786 exp(0.051935T) for sulfured apricots and k = 4.578252 + 1.144643T and n = 0.888040 + 0.145559V for non-sulfured apricots. It is possible to predict the moisture content of the product with the generalised Page model incorporating the effects of drying air temperature and velocity on the model constants and coefficients in the ranges T = 70,80 °C and V = 1,3 m s,1. This developed model showed acceptable agreement with the experimental results, explained the drying behaviour of the product and could also be used for engineering applications. Copyright © 2005 Society of Chemical Industry [source] Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet DecompositionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2007Yuanchang Xie Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error. [source] Simultaneous Quantitative Determination of Cadmium, Lead, and Copper on Carbon-Ink Screen-Printed Electrodes by Differential Pulse Anodic Stripping Voltammetry and Partial Least Squares RegressionELECTROANALYSIS, Issue 23 2008Michael Cauchi Abstract Water is a vital commodity for every living entity on the planet. However, water resources are threatened by various sources of contamination from pesticides, hydrocarbons and heavy metals. This has resulted in the development of concepts and technologies to create a basis for provision of safe and high quality drinking water. This paper focuses on the simultaneous quantitative determination of three common contaminants, the heavy metals cadmium, lead and copper. Multivariate calibration was applied to voltammograms acquired on in-house printed carbon-ink screen-printed electrodes by the highly sensitive electrochemical method of differential pulse anodic stripping voltammetry (DPASV). The statistically inspired modification of partial least squares (SIMPLS) algorithm was employed to effect the multivariate calibration. The application of data pretreatment techniques involving range-scaling, mean-centering, weighting of variables and the effects of peak realignment are also investigated. It was found that peak realignment in conjunction with weighting and SIMPLS led to the better overall root mean square error of prediction (RMSEP) value. This work represents significant progress in the development of multivariate calibration tools in conjunction with analytical techniques for water quality determination. It is the first time that multivariate calibration has been performed on DPASV voltammograms acquired on carbon-ink screen-printed electrodes. [source] Technical basis for polar and nonpolar narcotic chemicals and polycyclic aromatic hydrocarbon criteria.ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 7 2009Abstract A method is presented for extending the target lipid model (TLM) of narcotic toxicity to polar narcotic chemicals. The proposed polyparameter TLM extends the applicability of the TLM by including polar compounds and removing explicit chemical class corrections. The validity of the model is tested using a data set of 1,687 acute toxicity tests for 42 aquatic species, including fish, amphibians, arthropods, mollusks, polychaetes, coelenterates, protozoans, and algae, and 398 chemicals. The target lipid-water partition coefficient is computed using the Abraham polyparameter model. This replaces use of the octanol-water partition coefficient so that the partitioning of polar narcotic chemicals can be described correctly. The model predicts the log median lethal concentration with a root mean square error of 0.460 for nonpolar and polar chemicals and 0.501 for only polar chemicals. [source] Predicting pasture root density from soil spectral reflectance: field measurementEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2010B. H. KUSUMO This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350,2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g,1 soil. The average root density in the Fluvial soil (13.21 mg g,1) was twice that in the Allophanic soil (6.88 mg g,1). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r2 = 0.83, ratio prediction to deviation (RPD ) = 2.44, root mean square error of cross-validation (RMSECV ) = 1.96 mg g ,1) than in the Fluvial soil (r2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g ,1). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change. [source] Combined effect of factors associated with burdens on primary caregiverGERIATRICS & GERONTOLOGY INTERNATIONAL, Issue 2 2009Hyuma Makizako Background: It is argued that a multidimensional approach is necessary for burden assessment. Reducing caregiver burden is a social problem in the ageing Japan society. We examined the combined effect of factors affecting the care burden among community-dwelling handicapped people and their caregivers. Methods: The participants were 49 handicapped people (aged 53,104 years) who received home-visit rehabilitation, and their 49 caregivers (age 42,85 years). Caregivers were provided questionnaires consisting of questions on social support, subjective well-being, self-efficacy with regard to care continuation, the Motor Fitness Scale and caregiver burden. Care recipients were assessed using the Bedside Mobility Scale and the Barthel Index. Results: We prepared the hypothesis model using structural equation modeling with the bootstrap method within outcome measures. The hypothesis model did not fit the data well. The impact of the Motor Fitness Scale was shifted from the caregiver burden to care self-efficacy and well-being, having a cooperator for care and variable of spouse caregiver or others associated with caregiver well-being in the revised model. The fit of the revised model was acceptable (goodness of fit index, 0.903; comparative fit index, 0.998; root mean square error of approximation, 0.017). In the revised model, the care recipients' disabled state was associated with caregiver burden. In addition, higher burden and poor motor fitness of caregivers might lead to lower care self-efficacy in providing continuous care and lower caregiver well-being. Conclusion: These findings suggested that the program to