Molecular fingerprinting of different types of cheese to improve quality

Targeted Research Priorities

Technological properties of dairy matrices
The goal of this research is to identify the chemical compounds present in milk and cheese that may influence the sensory quality of cheddar cheese.

Indicators and control tools
This project aims to develop a tool for the early detection of chemical compounds responsible for sensory defects in Cheddar cheese.

Project Summary

The quality of cheese can vary from one production batch to another. Some batches of cheese may develop taste or aroma defects such as rancidity or bitterness, sometimes causing significant financial losses for the cheese industry.

This project aims to identify molecular markers associated with these sensory defects and with the aging of Cheddar cheese to better control its production and marketing. The team analyzes cheeses of varying qualities and ages from different cheese factories using advanced technologies such as:

• Near-infrared (NIR) spectroscopy to assess the chemical composition of the cheese
• Headspace gas chromatography-mass spectrometry (HD-GC-MS) for the identification of volatile compounds
• Liquid chromatography coupled with mass spectrometry (LC-MS) for the detection of bitter peptide compounds

These analytical methods are combined with principal component analysis (PCA) and ascending hierarchical classification (AHC) to identify cheese age groups. Combining results from NIR, HD-GC-MS, and LC-MS will enable the establishment of reliable chemical marker profiles associated with cheese age and its sensory properties, such as bitterness and rancidity. The goal is to develop a predictive model for monitoring and determining the age, sensory quality, and texture of Cheddar cheese.

Ultimately, this work could lead to a rapid online monitoring tool enabling continuous quality control during factory production, reducing losses and ensuring high-quality cheeses that meet quality standards. This innovative approach aims to provide processors with an effective means of improving product consistency and enhancing competitiveness.

Expected Results

• Chemical profiles of cheeses of different ages using HD-GC-MS and LC-MS
• Identification of the main chemical components enabling the classification of cheeses of different ages using the NIR method
• Identification of the main chemical components to classify cheeses according to their age and sensory quality (good, acceptable, or poor) through evaluation by a panel of sensory experts using the HD-GC-MS method
• Identification of volatile bitterness markers using the LC-MS method
• Identification of markers associated with cheese age
• Predictive models for the early detection of sensory characteristics of rancidity or bitterness and the determination of cheese age

The presence of chemical molecules associated with cheese age and sensory qualities that can be generated during the ripening (maturation) stage has already been reported in the literature. Consequently, the identification of molecules using analytical laboratory techniques (HD-GC-MS and LC-MS) will allow them to be detected in varying quantities, depending on the quality of the sampled cheeses, so that they can then be selected as molecular markers for the sensory defects of bitterness and rancidity. Furthermore, the high presence of peptic compounds containing hydrophobic molecules, which are correlated with the bitterness of the cheese, could also be identified and used to evaluate Cheddar cheese quality.

Main Achievements

• Training of a master’s student
• Presentation of results at the Novalait Forum Techno and during Consortium RITA outreach activities
• Presentation of lectures or posters at national and international conferences
• Writing of scientific and popular science articles
• Popular science reports tailored to industry partners participating in the project.