Combined application of electronic nose analysis and back-propagation neural network and random forest models for assessing yogurt flavor acceptability

Flavor acceptability is an important aspect of evaluating the quality of food products. Rapid flavor measurements and the detection of unsatisfactory products are thus necessary for the quality control of yogurt. To better evaluate the flavor acceptability of yogurt, this study used a method based on an electronic nose and nonlinear chemometric back-propagation neural network (BPNN) and random forest (RF) models. Initially, principal component analysis was applied to visualize the quality distribution of a set of yogurt samples, but it failed to distinguish between the satisfactory and unsatisfactory samples. However, the BPNN and RF models clearly discriminated between the two sample types, with accuracy values close to 100%. The RF model achieved better discrimination than the BPNN model, with an accuracy of 93.75% for three subsets of the samples with unsatisfactory flavor. In summary, the combination of an electronic nose and a nonlinear chemometric model is an effective system for the assessment of yogurt flavor acceptability.