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Classification of lignocellulosic biomass by weighted‐covariance factor fuzzy C‐means clustering of mid‐infrared and near‐infrared spectra
Author(s) -
Rammal Abbas,
Perrin Eric,
Vrabie Valeriu,
Bertrand Isabelle,
Chabbert Brigitte
Publication year - 2017
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2865
Subject(s) - cluster analysis , chemometrics , lignocellulosic biomass , principal component analysis , computer science , fuzzy clustering , pattern recognition (psychology) , partial least squares regression , artificial intelligence , mathematics , biological system , chemistry , machine learning , lignin , organic chemistry , biology
The analysis of lignocellulosic materials is crucial to optimizing the conversion efficiencies in biorefineries and to studying crop residue input to soil nutrient cycles. Mid‐infrared (MIR) and near‐infrared (NIR) spectroscopies are rapid, simple, and nondestructive methods for the determination of biomass compositions. However, the analysis of a small set of plant biomass is not generally possible with conventional methods of data processing, such as partial least squares. Additionally, IR spectra do not distribute spherically in the data space. To overcome these problems, we propose a weighted‐covariance factor fuzzy C‐means clustering method combined with bootstrapping. The algorithm can classify spherical and nonspherical clusters, in contrast to classic fuzzy C‐means, which is only adapted to spherical clusters. Bootstrapping enables resampling of the available spectra to generate several datasets on which the classification is performed. This unsupervised clustering methodology was tested to classify a small set of maize roots in soil according to genotype or period of their biodegradation process based on their NIR and MIR spectra. This methodology is applied to determine the optimal pretreatment of IR spectra, to study the contribution of the combination of MIR and NIR spectra and to compare the results on spectral and chemical data. The results show that the best methods of pretreatment are the first‐order Savitzky‐Golay derivative followed by standard normal variate. The MIR spectra produce a better result than NIR spectra for the initial characterization and for dynamic samples, while MIR spectra acquired on raw samples, without soluble extraction, provided better classification than wet chemistry.