In this study, two important sensorial parameters of beer quality – bitterness and grain taste – were correlated with data obtained after headspace solid phase microextraction – gas chromatography with mass spectrometric detection (HS-SPME–GC–MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME–GC–MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists.

The aim of this work was to use the FT-IR microspectroscopy technique, a union of a FT-IR spectrometer with a microscope, to discriminate fecal Escherichia coli strains from cows, chickens and humans and to compare the efficiency of this method with the genomic fingerprinting method, BOX-PCR. The obtained BOX-PCR profiles were able to correctly discriminate 93.75% of the chicken strains, 80% of the cow strains and 65% of the human strains. An efficient PLS-DA model was developed, using orthogonal signal correction and the second derivate of the FT-IR spectra. This model allowed the correct discrimination, according to the animal source, of all the E. coli strains analyzed. The bands in the FT-IR spectra that were responsible for the strains discrimination were in the region between 2816 and 3026cm−1 wavenumber, described as fatty acids. It was demonstrated that FT-IR microspectroscopy can be a suitable tool for fecal E. coli discrimination, because it is fast, easy to carry out and presents a flexible discrimination power.

Acidithiobacillus ferrooxidans is a Gram negative, acidophilic, chemolithoautotrophic bacterium that plays an important role in metal bioleaching. During bioleaching, the cells are subjected to changes in the growth temperature and nutrients starvation. The aim of this study was to gather information about the response of the A.ferrooxidans Brazilian strain LR to K2HPO4 starvation and heat stress through investigation of cellular morphology, chemical composition and differential proteome. The scanning electron microscopic results showed that under the tested stress conditions, A. ferrooxidans cells became elongated while the Fourier transform infrared spectroscopy (FT-IR) analysis showed alterations in the wavenumbers between 850 and 1,275 cm−1, which are related to carbohydrates, phospholipids and phosphoproteins. These findings indicate that the bacterial cell surface is affected by the tested stress conditions. A proteomic analysis, using 2-DE and tandem mass spectrometry, enabled the identification of 44 differentially expressed protein spots, being 30 due to heat stress (40°C) and 14 due to K2HPO4 starvation. The identified proteins belonged to 11 different functional categories, including protein fate, energy metabolism and cellular processes. The upregulated proteins were mainly from protein fate and energy metabolism categories. The obtained results provide evidences that A. ferrooxidans LR responds to heat stress and K2HPO4 starvation by inducing alterations in cellular morphology and chemical composition of the cell surface. Also, the identification of several proteins involved in protein fate suggests that the bacteria cellular homesostasis was affected. In addition, the identification of proteins from different functional categories indicates that the A. ferrooxidans response to higher than optimal temperatures and phosphate starvation involves global changes in its physiology.

Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) has been shown to provide a valuable technique to study the thermomechanical degradation of poly(ethylene terephthalate) (PET). MALDI-MS has been tested to monitor both the admixture of post-consumption bottle-grade PET (PET(pc)-btg) with virgin bottle-grade PET (PET(v)-btg) and the thermomechanical degradation effects on the chemical properties of PET(v)-btg. Principal component analysis of MALDI-MS data classify the samples into groups with specific features: a) PET-btg with intrinsic viscosities of 0.80 or 0.65-0.60 dL g(-1)): b) Processed or virgin PET with the same intrinsic viscosity; c) PET(v)-btg from PET containing PET(pc)-btg; and d) PET(v)-btg from different manufacturers. MALDI-MS data is therefore able to reveal the quality of PET-btg resins preventing frauds and illegal use of recycled PET-btg. (C) 2009 Elsevier Ltd. All rights reserved.

This paper reports the use of least-squares support vector machines (LS-SVM) for non-linear multivariate calibration in the determination of the alcohol content in the Brazilian spirit “cachaça” using near infrared spectroscopy. Fifty cachaça samples, with alcohol contents in the range of 20.9% to 46.5% v/v were used and the spectra were obtained at five different temperatures: 15°C, 20°C, 25°C, 30°C and 35°C. Two models were proposed: in the first, a single model was built, using the spectra from all five temperatures. In the second, the calibration set was composed of the spectra taken at four temperatures and the validation set was composed of spectra of the other temperature. All the combinations were made. In four of them, LS-SVM produced better predictions than PLS and in the other, the results were the same. These results indicate that LS-SVM can be an alternative when there is an influence of some physical variations, such as temperature, on near-infrared spectra.

Least-squares support vector machines (LS-SVM) were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R2, RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

Least-squares support vector machines (LS-SVM) were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.