Abstract
The aim of this work was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy as a rapid and accurate technique to detect and predict the onset of spoilage in fresh chicken breast fillets stored at 3, 8, and 30 A degrees C. Chicken breasts were excised from carcasses at 6 h post-mortem; cut in fillets; packed in air; stored at 3, 8, and 30 A(0)C; and periodically examined for FTIR, pH, microbiological analysis, and sensory assessment of freshness. Partial least squares regression allowed estimations of total viable counts (TVC), lactic acid bacteria (LAB), Pseudomonas spp., Brochothrix thermosphacta, Enterobacteriaceae counts and pH, based on FTIR spectral data. Analysis of an external set of samples allowed the evaluation of the predictability of the method. The correlation coefficients (R-2) for prediction were 0.798, 0.832, 0.789, 0.810, 0.857, and 0.880, and the room mean square error of prediction were 0.789, 0.658, 0.715, 0.701, 0.756 log cfu g(-1) and 0.479 for TVC, LAB, Pseudomonas spp., B. thermosphacta, Enterobacteriaceae, and pH, respectively. The spectroscopic variables that can be linked and used by the models to predict the spoilage/freshness of the samples, pH, and microbial counts were the absorbency values of 375 wave numbers from 1,700 to 950 cm(-1). A principal component analysis led to the conclusion that the wave numbers that ranges from 1,408 to 1,370 cm(-1) and from 1,320 to 1,305 cm(-1) are strongly connected to changes during spoilage. These wave numbers are linked to amides and amines and may be considered potential wave numbers associated with the biochemical changes during spoilage. Discriminant analysis of spectral data was successfully applied to support sensory data and to accurately bound samples freshness. According to the results presented, it is possible to conclude that FTIR spectroscopy can be used as a reliable, accurate, and fast method for real time freshness evaluation of chicken breast fillets during storage.

Abstract
Long period fiber gratings (LPFGs) were coated with iron (Fe) and exposed to oxidation in air and in water having different concentrations of sodium chloride (NaCl) to detect the formation of iron oxides and hydroxides. The process was monitored in real time by measuring the characteristics of the LPFGs attenuation bands. Thin films of Fe were deposited on top of silica (SiO2) substrates, annealed in air, and exposed to water with NaCl. The composition of the oxide and hydroxide layers was analyzed by SEM/EDS and X-ray diffraction. It observed the formation of oxide phases, Fe3O4 (magnetite), and Fe2O3 (hematite) when annealing in air, and Fe-2(OH)(3) Cl (hibbingite) and FeO(OH) (lepidocrocite) when exposed to water with NaCl. Results shows that Fe-coated LPFGs can be used as sensors for real-time monitoring of corrosion in offshore and in coastal projects where metal structures made of iron alloys are in contact with sea or brackish water. In addition, LPFGs coated with hematite were characterized for sensing, leading to the conclusion that the sensitivity to the refractive index of the surrounding medium can be tuned by proper choice of hematite thickness.

Abstract
A methodology based on Fourier transform infrared spectroscopy with attenuated total reflectance sampling technique, combined with multivariate analysis, was developed to monitor adulteration of extra virgin olive oil (EVOO) with peanut oil (PEO). Principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) allowed quantification of percentage of adulteration based on spectral data of 192 samples. Wavenumbers associated with the biochemical differences among several types of edible oils were investigated by principal component analysis. Two sets of frequencies were selected in order to establish a robust regression model. Set A consisted on the frequency regions from 600 to 1,800 cm-1 and from 2,750 to 3,050 cm-1. Set B comprised 17 discrete peak absorbance frequencies for which the communality value was higher than 0.6. Analysis of an external set of 25 samples allowed the validation and evaluation of the predictability of the models. When using a specific set of discrete peak absorbance frequencies, the R 2 coefficients for the prediction were 0.960 and 0.977, and the root mean square error (RMSE) were 1.49 and 1.05% V/V when using the PCR or PLS-R models, respectively. LDA was successful in the binary classification presence/absence of PEO in adulterated EVOO (with 5% V/V of less of PEO). LDA provided 92.3% correct classification for the calibration set and 88.3% correct classification when cross-validated. The lowest detectable concentration of PEO in EVOO was the lowest adulteration level studied, 0.5% V/V.

Abstract
Fourier transform infrared spectroscopy based on attenuated total reflectance sampling technique, combined with multivariate analysis methods was used to monitor the adulteration of pure sunflower oil (SO) with thermally deteriorated oil (TDO). Contrary to published research, in this work, SO was thermally deteriorated in the absence of foodstuff. SO samples were exposed to temperatures between 125 and 225°C from 6 to 24 h. Quantification of adulteration of SO with TDO, based on principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) applied to mid-infrared spectra and to their first and second derivatives is reported for the first time. Infrared frequencies associated with the biochemical differences between TDO samples deteriorated in different conditions were investigated by principal component analysis (PCA). LDA was effective in the twofold classification presence/absence of TDO in adulterated SO (with 5% V/V of less of TDO). It provided 93.7% correct classification for the calibration set and 91.3% correct classification when cross-validated. A detection limit of 1% V/V of TDO in SO was determined. Investigation of an external set of samples allowed the evaluation of the predictability of the models. The regression coefficient (R 2) for prediction was 0.95 and 0.96 and the RMSE was 2.1 and 1.9% V/V when using the PCR or PLS-R models, respectively, and the first derivative of spectra. To the best of our knowledge, no investigation of adulteration of SO with TDO based on PCR, PLS-R, and LDA has been reported so far.

Abstract
Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination ( > 0.933). Both the R-2, and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process.

Abstract
The aim of this work was to investigate the potential of Fourier transform infrared spectroscopy (FTIR) to detect and predict the bacterial load of salmon fillets (Salmo salar) stored at 3, 8 and 30 degrees C under three packaging conditions: air packaging (AP) and two modified atmospheres constituted by a mixture of 50%N-2/40%CO2/10%O-2 with lemon juice (MAPL) and without lemon juice (MAP). Fresh salmon samples were periodically examined for total viable counts (TVC), specific spoilage organisms (SSO) counts, pH, FTIR and sensory assessment of freshness. Principal components analysis (PCA) allowed identification of the wavenumbers potentially correlated with the spoilage process. Linear discriminant analysis (LDA) of infrared spectral data was performed to support sensory data and to accurately identify samples freshness. The effect of the packaging atmospheres was assessed by microbial enumeration and LDA was used to determine sample packaging from the measured infrared spectra. It was verified that modified atmospheres can decrease significantly the bacterial load of fresh salmon. Lemon juice combined with MAP showed a more pronounced delay in the growth of Brochothrix thermosphacta, Photobacterium phosphoreum, psychrotrophs and H2S producers. Partial least squares regression (PLS-R) allowed estimates of TVC and psychrotrophs, lactic acid bacteria, molds and yeasts, Brochothrix thermosphacta, Enterobacteriaceae, Pseudomonas spp. and H2S producer counts from the infrared spectral data: For TVC, the root mean square error of prediction (RMSEP) value was 0.78 log cfu g(-1) for an external set of samples. According to the results, FTIR can be used as a reliable, accurate and fast method for real time freshness evaluation of salmon fillets stored under different temperatures and packaging atmospheres.