Description

Producing high-quality products represents a key driver in the horticultural sector although the concept of quality has evolved substantially over the past few decades. From traditional attributes (visual and organoleptic) also representing the main focus of most of the quality standards and regulations, more recently other important aspects are gaining relevance: the consolidated consumer attention towards the nutritional value of fresh horticultural products and their increasing sensibility toward the environmental impact of production processes.

The intensive use of natural resources and external inputs for horticulture production is having a considerable impact on the environment. However, the investigation on the environmental and social impact of horticultural production is receiving limited attention compared with other food production. The adoption of LIP, with a proper certification, may thus be considered as a part of the above mentioned broader concept of quality.

On the other side, the higher prices paid for guarantees derived from these labels, as for social certification of sustainable production, increase the risk of frauds. Product authenticity is, in fact, a major concern both for consumers and for processors who are worried about unfair competition in the market (Reid et al., 2006). Thus the need for additional tools to prove the authenticity of the product. Production of vegetable crops under controlled environments (i.e. greenhouses) has expanded considerably over recent decades in Mediterranean areas (FAO, 2013). Initially, research efforts and the related introduction of technical innovations focused on high-quality, healthy products. However, concern with environmentally-sustainable production has risen in the last decade as industrial greenhouse crops are usually seen as entailing high environmental impact (Torellas et al., 2012). On the other hand, there is also plenty of evidence that greenhouse vegetable production may decrease the environmental impact compared to the field cultivation (Stanghellini, 2014).

Efficient use of resources (water and fertilizers) in irrigated greenhouse agriculture, are promising and increasingly adopted strategies to achieve better crop performance, improved nutritional and sensorial quality (Montesano et al., 2015; Montesano et al., 2018). With respect to traditional systems, soilless cultivation and, particularly, closed-cycle with recycling of nutrient solution (NS) produce a number of benefits, including the possibility to standardize the production process, to improve plant growth and yield, and to obtain higher efficiency in water and nutrient use. Moreover, it is also possible to modulate the regulation of secondary metabolism of plants by optimal control of nutrient solution composition or by imposing controlled stress, and to actuate biofortification processes, generally leading to improved nutritional value of products (Rouphael et al., 2018). Innovative technologies based on the use of sensor networks for fertigation management may considerably reduce water and fertilizers consumption and increase the overall use efficiency of those inputs and may lead to qualitative and quantitative improvements while preventing both under- and over-irrigation. Therefore, a first partial objective of this project will be to increase of the efficiency of water and fertilizers in soilless (with open and closed-cycles with recirculation of nutrients) and soil cultivation in unheated greenhouses in order to reduce the impact on the environment and on the society.

Normally, the most used instrumental techniques to measure quality attributes of fruits and vegetables are destructive and involve a considerable amount of manual work, primarily due to sample preparation. In addition, most of these analytical techniques are time-consuming and sometimes may require sophisticated equipments. Finally, they can be performed only on a limited number of specimens, and therefore their statistical relevance may be limited (Amodio et al., 2017a). Research has been focused on developing non-contact, rapid, environmental-friendly and accurate methods for non-invasive evaluation of quality in fruits and vegetables. Nowadays, there are a few emerging non-destructive analytical instruments and approaches for this task, including spectroscopy, hyperspectral imaging and computer vision (Liu et al., 2017).

Near-infrared spectroscopy has gained wide attention in the food sector due to its capacity of providing fingerprints of different products on the base of the interaction between their molecular structure and the incident light (Workman and Shenk, 2004) which is the result of different pre-harvest factors that also affect final composition and quality. The feasibility of NIRS-based analysis to evaluate quality attributes of fresh fruits for commercial application have been reported by numerous authors (Amodio et al., 2017b; Arendse et al., 2018).

Hyperspectral imaging (HSI) combines the principles of spectroscopy and conventional imaging or computer vision. It is mainly used for internal bruise and defect detection in fruits and vegetables (Xing et al. 2005; Ariana et al., 2010) but also to predict internal composition (Piazzolla et al. 2013 and 2017; Yang et al., 2015; Liu et al., 2017). Amodio et al. (2017a) showed the potentiality of hyperspectral imaging in the Vis-NIR spectral range to predict internal content of soluble solids, phenols and antioxidant activity of fennel heads. In addition, this technique provided important information about the maturity of fennel heads. Some studies successfully applied these methods for the discrimination of production origin and agricultural practices. NIR and HSI were in fact used for the classification of apples (Guo et al., 2013), persimmon (Khanmohammadi et al., 2014) and arabica coffee (Bona et al. 2017) from different origins. As for production systems (Sánchez et al., 2013) investigated the potentiality of NIRS technologies to discriminate green asparagus grown under organic and conventional methods. More recently, Amodio et al. (2017c) successfully discriminated conventionally and organically grown strawberries, being also able to identify two different types of organic production systems applied to the same genetic material on the same growing site.

All these studies have suggested multispectral and hyperspectral systems as valid tools to evaluate the quality of different agricultural products and, more interestingly, as a potential tool for product authentication.

In addition, Computer Vision Systems (CVS) may be applied to extend quality prediction and discrimination along the whole supply chain from harvesting up to consumers. CVS combines mechanics, optical instrumentation, electromagnetic sensing and digital image processing technology (Patel et al., 2012). Recently, CVSs have been used to assess quality and marketability of tomatoes (Arias et al., 2000), artichokes (Amodio et al., 2011), fresh-cut nectarines (Pace et al., 2011), fresh-cut lettuce (Pace et al., 2014), fresh-cut radicchio (Pace et al., 2015) and rocket leaves (Cavallo et al., 2017). Moreover, they have been applied for the prediction of internal quality of colored carrots (Pace et al., 2013). Even more interesting is the application of these systems during the post-packaging phase and along the whole distribution chain. Despite the relevance of quality evaluation of packaged products, few investigations were reported in the literature. Multi-spectral reflective image analysis has been applied to monitor the evolution and spoilage of leafy spinach covered by plastic materials (Lara et al., 2013); more recently, Cavallo et al. (2018) have proposed an application of image analysis by CVS for non-destructive and contactless evaluation of the quality of packaged fresh-cut lettuce. Therefore the interest of investigating the application of CVS to detect quality and shelf-life of packaged products.

Accordingly, a second partial objective of the project will be to assess the quality of products with conventional and innovative non-destructive approaches, providing innovative tools for discrimination of those obtained by LIP, and for quality prediction of products while in their package.

Finally, the possibility of using a non-destructive approach for increasing the information on product history (e.g. growing location and agricultural practice) may be considered as a baseline to develop marketing tools to promote the diffusion of the sustainable production system. Cost barrier is an obstacle for choosing low input products instead of the conventional, even if the environment is mentioned as a strong commitment (Padle and Foster, 2005; Krystallis and Chryssohoidis, 2005). Therefore, as the knowledge about consumer preferences for the adoption of LIP is still mattered of debate, a third partial objective of this project will be to test the hypothesis of the influence of quality certification of LIP practices resulting from ND methods on consumer choices and their willingness to pay, in order to produce adequate and realistic marketing strategies.