Classification of cereal flours by chemometric analysis of MIR spectra.
Different kinds of cereal flours submitted to various technological treatments were classified on the basis of their mid-infrared spectra by pattern recognition techniques. Classification in the wavelet domain was achieved by using the wavelet packet transform for efficient pattern recognition (WPTER) algorithm, which allowed singling out the most discriminant spectral regions. Principal component analysis (PCA) on the selected features showed an effective clustering of the analyzed flours. Satisfactory classification models were obtained both on training and test samples. Furthermore, mixtures of varying composition of the studied flours were distributed in the PCA space according to their composition.
Dipartimento di Chimica, Università di Modena e Reggio Emilia, Via Campi 183, 41100 Modena, Italy.
Marina
M
Cocchi
Giorgia
G
Foca
Mara
M
Lucisano
Andrea
A
Marchetti
M Ambrogina
MA
Pagani
Lorenzo
L
Tassi
Alessandro
A
Ulrici
D000465:Algorithms; D018554:Avena / Q000737:chemistry; D002523:Edible Grain / Q000737:chemistry*; D019612:Fagopyrum / Q000737:chemistry; D005433:Flour / Q000145:classification*; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared; D014908:Triticum / Q000737:chemistry