Optimal preprocessing and FCM clustering of MIR, NIR and combined MIR-NIR spectra for classification of maize roots

Abstract

InfraRed spectroscopy (IR) provides useful information of the molecular composition of biological systems. Mid-InfraRed (MIR) spectroscopy reflects fundamental molecular vibrations whereas Near-InfraRed (NIR) spectroscopy exhibits the overtones and combinations of fundamental vibrations and bonds. In most applications, the samples are mixed with potassium bromide (KBr) powder, or simply unmixed. Two technics are investigated: IR absorption on mixed samples and Diffuse Reflectance IR Fourier Transform (DRIFT) on unmixed samples. IR spectra are collected in either MIR or NIR regions. However, the preprocessing of IR spectra, the choice of the spectral band and the combination of MIR-NIR information are important factors that could substantially influence analyses. This study investigates these factors while attempting to retrieve three different genotypes of maize roots via a Fuzzy C-Mean (FCM) classification of IR spectra. A bootstrapping procedure is used as the number of samples is limited. Results show that KBr spectroscopy is better than DRIFT spectroscopy for MIR region; MIR provides equivalent information as NIR for DRIFT spectroscopy; combination of MIR-NIR information gives preprocessing independent results. Several distances are tested in FCM classification. The city bloc distance gives optimal results compared with Euclidean, Chebyshev, correlation and diagonal distance.