reduce caregiver burden should focus on aspects of the care recipients' disabled state, the caregivers' well-being, fitness, and care self-efficacy. [source] A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications,HUMAN MUTATION, Issue 5 2008Indrani Halder Abstract Autosomal ancestry informative markers (AIMs) are useful for inferring individual biogeographical ancestry (I-BGA) and admixture. Ancestry estimates obtained from Y and mtDNA are useful for reconstructing population expansions and migrations in our recent past but individual genomic admixture estimates are useful to test for association of admixture with phenotypes, as covariate in association studies to control for stratification and, in forensics, to estimate certain overt phenotypes from ancestry. We have developed a panel of 176 autosomal AIMs that can effectively distinguish I-BGA and admixture proportions from four continental ancestral populations: Europeans, West Africans, Indigenous Americans, and East Asians. We present allele frequencies for these AIMs in all four ancestral populations and use them to assess the global apportionment of I-BGA and admixture diversity among some extant populations. We observed patterns of apportionment similar to those described previously using sex and autosomal markers, such as European admixture for African Americans (14.3%) and Mexicans (43.2%), European (65.5%) and East Asian affiliation (27%) for South Asians, and low levels of African admixture (2.8,10.8%) mirroring the distribution of Y E3b haplogroups among various Eurasian populations. Using simulation studies and pedigree analysis we show that I-BGA estimates obtained using this panel and a four-population model has a high degree of precision (average root mean square error [RMSE]=0.026). Using ancestry,phenotype associations we demonstrate that a large and informative AIM panel such as this can help reduce false-positive and false-negative associations between phenotypes and admixture proportions, which may result when using a smaller panel of less informative AIMs. Hum Mutat 29(5), 648,658, 2008. © 2008 Wiley-Liss, Inc. [source] Potential of low cost close-range photogrammetry system in soil microtopography quantificationHYDROLOGICAL PROCESSES, Issue 10 2009Mohamed A. M. Abd Elbasit Abstract Soil microtopography is a dynamic soil property which affects most soil-surface and water interaction processes. The importance of soil microtopography has been recognized for a long time, but only limited reports are available in the literature. In this study, the potential of using consumer-grade cameras and close-range photogrammetry procedures to quantify soil microtopography at plot-scale level (,1 m2) were assessed. Five fabricated gypsum surfaces with different degrees of roughness were used to simulate the soil surface conditions with different soil aggregates. The surfaces' digital elevation model (DEM) was generated using the photogrammetry system (PHM) involving a consumer-grade camera, and pin-microrelief meter (PM). The DEM generated using the PHM was assessed for accuracy, roughness indices (RI), depression area percentage (DA%), depression storage capacity (DSC), and micro-rills delineation in comparison with the PM. The accuracy was evaluated using the root mean square error (RMSE) in the x-, y-, and z-directions. Visual comparison between the 3D-visions of the DEM showed strong agreement between the DEM generated by the PHM and the PM, and between the PHM and the 2D images for the different gypsum surfaces. The average RMSE in the x-. y-, and z-direction were 2·08, 1·52, and 0·82 mm for the rough surface, and 4·42, 1·65, and 3·22 mm for the smooth surface. The RIs calculated from the two methods were highly correlated. The small discrepancy between the two methods was discussed. The micro-rills delineation was also similar for the two methods regarding the network density. The grid size did not effect the RI calculation, and has a strong influence on the DA%, DSC, and the delineated micro-rills orders. Results suggest that a consumer-grade camera and close-range photogrammetry have the potential to quantify the soil microtopography. Copyright © 2009 John Wiley & Sons, Ltd. [source] Evaluating best evaporation estimate model for water surface evaporation in semi-arid region, IndiaHYDROLOGICAL PROCESSES, Issue 8 2008Shakir Ali Abstract Evaluating performances of four commonly used evaporation estimate methods, namely; Bowen ratio energy balance (BREB), mass transfer (MT), Priestley,Taylor (PT) and pan evaporation (PE), based on 4 years experimental data, the most effective and the reliable evaporation estimates model for the semi-arid region of India has been derived. The various goodness-of-fit measures, such as; coefficient of determination (R2), index of agreement (D), root mean square error (RMSE), and relative bias (RB) have been chosen for the performance evaluation. Of these models, the PT model has been found most promising when the Bowen ratio, , is known a priori, and based on its limited data requirement. The responses of the BREB, the PT, and the PE models were found comparable to each other, while the response of the MT model differed to match with the responses of the other three models. The coefficients, , of the BREB, µ of the MT, , of the PT and KP of the PE model were estimated as 0·07, 2·35, 1·31 and 0·65, respectively. The PT model can successfully be extended for free water surface evaporation estimates in semi-arid India. A linear regression model depicting relationship between daily air and water temperature has been developed using the observed water temperatures and the corresponding air temperatures. The model helped to generate unrecorded water temperatures for the corresponding ambient air temperatures. Copyright © 2007 John Wiley & Sons, Ltd. [source] Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero Basin, SpainHYDROLOGICAL PROCESSES, Issue 8 2005Antonio Ceballos Abstract The objective of this study was to validate the soil moisture data derived from coarse-resolution active microwave data (50 