In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non-disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PLS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P>0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one.

Successful preparation of polymer nanocomposites, exploiting graphene-related materials, via melt mixing technology requires precise design, optimization and control of processing. In the present work, the effect of different processing parameters during the preparation of poly (butylene terephthalate) nanocomposites, through ring-opening polymerization of cyclic butylene terephthalate in presence of graphite nanoplatelets (GNP), was thoroughly addressed. Processing temperature (240°C or 260°C), extrusion time (5 or 10 minutes) and shear rate (50 or 100 rpm) were varied by means of a full factorial design of experiment approach, leading to the preparation of polybutylene terephthalate/GNP nanocomposite in 8 different processing conditions. Morphology and quality of GNP were investigated by means of electron microscopy, X-ray photoelectron spectroscopy, thermogravimetry and Raman spectroscopy. Molecular weight of the polymer matrix in nanocomposites and nanoflake dispersion were experimentally determined as a function of the different processing conditions. The effect of transformation parameters on electrical and thermal properties was studied by means of electrical and thermal conductivity measurement. Heat and charge transport performance evidenced a clear correlation with the dispersion and fragmentation of the GNP nanoflakes; in particular, gentle processing conditions (low shear rate, short mixing time) turned out to be the most favourable condition to obtain high conductivity values. . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ ©2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ higher thermal conductivity [28,29]. In a recent paper, we demonstrated that the addition of hightemperature-annealed rGO in a polymer matrix leads to a thermal conductivity which is about 2-fold those of poly (butylene terephthalate) containing pristine rGO (higher defectiveness) or GNP [30], further confirming the need of high quality nanoflakes for the preparation of highly thermally conductive polymer nanocomposites. On the other hand, the control of GRM organization into a polymer matrix remains crucial in terms of nanoparticle distribution and quality of contacts between particles. Attempts in precisely controlling orientation and contacts between nanoparticles indeed resulted in an improvement of thermal transfer [31-33] but the methods adopted for the preparation of these nanocomposites are hardly up-scalable or requires very high filler concentrations. Finally, the reduction of interfacial thermal resistance was also pursued by nanoparticle functionalization [34][35][36], despite the effectiveness of this strategy may be also related to the lateral size of the nanoflakes [37]. Recently, the preparation of polymer nanocomposites was obtained by in-situ ring-opening polymerization of cyclic butylene terephthalate (CBT) oligomers into poly (butylene terephthalate), ...

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans.

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser—MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R2 of prediction (R2PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R2PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer.

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