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The general aim of the project SUS&LOW is to increase the sustainability of fresh produce by testing and implementing lowinput agricultural practices (LIP) with positive impact on product quality with the support of nondestructive (ND) tools for realtime quality assessment and for product discrimination. Additionally, new marketing strategies are generated to better support the added value of the products and to satisfy the final consumers' preferences. The SUS&LOW project consists of three work packages (WP) and the adopted methodology used two model crops: rocket salad and tomato. The WP1, focused on the reduction of agricultural inputs, showed that sensorbased fertigation management might improve sustainability of soilless cultivation. Results coming from WP2, aimed to the evaluation of ND techniques, outlined the high potentiality of hyperspectral imaging (HSI) and Fourier transformednear infrared (FTNIR) techniques for the authentication of sustainable growing methods. Moreover, project activities' proved computer vision system (CVS) as an effective tool for evaluating the product quality also through the bag. The WP3, dealing with marketing strategies, indicated a positive approach of consumers compared to LIP products certified through a visual storytelling platform. [ABSTRACT FROM AUTHOR]

Presents methods for the automatic extraction of fuzzy rules for reactive robot controllers. Definition of fuzzy controller; Application of a product-max inference; Comparison of the two learning methods; Use of real acquired sensorial data; Control system for TRC Labmate mobile robot indoor navigation.

White table grape cv. Italia is a typical component of the Mediterranean diet and a source of phenolic compounds, particularly abundant in the skin portion. The aim of this study was to characterize the phenolic profile of the table grape skin and to assess its stability after the in vitro digestion process. The main phenolic compounds identified by the HPLC-DAD analysis were: procyanidin B 1 , caftaric acid, catechin, coutaric acid, quercetin 3-glucuronide and quercetin 3-glucoside. All compounds showed a good stability after in vitro digestion (from 43 to 80%). Moreover, the influence of grape skin polyphenols on the modulation of ROS and GSH levels was evaluated in basal and in stressed conditions on human intestinal cells (HT-29). In basal conditions, a higher polyphenol concentrations exerted pro-oxidant effect corresponding to high ROS level and low GSH content. This effect was probably due to the polyphenolic oxidation in cell culture condition with consequent production of hydrogen peroxide. Otherwise, in stressed conditions, grape skin polyphenols exerted antioxidant effects up to 1.3 × 10 −6  μg/g and restored the stress-related GSH reduction. The in vitro digestion process attenuated the biological effect of grape skin polyphenols on intestinal cell line (HT-29). In conclusion, grape skin polyphenols showed different behavior in relation to their concentrations and to the intracellular ROS levels. [ABSTRACT FROM AUTHOR]

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

The paper describes the developed hardware and software components of a computer vision system that extracts colour parameters from calibrated colour images and identifies non-destructively the different quality levels exhibited by lettuce (either whole or fresh-cut) during storage. Several colour parameters extracted by computer vision system have been evaluated to characterize the product quality levels. Among these, brown on total and brown on white proved to achieve a good identification of the different quality levels on whole and fresh-cut lettuce (P-value<0.0001). In particular, these two parameters were able to discriminate three levels: very good or good products (quality levels from 5 to 4), samples at the limit of marketability (quality level of 3) and waste items (quality levels from 2 to 1). Quality levels were also chemically and physically characterized. Among the parameters analysed, ammonia content proved to discriminate the marketable samples from the waste in both product's typologies (either fresh-cut or whole); even the two classes of waste were well discriminated by ammonia content (P-value<0.0001). A function that infers quality levels from the extracted colour parameters has been identified using a multi-regression model (R 2 =0.77). Multi-regression also identified a function that predicts the level of ammonia (an indicator of senescence) in the iceberg lettuce from a colour parameter provided by the computer vision system (R 2 =0.73), allowing a non-destructive evaluation of a chemical parameter that is particularly useful for the objective assessment of lettuce quality. The developed computer vision system offers flexible and simple non-destructive tool that can be employed in the food processing industry to monitor the quality and shelf life of whole and fresh-cut lettuce in a reliable, objective and quantitative way., (Copyright © 2014 Elsevier Ltd. All rights reserved.)