Classification of Rice by Combining Electronic Tongue and Nose

Food Analytical Methods

Volumn 8, Issue 8, 2015, Pages 1893-1902

  Lu, Lin ^a,b   Deng, Shaoping ^a   Zhu, Zhiwei ^b   Tian, Shiyi ^a  

^a ZHEJIANG GONGSHANG UNIVERSITY   (China)

^b CHINA NATIONAL RICE RESEARCH INSTITUTE   (China)

Author keywords

Electronic nose; Electronic tongue; KNN; LLE; SVM

Indexed keywords

ELECTRONIC TONGUES; FORECASTING; LEARNING ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

ELECTRONIC SIGNALS; ELECTRONIC SYSTEMS; K NEAREST NEIGHBOR (KNN); KNN; LLE; LOCALLY LINEAR EMBEDDING; PREDICTION ACCURACY; SVM;

ELECTRONIC NOSE;

EID: 84938293304     PISSN: 19369751     EISSN: 1936976X     Source Type: Journal    
DOI: 10.1007/s12161-014-0070-x     Document Type: Article

References (38)

1. Abud-Archila M, Courtois F, Bonazzi C, Bimbenet JJ (2000) A compartmental model of thin-layer drying kinetics of rough rice. Dry Technol 18:1389–1414
2. Banerjee R, Tudu B, Shaw L, Jana A, Bhattacharyya N, Bandyopadhyay R (2012) Instrumental testing of tea by combining the responses of electronic nose and tongue. J Food Eng 110:356–363
3. Bett-Garber KL, Champagne ET, McClung AM, Moldenhauer KA, Linscombe SD, McKenzie KS (2001) Categorizing rice cultivars based on cluster analysis of amylose content, protein content and sensory attributes. Cereal Chem 78:551–558
4. Bleibaum RN, Stone H, Tan T, Labreche S, Saint-Martin E, Isz S (2002) Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Qual Prefer 13(6):409–422
5. Cagampang GB, Perez CM, Juliano BO (1973) A gel consistency test for the eating quality of rice. J Sci Food Agric 24:1589–1594
6. Chen QQ, Song GQ, Ouyang JR (1991) Study on the quality difference of indica hybrid and conventional rice. Sci Agric Sinica 24(2):43–50
7. Cheng FM, Zhong LJ, Wang F, Zhang GP (2005) Differences in cooking and eating properties between chalky and translucent parts in rice grains. Food Chem 90:39–46
8. Ciosek P, Brzózka Z, Wróblewski W (2006) Electronic tongue for flow through analysis of beverages. Sensor Actuat B-Chem 118:454–460
9. Comak E, Arslan A, Turkoglu I (2007) A decision support system based on support vector machines for diagnosis of the heart valve diseases. Comput Biol Med 37:21–27
10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
11. Derycke V, Veraverbeke WS, Vandeputte GE, De Man W, Hoseney RC, Delcour JA (2005) Impact of protein on pasting and cooking properties of nonparboiled and parboiled rice. J Cereal Chem 82(4):468–474
12. Dong YJ, Tsuzuki E, Terao H (2001) Trisomic genetic analysis of aroma in three Japanese native rice varieties (Oryza sativa L.). Euphytica 117:191–196
13. Fix E, Hodges JL (1951) Discriminatory analysis, nonparametric discriminators: consistency properties. Technical Report 4. Randolph Field, Texas: USAF School of Aviation Medicine
14. Guo G, Li SZ, Chan KL (2001) Support vector machines for face recognition. Image Vision Comput 19:631–638
15. Hu XH (2011) Research on design and construction of electronic nose platform. Zhejiang Gongshang University, China
16. Kundu PK, Chatterjee A, Panchariya PC (2011) Electronic tongue system for water sample authentication: a slantlet-transform-based approach. IEEE T Instrum Meas 60(6):1959–1966
17. Martin M, Fitzgerald MA (2002) Proteins in rice influence cooking properties. J Cereal Sci 36:285–294
18. Ouyang Q, Zhao JW, Chen QS (2013) Classification of rice wine according to different marked ages using a portable multi-electrode electronic tongue coupled with multivariate analysis. Food Res Int 51:633–640
