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Abstract

Pseudomonas fluorescens is a psychrotrophic species associated with milk spoilage because of its lipolytic and proteolytic activities. Consequently, monitoring P. fluorescens or its antecedent activity in milk is critical to preventing quality defects of the product and minimizing food waste. Therefore, in this study, untargeted metabolomics and peptidomics were used to identify the changes in milk related to P. fluorescens activity by simulating the low-temperature conditions usually found in milk during the cold chain. Both unsupervised and supervised multivariate statistical approaches showed a clear effect caused by the P. fluorescens inoculation on milk samples. Our results showed that the levels of phosphatidylglycerophosphates and glycerophospholipids were directly related to the level of contamination. In addition, our metabolomic approach allowed us to detect lipid and protein degradation products that were directly correlated with the degradative metabolism of P. fluorescens. Peptidomics corroborated the proteolytic propensity of P. fluorescens-contaminated milk, but with lower sensitivity. The results obtained from this study provide insights into the alterations related to P. fluorescens 39 contamination, both pre and post heat treatment. This approach could represent a potential tool to retrospectively understand the actual quality of milk under cold chain storage conditions, either before or after heat treatments.
Lingua originaleInglese
pagine (da-a)1173-N/A
RivistaFoods
Volume10
Numero di pubblicazione6
DOI
Stato di pubblicazionePubblicato - 2021

OSS delle Nazioni Unite

Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile

  1. SDG 3 - Salute e benessere
    SDG 3 Salute e benessere

All Science Journal Classification (ASJC) codes

  • Scienze Alimentari
  • Microbiologia
  • Salute (scienze sociali)
  • Professioni della salute (varie)
  • Botanica

Keywords

  • Foodomics
  • Milk contamination
  • Milk quality
  • Molecular marker
  • Spoilage
  • Untargeted profiling

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