Alessandra Borin

This work shows the efficiency of wash waters from lipopeptide production as a remediation strategy to treat urban water samples contaminated with p-cresol. The harvesting step in surfactin production involved a centrifugation step, generating a major soluble fraction and a fraction that is adsorbed to the biomass. The adsorbed fraction was recovered by washing steps. These wash waters containing lipopeptides (mostly surfactins), were successfully used to adsorb and solubilize p-cresol. The method of decontamination applied to an artificially contaminated natural water was monitored using a biosensor based on laccase/magnetic nanoparticles. Given the amount of surfactin within the wash water, the removal of p-cresol from artificially contaminated water was approximately 46.0%. This result confirms the successful and sustainable application of surfactin-rich wash waters to remove p-cresol from artificially contaminated natural water. The adsorption mechanism is potentially based on a multi-layer adsorption process, considering Langmuir and Freundlich adsorption isotherms. GRAPHICAL ABSTRACT

The aim of the present work was to evaluate molar ratio iron: zinc and the levels of folic acid in biscuit and snacks commercialized in Brazil, prepared with folic acid and iron fortified flours. These nutrients are important for human nutrition; however, iron can have a negative effect on zinc absorption. Molar ratio iron: zinc can indicate if there will be any problems for absorption of these nutrients. The folic acid content varied from 58 to 433 mu g/100g and iron and zinc levels varied from 2.9 to 9.4 mg/100g and from 0.2 to 1.3 mg/100g, respectively, for 75 analyzed samples. The average iron contents observed in the products and molar ratio iron: zinc (in average 8:1 for biscuits and 12.8:1 for snacks) could result in problems with the zinc absorption. Moreover, principal component analyses (PCA) indicated low uniformity in the distribution of minerals and vitamin in the majority of the samples, mainly among brands. The results indicated that for the majority of the samples tested folic acid and iron content was higher than expected for flours and could be useful to governmental authorities in their evaluation program of flour fortification.

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

This paper reports an approach for quantification of Lactobacillus in fermented milk, grown in a selective medium (MRS agar), by use of digital colour images of Petri plates easily obtained by use of a flatbed scanner. A one-dimensional data vector was formed to characterize each digital image on the basis of the frequency-distribution curves of the red (R), green (G), and blue (B) colour values, and quantities derived from them, for example lightness (L), relative red (RR), relative green (RG), and relative blue (RB). The frequency distributions of hue, saturation, and intensity (HSI) were also calculated and included in the data vector used to describe each image. Multivariate non-linear modelling using the least-squares support vector machine (LS-SVM) and a linear model based on PLS regression were developed to relate the microbiological count and the frequency vector. Feasibly models were developed using the LS-SVM and errors were below than 10% for Lactobacillus quantification, indicating the proposed approach can be used for automatic counting of colonies.

Multivariate quality control in conjunction with Fourier transform infrared Spectroscopy (FTIR) were used to qualitatively detect the class and the condition of lubricating oils. The multivariate approach was based on principal component analysis (PCA), first to classify the lubricating oil type (mineral, synthetic and semi-synthetic) and then to develop two control charts: a T² chart using the most significant principal components and a Q chart with the PC not used in the first chart. From these two charts it was possible to identify oil samples, based on a viscosity parameter, which were out of the pattern normally present in utilizable lubricating oils.Controle de qualidade multivariado, em conjunto com espectroscopia no infravermelho por transformada de Fourier (FTIR), foi usado para detectar qualitativamente o tipo e a condição de óleos lubrificantes. O procedimento multivariado baseou-se na análise de componentes principais (PCA), primeiro para classificar o tipo do lubrificante (mineral, sintético e semi-sintético) e depois para desenvolver duas cartas de controle: uma carta T² usando os componentes principais mais significativos e uma carta Q com os componentes principais não utilizados na primeira carta. A partir destas duas cartas, foi possível identificar amostras de óleo, baseado no parâmetro da viscosidade, que se encontravam fora do padrão normalmente presente em lubrificantes em condições de uso