Discriminating Fruits and Vegetables using Innovative and Non-Destructive Techniques

Abstract

Authentication of products is one of the greatest challenges for agriculture: manufacturers and consumers are, in fact, concerned about unfair competition and finding on the market products, which do not correspond to the declared specifications. Increased consumer awareness and the recent organic scandals are therefore directing the research towards the development of new techniques for food safety and traceability. There are different traditional solutions to solve these problems, which are time, cost, and labor consuming. Hence, there is a need for complementary methods to the traditional ones, to increase the consumer information. Non-destructive optical techniques, in combination with chemometric methods, have been used for rapid determination of the quality of the fruit and vegetable products. This research will focus on the application of non-destructives technologies such as VIS-NIR spectroscopy and hyperspectral images to authenticate fruit and vegetable products obtained with sustainable production techniques. Thanks to these techniques, it is possible to obtain what is called the spectral footprint or fingerprint, which is the result of several factors, including agronomic practices, that influence the composition and final quality of each product. The research activity is divided into two main lines: 1. discrimination of organic from conventional products; 2. discrimination of products from sustainable agriculture obtained reducing agronomic inputs. Tomatoes, and potato, widespread and available all year round are chosen as product models for the first line, whereas tomato and salad leaves will be used for the second research line. We expect that the results of the research will confirm the capability to discriminate fruit and vegetable products obtained with low agronomic input, increasing the available tool for consumer information.

Contactless and non-destructive quality assessment of fresh-cut fruits and vegetables

Abstract

Computer Vision System (CVS) is an innovative contactless and non-destructive methodology that combines mechanics, optical instrumentation, electromagnetic sensing and digital image processing technology to predict and discriminate the products quality. CVS can automatically extract from an image the most discriminative colour features, related to the quality of the product analyzed, and can assess and predict its quality through the construction of mathematical models and algorithms. This Ph.D. project is aimed at developing and validating CVS for the quality assessment of packaged rocket leaves and of common commercial fresh-cut fruits (apple and melon) to provide an innovative method for the quality prediction of products through the packaging material. This activity well be carried out within the PRIN project SUS&LOW (Sustaining Low-impact Practices In Horticulture Through Non-destructive Approach To Provide More Information On Fresh Produce History & Quality) whose purpose is to support the quality of productions and of the environment through low- input agricultural practices and non-destructive quality assessment. During the first year, three experiments on rocket leaves, obtained using different low impact agricultural practices, were realized at the Institute of Science of Food Production of National Research Council. Rocket leaves were stored at 10 °C and the quality level (QL) was assessed using a 5 to 1 rating scale. At each QL leaves were subjected to destructive quality characterization and to non-destructive analysis by CVS. Preliminary results showed that the CVS can be a valid non-destructive and contactless technology for the QL assessment of unpackaged rocket leaves. Moreover, preliminary trials were conducted on fresh-cut apples to select cultivars more suitable to postharvest handlings.