Rapid Determination of Nitrogen and Phosphorus in Dairy Farm Slurry Via Near-Mid Infrared Fusion Spectroscopy Technology
SUN Di1, 2, LI Meng-ting1, MU Mei-rui1, ZHAO Run1*, ZHANG Ke-qiang1*
1. Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
2. College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Abstract:Rapid and accurate determination of the nitrogen (N) and phosphorus (P) in the slurry/biogas slurry has been a technical bottleneck is urgently needed for the large-scale dairy farms in China on their ways of planting and breeding combination. Conventional wet chemical measuring methods in the laboratory were difficult to meet the practical demand of rapid quantitative determination on the N and P before recycling the dairy farm slurry back to the field. An indigenized rapid detection method of N and P through the full chain of slurry movement in large-scale dairy farms was developed based on the near-infrared (NIR), mid-infrared (MIR) and near-mid infrared (NIR-MIR) spectral fusion technology. A total of 144 slurry samples were collected along with the entire process links (manure collecting gutter, slurry tank, lagoon, etc.) from 27 large-scale dairy farms in Tianjin. The spectral data of 12 000~4 000 and 4 000~650 cm-1 were collected by the Fourier transform near-infrared spectrometer (FT-NIRS) and mid-infrared spectrometer (FT-MIRS). Pretreatment methods involved the normalization, baseline and SNV were performed on the whole NIR, MIR and NIR-MIR data.NIR and MIR spectral characteristics were analyzed. The concentration gradient method was used for the sample diversity. NIR and MIR models of the total nitrogen (TN) and total phosphorus (TP) in the slurry were constructed by the partial least squares (PLS), interval partial least squares (IPLS) and synergy interval partial least squares (SIPLS). The results of slurry TN models were preferable, while the optimal models between the NIR and MIR were equivalent. The prediction performance of the TP model for the slurry was unsatisfactory that difficult of practical application. The R2pred of the optimal SIPLS models for NIR and MIR were only 0.790 and 0.631, respectively. The residual predictive deviation (RPD) was 2.213 and 1.479 respectively. And the ratio of performance to interquartile range (RPIQ) was 3.616 and 2.351, respectively. In order to realize the simultaneous and effective determination and analysis of the N and P in the slurry meanwhile further improve the overall prediction performance of the model, the NIR-MIR fusion model of the N and P in the slurry was established integrated the NIR with MIR spectral data, with the spectral range of 12 000~650 cm-1. The prediction performance behaved well overall. IPLS fusion model performed the optimum. The R2pred was 0.970 and 0.861 respectively. RPD was 5.615 and 2.684 respectively. RPIQ was 12.874 and 4.394 respectively. It was better than the single NIR model and MIR model. In particular, the optimal fusion model of the TP was 0.071 and 0.170 which was higher than that of the single NIR and MIR models. The results showed that exact and rapid determination of the N and P through the full chain links of slurry movement in large-scale dairy farms via the near-mid infrared spectroscopy fusion technology could be available for the scientific slurry recycling to the farmland.
Key words:Slurry movement; Full chain links; Near-mid infrared spectroscopy; Rapid determination; Nitrogen and phosphorus contents; Fusion model
[1] LIU Xiao-yong, WANG Xiu-bin, LI Shu-tian(刘晓永, 王秀斌, 李书田). Environmental Science(环境科学), 2018, 39(12): 5723.
[2] MA Yan-ru, MENG Hai-bo, SHEN Yu-jun, et al(马艳茹, 孟海波, 沈玉君, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(15): 245.
[3] ZHAO Run, ZHANG Hui-jie, LIU Qi, et al(赵 润, 张蕙杰, 刘 琦, 等). Environmental Protection(环境保护), 2019, 47(9): 69.
[4] LI Meng-ting, SUN Di, MU Mei-rui, et al(李梦婷, 孙 迪, 牟美睿, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(20): 27.
[5] General Office of Ministry of Agriculture and Rural Affairs, General Office of Ministry of Ecology and Environment(农业农村部办公厅, 生态环境部办公厅). Guiding Opinions on Promoting the Use of Livestock and Poultry Manure for Returning Farmaland to Strengthen the Control of Farming Pollution in Accordance With Law (No.84)(关于促进畜禽屎污染还田利用依法加强养殖污染治理的指导意见),2019.
[6] General Office of Ministry of Agriculture and Rural Affairs, General Office of Ministry of Ecology and Environment(农业农村部办公厅, 生态环境部办公厅). General Office of Ministry of Agriculture and Rural Affairs, General Office of Ministry of Ecology and Environment. Notice on Further Clarifying the Requirements of Livestock Manure Returning to Farmland and Strengthening the Supervision of Breeding Pollution (No.23)(关于进一步明确畜禽类污还田利用要求强化养殖污染监管的通知),2020.
[7] Birgül A, Papke G, Sundrum A. Animal Feed Science & Technology, 2013, 185(1-2): 53.
[8] Finzi A, Oberti R, Negri A S, et al. Biosystems Engineering, 2015, 134: 42.
[9] Bedina F C B, Faust M V, Guarneri G A, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 245: 118834.
[10] Awhangboad L, Bendoulab R, Roger J M. Chemometrics and Intelligent Laboratory Systems, 2020, 196: 103905.
[11] LIANG Hao, HUANG Yuan-ping, SHEN Guang-hui, et al(梁 浩, 黄圆萍, 沈广辉, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(10): 220.
[12] YANG Zeng-ling, HUANG Yuan-ping, SHEN Guang-hui, et al(杨增玲, 黄圆萍, 沈广辉, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(5): 356.
[13] Bustamante M A, Nogués I, Jones S, et al. Scientific Reports, 2019, 9(1): 6489.
[14] Cao W, Cao C, Guo L, et al. International Journal of Hydrogen Energy, 2016, 41(48): 22722.
[15] Yang Y, Du W, Cui Z, et al. Microchemical Journal, 2020, 158: 105226.
[16] Mcbratney A, Fernandez-Ahumada E, Palagos B, et al. TrAC Trends in Analytical Chemistry, 2010, 29(9): 1073.
[17] GE Xiang-yu, DING Jian-li, WANG Jing-zhe, et al(葛翔宇, 丁建丽, 王敬哲, 等). Acta Optica Sinica(光学学报), 2018, 38(10): 385.
[18] Nawar S, Mouazen A M. Catena, 2017, 151: 118.
[19] CHU Xiao-li(褚小立). The Near Infrared Spectral Analysis Technology and Practical Manual(近红外光谱分析技术实用手册). Beijing: China Machine Press(北京:机械工业出版社), 2016. 4.
[20] ZHAO Run, YANG Ren-jie, MU Mei-rui, et al(赵 润, 杨仁杰, 牟美睿, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(15): 217.