Prediction of human responses to dairy odor using an electronic nose and neural networks

Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative measurements from instruments. In this work, a prediction system was designed and developed to use instruments to predict human assessment of odors from common dairy operations. The model targets are the human responses to odor samples evaluated using a general pleasantness scale ranging from-11 (extremely unpleasant) to +11 (extremely pleasant). The model inputs were the electronic nose measurements. Three different neural networks, a Levenberg-Marquardt back-propagation neural network (LMBNN), a scaled conjugate gradient back-propagation neural network (CGBNN), and a resilient back-propagation neural network (RPBNN), were applied to connect these two sources of information (human assessments and instrument measurements). The results showed that the LMBNN model can predict human assessments with accuracy as high as 78% within a 10% range and as high as 63% within a 5% range of the targets in independent validation. In addition, the LMBNN model performed with the best stability in both training and independent validation.