km) from the ERS scatterometer. The retrieval technique is based on a change detection method coupled with a data-based modelling approach to account for seasonal vegetation dynamics. The technique is able to derive information about the soil moisture content corresponding to the degree of saturation of the topmost soil layer (,5 cm). To estimate profile soil moisture contents down to 100 cm depth from the scatterometer data, a simple two-layer water balance model is used, which generates a red noise-like soil moisture spectrum. The retrieval technique had been successfully applied in the Ukraine in a previous study. In this paper, the performance of the model in a semi-arid Mediterranean environment characterized by low annual precipitation (400 mm), hot dry summers and sandy soils is investigated. To this end, field measurements from the REMEDHUS soil moisture station network in the semi-arid parts of the Duero Basin (Spain) were used. The results reveal a significant coefficient of determination (R2 = 0·75) for the averaged 0,100 cm soil moisture profile and a root mean square error (RMSE) of 2·2 vol%. The spatial arrangement of the REMEDHUS soil moisture stations also allowed us to study the influence of the small-scale variability of soil moisture within the ERS scatterometer footprint. The results show that the small-scale variability in the study area is modest and can be explained in terms of texture fraction distribution in the soil profiles. Copyright © 2004 John Wiley & Sons, Ltd. [source] Simulating daily soil water under foothills fescue grazing with the soil and water assessment tool model (Alberta, Canada)HYDROLOGICAL PROCESSES, Issue 15 2004Emmanuel Mapfumo Abstract Grazing is common in the foothills fescue grasslands and may influence the seasonal soil-water patterns, which in turn determine range productivity. Hydrological modelling using the soil and water assessment tool (SWAT) is becoming widely adopted throughout North America especially for simulation of stream flow and runoff in small and large basins. Although applications of the SWAT model have been wide, little attention has been paid to the model's ability to simulate soil-water patterns in small watersheds. Thus a daily profile of soil water was simulated with SWAT using data collected from the Stavely Range Sub-station in the foothills of south-western Alberta, Canada. Three small watersheds were established using a combination of natural and artificial barriers in 1996,97. The watersheds were subjected to no grazing (control), heavy grazing (2·4 animal unit months (AUM) per hectare) or very heavy grazing (4·8 AUM ha,1). Soil-water measurements were conducted at four slope positions within each watershed (upper, middle, lower and 5 m close to the collector drain), every 2 weeks annually from 1998 to 2000 using a downhole CPN 503 neutron moisture meter. Calibration of the model was conducted using 1998 soil-water data and resulted in Nash,Sutcliffe coefficient (EF or R2) and regression coefficient of determination (r2) values of 0·77 and 0·85, respectively. Model graphical and statistical evaluation was conducted using the soil-water data collected in 1999 and 2000. During the evaluation period, soil water was simulated reasonably with an overall EF of 0·70, r2 of 0·72 and a root mean square error (RMSE) of 18·01. The model had a general tendency to overpredict soil water under relatively dry soil conditions, but to underpredict soil water under wet conditions. Sensitivity analysis indicated that absolute relative sensitivity indices of input parameters in soil-water simulation were in the following order; available water capacity > bulk density > runoff curve number > fraction of field capacity (FFCB) > saturated hydraulic conductivity. Thus these data were critical inputs to ensure reasonable simulation of soil-water patterns. Overall, the model performed satisfactorily in simulating soil-water patterns in all three watersheds with a daily time-step and indicates a great potential for monitoring soil-water resources in small watersheds. Copyright © 2004 John Wiley & Sons, Ltd. [source] Representing elevation uncertainty in runoff modelling and flowpath mappingHYDROLOGICAL PROCESSES, Issue 12 2001Theodore A. Endreny Abstract Vertical inaccuracies in terrain data propagate through dispersal area subroutines to create uncertainties in runoff flowpath predictions. This study documented how terrain error sensitivities in the D8, Multiple Flow (MF), DEMON, D-Infinity and two hybrid dispersal area algorithms, responded to changes in terrain slope and error magnitude. Runoff dispersal areas were generated from convergent and divergent sections of low, medium, and high gradient 64-ha parcels using a 30 m pixel scale control digital elevation model (DEM) and an ensemble of alternative realizations of the control DEM. The ensemble of alternative DEM realizations was generated randomly to represent root mean square error (RMSE) values ranging from 0·5 to 6 m and spatial correlations of 0 to 0·999 across 180 m lag distances. Dispersal area residuals, derived by differencing output from control and ensemble simulations, were used to quantify the spatial consistency of algorithm dispersal area predictions. A maximum average algorithm consistency of 85% was obtained in steep sloping convergent terrain, and two map analysis techniques are recommended in maintaining high spatial consistencies under less optimum terrain conditions. A stochastic procedure was developed to translate DEM error into dispersal area probability maps, and thereby better represent uncertainties in runoff modelling and management. Two uses for these runoff probability maps include watershed management indices that identify the optimal areas for intercepting polluted runoff as well as Monte-Carlo-ready probability distributions that report the cumulative pollution impact of each pixel's downslope dispersal area. Copyright © 2001 John Wiley & Sons, Ltd. [source] Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th centuryINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2008Yasuyuki