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Partial Least-squares Method (partial + least-square_method)
Selected AbstractsUse of mid-infrared spectroscopy in the diffuse-reflectance mode for the prediction of the composition of organic matter in soil and litterJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 3 2008Bernard Ludwig Abstract Mid-infrared spectroscopy (MIRS) is assumed to be superior to near-infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid-infrared region including part of the near-infrared region (7000,400,cm,1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C CPMAS-NMR spectroscopy. A partial least-square method and cross-validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid-infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF , 0.70, 0.8 , a , 1.2, r , 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O-alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl,to,aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse-reflectance mode were generally not superior to those of NIRS. [source] Near-infrared spectroscopy can predict the composition of organic matter in soil and litterJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 2 2006Thomas Terhoeven-Urselmans Abstract The usefulness and limitations of near-infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near-infrared reflectance (VIS-NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS-NIR region (400,2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C-CPMAS-NMR spectroscopy. A modified partial least-square method and cross-validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near-infrared spectroscopy predicted well the C : N ratios, the percentages of O-alkyl C and alkyl C, the ratio of alkyl C to O-alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross-validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 , a , 1.2, r , 0.8, and 1.4 , RSC , 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N-mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter. [source] Determination of tocopherols and phytosterols in sunflower seeds by NIR spectrometryEUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY, Issue 5 2007Alicia Ayerdi Gotor Abstract The objective of this work was to develop a near-infrared reflectance spectrometry (NIRS) calibration estimating the tocopherol and phytosterol contents in sunflower seeds. Approximately 1000 samples of grinded sunflower kernels were scanned by NIRS at 2-nm intervals from 400 to 2500,nm. For each sample, standard measurements of tocopherol and phytosterol contents were performed. The total tocopherol content was obtained by high-performance liquid chromatography coupled with a fluorescence detector, while the total phytosterol content was assessed by gas chromatography. For tocopherol, the calibration data set ranged from 175 to 1005,mg/kg oil (mean value around 510,±,140,mg/kg oil), whereas for the phytosterol content, the calibration data set ranged from 180 to 470,mg/100,g oil (mean value of 320,±,50,mg/100,g oil). The NIRS calibration showed a relatively good correlation (R2,=,0.64) between predicted by NIRS and real values for the total tocopherol content but a poor correlation for the total phytosterol content (R2,=,0.27). These results indicate that NIRS could be useful to classify samples with high and low tocopherol content. In contrast, the estimation of phytosterol contents by NIRS needs further investigation. Moreover, in this study, calibration was obtained by a modified partial least-squares method; the use of other mathematical treatments can be suitable, particularly for total phytosterol content estimation. [source] Determination of %polyvinyl alcohol in vinyl acetate-alcohol resins by quantitative near infrared spectroscopic analysisADVANCES IN POLYMER TECHNOLOGY, Issue 1 2010S. H. Patel Abstract A number of vinyl acetate-alcohol resins (VAAR) samples were collected during partial hydrolysis of poly(vinyl acetate) at different conversions levels (<30%). Using these VAAR samples with known OH (hydroxyl) content, it has been demonstrated that near infrared (NIR) spectroscopic data produced a near perfect fit for the calibration of OH content. A 4-factor partial least-squares method was employed and gave the best results. Further work also confirmed that NIR, operating at a well-controlled environment, is able to quantify, with great precision, the OH content of the selected model compound. In the case for the OH content analysis of VAAR resins, solvent mix ratio (methyl acetate: methanol) and temperature have been identified to be the two most influential factors on the analytical results. © 2010 Wiley Periodicals, Inc. Adv Polym Techn 29:1,10, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/adv.20166 [source] Modeling and predicting binding affinity of phencyclidine-like compounds using machine learning methodsJOURNAL OF CHEMOMETRICS, Issue 1 2010Ozlem Erdas Abstract Machine learning methods have always been promising in the science and engineering fields, and the use of these methods in chemistry and drug design has advanced especially since the 1990s. In this study, molecular electrostatic potential (MEP) surfaces of phencyclidine-like (PCP-like) compounds are modeled and visualized in order to extract features that are useful in predicting binding affinities. In modeling, the Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map (SSOM). The resulting maps are visualized using electrostatic potential (ESP) values. These values also provide features for a prediction system. Support vector machines and partial least-squares method are used for predicting binding affinities of compounds. Copyright © 2009 John Wiley & Sons, Ltd. [source] | ||||||||||