Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning |
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Authors: | Somsubhra Chakraborty Bhabani S. Das Md. Nasim Ali Bin Li M.C. Sarathjith K. Majumdar D.P. Ray |
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Affiliation: | 1. IRDM Faculty Centre, Ramakrishna Mission Vivekananda University, Kolkata 700103, India;2. Department of Agricultural and Food Engineering, IIT, Kharagpur 721302, India;3. Department of Experimental Statistics, Louisiana State University, 61 Agriculture Administration Building, Baton Rouge, LA 70803, USA;4. Soil Testing Laboratory, Kalimpong 734301, India;5. National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700040, India |
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Abstract: | 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 = 13.38 μg g?1 h?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. |
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Keywords: | Compost Fluorescein diacetate hydrolysis Artificial neural network Savitzky–Golay Visible near infrared diffuse reflectance spectroscopy |
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