期刊
TALANTA
卷 80, 期 5, 页码 1832-1837出版社
ELSEVIER
DOI: 10.1016/j.talanta.2009.10.030
关键词
Infrared; Near infrared; Data fusion; Olive cultivar; Variable selection; Wavelet compression
资金
- National Ministry of University
- University of Genoa
The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated. Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together) In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers). LDA was preceded either by feature selection or variable compression For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression. Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression. Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar (C) 2009 Elsevier B.V. All rights reserved.
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