Quantitative analysis of index factors in agricultural compost by infrared spectroscopy

Guangrong Shen; Yanchi Chen; Jingying Zhang; Yu Wu; Yang Yi; Shengyong Li; Shan Yin

doi:10.1016/j.heliyon.2023.e14010

Quantitative analysis of index factors in agricultural compost by infrared spectroscopy

Heliyon. 2023 Feb 24;9(3):e14010. doi: 10.1016/j.heliyon.2023.e14010. eCollection 2023 Mar.

Authors

Guangrong Shen^{1

2

3}, Yanchi Chen^{1

2}, Jingying Zhang^{1

2}, Yu Wu^{1

2}, Yang Yi^{3

4}, Shengyong Li⁵, Shan Yin^{1

2

3}

Affiliations

¹ School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China.
² Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China.
³ Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station,Ministry of Science and Technology, Ministry of Education, Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai, 200240, China.
⁴ Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, National Innovation Alliance of National Forestry and Grassland Administration on Afforestation and Landscaping of Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai, 200232, China.
⁵ Department of Civil Engineering, Hetao College, Yunzhong Street, Bayannur City, Inner Mongolia, 015000, China.

Abstract

Hyperspectral technology, with its high spectrum resolution and nanometer continuous spectral information acquisition ability, provide a possibility for rapidly and nondestructive evaluating compost maturity. In this study, the near-infrared spectroscopy (NIRS) analysis techniques was used to analyze quantitatively organic matter (OM) content, total nitrogen (TN) content and carbon-nitrogen (C/N) ratio in compost based on two different composting procedures. In the basis of spectra preprocessing and strategies of variable selection, the nonlinear modeling LBC-siPLS-PLSR for OM, MSC-SPA-PLSR for TN and R-SPA-PLSR for C/N ratio was respectively constructed using partial least squares regression (PLSR). LBC-siPLS-PLSR, MSC-SPA-PLSR and R-SPA-PLSR provided a better prediction capability with root mean square error of prediction, the coefficient of determination for prediction and residual predictive deviation values of 4.061, 0.746 and 2.02 for OM, values of 0.205, 0.65 and 1.71 for TN and values of 1.11, 0.706 and 2.07 for C/N ratio, respectively. These results showed that the NIRS technique could be fitted to each element, using specific spectrum pretreatment, in order to achieve an acceptable accuracy in the prediction.

Keywords: Aerobic composting; Inversion model; Organic matter; Rice straw; Spectral analysis.