Aono Abstract Changes in springtime temperature in Kyoto, Japan, since the 9th century were reconstructed, using the phenological data series for cherry tree (Prunus jamasakura), deduced from old diaries and chronicles. Phenological data for 732 years was made available by combining data from previous studies. The full-flowering date of cherry trees fluctuates in accordance with temperature conditions during February and March. Full-flowering dates were closely related to the March mean temperature by means of a temperature accumulation index, in which plant growth is considered to be an exponential function of temperature. Calibration enabled accurate estimation of temperatures in the instrumental period, after 1880; the root mean square error (RMSE) of temperature estimates was determined to be within 0.1 °C, after smoothing by local linear regression over time spans of 31 years. The results suggested the existence of four cold periods, 1330,1350, 1520,1550, 1670,1700, and 1825,1830, during which periods the estimated March mean temperature was 4,5 °C, about 3,4 °C lower than the present normal temperature. These cold periods coincided with the less extreme periods, known as the Wolf, Spoerer, Maunder, and Dalton minima, in the long-term solar variation cycle, which has a periodicity of 150,250 years. The sunspot cycle length, a short-term solar variation cycle, was also compared with the temperature estimates, with the result that a time lag of about 15 years was detected in the climatic temperature response to short-term solar variation. Copyright © 2007 Royal Meteorological Society [source] Missing data estimation for 1,6,h gaps in energy use and weather data using different statistical methodsINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 13 2006David E. Claridge Abstract Analysing hourly energy use to determine retrofit savings or diagnose system problems frequently requires rehabilitation of short periods of missing data. This paper evaluates four methods for rehabilitating short periods of missing data. Single variable regression, polynomial models, Lagrange interpolation, and linear interpolation models are developed, demonstrated, and used to fill 1,6,h gaps in weather data, heating data and cooling data for commercial buildings. The methodology for comparing the performance of the four different methods for filling data gaps uses 11 1-year data sets to develop different models and fill over 500 000 ,pseudo-gaps' 1,6,h in length for each model. These pseudo-gaps are created within each data set by assuming data is missing, then these gaps are filled and the ,filled' values compared with the measured values. Comparisons are made using four statistical parameters: mean bias error (MBE), root mean square error, sum of the absolute errors, and coefficient of variation of the sum of the absolute errors. Comparison based on frequency within specified error limits is also used. A linear interpolation model or a polynomial model with hour-of-day as the independent variable both fill 1,6 missing hours of cooling data, heating data or weather data, with accuracy clearly superior to the single variable linear regression model and to the Lagrange model. The linear interpolation model is the simplest and most convenient method, and generally showed superior performance to the polynomial model when evaluated using root mean square error, sum of the absolute errors, or frequency of filling within set error limits as criteria. The eighth-order polynomial model using time as the independent variable is a relatively simple, yet powerful approach that provided somewhat superior performance for filling heating data and cooling data if MBE is the criterion as is often the case when evaluating retrofit savings. Likewise, a tenth-order polynomial model provided the best performance when filling dew-point temperature data when MBE is the criterion. It is possible that the results would differ somewhat for other data sets, but the strength of the linear and polynomial models relative to the other models evaluated seems quite robust. Copyright © 2006 John Wiley & Sons, Ltd. [source] Inter-particle contact heat transfer model: an extension to soils at elevated temperaturesINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 2 2005W. H. Leong Abstract A simple ,inter-particle contact heat transfer' model for predicting effective thermal conductivity of soils at moderate temperatures (0,30°C) has been extended up to 90°C. The extended model accounts for latent heat transport by water vapour diffusion in soil air above the permanent wilting point; below that point, the soil thermal conductivity is approximated by linear interpolation without latent heat effect. By and large the best results are obtained when the latent heat is used only in the ,self consistent approximation' model with an overall root mean square error of 35% for all soils under consideration or 26% when excluding volcanic soils. This option can also be applied to moderate temperatures at which the enhanced heat transfer is negligibly small. Copyright © 2005 John Wiley & Sons, Ltd. [source] Trust in Nurses Scale: construct validity and internal reliability evaluationJOURNAL OF ADVANCED NURSING, Issue 3 2010Laurel E. Radwin radwin l.e. & cabral h.j. (2010) Trust in Nurses Scale: construct validity and internal reliability evaluation. Journal of Advanced Nursing66(3), 683,689. Abstract Aim., This paper is a report of the continued psychometric evaluation of the Trust in Nurses Scale. Background., Qualitative analyses indicate that trust in nurses is critically important to adult patients. Instruments that distinctively measure this concept are lacking. A middle-range theory of patient-centred nursing care provided the theoretical basis for the Trust in Nurses Scale. Content validity was assessed by an expert panel and patient interviews. Construct validity and reliability were found acceptable using multi-trait/multi-item analysis techniques. These findings were previously reported. Methods., Construct validity and