Development of a HS-GC-IMS predictive method for estimating meat spoilage via VOCs profiling

Area of Science:

• Food Science
• Analytical Chemistry
• Microbiology

Background:

• Fresh meat spoilage due to microbial growth necessitates rapid hygiene quality assessment.
• Current methods for assessing meat quality are often time-consuming in vitro tests.
• Ensuring food safety and minimizing economic losses requires efficient quality control measures.

Purpose of the Study:

• To establish a correlation between the meat volatilome and microbial counts for rapid hygiene quality assessment.
• To evaluate Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) and SPME-GC-MS as alternatives to traditional microbial testing.
• To identify key volatile compounds indicative of meat spoilage.

Main Methods:

• Analysis of refrigerated raw meat volatilome using HS-GC-IMS and SPME-GC-MS.
• Correlation of volatilome data with Total Viable Counts (TVCs) using Partial Least Squares Regression (PLSR).
• Non-targeted modeling for spoilage marker detection without sample pretreatment.

Main Results:

• HS-GC-IMS demonstrated superior performance over HS-GC-MS, achieving high predictive model accuracy (R² > 0.80).
• HS-GC-IMS showed enhanced sensitivity for early detection of spoilage markers.
• 3-methyl-1-butanol was identified as a key indicator of meat end-of-shelf-life.

Conclusions:

• HS-GC-IMS provides a rapid, culture-independent method for real-time monitoring of meat freshness.
• This technique serves as a viable alternative to traditional microbial testing for hygiene quality control.
• The findings contribute to improved food safety and reduced economic losses in the meat industry.