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Calibration Models (calibration + models)
Selected AbstractsPredicting 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] Hyperspectral NIR image regression part II: dataset preprocessing diagnosticsJOURNAL OF CHEMOMETRICS, Issue 3-4 2006James Burger Abstract When known reference values such as concentrations are available, the spectra from near infrared (NIR) hyperspectral images can be used for building regression models. The sets of spectra must be corrected for errors, transformed to reflectance or absorbance values, and trimmed of bad pixel outliers in order to build robust models and minimize prediction errors. Calibration models can be computed from small (<100) sets of spectra, where each spectrum summarizes an individual image or spatial region of interest (ROI), and used to predict large (>20,000) test sets of spectra. When the distributions of these large populations of predicted values are viewed as histograms they provide mean sample concentrations (peak centers) as well as uniformity (peak widths) and purity (peak shape) information. The same predicted values can also be viewed as concentration maps or images adding spatial information to the uniformity or purity presentations. Estimates of large population statistics enable a new metric for determining the optimal number of model components, based on a combination of global bias and pooled standard deviation values computed from multiple test images or ROIs. Two example datasets are presented: an artificial mixture design of three chemicals with distinct NIR spectra and samples of different cheeses. In some cases it was found that baseline correction by taking first derivatives gave more useful prediction results by reducing optical problems. Other data pretreatments resulted in negligible changes in prediction errors, overshadowed by the variance associated with sample preparation or presentation and other physical phenomena. Copyright © 2007 John Wiley & Sons, Ltd. [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] Data reconciliation of concentration estimates from mid-infrared and dielectric spectral measurements for improved on-line monitoring of bioprocessesBIOTECHNOLOGY PROGRESS, Issue 2 2009Michal Dabros Abstract Real-time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier-transform mid-infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off-line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration. To address this problem, the predicted metabolite and biomass concentrations, along with off-gas analysis and base addition measurements, are reconciled in real-time based on the closure of mass and elemental balances. A statistical test is used to confirm the integrity of the balances, and a non-negativity constraint is used to guide the data reconciliation algorithm toward positive concentrations. It is verified experimentally that the proposed approach reduces the standard error of prediction without the need for additional off-line analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] Application of Multiway Chemometric Techniques for Analysis of AC Voltammetric DataELECTROANALYSIS, Issue 3-5 2009Aleksander Jaworski Abstract Three multiway calibration techniques have been applied for determining of the suppressor concentration in industrial copper electrometallization baths used in semiconductor manufacturing. Parallel factor analysis (PARAFAC) for multiway array decomposition coupled with inverse least squares (ILS) regression (PARAFAC/ILS), direct trilinear decomposition (DTLD) coupled with ILS (DTLD/ILS), and multilinear partial least squares (N-PLS) regression were employed to develop and test calibration models based on trilinear AC voltammetric data. All techniques employed comparatively produce reliable calibration model and provide quantitative information about its robustness. [source] Calibration model of microbial biomass carbon and nitrogen concentrations in soils using ultraviolet absorbance and soil organic matterEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2008X. Xu Summary There is a need for a rapid, simple and reliable method of determining soil microbial biomass (SMB) for all soils because traditional methods are laborious. Earlier studies have reported that SMB-C and -N concentrations in grassland and arable soils can be estimated by measurement of UV absorbance in soil extracts. However, these previous studies focused on soils with small soil organic matter (SOM) contents, and there was no consideration of SOM content as a covariate to improve the estimation. In this study, using tropical and temperate forest soils with a wide range of total C (5,204 mg C g,1 soil) and N (1,12 mg N g,1 soil) contents and pH values (4.1,5.9), it was found that increase in UV absorbance of soil extracts at 280 nm (UV280) after fumigation could account for 92,96% of the variance in estimates of the SMB-C and -N concentrations measured by chloroform fumigation and extraction (P < 0.001). The data were combined with those of earlier workers to calibrate UV-based regression models for all the soils, by taking into account their varying SOM content. The validation analysis of the calibration models indicated that the SMB-C and -N concentrations in the 0,5 cm forest soils simulated by using the increase in UV280 and SOM could account for 86,93% of the variance in concentrations determined by chloroform fumigation and extraction (P < 0.001). The slope values of linear regression equations between measured and