reliability of the Trust in Nurses Scale was assessed in 2007 using data collected during 2004,2005 from 187 hospitalized patients in a haematology-oncology setting. Trust in nurses (the latent factor) was operationalized by five items (manifest variables) using confirmatory factor analyses. Fit statistics included comparative fit index, Tucker-Lewis Index, root mean square error of approximation and the standardized root mean square residual. Internal consistency reliability was assessed using coefficient alpha. Findings., Both a five-item and a four-item version demonstrate acceptable psychometric properties. The five-item version met three fit statistics criteria. Fifty-nine per cent of the variance was explained. A four-item version met all fit statistics criteria. Sixty-six per cent of the variance was explained. Acceptable internal consistency reliability was found for both versions. Conclusion., Previous psychometric testing of the Trust in Nurses Scale provided evidence of the instrument's reliability, content validity and construct validity. The presented analyses further support construct validity. Thus, cumulative findings indicate that the instrument measures with a few items the underlying concept of trust. [source] Selection of individual variables versus intervals of variables in PLSRJOURNAL OF CHEMOMETRICS, Issue 2 2010Masoud Shariati-Rad Abstract The selection abilities of the two well-known techniques of variable selection, synergy interval-partial least-squares (SiPLS) and genetic algorithm-partial least-squares (GA-PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA-PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA-PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA-PLS was not significant. For real data of less overlapped components (metabolite dataset), GA-PLS that tends to select far fewer variables did not give significantly better root mean square error of cross-validation (RMSECV), cross-validated R2 (Q2), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA-PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA-PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd. [source] Impartial graphical comparison of multivariate calibration methods and the harmony/parsimony tradeoffJOURNAL OF CHEMOMETRICS, Issue 11-12 2006Forrest Stout Abstract For multivariate calibration with the relationship y,=,Xb, it is often necessary to determine the degrees of freedom for parsimony consideration and for the error measure root mean square error of calibration (RMSEC). This paper shows that degrees of freedom can be estimated by an effective rank (ER) measure to estimate the model fitting degrees of freedom and the more parsimonious model has the smallest ER. This paper also shows that when such a measure is used on the X-axis, simultaneous graphing of model errors and other regression diagnostics is possible for ridge regression (RR), partial least squares (PLS) and principal component regression (PCR) and thus, a fair comparison between all potential models can be accomplished. The ER approach is general and applicable to other multivariate calibration methods. It is often noted that by selecting variables, more parsimonious models are obtained; typically by multiple linear regression (MLR). By using the ER, the more parsimonious model is graphically shown to not always be the MLR model. Additionally, a harmony measure is proposed that expresses the bias/variance tradeoff for a particular model. By plotting this new measure against the ER, the proper harmony/parsimony tradeoff can be graphically assessed for RR, PCR and PLS. Essentially, pluralistic criteria for fairly valuating and characterizing models are better than a dualistic or a single criterion approach which is the usual tactic. Results are presented using spectral, industrial and quantitative structure activity relationship (QSAR) data. Copyright © 2007 John Wiley & Sons, Ltd. [source] Tikhonov regularization in standardized and general form for multivariate calibration with application towards removing unwanted spectral artifactsJOURNAL OF CHEMOMETRICS, Issue 1-2 2006Forrest Stout Abstract Tikhonov regularization (TR) is an approach to form a multivariate calibration model for y,=,Xb. It includes a regulation operator matrix L that is usually set to the identity matrix I and in this situation, TR is said to operate in standard form and is the same as ridge regression (RR). Alternatively, TR can function in general form with L,,,I where L is used to remove unwanted spectral artifacts. To simplify the computations for TR in general form, a standardization process can be used on X and y to transform the problem into TR in standard form and a RR algorithm can now be used. The calculated regression vector in standardized space must be back-transformed to the general form which can now be applied to spectra that have not been standardized. The calibration model building methods of principal component regression (PCR), partial least squares (PLS) and others can also be implemented with the standardized X and y. Regardless of the calibration method, armed with y, X and L, a regression vector is sought that can correct for irrelevant spectral variation in predicting y. In this study, L is set to various derivative operators to obtain smoothed TR, PCR and PLS regression vectors in order to generate models robust to noise and/or temperature effects. Results of this smoothing process are examined for spectral data without excessive noise or other artifacts, spectral data with additional noise added and spectral data exhibiting temperature-induced peak shifts. When the noise level is small, derivative operator smoothing was found to slightly degrade the root mean square error of validation (RMSEV) as well as the prediction variance indicator represented by the regression vector 2-norm thereby deteriorating the model harmony (bias/variance tradeoff). The effective rank (ER) (parsimony) was found to decrease with smoothing and in doing so; a harmony/parsimony tradeoff is formed. For the temperature-affected data and some of the