Rice Odours’ Readings Investigation Using Principal Component Analysis

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.

     

     


  • Keywords


    Data Reduction; e-nose; Principle Component Analysis (PCA)

  • References


      [1] Clark NR, Ma'ayan A. Introduction to statistical methods to analyze large data sets: principal components analysis. Science signaling. 2011;4(190):tr3.

      [2] Clarke R, Ressom HW, Wang A, Xian J, Liu MC, Gehan EA, et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer. 2008;8(1).

      [3] Fan J, Lv J. A Selective Overview of Variable Selection in High Dimensional Feature Space. 2010;20(1).

      [4] Mwangi B, Tian TS, Soares JC. A review of feature reduction techniques in neuroimaging. Neuroinformatics. 2014;12(2):229-44.

      [5] Johnson R, Wichern D. Principle Components. Applied Multivariate Statistical Analysis. United States of America: Pearson; 2014. p. 430.

      [6] Mizumukai K, Sato T, Tabeta S, Kitazawa D. Numerical studies on ecological effects of artificial mixing of surface and bottom waters in density stratification in semi-enclosed bay and open sea. Ecological Modelling. 2008;214(2-4):251-70.

      [7] Wunderlin DA, Pilar DMD, Valeria AM, Fabiana PS, Cecilia HA, Angeles BMDL. Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study : Suquia River Basin (Cordoba-Argentina). Wat Res. 2001;35(12).

      [8] Harada M, Yoshida I. Distribution of Sulfides and Heavy Metals of Bottom Sediments in lake Koyama. Transactions of Rural Planning. Journal of Rural Planning Association. 2001;20:64-6.

      [9] Oketola AA, Adekolurejo SM, Osibanjo O. Water Quality Assessment of River Ogun using Multivariate Statistical Techniques. Journal of Enviromental Protection. 2013;4(05):466.

      [10] Koch I. Analysis of Multivariate and High-Dimensional Data. United States of America: Cambridge; 2014.

      [11] Principle Component Analysis- A Realization of Classification Success in Multi Sensor Data Fusion [Internet]. 2012.

      [12] Champagne TE. Rice Aroma and Flavor : A Literature Review. Cereal Chemistry Journal. 2008;85:445.

      [13] Bergman CJ, Bhattacharya KR, Ohtsubo K. Rice End-Use Quality Analysis. Rice: Chemistry and Technology. 3rd ed. USA: AACC Intl. PRESS; 2004. p. 415-72.


 

View

Download

Article ID: 13803
 
DOI: 10.14419/ijet.v7i2.29.13803




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.