Abstract
Battling meat adulteration is essential in preserving both public health and a fair market. This study depicts a method of reducing adulteration in meats, in which Electronic-nose (E-nose) and gas chromatography-mass spectrometer (GC-MS) were applied to identify adulterants in mutton. Duck meat was selected as a model adulterant due to its lower cost and similarity in flavor to mutton, as well as being frequently adulterated in China. Qualitative and quantitative analysis were conducted using linear regression, fisher linear discriminant analysis (FLDA), and multilayer perceptron neural networks analysis (MLPN) on E-nose signals. Several fingerprint volatile organic chemicals (VOC) were identified by GC-MS to validate the E-nose results. Multivariate partial least square regression (PLS) was carried out to study the relationships between GC-MS and E-nose. The results of GC-MS confirmed that E-nose can be used to identify duck adulteration in mutton, with a minimum detection ratio of 10%. This method proved that rapid detection of mutton adulterated duck meat using E-nose has a high accuracy, which has reduced detecting time and improved detection efficiency.
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