Sensory based quality control utilising an electronic nose and GC-MS analyses to predict end-product quality from raw materials.

The objective of the present study was to investigate if an electronic nose, comprising six metal oxide sensors (MOS) could predict the sensory quality of porcine meat loaf, based on measuring the volatiles in either the raw materials or the meat loaf produced from those raw materials. A multivariate data analysis strategy involving analysis of variance partial least squares regression (APLSR) and principal component analysis (PCA) was used to determine causal and predictive relationships between the raw material and meat loaf samples, sensory analysis, electronic nose, and GC-MS measurements. The results showed that the six MOS sensors in the Danish odour sensor system (DOSS) could detect the raw materials that led to unacceptable products, as determined by sensory profiling and in-house sensory quality control (QC), and separate those raw materials from each other, based on the volatile composition, as determined by GC-MS. However, the electronic nose was unable to detect all the sensory unacceptable meat loaf samples themselves due to changes in the volatile composition after cooking. Analysis of the GC-MS compounds identified from raw materials and meat loaf samples indicate that two MOS sensors mainly responded to alcohols and to a lesser degree to aldehydes and alkanes, whereas two other sensors most likely responded to low molecular weight sulphur compounds. Thus, the results indicate that measuring volatiles with the MOS sensors in the DOSS system, on raw materials for processed meat products, may be a feasible strategy in sensory based quality control, and may also have potential in predicting the sensory quality of the end product.