Development and validation of an APCI-MS / GC-MS approach for the classification and prediction of cheddar cheese maturity - Gan, Heng-Hui; Bingnan, Yan; Linforth, Rob S.T.; Fisk, Ian D. (Elsevier, 2016)
Headspace techniques have been extensively employed in food analysis to measure volatile compounds, which play a central role in the perceived quality of food. In this study atmospheric pressure chemical ionisation-mass spectrometry (APCI-MS), coupled with GC-MS (gas chromatography–mass spectrometry), was used to investigate the complex mix of volatile compounds present in Cheddar cheese of different maturity, processing and recipes to enable characterization of the cheeses based on their ripening stages. Partial Least Square-Linear Discriminant Analysis (PLS-DA) provided a 70% success rate in correct prediction of the age of the cheeses based on their key headspace volatile profiles. In addition to predicting maturity, the analytical results coupled with chemometrics offered a rapid and detailed profiling of the volatile component of Cheddar cheeses, which could offer a new tool for quality assessment and accelerate product development timelines.