noisy data, derivative operator smoothing decreases the RMSEV, but at a cost of greater values for . The ER was found to increase and hence, the parsimony degraded. A simulated data set from a previous study that used TR in general form was reexamined. In the present study, the standardization process is used with L set to the spectral noise structure to eliminate undesirable spectral regions (wavelength selection) and TR, PCR and PLS are evaluated. There was a significant decrease in bias at a sacrifice to variance with wavelength selection and the parsimony essentially remains the same. This paper includes discussion on the utility of using TR to remove other undesired spectral patterns resulting from chemical, environmental and/or instrumental influences. The discussion also incorporates using TR as a method for calibration transfer. Copyright © 2006 John Wiley & Sons, Ltd. [source] MODELING VARIETAL EFFECT ON THE WATER UPTAKE BEHAVIOR OF MILLED RICE (ORYZA SATIVA L.) DURING SOAKINGJOURNAL OF FOOD PROCESS ENGINEERING, Issue 6 2007B.K. YADAV ABSTRACT Milled rice is soaked until saturation before cooking and other processing. The soaking behavior of the milled rice is affected by varietal factor as well as initial moisture content (M0) of the samples. In the present study, tests were performed for milled whole kernels of 10 rice varieties ranging from low to high amylose content (16,29% d.b.) with three initial moisture levels (approximately 8, 12 and 16% d.b.) for monitoring water uptake in rice kernels during soaking at room temperature (25 ± 1C), in relation to the varietal differences manifested by the physicochemical properties. The water uptake by milled rice kernels took place at a faster rate in the beginning and was followed by a diminishing rate finally leading to a saturated value during soaking. The water uptake of the kernels during soaking could be best expressed by a modified exponential relationship with R2 values ranging from 0.971 to 0.998 for all varieties. The slope of the fitted straight line between actual and estimated moisture contents of milled rice during soaking using a modified exponential relationship was about unity (0.998) with a high R2 value of 0.989 and a root mean square error of 1.2% d.b. The parameters of the fitted model were the function of the M0 and the physicochemical properties of the milled rice. Using developed relationship, the water uptake of the milled rice during soaking could be estimated from its M0 and the physicochemical properties within±10% of the actual values. PRACTICAL APPLICATIONS This information would be useful for the scientific world working on the soaking characteristics of various varieties of rice, mainly for the modeling of the soaking process. It could also be used as a tool in selecting the rice varieties to meet their desired water uptake properties in relation to their psychochemical properties by rice breeder scientists. [source] THIN-LAYER DRYING KINETICS OF SESAME HULLS UNDER FORCED CONVECTION AND OPEN SUN DRYINGJOURNAL OF FOOD PROCESS ENGINEERING, Issue 3 2007MAJDI A. AL-MAHASNEH ABSTRACT Sesame hulls are a useful by-product of the sesame processing industry. The sesame hulls are produced at a high moisture content (68% wet basis) and need further drying to prevent deterioration. In this study, both open sun drying (OSD) and forced convection drying (FCD) at 42, 55, and 76C and 1.2 m/s air velocity were investigated. Six common thin-layer drying models were fitted to the experimental data. Several statistical parameters were used to evaluate the performance of thin-layer drying models, including r2, x2, root mean square error (RMSE) and residuals. Sesame hull drying was found to take place completely in the falling rate region. The modified Page model was found to describe OSD data well, while the Wang and Singh model was the best model for describing FCD. Effective diffusivity was found to be 1.89 × 10 - 8 m2/s and 7.36 × 10 - 10 to 1.20 × 10 - 9 m2/s for OSD and FCD, respectively. Activation energy was also found to be 12.95 kJ/mol for FCD. [source] MONTE CARLO SIMULATION OF FAR INFRARED RADIATION HEAT TRANSFER: THEORETICAL APPROACHJOURNAL OF FOOD PROCESS ENGINEERING, Issue 4 2006F. TANAKA ABSTRACT We developed radiation heat transfer models with the combination of the Monte Carlo (MC) method and computational fluid dynamic approach and two-dimensional heat transfer models based on the fundamental quantum physics of radiation and fluid dynamics. We investigated far infrared radiation (FIR) heating in laminar and buoyancy airflow. A simple prediction model in laminar airflow was tested with an analytical solution and commercial software (CFX 4). The adequate number of photon tracks for MC simulation was established. As for the complex designs model, the predicted results agreed well with the experimental data with root mean square error of 3.8 K. Because food safety public concerns are increasing, we applied this model to the prediction of the thermal inactivation level by coupling with the microbial kinetics model. Under buoyancy airflow condition, uniformity of FIR heating was improved by selecting adequate wall temperature and emissivity. [source] MATHEMATICAL MODELLING OF THIN-LAYER DRYING OF KIWIFRUIT SLICESJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 2009M DOYMAZ ABSTRACT The effect of temperature on the drying kinetics of kiwifruit was investigated. The drying process was carried out at temperatures of 50, 55 and 60C, air velocity of 2.4 m/s and relative humidity between 10,25%. Drying time decreased considerably with increased air temperature. Drying process took place in the falling rate period. Twelve mathematical models available in the literature were fitted to the experimental data. The models were compared by four statistical parameters; i.e., determination of coefficient, mean relative percent error, reduced chi-square and root mean square error, and the best-fit model was selected. The modified Henderson and Pabis and Verma et al. models were given the best results in describing thin-layer drying