Combined Effect of Dipping in Oxalic or in Citric Acid and Low O2 Modified Atmosphere, to Preserve the Quality of Fresh-Cut Lettuce during Storage.

Leaf edge browning is the main factor affecting fresh-cut lettuce marketability. Dipping in organic acids as well as the low O2 modified atmosphere packaging (MAP), can be used as anti-browning technologies. In the present research paper, the proper oxalic acid (OA) concentration, able to reduce respiration rate of fresh-cut iceberg lettuce, and the suitable packaging materials aimed to maintaining a low O2 during storage, were selected. Moreover, the combined effect of dipping (in OA or in citric acid) and packaging in low O2 was investigated during the storage of fresh-cut iceberg lettuce for 14 days. Results showed a significant effect of 5 mM OA on respiration rate delay. In addition, polypropylene/polyamide (PP/PA) was select as the most suitable packaging material to be used in low O2 MAP. Combining OA dipping with low O2 MAP using PP/PA as material, resulted able to reduce leaf edge browning, respiration rate, weight loss and electrolyte leakage, preserving the visual quality of fresh-cut lettuce until 8 days at 8 °C.

Quality evaluation of table grapes during storage by using 1H NMR, LC-HRMS, MS-eNose and multivariate statistical analysis.

Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.