Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data.

The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.

The health-nutrition dimension: a methodological approach to assess the nutritional sustainability of typical agro-food products and the Mediterranean diet.

BACKGROUND: The aim of this paper is to provide a methodological approach to evaluate the nutritional sustainability of typical agro-food products, representing Mediterranean eating habits and included in the Mediterranean food pyramid. RESULTS: For each group of foods, suitable and easily measurable indicators were identified. Two macro-indicators were used to assess the nutritional sustainability of each product. The first macro-indicator, called 'business distinctiveness', takes into account the application of different regulations and standards regarding quality, safety and traceability as well as the origin of raw materials. The second macro-indicator, called 'nutritional quality', assesses product nutritional quality taking into account the contents of key compounds including micronutrients and bioactive phytochemicals. For each indicator a 0-10 scoring system was set up, with scores from 0 (unsustainable) to 10 (very sustainable), with 5 as a sustainability benchmark value. The benchmark value is the value from which a product can be considered sustainable. A simple formula was developed to produce a sustainability index. CONCLUSION: The proposed sustainability index could be considered a useful tool to describe both the qualitative and quantitative value of micronutrients and bioactive phytochemical present in foodstuffs. This methodological approach can also be applied beyond the Mediterranean, to food products in other world regions. © 2018 Society of Chemical Industry.

Investigating the origin of tomatoes and triple concentrated tomato pastes through multielement determination by inductively coupled plasma mass spectrometry and statistical analysis.

The concentration of 32 elements (Al, As, Ba, Be, Ca, Cd, Ce, Cu, Dy, Fe, K, La, Lu, Mg, Mn, Na, Nd, Pb, Rb, Sm, Sr, Th, U, V, Zn) was determined in tomatoes harvested in different four Italian regions and in triple concentrated tomato paste samples coming from Italy, China, Greece and California. The resulting multielement profiles were processed using three chemometric techniques to evaluate the possibility of discrimination between different cultivation areas. The closed-vessel microwave digested samples were diluted and analyzed by DRC-ICP-MS with CH(4) as reaction gas. The accuracy of the proposed method was considered acceptable (values in the range 75-120%) for 25 out of the 35 elements of the reference material NCS ZC85006 Tomato. The origin of tomato fruits and the areas of production as "Italy" and "non-Italy" of the triple concentrated pastes were evaluated by three supervised pattern recognition procedures, linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA) and K-nearest neighbors (KNN).