Non-Destructive Discrimination of Rocket Leaves
WP 2
Jul.2020 - Dec. 2020
This research focuses on the application of non-destructive technologies to authenticate products obtained with sustainable production techniques. Rocket leaves were grown soilless and soil-base at CNR - ISPA (WP1), comparing cultural practices with different degrees of inputs, in relation to the use of water and fertilizers. In this trial, we are acquiring the spectral data of rocket leaves using both a spectrophotometer and hyperspectral scanners. Spectra are being analyzed in order to study the impact of LIP on spectra variation and to individuate the selected spectral range significantly affected by the agricultural practices. The objective is to non-destructively discriminate rocket leaves according to the degree of sustainability of cultural practices, by building a classification model. The performance of the model is being evaluated.
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-destructive 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.
This trial focuses on discrimination of rocket leaves from sustainable agriculture obtained reducing agronomic inputs, and to predict its internal quality. To this end, we measured the reflected radiation, which in the NIR is the result of the molecular vibrations of boundaries C-H, N-H, and O-H. The near-infrared spectra consist of the fundamental molecular absorptions, generally overlapping. After spectra acquisition, we need to process our data through the application of sophisticated statistical approaches like multivariate techniques for either regression or classification purposes.
We expect the results of the research will confirm the capability to discriminate rocket leaves obtained with low agronomic input, increasing the available information for consumers and the authentication tools for growers.
