Endosperm structure and Glycemic Index of Japonica Italian rice varieties.

Introduction: Given that rice serves as a crucial staple food for a significant portion of the global population and with the increasing number of individuals being diagnosed with diabetes, a primary objective in genetic improvement is to identify and cultivate low Glycemic Index (GI) varieties. This must be done while ensuring the preservation of grain quality. Methods: 25 Italian rice genotypes were characterized calculating their GI "in vivo" and, together with other 29 Italian and non-Italian genotypes they were studied to evaluate the grain inner structure through Field Emission Scanning Electron Microscopy (FESEM) technique. Using an ad-hoc developed algorithm, morphological features were extracted from the FESEM images, to be then inspected by means of multivariate data analysis methods. Results and Discussion: Large variability was observed in GI values (49 to 92 with respect to glucose), as well as in endosperm morphological features. According to the percentage of porosity is possible to distinguish approximately among rice varieties having a crystalline grain (< 1.7%), those intended for the preparation of risotto (> 5%), and a third group having intermediate characteristics. Waxy rice varieties were not united by a certain porosity level, but they shared a low starch granules eccentricity. With reference to morphological features, rice varieties with low GI (<55) seem to be characterized by large starch granules and low porosity values. Our data testify the wide variability of Italian rice cultivation giving interesting information for future breeding programs, finding that the structure of the endosperm can be regarded as a specific characteristic of each variety.

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments.

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.

Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy.

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.