Metabolomics-based component profiling of hard and semi-hard natural cheeses with gas chromatography/time-of-flight-mass spectrometry, and its application to sensory predictive modeling.

Gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) was used to analyze hydrophilic low molecular weight components, including amino acids, fatty acids, amines, organic acids, and saccharides, in cheese, and the sensometric application for practical metabolomic studies in the food industry is described. Derivatization of target analytes was conducted prior to the GC/TOF-MS analysis. Data on 13 cheeses, six Cheddar cheeses, six Gouda cheeses and one Parmigiano-Reggiano cheese, were analyzed by multivariate analysis. The uniqueness of the Parmigiano-Reggiano cheese metabolome was revealed. Principal component analysis (PCA) showed no grouping of the Cheddar cheeses and Gouda cheeses according to production method or country of origin. The PCA loading plot confirms that many amino acids contribute positively to PC1, suggesting that PC1 is closely related to degradation of proteins, and that lactic acid contributed positively to PC2, whereas glycerol contributed negatively to PC2, suggesting that factors regarding degradation of carbohydrates and fats were expressed in PC2. Partial least squares (PLS) regression models were constructed to predict the relationship between the metabolite profile and two sensory attributes, "Rich flavor" and "Sour flavor", which were related to maturation. The compounds that play an important role in constructing each sensory prediction model were identified as 12 amino acids and lactose for "Rich flavor", and 4-aminobutyric acid, ornithine, succinic acid, lactic acid, proline and lactose for "Sour flavor". The present study revealed that metabolomics-based component profiling, focusing on hydrophilic low molecular weight components, was able to predict the sensory characteristics related to ripening.