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Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

Chakraborty, Somsubhra, Das, Bhabani S., Nasim Ali, Md., Li, Bin, Sarathjith, M.C., Majumdar, K., Ray, D.P.
Waste management 2014 v.34 pp. 623-631
algorithms, artificial intelligence, composts, enzyme activity, hydrolysis, microbial activity, multivariate analysis, neural networks, prediction, principal component analysis, rapid methods, reflectance, reflectance spectroscopy, spectral analysis, waste management
The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r2=0.91 and RMSE=13.38μgg−1h−1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.