simulated values were 0.94 ± 0.03 and 0.94 ± 0.04, respectively, for the SMB-C and -N. However, simulation using the regression equations obtained by using only the data for forest profile soils gave less good agreement with measured values. Hence, the calibration models obtained by using the increase in UV280 and SOM can give a rapid, simple and reliable method of determining SMB for all soils. [source] Modified evolving window factor analysis for process monitoringJOURNAL OF CHEMOMETRICS, Issue 9 2004S. Kamaledin Setarehdan Abstract Reaction process monitoring and control are usually involved with direct measurement or indirect model-based prediction of concentration profiles of the constituents of interest in a chemical reaction at regular time intervals. These approaches are expensive, time-consuming and sometimes impossible. On the other hand, application of so-called ,calibration-free' techniques such as EFA and EWFA to spectral data usually provides important information regarding the structural variations in the chemical system without identification of the chemical components responsible for the variations. In this paper a novel spectral data pre-processing algorithm is presented which helps EWFA to extract the concentration trends of the components of interest within the reaction. The proposed algorithm uses the pure spectrum of the component of interest to develop a so-called ,weighting filter' which is applied to the input spectral information before EWFA. The algorithm was applied to a real Raman spectral data set obtained from a pre-treatment distillation column used for removing unwanted heavy/cyclic hydrocarbons from naphtha in an oil company. Comparison of the concentration trends resulting from the proposed algorithm with those obtained using conventional PLS1 models shows that the new calibration-free and on-line algorithm outperforms the calibration models obtained by difficult and expensive laboratory work. Copyright © 2005 John Wiley & Sons, Ltd. [source] Theory of net analyte signal vectors in inverse regressionJOURNAL OF CHEMOMETRICS, Issue 12 2003Rasmus Bro Abstract The net analyte signal and the net analyte signal vector are useful measures in building and optimizing multivariate calibration models. In this paper a theory for their use in inverse regression is developed. The theory of net analyte signal was originally derived from classical least squares in spectral calibration where the responses of all pure analytes and interferents are assumed to be known. However, in chemometrics, inverse calibration models such as partial least squares regression are more abundant and several tools for calculating the net analyte signal in inverse regression models have been proposed. These methods yield different results and most do not provide results that are in accordance with the chosen calibration model. In this paper a thorough development of a calibration-specific net analyte signal vector is given. This definition turns out to be almost identical to the one recently suggested by Faber (Anal. Chem. 1998; 70: 5108,5110). A required correction of the net analyte signal in situations with negative predicted responses is also discussed. Copyright © 2004 John Wiley & Sons, Ltd. [source] Non-parametric statistical methods for multivariate calibration model selection and comparison,JOURNAL OF CHEMOMETRICS, Issue 12 2003Edward V. Thomas Abstract Model selection is an important issue when constructing multivariate calibration models using methods based on latent variables (e.g. partial least squares regression and principal component regression). It is important to select an appropriate number of latent variables to build an accurate and precise calibration model. Inclusion of too few latent variables can result in a model that is inaccurate over the complete space of interest. Inclusion of too many latent variables can result in a model that produces noisy predictions through incorporation of low-order latent variables that have little or no predictive value. Commonly used metrics for selecting the number of latent variables are based on the predicted error sum of squares (PRESS) obtained via cross-validation. In this paper a new approach for selecting the number of latent variables is proposed. In this new approach the prediction errors of individual observations (obtained from cross-validation) are compared across models incorporating varying numbers of latent variables. Based on these comparisons, non-parametric statistical methods are used to select the simplest model (least number of latent variables) that provides prediction quality that is indistinguishable from that provided by more complex models. Unlike methods based on PRESS, this new approach is robust to the effects of anomalous observations. More generally, the same approach can be used to compare the performance of any models that are applied to the same data set where reference values are available. The proposed methodology is illustrated with an industrial example involving the prediction of gasoline octane numbers from near-infrared spectra. Published in 2004 by John Wiley & Sons, Ltd. [source] Wavelength selection with Tabu SearchJOURNAL OF CHEMOMETRICS, Issue 8-9 2003J. A. Hageman Abstract This paper introduces Tabu Search in analytical chemistry by applying it to wavelength selection. Tabu Search is a deterministic global optimization technique loosely based on concepts from artificial intelligence. Wavelength selection is a method which can be used for improving the quality of calibration