Abstract
Soil types have traditionally been determined by soil physical and chemical properties, diagnostic horizons and pedogenic processes based on a given classification system. This is a laborious and time consuming process. Near infrared (NIR) spectroscopy can comprehensively characterize soil properties, and may provide a viable alternative method for soil type recognition. Here, we presented a partial least squares discriminant analysis (PLSDA) method based on the NIR spectra for the accurate recognition of the types of 230 soil samples collected from farmland topsoils (0–10 cm), representing 5 different soil classes (Albic Luvisols, Haplic Luvisols, Chernozems, Eutric Cambisols and Phaeozems) in northeast China. We found that the PLSDA had an internal validation accuracy of 89% and external validation accuracy of 83% on average, while variable selection with the genetic algorithm (GA and GA-PLSDA) improved this to 92% and 93%. Our results indicate that the GA variable selection technique can significantly improve the accuracy rate of soil type recognition using NIR spectroscopy, suggesting that the proposed methodology is a promising alternative for recognizing soil types using NIR spectroscopy.
Highlights
Soil types have traditionally been determined by soil physical and chemical properties, diagnostic horizons and pedogenic processes based on a given classification system
Soil is a heterogeneous mixture of minerals and organic matter created by long-term pedogenic processes, which result in a variety of distinct types with differing properties and qualities
Chemometrics methods used in identification and classification of soils include principal component analysis (PCA)[4,12,13], partial least squares (PLS) and artificial neural networks (ANN)[14], cluster analysis[15], linear discriminant analysis (LDA)[16], soft independent modeling of class analogy (SIMCA)[16] and partial least squares discriminant analysis (PLSDA)[17]
Summary
Soil types have traditionally been determined by soil physical and chemical properties, diagnostic horizons and pedogenic processes based on a given classification system. Chemometrics methods used in identification and classification of soils include principal component analysis (PCA)[4,12,13], partial least squares (PLS) and artificial neural networks (ANN)[14], cluster analysis[15], linear discriminant analysis (LDA)[16], soft independent modeling of class analogy (SIMCA)[16] and partial least squares discriminant analysis (PLSDA)[17]. Among these methods, PLSDA is effective at classification tasks[18,19] and has the capacity to deal with data multicollinearity[20]
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