Application of a path-modelling approach for deciphering causality relationships between microbiota, volatile organic compounds and off-odour profiles during meat spoilage

TLDR

Meat spoilage is driven by bacterial metabolism that degrades sensory qualities, and studying it requires microbiological, physicochemical, and sensorial data. The study aimed to use a multiblock path‑modelling workflow to uncover causal links among microbiota composition, volatile organic compounds, and off‑odour profiles during spoilage. Using the Path‑ComDim approach on a large‑scale dataset from fresh turkey sausages, the authors quantified a priori causal relationships, identified key spoilage responses, and validated the assumptions. The analysis confirmed dynamic spoilage causality, linking off‑odour evolution to VOCs such as ethanol, ethyl acetate, acetoin, and diacetyl produced by bacteria like *Lactococcus piscium* and *Leuconostoc gelidum*, and demonstrated that Path‑ComDim is a promising tool for other complex biological datasets.

Abstract

Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.