models. Tabu Search uses basic, problem-specific operators to explore a search space, and memory to keep track of parts already visited. Several implementational aspects of wavelength selection with Tabu Search will be discussed. Two ways of memorizing the search space are investigated: storing the actual solutions and storing the steps necessary to create them. Parameters associated with Tabu Search are configured with a Plackett,Burman design. In addition, two extension schemes for Tabu Search, intensification and diversification, have been implemented and are applied with good results. Eventually, two implementations of wavelength selection with Tabu Search are tested, one which searches for a solution with a constant number of wavelengths and one with a variable number of wavelengths. Both implementations are compared with results obtained by wavelength selection methods based on simulated annealing (SA) and genetic algorithms (GAs). It is demonstrated with three real-world data sets that Tabu Search performs equally well as and can be a valuable alternative to SA and GAs. The improvements in predictive abilities increased by a factor of 20 for data set 1 and by a factor of 2 for data sets 2 and 3. In addition, when the number of wavelengths in a solution is variable, measurements on the coverage of the search space show that the coverage is usually higher for Tabu Search compared with SA and GAs. Copyright © 2003 John Wiley & Sons, Ltd. [source] PREDICTION OF TEXTURE IN GREEN ASPARAGUS BY NEAR INFRARED SPECTROSCOPY (NIRS)JOURNAL OF FOOD QUALITY, Issue 4 2002D. PEREZ NIR spectroscopy was used to estimate three textural parameters of green asparagus: maximum cutting force, energy and toughness. An Instron 1140 Texturometer provided reference data. A total of 199 samples from two asparagus varieties (Taxara and UC-157) were used to obtain the calibration models between the reference data and the NIR spectral data. Standard errors of cross validation (SECV) and r2 were (5.73, 0.84) for maximum cutting force, (0.58, 0.66) for toughness, and (0.04, 0.85) for cutting energy. The mathematical models developed as calibration models were tested using independent validation samples (n =20); the resulting standard errors of prediction (SEP) and r2 for the same parameters were (6.73, 0.82), (0.61, 0.57) and (0.04, 0.89), respectively. For toughness, substantially improved r2 (0.85) and SEP (0.36) when four samples exhibiting large residual values were removed. The results indicated that NIRS could accurately predict texture parameters of green asparagus. [source] Analysis of low content drug tablets by transmission near infrared spectroscopy: Selection of calibration ranges according to multivariate detection and quantitation limits of PLS modelsJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 12 2008Manel Alcalą Abstract The content uniformity of low dose products is a major concern in the development of pharmaceutical formulations. Near infrared spectroscopy may be used to support the design and optimization of potent drug manufacturing processes through the analysis of blends and tablets in a relatively short time. A strategy for the selection of concentration ranges in the development of multivariate calibration is presented, evaluating the detection and quantitation limits of the obtained multivariate models. The strategy has been applied to the determination of an active principle in pharmaceutical tablets of low concentration (0,5%, w/w), using Fourier Transform Near Infrared (FT-NIR) transmission spectroscopy. The quantitation and detection limits decreased as the upper concentration level of the calibration models was reduced. The results obtained show that the selection of concentration ranges is a critical aspect during model design. The selection of wide concentration ranges with high levels is not recommended for the determination of analytes at minor levels (<1%, w/w), even when the concentration of interest is within the range of the model. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:5318,5327, 2008 [source] In-line measurement of a drug substance via near infrared spectroscopy to ensure a robust crystallization processJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 11 2006George X. Zhou Abstract The crystallization of Etoricoxib, a polymorphic compound, has been optimized and controlled by seeding with the desired polymorph at a moderate supersaturation condition. To enhance the process robustness, near infrared spectroscopy (NIRS) has been evaluated as an inline measurement method for the concentration of Etoricoxib prior to seeding in the crystallization process. In this NIRS method, a spectral discriminant analysis based on principal component analysis (PCA) was established to detect the presence of solids produced by premature crystallization, or bubbles in the path of light. Once a spectrum was qualified as that of clear solution, concentration of Etoricoxib was calculated by a NIRS calibration model built with partial least squares (PLS) regression and with offline HPLC analysis as the reference method. This model was accurate with a standard error of cross validation (SECV) less than 1.2 mg/g Etoricoxib and a standard error of prediction (SEP) less than 1.7 mg/g over the concentration range from 50 to 170 mg/g, temperature range from 49 to 65°C, and different sources of materials. In addition, all aspects of the offline HPLC method, especially the sampling procedure, were optimized to provide an accurate reference for NIRS calibration