Skip to main content
SpringerLink
Log in

Classification of Rice by Combining Electronic Tongue and Nose

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

One hundred twenty indica rice samples were determined by electronic tongue and electronic nose. The potential of the combinational approaches of electronic tongue and nose for rice analysis, with the aim of differentiating conventional and hybrid rice, was investigated. Principal component analysis (PCA) and locally linear embedding (LLE) were used to preprocess data from electronic systems. Support vector machine (SVM) model and K-nearest neighbors (KNN) model were established with the values from PCA and LLE algorithms as attributes. For the combination of electronic tongue and nose, the prediction accuracies of PCA-SVM, PCA-KNN, LLE-SVM, and LLE-KNN models were 55, 55, 85, and 80 %. The LLE-based models achieved better prediction accuracies than PCA-based models. These results demonstrated that LLE algorithm coupled with SVM or KNN for the combined electronic signals was effective in extracting and analyzing features for detecting rice. The LLE-SVM model achieved a little higher accuracy than the LLE-KNN model. It can be concluded that the combination of electronic systems coupled with LLE-based model have a great potential in the prediction of rice types.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cagampang GB, Perez CM, Juliano BO (1973) A gel consistency test for the eating quality of rice. J Sci Food Agric 24:1589–1594

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Ciosek P, Brzózka Z, Wróblewski W (2006) Electronic tongue for flow through analysis of beverages. Sensor Actuat B-Chem 118:454–460

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Fix E, Hodges JL (1951) Discriminatory analysis, nonparametric discriminators: consistency properties. Technical Report 4. Randolph Field, Texas: USAF School of Aviation Medicine

  • Guo G, Li SZ, Chan KL (2001) Support vector machines for face recognition. Image Vision Comput 19:631–638

    Article  Google Scholar 

  • Hu XH (2011) Research on design and construction of electronic nose platform. Zhejiang Gongshang University, China

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Martin M, Fitzgerald MA (2002) Proteins in rice influence cooking properties. J Cereal Sci 36:285–294

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Pinson SRM (1994) Inheritance of aroma in six rice cultivars. Crop Sci 34:1151–1157

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Thissen U (2004) Comparing support vector machines to PLS for spectral regression applications. Chemometr Intell Lab 73(2):169–179

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Vapnik VN (2004) Statistical learning theory. Electronics Industry, Beijing

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Xiaobo Z, Jiewen Z, Povey MJW, Holmes M, Hanpin M (2010) Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta 667:14–32

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Zhou B, Wang J (2011) Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens. Sensor Actuat B-Chem 160:15–21

    Article  CAS  Google Scholar 

  • 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

    Google Scholar 

Download references

Acknowledgments

The project was supported by the High-Tech Research and Development (863) Program (No. 2011AA1008047) and the Research Foundation of Education Department of Zhejiang Province (Y201327111).

Conflict of Interest

Lin Lu declares that he has no conflict of interest. Shaoping Deng declares that he has no conflict of interest. Zhiwei Zhu declares that he has no conflict of interest. Shiyi Tian declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

Author information

Authors and Affiliations

  1. College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, 310035, China

    Lin Lu, Shaoping Deng & Shiyi Tian

  2. Rice Product Quality Supervision and Inspection Center, Ministry of Agriculture, China National Rice Research Institute, Hangzhou, 310006, China

    Lin Lu & Zhiwei Zhu

Authors
  1. Lin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaoping Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiwei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiyi Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shiyi Tian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, L., Deng, S., Zhu, Z. et al. Classification of Rice by Combining Electronic Tongue and Nose. Food Anal. Methods 8, 1893–1902 (2015). https://doi.org/10.1007/s12161-014-0070-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-014-0070-x

Keywords

Search

Navigation