title: Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning.
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 r(2)=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.
authors.affiliation: IRDM Faculty Centre, Ramakrishna Mission Vivekananda University, Kolkata 700103, India. Electronic address: som_pau@yahoo.com.
authors.forename: Somsubhra
authors.initials: S
authors.lastname: Chakraborty
authors.affiliation: Department of Agricultural and Food Engineering, IIT, Kharagpur 721302, India.
authors.forename: Bhabani S
authors.initials: BS
authors.lastname: Das
authors.affiliation: IRDM Faculty Centre, Ramakrishna Mission Vivekananda University, Kolkata 700103, India.
authors.forename: Md Nasim
authors.initials: MN
authors.lastname: Ali
authors.affiliation: Department of Experimental Statistics, Louisiana State University, 61 Agriculture Administration Building, Baton Rouge, LA 70803, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Agricultural and Food Engineering, IIT, Kharagpur 721302, India.
authors.forename: M C
authors.initials: MC
authors.lastname: Sarathjith
authors.affiliation: Soil Testing Laboratory, Kalimpong 734301, India.
authors.forename: K
authors.initials: K
authors.lastname: Majumdar
authors.affiliation: National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700040, India.
authors.forename: D P
authors.initials: DP
authors.lastname: Ray
mesh_terms: D001185:Artificial Intelligence; D057075:Enzyme Assays / Q000379:methods*; D004798:Enzymes / Q000032:analysis; D007194:India; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012037:Refuse Disposal; D012988:Soil Microbiology; D019265:Spectroscopy, Near-Infrared
chemical_list: D004798:Enzymes
keywords: Artificial neural network; Compost; Fluorescein diacetate hydrolysis; Savitzky–Golay; Visible near infrared diffuse reflectance spectroscopy