Rapid Identification of Rice Samples Using an Electronic Nose

• Published in: Journal of bionics engineering Vol. 6; no. 3; pp. 290 - 297
• Main Authors: Zheng, Xian-zhe, Lan, Yu-bin, Zhu, Jian-min, Westbrook, John, Hoffmann, W.C., Lacey, R.E.
• Format: Journal Article
• Language: English
• Published: Singapore Elsevier Ltd 01-09-2009; Springer Singapore
• Subjects: Artificial Intelligence; Biochemical Engineering; Bioinformatics; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; data analysis; electronic nose; Engineering; identification; Oryza sativa; pattern recognition; rice grain; electronic nose; identification; data analysis; rice grain; pattern recognition
• ISSN: 1672-6529; 2543-2141
• DOI: 10.1016/S1672-6529(08)60122-5

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320). Samples of 5 g of each variety of rice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors. The Cyranose-320 was able to differentiate between varieties of rice. The chemical composition of the rice odors for differentiating rice samples needs to be investigated. The optimum parameter settings should be considered during the Cyranose-320 training process especially for multiple samples, which are helpful for obtaining an accurate training model to improve identification capability. Further, it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy. A reduced number of sensors could potentially shorten the data processing time, and could be used to establish an application procedure and reduce the cost for a specific electronic nose. Further research is needed for developing analytical procedures that adapt the Cyranose-320 as a tool for testing rice quality.