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Simultaneous determination of ash content and protein in wheat flour using infrared reflection techniques and partial least-squares regression (PLS)
Abstract
As técnicas de espectroscopia por reflexão no infravermelho próximo (NIRRS) e por reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS) foram empregadas com o método de regressão multivariado por mínimos quadrados parciais (PLS) para a determinação simultânea dos teores de proteína e cinza em amostras de farinha de trigo da variedade Triticum aestivum L. Foram coletados espectros no infravermelho em duplicata de 100 amostras, empregando-se acessórios de reflexão difusa. Os teores de proteína (8,85-13,23%) e cinza (0,330-1,287%), empregados como referência, foram determinados pelo método Kjeldhal e método gravimétrico, respectivamente. Os dados espectrais foram utilizados no formato log(1/R), bem como suas derivadas de primeira e segunda ordem, sendo pré-processados usando-se os dados centrados na média (MC) ou escalados pela variância (VS) ou ambos. Cinqüenta e cinco amostras foram usadas para calibração e 45 para validação dos modelos, adotando-se como critério de construção os valores mínimos do erro padrão de calibração (SEC) e do erro padrão de validação (SEV). Estes valores foram inferiores a 0,33% para proteína e a 0,07% para cinza. Os métodos desenvolvidos apresentam como vantagens a não agressão ao ambiente, bem como permitem uma determinação direta, simultânea, rápida e não destrutiva dos teores de proteína e cinza em amostras de farinha de trigo.
... For a model to be properly adjusted, the validation set must produce an SEV value like that of the SEC. SEV values excessively higher than the SEC indicate over-adjusted models, that is, the regression considers data that are not actually correlated, such as noise and other systematic errors (Ferrão et al. 2004). ...
Wood can be used for fuel by direct burning, or as a raw material for other fuels; however, it is necessary to evaluate the energy properties to ensure the optimal use of this material. The most relevant characteristics to be analyzed are the higher heating value, volatile material content, fixed carbon content, and ash content. Along with the traditional methods, there are also non-destructive evaluations that are optimized for speed and reliability. Among these methods, visible spectroscopy and near-infrared spectroscopy have been proven to be robust for the prediction of several wood properties. The aim of this study was to evaluate the potential of visible spectroscopy and near-infrared spectroscopy for species discrimination and prediction of higher heating value, volatile material content, fixed carbon content, and ash content for Eucalyptus saligna, Eucalyptus dunnii, and Eucalyptus benthamii woods. For this purpose, multivariate principal component analysis and partial least squares regression were applied to the collected spectra. The principal component analysis satisfactorily discriminated the three species, explaining 99% of the variance of the visible spectroscopy spectra and 73% of that of the near-infrared spectra. The estimation of energetic properties through partial least squares regression was satisfactory for both visible spectroscopy and near-infrared spectroscopies, which presented calibration R² values close to 1 and low errors for all properties studied.
... The values of Table 2 indicated that performance is the same for the models, because of high similarity in their values. The SEC to SEV ratio above 0.75 is a parameter indicative that the models are well-adjusted (Ferrão, Carvalho, Müller, & Davanzo, 2004), as such both models were well-adjusted presenting a 0.81 ratio value. ...
... Generally, noise and systematic behavior are undesirable features in the spectra. To minimize the noise of the calibration and validation spectra sets in PLS calibration models, the spectra were treated using multiplicative scattering correction (MSC) and compared with the original spectra set [19]. All models using MSC presented a decrease in the RMSECV (root mean square error of crossvalidation) values. ...
A rapid method for the quantitative determination of the hydroxyl value (OHV) of hydroxylated soybean oils by HATR/FTIR spectroscopy is described. Calibration standards were prepared by the formic acid/hydrogen peroxide method and OH values were determined by the official method of AOCS Tx 1a-66, covering an analytical range of 3.5–125 mg of KOH/g of sample. A partial least squares (PLS) calibration model for the prediction of the hydroxyl value (OHV) was developed based on eight different spectral subregions between 3,150 and 990 cm−1 and combinations of them. On average, 36 samples were used for the modeling and 17 were used for external validation. The resulting calibration was linear over the analytical range and had a standard deviation of 2.334. Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oils predicted by the PLS model to the values obtained by the AOCS standard method. A correlation coefficient of R
2 = 0.9843 and RMSEC and RMSEP values of, respectively, 3.393 and 3.643 were obtained. After the calibration of the spectrometer, the OHV could be obtained in 2–3 min per sample, a major improvement over conventional wet chemical methods. The advantages of these methodologies are that they do not destroy the sample, have a lower cost, expedite the analysis and do not produce residues. Therefore, they may yield excellent results when used to quantify OHV of soybean polyols obtained by hydroxylation reaction.
