Ronei J. Poppi

Discrimination of management effects on soil parameters by using principal component analysis: a multivariate analysis case study

One of the major interests in soil analysis is the integrated evaluation of soil properties, which might be indicators of soil quality. Unsupervised methods of multivariate statistics are powerful tools for this integrated assessment and can help soil researchers to extract much more information from their data. A multivariate study was carried out in three farms from Guaira, State of Sao Paulo, Brazil. Conventionally managed plots that intensively utilized pesticides and chemical fertilizers were compared with both non-disturbed forest areas and alternatively managed plots. The latter were under ecological farming employing effective microorganisms (EM) integrated with crop residues. Eight soil parameters were determined for each plot. Hierarchical cluster analysis (HCA) was used to verify the similarity among the plots. The multivariate approach of principal component analysis (PCA) allowed us to distinguish the areas as a function of the soil management and determine which are the most important parameters to characterize them. The forest areas presented higher microbial biomass with lower cellulolytics population than at cultivated sites. The alternative plots were characterized by higher microbial biomass and polysaccharide content with lower phosphate solubilizers and cellulolytics microorganisms colony counts than at the conventional areas. The higher observed levels of microbial biomass and polysaccharide content in the alternative areas can be attributed to the effects of the alternative soil amendment. All these effects can be clearer globally visualized with the aid of PCA, through the biplots.

Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.

Determination of the total unsaturation in vegetable oils by Fourier transform Raman spectroscopy and multivariate calibration

Abstract In this paper multivariate calibration was used in conjunction with NIR-Fourier transform Raman spectroscopy for determination of the total degree of unsaturation in vegetable oils. For this purpose, different vegetable oils and some mixtures were employed as calibration standards. A calibration model based on partial least squares (PLS) was constructed and used to analyse oils with iodine values ranging from 17 to 130. This methodology is rapid, simple and can be easily used in quality control laboratories or to monitor industrial process on line.

Application of two- and three-way chemometric methods in the study of acetylsalicylic acid and ascorbic acid mixtures using ultraviolet spectrophotometry

AbstractIn this work, mixtures of acetylsalicylic acid (ASA) and ascorbic acid (AA) were studied by ultraviolet spectrophotometry(210–300nm) using parallel factor analysis (PARAFAC) and partial least square (PLS). The study was carried out in thepH range from 1.0 to 5.5 and with a concentration range from 1.0 −10 5 to 1.0 10 −4 moll −1 of both analytes. PARAFACwas used for spectra deconvolution, pK a estimation for both acids and to check the presence of salicylic acid (SA), dueto the possible ASA decomposition. The estimated first p K a was equal to 3.41 and 4.10 for ASA and AA, respectively.Multivariate calibration models using PLS at different pH and N-way PLS were elaborated for simultaneous determination ofASA and AA in pharmaceutical samples. The best models for the system were obtained with N-way PLS2 and PLS2 at pH1.1. The results obtained for simultaneous determination of ASA and AA in samples were in agreement to the values specifiedby the manufacturers and the recovery was between 97.6 and 103.6%. Nevertheless, these models failed to predict ASAdecomposition to SA in simulated samples. Thus, a new PLS-pH1 model considering SA was built and applied successfullyin simulated samples. ©2000 Elsevier Science B.V. All rights reserved.

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing.

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.

Estado da arte de figuras de mérito em calibração multivariada

The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.

Avaliação do Uso de Métodos Quimiométricos em Análise de Solos

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

Determination of organic matter in soil using near-infrared spectroscopy and partial least squares regression

An alternative approach for the determination of organic matter content in soil is proposed, using near infrared spectroscopy and partial least squares regression for data modeling. This method was applied to two different types of soils: Oxisol and Ultisol. One hundred soil samples, collected at different depth intervals, with organic matter content from 0.40 to 4.83 mg g−1 were obtained from the Instituto Agronômico (Campinas-Brazil). For test the reference data were obtained by titration of the excess of Cr(VI) with standard Fe(II) solution, after sample digestion with potassium dichromate in an acid medium in digestion tubes. The organic matter contents determined by the so called digestion tube method were also correlated with those obtained by near infrared spectroscopy, using partial least squares regression. A root mean squares error of prediction equal to 0.20 mg g−1 was obtained. The digestion tube method was also compared with the Walkley–Black method for twelve soil samples and no difference in results for the organic matter determination was found.

Combining standard addition method and second-order advantage for direct determination of salicylate in undiluted human plasma by spectrofluorimetry

Second-order advantage turns possible a determination in the presence of unknown interferences. This work presented an application of the second-order advantage provided by parallel factor analysis (PARAFAC). The aim was the direct determination of salicylic acid (SA), the main product of aspirin degradation, in undiluted human plasma by spectrofluorimetry. The strategy of this analysis combined the use of PARAFAC, for extraction of the pure analyte signal, with the standard addition method, for a determination in the presence of a strong matrix effect caused by the quenching effect of the proteins present in the plasma. For each sample, four standard additions were performed, in triplicates. A specific PARAFAC model was built for each triplicate of each sample, from three-way arrays formed by 436 emission wavelengths, 7 excitation wavelengths and 5 measurements (sample plus 4 additions). In all the cases, the models were built with three factors and explained more than 99.90% of the total variance. The obtained loadings were related to SA and two background interferences. The scores related to SA were used for a linear regression in the standard addition method. Good results were obtained for determinations in the SA concentration range from 3.0 to 24.0mugml(-1), providing errors of prediction between 0.7 and 6.3%.

Standard addition method applied to the urinary quantification of nicotine in the presence of cotinine and anabasine using surface enhanced Raman spectroscopy and multivariate curve resolution

In this work, urinary nicotine was determined in the presence of the metabolite cotinine and the alkaloid anabasine using surface enhanced Raman spectroscopy and colloidal gold as substrate. Spectra were decomposed using the multivariate curve resolution-alternating least squares method, and pure contributions were recovered. The standard addition method was applied by spiking urine samples with known amounts of the analyte and relative responses from curve resolution were employed to build the analytical curves. The use of multivariate curve resolution in conjunction with standard addition method showed to be an effective strategy that minimized the need for reagent and time-consuming procedures. The determination of the alkaloid nicotine was successfully accomplished at concentrations 0.10, 0.20 and 0.30 μg mL(-1) and total error values less than 10% were obtained.