of kiwifruits. The effective diffusivity of water during air-drying varied from 1.743 to 2.241 × 10,10 m2/s over the temperature range investigated, with activation energy equal to 22.48 kJ/mol. PRACTICAL APPLICATIONS Drying can be described as an industrial preservation method in which water content and activity of agricultural products are decreased by heated air to minimize biochemical, chemical and microbiological deterioration. Kiwifruit has a very short life because of softening and vitamin loss during cold storage. The use of drying prolongs the shelf-life of the kiwifruit, as the water content reduction slows down deterioration reactions. In this study, drying characteristics of kiwifruits were studied in a convectional hot-air dryer. The objectives of the present study were to determine experimentally the thin-layer drying characteristics and rehydration capacity of samples, and to fit the experimental data to 12 mathematical models available from the literature. [source] PRELIMINARY EVALUATION OF THE APPLICATION OF THE FTIR SPECTROSCOPY TO CONTROL THE GEOGRAPHIC ORIGIN AND QUALITY OF VIRGIN OLIVE OILSJOURNAL OF FOOD QUALITY, Issue 4 2007ALESSANDRA BENDINI ABSTRACT A rapid Fourier transform infrared (FTIR) attenuated total reflectance spectroscopic method was applied to determine qualitative parameters such as free fatty acid (FFA) content and the peroxide value (POV) in virgin olive oils. Calibration models were constructed using partial least squares regression on a large number of virgin olive oil samples. The best results (R2 = 0.955, root mean square error in cross validation [RMSECV] = 0.15) to evaluate FFA content expressed in oleic acid % (w/w) were obtained considering a calibration range from 0.2 to 9.2% of FFA relative to 190 samples. For POV determination, the result obtained, built on 80 olive oil samples with a calibration range from 11.1 to 49.7 meq O2/kg of oil, was not satisfactory (R2 = 0.855, RMSECV = 3.96). We also investigated the capability of FTIR spectroscopy, in combination with multivariate analysis, to distinguish virgin olive oils based on geographic origin. The spectra of 84 monovarietal virgin olive oil samples from eight Italian regions were collected and elaborated by principal component analysis (PCA), considering the fingerprint region. The results were satisfactory and could successfully discriminate the majority of samples coming from the Emilia Romagna, Sardinian and Sicilian regions. Moreover, the explained variance from this PCA was higher than 96%. PRACTICAL APPLICATIONS The verification of the declared origin or the determination of the origin of an unidentified virgin olive oil is a challenging problem. In this work, we have studied the applicability of Fourier transform infrared coupled with multivariate statistical analysis to discriminate the geographic origin of virgin olive oil samples from different Italian regions. [source] Nondestructive Assessment of Lipid Oxidation in Minced Poultry Meat by Autofluorescence SpectroscopyJOURNAL OF FOOD SCIENCE, Issue 1 2000J.P. Wold ABSTRACT: To develop a rapid method to assess lipid oxidation, autofluorescence spectra (excitation wavelengths 365, 380, and 400 nm) from large samples (17 cm2) of minced poultry meat were collected by an optical system to determine directly lipid oxidation level. The same samples were also measured by 2-thiobarbituric acid method (TBARS). High correlations could be made between the TBARS method and autofluorescence spectra, especially those from 380 nm excitation. Partial least squares regression resulted in a root mean square error of 0.15 (R = 0.87) for chicken meat and 0.24 (R = 0.80) for mechanically recovered turkey meat. Classification analysis between fresh (TBARS < 0.25) and rancid (TBARS > 0.25) samples was done with high success rates. Autofluorescence spectroscopy might be well suited for rapid on-line determination of lipid oxidation level in minced poultry meat. [source] Separation of dyes using composite carbon membranesAICHE JOURNAL, Issue 7 2009Sonny Sachdeva Abstract A composite, clay supported carbon membrane has been synthesized by carbonization of a blend of polyethylene glycol and phenol formaldehyde resin and the membrane thus obtained is characterized by separation of dyes. This membrane is subjected to permeability test using pure water which is found to be considerably higher than that reported in literature. It is subsequently shown to reject Acid Orange 7 dye from water with the rejection dependent on pressure and concentration of the dye which is typical phenomenon observed for a charged membrane. The separation data has been analyzed using the Space charge model and the membrane charge is estimated by minimizing the root mean square error between the experimental results and those calculated from the model. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Estimation of soil water content and evapotranspiration from irrigated cropland on the North China PlainJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 5 2008Jie Jiang Abstract For nearly 30 y, cropland on the North China Plain (NCP) has been irrigated primarily by pumping groundwater with no sustainable management strategy. This has caused a continuous decline of the water table. A sustainable groundwater management and irrigation strategy must be established in order to prevent further decline of the water table; to do this, one must quantify soil water content and daily rates of deep percolation and locate evapotranspiration from irrigated cropland. For that purpose, we developed a three-layer soil,water balance (SWB) model based on an approach described by Kendy et al. (2003). In this model, the unsaturated soil zone is divided into three layers: a surface active layer, a middle active soil layer, and a lowest passive soil layer. The middle and the lowest layers dynamically change with the development of crop rooting depth. A simple "tipping bucket" routine and an exponential equation are used to redistribute soil water in the three soil layers. The actual evapotranspiration estimated