The aim of this work was to investigate the applicability of non-destructive techniques in monitoring freshness decay of fresh-cut Valerianella locusta L. during storage at different temperature. The sampling was performed for 15 days for Valerianella samples preserved at 4 and 10 °C, and for 7 days for samples stored at 20 °C. The quality decay of samples was evaluated by quality parameters (pH, water content, total phenols, chlorophyll a fluorescence) and by non-destructive systems (electronic nose and visible-near infrared spectroscopy). Cluster Analysis (CA) was performed on quality indices and four clusters were identified, namely "fresh", "acceptable", "spoiled" and "very spoiled". Principal Component Analysis (PCA) was applied on the electronic nose data in order to evaluate the feasibility of this technique as a rapid and non-destructive approach for monitoring the freshness of fresh-cut Valerianella during storage. Linear Discriminant Analysis (LDA) and PLS-discriminant analysis (PLS-DA) models were developed to test the performance of electronic nose and VIS-NIR, respectively, to classify samples in the four classes of freshness. The average value of samples correctly classified using LDA was 95.5% and the cross validation error rate was equal to 8.7%. The results obtained from PLS-DA models, in validation, gave a positive predictive value (PPV) of classification between 74% and 96%. Finally, predictive models were performed using Partial Least Squares (PLS) regression analysis between quality indices and VIS-NIR data. RPD values <3 were obtained for water content and pH. Excellent results were obtained for total phenols with Rcv(2) and RPD equal to 0.89 and 3.19, and for chlorophyll a fluorescence with Rcv(2) and RPD equal to 0.92 and 3.22, respectively. Results demonstrated that electronic nose and VIS-NIR are complementary techniques able to support the conventional techniques in the shelf-life assessment of fresh-cut V. locusta L. providing information useful for a better management of the product along the distribution chain.