ABSTRACT
This paper presents the use of a tin-oxide sensor array and self-organized map (SOM)-based E-nose for analysis of volatile bread aroma and explores its ability to cluster bread odor data according to the freshness of bread. A low cost tin-oxide sensor array based electronic nose system has been used for the classification of state of freshness of bread. The sensor data was acquired for a period of 3 weeks, and an unsupervised self-organizing map (SOM) model was trained using this data to correlate the sensor response to classify the bread as fresh and stale. A comparative evaluation of 3 week' of bread data was carried out using the SOM. The results suggest that the system developed is able to predict the state of bread as fresh and stale up to 98% accuracy if the test bread data sets are of the same week. The classification accuracy reduces to 75–85% if test bread data sets are from different weeks. The model is also applied on three different brands of bread and similar classification results are obtained.
ACKNOWLEDGMENT
The authors B. Botre and D. Gharpure thank CSIR, India, for financial support.
Notes
W1, W2, and W3: Test bread data of week 1, 2, and 3, respectively. SOM1, SOM2, and SOM3: SOM model for week 1, 2, and 3 training data set, respectively.