models. The application of this method at a pilot plant has demonstrated its capability of accurately measuring the process concentration of Etoricoxib as well as detecting the presence of solids produced by premature crystallization before seeding. © 2006 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:2337,2347, 2006 [source] THE USE OF NEAR INFRARED REFLECTANCE SPECTROMETRY FOR CHARACTERIZATION OF BROWN ALGAL TISSUE,JOURNAL OF PHYCOLOGY, Issue 5 2010Kyra B. Hay Measuring qualitative traits of plant tissue is important to understand how plants respond to environmental change and biotic interactions. Near infrared reflectance spectrometry (NIRS) is a cost-, time-, and sample-effective method of measuring chemical components in organic samples commonly used in the agricultural and pharmaceutical industries. To assess the applicability of NIRS to measure the ecologically important tissue traits of carbon, nitrogen, and phlorotannins (secondary metabolites) in brown algae, we developed NIRS calibration models for these constituents in dried Sargassum flavicans (F. K. Mertens) C. Agardh tissue. We then tested if the developed NIRS models could detect changes in the tissue composition of S. flavicans induced by experimental manipulation of temperature and nutrient availability. To develop the NIRS models, we used partial least squares regression to determine the statistical relationship between trait values determined in laboratory assays and the NIRS spectral data of S. flavicans calibration samples. Models with high predictive power were developed for all three constituents that successfully detected changes in carbon, nitrogen, and phlorotannin content in the experimentally manipulated S. flavicans tissue. Phlorotannin content in S. flavicans was inversely related to nitrogen availability, and nitrogen, temperature, and tissue age interacted to significantly affect phlorotannin content, demonstrating the importance of studies that investigate these three variables simultaneously. Given the speed of analysis, accuracy, small tissue requirements, and ability to measure multiple traits simultaneously without consuming the sample tissue, NIRS is a valuable alternative to traditional methods for determining algal tissue traits, especially in studies where tissue is limited. [source] Prediction of sensory textural properties from rheological analysis for process cheeses varying in emulsifying salt, protein and moisture contentsJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 4 2007Colm D Everard Abstract Textural characteristics of process cheeses varying in emulsifying salt (disodium phosphate), protein and moisture contents were evaluated by rheological compression using texture profile analysis and by sensory evaluation. The primary objective of this study was to predict sensory textural parameters using instrumental rheological parameters. All sensory parameters correlated with one or more instrumental parameters, e.g. rheological firmness versus sensory firmness (R = 0.98, P < 0.001), rheological chewiness versus sensory rubbery (R = 0.92, P < 0.001) and rheological chewiness versus sensory chewy (R = 0.86, P < 0.001). Partial least squares calibration models were developed for each of nine sensory parameters using instrumental parameters. Principal component analysis of instrumental and sensory parameters illustrated relationships among parameters. It was shown that instrumental parameters could be used to supplement sensory evaluation of process cheese texture. Increasing emulsifying salt content increased firmness, springiness and chewiness and decreased adhesiveness, mouthcoating and mass formation. Increasing protein content resulted in increased fracture strain and stress and chewiness and decreased melting. Increasing moisture content increased cohesiveness and decreased firmness and chewiness. Copyright © 2007 Society of Chemical Industry [source] Detection of inverted beet sugar adulteration of honey by FTIR spectroscopyJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 8 2001S Sivakesava Abstract A combination of Fourier transform infrared (FTIR) spectroscopy and multivariate statistics as a screening tool for the determination of beet medium invert sugar adulteration in three different varieties of honey is discussed. Honey samples with different concentrations of beet invert sugar were scanned using the attenuated total reflectance (ATR) accessory of the Bio-Rad FTS-6000 Fourier transform spectrometer. The spectral wavenumber region between 950 and 1500,cm,1 was selected for partial least squares (PLS) regression to develop calibration models for beet invert sugar determination in honey samples. Results from the PLS (first derivative) models were slightly better than those obtained with other calibration models. Predictive models were also developed to classify beet sugar invert in three different varieties of honey samples using discriminant analysis. Spectral data were compressed using the principal component method, and linear discriminant and canonical variate analyses were used to detect the level of beet invert sugar in honey samples. The best predictive model for adulterated honey samples was achieved with canonical variate analysis, which successfully classified 88,94 per cent of the validation set. The present study demonstrated that Fourier transform infrared spectroscopy could be used for rapid detection of beet invert sugar adulteration in different varieties of honey. © 2001 Society of Chemical Industry [source] | |||||||||||||