Near-infrared (NIR) spectroscopy has been attempted to determine ash and moisture of wheat flour nondestructively combined with characteristic bands selection methods. Pearson product-moment correlation coefficients and interval partial least squares (iPLS) were performed comparatively to choose characteristic bands associated with ash and moisture distributions. Through comparing the performance of developed models with new samples, the optimal models of ash and moisture were obtained with the characteristic bands of 4000-5500, 6708-7304 and 4000-4896, 5504-6704 cm-1, respectively. The root mean square errors of prediction were 0.019 and 0.088 % for ash and moisture, respectively. The results suggest that NIR technique combining with iPLS has significant potential to quantitatively analyze ash and moisture in wheat flour; moreover, it could indicate the related spectral contributions.
Os anos 90 foram marcados pela utilização da espectroscopia no infravermelho médio para a análise qualitativa e quantitativa de alimentos. Wilson, em 1990, introduz as potencialidades da região do infravermelho médio e as técnicas de reflexão: reflexão total atenuada (ATR), detecção fotoacústica e reflexão difusa (DRIFTS), para análise de alimentos e sua utilização no controle industrial.
Fourier transform infrared (FTIR) technology has substantial potential as a quantitative quality control tool for the food industry. FTIR analysis methods are convenient, rapid and automatable, and in conjunction with attenuated total reflectance (ATR) technology, dramatically simplify sample handling. The key elements of FTIR spectroscopy are presented along with a selection of methods under development, including analyses of milk, meats, fats and oils, butter, sweetened condensed milk and juices. The examples presented provide clear evidence of the benefits of FTIR in quality control applications.
Ash content is one of matters of concern in a viewpoint of breeding programme for Japanese home-grown wheat. A near-infrared diffuse reflectance (NIR) technique was applide to determination of ash in 60% extraction flours from 2 varieties of the wheat. A distinct tendency that flour of a finer particle size has a higher ash content was observed in the NIR spectrum, resulting in an unstable property of diffuse reflectance which was affected by mutural interaction of moisture in the flour with its flour particle. As a result of calibrations performed, the most accurate calibration was obtained with a multiple linear regression analysis using second derivative of absorbances at 4 specific wavelengths involving an absorbance at 2344nm which was assigend to a bran-related substance in the flour. It is possible to determine ash indirectly in connection with an inclusion of the branrelated fraction in the flour. However, prediction of ash in "unknown" wheat flours from different varieties from those used for performing the calibration resulted in serious errors on account of a bias effect between the varieties.
It is shown that reduction of particle size for ground durum wheat does not cause a concomitant decrease in the amount of specular reflection observed in the mid-infrared spectrum of the neat sample. Specular reflection can be reduced, but not completely eliminated, by dilution of the wheat in a nonabsorbing matrix such as KCl. The lack of dependence of mid-infrared diffuse reflection spectra of neat ground durum wheat on particle size is explained by the morphology of the surface of each particle and the low effective depth of penetration of mid-infrared radiation into particles of very high absorptivity. It is concluded that mid-infrared diffuse reflection spectra of wheats do not provide information characteristic of the overall composition of the wheat when the particle size exceeds a few micrometers, but that near-infrared diffuse reflection spectra will provide representative analytical data even when the particle size is large.
Partial least-squares regression (PLSR) was used to generate three Raman and three near-infrared reflectance (NIRR) models for the spectroscopic determination of total dietary fiber (TDF) of a wide variety of cereal foods. To allow comparison of the spectral techniques, both analyses used the same sets of samples (ncal = 63, nval = 63). Six models were optimized by full leave-one-out cross-validation (CV), including a smoothed, a first-, and a second-derivative model for each spectral technique. Both kinds of raw spectral data required correction of interfering baseline and amplitude variations. Derivative preprocessing generally reduced the number of latent variables (LVs) for both spectral types and significantly reduced the CV error of the Raman models. The derivative treatments enhanced the influence of select vibrational-bandwidth-sized features in the Raman data (64-84 cm-1). The Savitzky-Golay derivative calculation method was better for the NIRR data, while the gap-difference method was better for the Raman data, which had a higher level of baseline noise. Raman models required 6 to 9 latent variables while NIRR models required 10 to 14 LVs. The root-mean-squared CV model errors were 2-2.3% TDF for all six models, and the three Raman models had root-mean-squared prediction errors (RMSEPs) in the range 2.8-3.2% TDF, with the best model being generated from second-derivative data. First-derivative data provided the best NIRR model, and for all three NIRR models the RMSEP spanned 2.4-2.9%. For some types of samples, it is suggested that the Raman method is limited by its sampling technique and could be improved with more densely packed, larger-area specimens. The regression vectors of the Raman models seem more easily interpretable than NIRR models. Either spectral method appears capable of acheiving an acceptable level of error; TDF reference method precision was 0,68% TDF, while the product label information had an error of 2.8% TDF relative to the reference.