Abstract
This paper presents the performance analysis of a new geometry sensing configuration for refractive index, based on surface plasmon resonance (SPR) in photonic crystal fiber (PCF) D-type optical fiber with a thin gold layer, using the finite element method (FEM). The configuration is analyzed in terms of the loss. The results are compared with a conventional SPR D-type and with a PCF D-type optical fiber sensor for refractive index measurement. The simulation results show an improvement of the sensitivity and resolution (3.70x10(3)nm/RIU and 2.72x10(-5)RIU, respectively, when considering an accurately spectral variation detection of 0.1nm).

Abstract
This paper presents the performance analysis of a sensing configuration of refractive index, based on surface plasmon resonance (SPR) in microstructured D-type optical fiber with a thin gold layer, using the finite-element method. The configuration is analyzed in terms of the loss and distribution Poynting vector. The results are compared with a conventional SPR D-type optical fiber sensor for refractive index measurement. The simulation results show an improvement of the sensitivity and resolution (10 x 10(3) nm/RIU and 9.8 x 10(-6) RIU, respectively, when considering an accurately spectral variation detection of 0.1 nm).

Abstract
A new sensing system for the detection of thermal deterioration of extra virgin olive oil based on long period fibre grating is reported. It is demonstrated the feasibility of long period fibre grating sensor for the analysis of high refractive index edible oils. The detection principle is the wavelength dependence of the attenuation bands of a TiO2-coated long period fibre grating on the changes in the refractive index of the medium surrounding the cladding surface of the optical fibre. The quality of the sensor was tested by measuring the wavelength shift of the attenuation bands in response to thermal deterioration of an edible oil (extra virgin olive oil) with refractive index higher than the fibre cladding. Absorption spectroscopy has allowed the effects of thermal deterioration to be detected, for example, in the decreasing of the absorption band at 677 nm, attributed to chlorophyll A. A detection limit of about 5 min at 180 A degrees C and of about 2 min at 225 A degrees C was observed for the sensing system. The proposed sensing system could lead to the realisation of a biochemical sensor for the food industry. The change in refractive index of extra virgin olive oil as a function of heating time and temperature was systematically measured for the first time.

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
The phenolic compound concentration of olives and olive oil is typically quantified using HPLC; however, this process is expensive and time consuming. The purpose of this work was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy combined with chemometrics, as a rapid tool for the quantitative prediction of phenol content and antioxidant activity in olive fruits and oils from "Cobran double dagger osa" cultivar. Normalized spectral data using standard normal variate (SNV) and first and second Savitzky-Golay derivatives were used to build calibration models based on principal component regression (PCR) and on partial least squares regression (PLS-R), the performance of both models have been also compared. It was shown the possibility of establishing optimized regression models using the combined frequency regions of 3050-2750 and 1800-790 cm(-1) instead of the full mid-infrared spectrum was shown. It was concluded that, in general, the first derivative of data and PLS-R models offered enhanced results. Low root-mean-square error (RMSE) and high correlation coefficients (R (2)) for the calibration and for the validation sets were obtained.