is partitioned into soil evaporation and crop transpiration using a dual crop coefficient reference approach. At first, the model was calibrated using data obtained from five deficiently irrigated field plots located at an experimental site in the NCP between 1998 and 2003. Then, the model was validated by comparing estimated soil water contents with measured ones at three other plots with nondeficient irrigation. The estimates of actual evapotranspiration were compared with those measured with a large-scale weighing lysimeter (3 m2). The index of agreement (IA) for soil water contents varied between 0.62 and 0.80; the concordance correlation coefficient (CCC) and the root mean square error obtained from the same comparison were 0.34,0.65 and 0.043,0.074,cm3,cm,3, respectively. The rates of 10 d mean evapotranspiration estimated by the model show a good fit to those measured by the large-scale lysimeter; this is indicated by IA = 0.94 and CCC = 0.88. Our results indicate that at the irrigated cropland on the plain, deep soil water,percolation rates are usually <200,mm y,1 under nondeficient-irrigation conditions. [source] BP NEURAL NETWORK FOR EVALUATING SENSORY TEXTURE PROPERTIES OF COOKED SAUSAGEJOURNAL OF SENSORY STUDIES, Issue 6 2009QING-LI DONG ABSTRACT In order to replace sensory evaluation by instrumental measurement with more accuracy for texture properties of cooked sausage, correlation analysis between sensory and instrumental texture was established by multiple regression and back propagation (BP) neural network, respectively. Effect of different fat, salt, moisture and starch addition on the texture of cooked sausage was also investigated in this paper. It indicated that the accuracy and goodness of fit of predicting sensory hardness, cohesiveness and juiciness by BP neural network were more significant than those by multiple regressions with lower root mean square error and standard error of prediction. Although both accuracy and bias factors of two models were in acceptable range, BP neural network provides an accurate and selective method for predicting sensory texture evaluation in similar meat products. PRACTICAL APPLICATIONS The effect of different fat, salt, moisture and starch addition on textural properties of cooked sausage could be valuable to the meat industry in order to select the appropriate components for improving the texture of sausage. Artificial neural network technology used in this study can be useful for the fast, on-time and convenient detection of texture measurement by instrumental instead of sensory evaluation. [source] Investigating the Fate and Transport of Escherichia coli in the Charles River, Boston, Using High-Resolution Observation and Modeling,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2008Ferdi L. Hellweger Abstract:, The processes affecting the fate and transport of Escherichia coli in surface waters were investigated using high-resolution observation and modeling. The concentration patterns in Boston's Charles River were observed during four sampling events with a total of 757 samples, including two spatial surveys with two along-river (1,500 m length) and three across-river (600 m length) transects at approximately 25-m intervals, and two temporal surveys at a fixed location (Community Boating) over seven days at hourly intervals. The data reveal significant spatial and temporal structure at scales not resolved by typical monitoring programs. A mechanistic, time-variable, three-dimensional coupled hydrodynamic and water quality model was developed using the ECOMSED and RCA modeling frameworks. The computational grid consists of 3,066 grid cells with average length dimension of 25 m. Forcing functions include upstream and downstream boundary conditions, Stony Brook, and Muddy River (major tributaries) combined sewer overflow (CSO) and non-CSO discharge and wind. The model generally reproduces the observed spatial and temporal patterns. This includes the presence and absence of a plume in the study area under similar loading, but different hydrodynamic conditions caused by operation of the New Charles River Dam (downstream) and wind. The model also correctly predicts an episode of high concentrations at the time-series station following seven days of no rainfall. The model has an overall root mean square error (RMSE) of 250 CFU/100 ml and an error rate (above or below the USEPA-recommended single sample criteria value of 235 CFU/100 ml) of 9.4%. At the time series station, the model has an RMSE of 370 CFU/100 ml and an error rate of 15%. [source] EVALUATION OF LIGHT DETECTION AND RANGING (LIDAR) FOR MEASURING RIVER CORRIDOR TOPOGRAPHY,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 1 2002Zachary H. Bowen ABSTRACT: LIDAR is relatively new in the commercial market for remote sensing of topography and it is difficult to find objective reporting on the accuracy of LIDAR measurements in an applied context. Accuracy specifications for LIDAR data in published evaluations range from 1 to 2 m root mean square error (RMSEx,y) and 15 to 20 cm RMSEz. Most of these estimates are based on measurements over relatively flat, homogeneous terrain. This study evaluated the accuracy of one LIDAR data set over a range of terrain types in a western river corridor. Elevation errors based on measurements over all terrain types were larger (RMSEz equals 43 cm) than values typically reported. This result is largely attributable to horizontal positioning limitations (1 to 2 m RMSEx,y) in areas with variable terrain and large topographic relief. Cross-sectional profiles indicated algorithms that were effective for removing vegetation in relatively flat terrain were less effective near the active channel where dense vegetation was found in a narrow band along a low terrace. LIDAR provides relatively accurate data at densities (50,000 to 100,000 points per km2) not feasible with other survey technologies. Other options for projects requiring higher accuracy include low-altitude aerial photography and intensive ground surveying. [source] | |||||||||||||||||||||||||