19. Pathange LP, Mallikarjunan P, Marini RP, O’Keefe S, Vaughan D (2006) Non-destructive evaluation of apple maturity using an electronic nose system. J Food Eng 77:1018–1023
20. Pinson SRM (1994) Inheritance of aroma in six rice cultivars. Crop Sci 34:1151–1157
21. Rodríguez SD, Barletta DA, Wilderjans TF, Bernik DL (2014) Fast and efficient food quality control using electronic noses: adulteration detection achieved by unfolded cluster analysis coupled with time-window selection. Food Anal Method. doi:10.1007/s12161-014-9841-7
22. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326
23. Schulbach KF, Rouseff RL, Sims CA (2004) Relating descriptive sensory analysis to gas chromatography/olfactometry ratings of fresh strawberries using partial least squares regression. J Food Sci 69:273–277
24. Singh V, Okadome H, Toyoshima H, Isobe S, Ohtsubo K (2000) Thermal and physicochemical properties of rice grain flour and starch. J Agric Food Chem 48:2639–2647
25. Teye E, Huang X, Han F, Botchway F (2014) Discrimination of cocoa beans according to geographical origin by electronic tongue and multivariate algorithms. Food Anal Method 7:360–365
26. Thissen U (2004) Comparing support vector machines to PLS for spectral regression applications. Chemometr Intell Lab 73(2):169–179
27. Tian SY, Deng SP, Chen ZX (2007) Multifrequency large amplitude pulse voltammetry: a novel electrochemical method for electronic tongue. Sensor Actuat B-Chem 21:1049–1056
28. Tran TU, Suzuki K, Okadome H, Homma S, Ohtsubo KI (2004) Analysis of the tastes of brown rice and milled rice with different milling yields using a taste sensing system. Food Chem 88(4):557–566
29. Tran TU, Suzuki K, Okadome H, Ikezaki H, Homma S, Ohtsubo KI (2005) Detection of changes in taste of japonica and indica brown and milled rice (Oryza sativa L.) during storage using physicochemical analyses and a taste sensing system. J Agric Food Chem 53(4):1108–1118
30. Vapnik VN (2004) Statistical learning theory. Electronics Industry, Beijing
31. Versari A, Parpinello GP, Ricci A, Meglioli M (2013) Relationship between chemical markers and sensory score of traditional balsamic vinegars using a screening approach combined with rapid assessment methods. Food Anal Method 6:1697–1703
32. Wall ME, Rechtsteiner A, Rocha LM (2003) Singular value decomposition and principal component analysis. In: Berrar DP, Dubitzky W, Granzow M (eds) A practical approach to microarray data analysis, chapter 5. Kluwer Academic, Norwell, MA, pp 91–109
33. Wei Z, Wang J, Ye L (2011) Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue. Biosens Bioelectron 26(2):4767–4773
34. Xiaobo Z, Jiewen Z, Povey MJW, Holmes M, Hanpin M (2010) Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta 667:14–32
35. Xie LH, Chen N, Duan BW, Zhu ZW, Liao XY (2008) Impact of proteins on pasting and cooking properties of waxy and non-waxy rice. J Cereal Sci 47:372–379
36. Zheng XZ, Lan YB, Zhu JM, Westbrook J, Hoffmann WC, Lacey RE (2009) Rapid identification of rice samples using an electronic nose. J Bionic Eng 6:290–297
37. Zhou B, Wang J (2011) Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens. Sensor Actuat B-Chem 160:15–21
38. Zhu ZW, Chen N, Wang DY, Zhang XF, Yao Q, Min J, Liao XY (2004) Analysis on variation and difference for rice quality traits among different types of rice. Chinese J Rice Sci 18(4):315–320