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.

Abstract
A methodology based on Fourier transform infrared (FTIR) spectroscopy, combined with multivariate analysis methods, was applied in order to monitor extra virgin olive oils produced from three distinct cultivars on different maturation stages. For the first time, this kind of methodology is used for the simultaneous discrimination of the maturation stage, and different cultivars. Principal component analysis and discriminant analysis were utilised to create a model for the discrimination of olive oil samples. Partial least squares regression was employed to design calibration models for the determination of chemical parameters. The performance of these models was based on the multiple coefficient of determination (R-2), the root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV). The prediction models for the chemical parameters resulted in a R-2 ranged from 0.93 to 0.99, a RMSEC ranged from 1% to 4% and a RMSECV from 2% to 5%. It has been shown that this kind of approach allows to distinguish the different cultivars, and to clearly discern the different maturation stages, in each one of these distinct cultivars. Furthermore, the results demonstrated that FTIR spectroscopy in tandem with chemometric techniques allows the creation of viable and accurate models, suitable for correlating the data collected by FTIR spectroscopy, with the chemical composition of the EVOOs, obtained by standard methods.

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 interaction between gold sub-nanometer clusters composed of ten atoms (Au10) and tetrakis(4-sulfonatophenyl)porphyrin (TPPS) was investigated through various spectroscopic techniques. Under mild acidic conditions, the formation, in aqueous solutions, of nanohybrid assemblies of porphyrin J-aggregates and Au10 cluster nanoparticles was observed. This supramolecular system tends to spontaneously cover glass substrates with a co-deposit of gold nanoclusters and porphyrin nanoaggregates, which exhibit circular dichroism (CD) spectra reflecting the enantiomorphism of histidine used as capping and reducing agent. The morphology of nanohybrid assemblies onto a glass surface was revealed by atomic force microscopy (AFM), and showed the concomitant presence of gold nanoparticles with an average size of 130 nm and porphyrin J-aggregates with lengths spanning from 100 to 1000 nm. Surface-enhanced Raman scattering (SERS) was observed for the nanohybrid assemblies.

Abstract

Abstract
n-Alkanes and long-chain alcohols (LCOH) have been used as faecal markers to assess the feeding behaviour of both wild and domestic herbivore species. However, their chemical analysis is time-consuming and expensive, making it necessary to develop more expeditious methodologies to evaluate concentrations of these markers. This work aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) technology in the near infrared (NIR) and mid infrared (MIR) intervals, for the determination of n-alkane and LCOH concentrations of different plant species and faecal samples of domestic herbivores. Spectra of 33 feed samples, namely L. perenne, T. repens, U. gallii, short heathers (mixture of Erica spp. and Calluna vulgaris), improved pasture grasses (mixture of L. perenne and A. capillaris), heath grasses (mixture of P. longifolium and A. curtissii), improved pasture species (mixture of L. perenne, T. repens and A. capillaris) and herbaceous species (mixture of all herbaceous species found in the plot)) and 181 faecal samples (cattle and horses) were recorded. In order to develop calibrations for the prediction of n-alkanes and LCOH concentrations, partial least squares (PLS) regression was used. Regarding the models developed for plant species, the best results were observed for the calibrations using NIR. The best external validation coefficients of determination (R(2)v) obtained were 0.90 and 0.79 for LCOH and n-alkanes, respectively. For faecal samples, in the NIR interval, results indicate similar external validation predictions (R(2)v) for both animal species (0.64). On the contrary, in the MIR interval, differences between cattle (0.70) and horses (0.57) faecal samples in R(2)v were observed. Regarding the models created for both animal species faeces, LCOH (C-26-OH and C-30-OH concentrations ranging from 713.3 to 4451.9 mg/kg DM, respectively; R(2)v values ranging from 0.72 to 0.95) and n-alkanes (C31 and C33 concentrations ranging from 112.8 to 643.2 mg/kg DM, respectively; R(2)v values ranging from 0.19 to 0.90) present in higher concentrations tended to be those with better estimates. Results obtained suggest that the selection of the technique to be used may depend on the type of matrix, being the homogeneity of the matrices one of the most important factors for its success. In order to improve the accuracy and robustness of the models created for the estimation of the concentrations of these markers using these methodologies, the database (greater variability) used for the calibrations of these models must be increased.