论文信息 - Regression model on electronic nose data from aromatic rice samples

Regression model on electronic nose data from aromatic rice samples

As of today, aroma of rice is measured by an expert sensory panel and they assign scores like `+', `++', `+++' and `NA' for mild, medium, strong and non aromatic varieties of rice respectively. This method of human panel testing is very subjective with numerous problems like inaccuracy, non-repeatability and it is laborious and time consuming also. On the other hand, the analytical instruments, which are used for this purpose are prohibitively expensive and are available in the laboratories only. It is in this pursuit, an electronic nose with an array of gas sensors has been developed for aroma measurement of rice. This user friendly and low cost electronic nose may be extremely useful for rice scientists, researchers and exporters to determine the aroma of aromatic rice. In this paper, we describe the experimental setup and the regression model for classification of rice samples. With unknown rice samples, aroma based classification accuracy by multi-sensor electronic nose using the regression model, has been found to be more than 80%.

[1] H. Troy Nagle,et al. Performance of the Levenberg–Marquardt neural network training method in electronic nose applications , 2005 .

[2] Manuela O’Connell,et al. A practical approach for fish freshness determinations using a portable electronic nose , 2001 .

[3] T. Eklöv,et al. Selection of variables for interpreting multivariate gas sensor data , 1999 .

[4] J. W. Arnold,et al. Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage , 2002 .

[5] Nabarun Bhattacharyya,et al. Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks , 2008, IEEE Transactions on Instrumentation and Measurement.

[6] M. C. Horrillo,et al. Identification of typical wine aromas by means of an electronic nose , 2004, Proceedings of IEEE Sensors, 2004..

[7] A. Legin,et al. Towards reliable estimation of an "electronic tongue" predictive ability from PLS regression models in wine analysis. , 2012, Talanta.

[8] Giovanni Sparacino,et al. Assessment of linear regression techniques for modeling multisensor data for non-invasive continuous glucose monitoring , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9] R. Bandyopadhyay,et al. Classification of aromatic and non-aromatic rice using electronic nose and artificial neural network , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.