Rapid measuring and modelling flavour quality changes of oxidised chicken fat by electronic nose profiles through the partial least squares regression analysis.

The objective of this study was to investigate whether an electronic nose, comprising 18 metal oxide semiconductor gas sensors, could be used for measuring and modelling flavour quality changes of refined chicken fat during controlled oxidation. Partial least squares regression (PLSR) was applied to determine the predictive relationships between the chemical parameters, GC-MS data, free fatty acid profiles and electronic nose responses for controlled oxidation of refined chicken fat. The results showed that peroxide value (PV) and acid value (AV) were significantly well predicted by the electronic nose responses, whereas p-anisidine value (p-AV) was found to be fairly well predicted especially for deeply oxidised chicken fat. Thus, this study gave evidence of the electronic nose system to be a promising device for future at- or on-line implementation in oxidation control of chicken fat for producing meat flavourings.