Neural network based electronic nose for discrimination of fish sauces

Fish sauce is one of the signature condiments in various cuisines in many countries. In this study, fish sauces are successfully discriminated depending on their quality indicated by the level of Total Nitrogen (TN) content. We introduce an electronic nose (E-nose) technology to measure the odor of fish sauce. Feature extraction methods are also performed in order to obtain information relevant to the odor discrimination task. Four features based on both steady-state and transient responses are extracted from each response signal obtained from the E-nose. The principal component analysis (PCA) is also introduced to reduce the dimensionality of feature vector for avoiding the curse of dimensionality. The backpropagation (BP) learning algorithm is then used for the discrimination. Using this method, we achieve high discrimination performance to the fish sauces.

[1]  T. Kurata,et al.  Quality and sensory acceptance of fish sauce partially substituting sodium chloride or natural salt with potassium chloride during the fermentation process , 2003 .

[2]  Mark Bücking,et al.  A novel electronic nose based on miniaturized SAW sensor arrays coupled with SPME enhanced headspace-analysis and its use for rapid determination of volatile organic compounds in food quality monitoring , 2006 .

[3]  Changsheng Xie,et al.  ‘Sensory analysis’ of Chinese vinegars using an electronic nose , 2008 .

[4]  J. Järnberg,et al.  Microbial volatile organic compounds. , 2009, Critical reviews in toxicology.

[5]  Anne-Claude Romain,et al.  Using the classification model of an electronic nose to assign unknown malodours to environmental sources and to monitor them continuously , 2000 .

[6]  Tao Yu,et al.  Comparison of Algorithms for an Electronic Nose in Identifying Liquors , 2008 .

[7]  Hongmei Zhang,et al.  Quality grade identification of green tea using E-nose by CA and ANN , 2008 .

[8]  Frank Stam,et al.  Packaging effects of a novel explosion-proof gas sensor , 2003 .

[9]  C. Distante,et al.  On the study of feature extraction methods for an electronic nose , 2002 .

[10]  A. Fazlara,et al.  Chemical and microbial properties of mahyaveh, a traditional Iranian fish sauce , 2012 .

[11]  Changsheng Xie,et al.  Characterization of Chinese vinegars by electronic nose , 2006 .

[12]  Zhiwei Zhu,et al.  Chemical and sensory changes associated Yu-lu fermentation process – A traditional Chinese fish sauce , 2007 .

[13]  Takamichi Nakamoto,et al.  Study on the odor classification in dynamical concentration robust against humidity and temperature changes , 2008 .

[14]  K. Tu,et al.  Application of electronic nose in Chinese spirits quality control and flavour assessment , 2012 .

[15]  Jun Wang,et al.  Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals , 2009 .

[16]  Y. Ohta,et al.  Microflora and Chemical Assessment of an Indonesian Traditional Fermented Fish Sauce "Bakasang" , 1995 .

[17]  M. Daeschel,et al.  FISH SAUCE PRODUCTS AND MANUFACTURING: A REVIEW , 2001 .

[18]  J. W. Park,et al.  Seasonal effects on the physicochemical characteristics of fish sauce made from capelin (Mallotus villosus) , 2007 .

[19]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .