Non-Destructive Analysis of Low-Impact Grown Tomatoes
WP 2
Dec. 2020 - Feb. 2021
This research focuses on the application of non-destructive technologies to authenticate products obtained with sustainable production techniques. Tomatoes were grown at CNR – ISPA, under greenhouse conditions using both closed-cycle and open-cycle soilless systems. Advanced sensor-based technologies are used to reduce the environmental impact also in open-cycle systems. In this trial, we are acquiring the spectral data of tomatoes using both a spectrophotometer and hyperspectral scanners. In addition, quality attributes are also being determined using conventional methods and devices, and then correlated with the spectral information. The objective is to non-destructively assess the internal quality of tomatoes and to discriminate them according to the degree of sustainability of cultural practices by building a prediction and a classification model, respectively. The performance of each model is 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 the prediction of internal quality and the discrimination of tomatoes obtained from sustainable agriculture in relation to reduced agronomic inputs. To this end, we measured the reflected radiation of tomatoes measured by a hyperspectral scanner and a spectrophotometer, 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 process our data through the application of sophisticated statistical approaches like multivariate techniques for either regression or classification purposes. A prediction and a classification model is built to predict the internal quality and to discriminate tomatoes, respectively. The performance of models is being evaluated.
We expect that the results of the research will confirm the capability to predict internal quality and discriminate tomatoes obtained with low agronomic input, increasing the available tool for consumers and the authentication tools for growers.
