Rapid Detection of Total Nitrogen Through the Manure Movement of in Large-Scale Dairy Farm by Near-Infrared Diffuse Reflectance Spectroscopy
WANG Peng1,2, SUN Di2, MU Mei-rui3, LIU Hai-xue3, ZHANG Ke-qiang2, MENG Xiang-hui1, YANG Ren-jie1*, ZHAO Run2*
1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384,China
2. Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191,China
3. Laboratory of Agricultural Analysis, Tianjin Agricultural University, Tianjin 300384,China
Abstract:In order to achieve on-site, rapid and relatively accurate acquire the total nitrogen (TN) content of manure in the large-scale dairy farm that is going through the whole sections from the barn to the farmland before, 111 manure samples during the whole process (collection-separation-stacking) were collected from a typical large-scale dairy farm in Tianjin lasting for 6 consecutive days. All samples were dried using electrothermal blast dryer, rushed and sifted through 18 meshes. The total nitrogen contents were measured by Kjeldahl nitrogen analyzer, and the range of concentration is 0.20%~3.86%. Near-infrared (NIR) diffuse reflectance spectra of all samples were collected in the range of 4 000~12 000 cm-1 using the PerkinElmer Fourier NIR spectrometer. 17 outlier samples were removed based on Monte Carlo cross validation method. The NIR diffuse reflectance spectra of the remaining 94 samples were pretreated by SG first derivative and denoising. Then, the principal component analysis was adopted to obtain the variation of samples in the whole process of fecal treatment in large-scale dairy farm. The first two principal components (PCA) can explain 89% of the total variance. The results of PCA showed that the properties and components of fresh feces of dairy cows in different reproductive ages were similar. From feces to manure, the properties and components of fresh feces were not changed any more. However, the properties and components were greatly changed in the bedding stage. Therefore, it is necessary to establish a global TN quantitative analysis model suitable for the whole process of fecal treatment for realizing the real-time and rapid detection of TN in the whole process of fecal treatment in large-scale dairy farms. 94 samples were divided into calibration and prediction sets based on K-S method. 63 samples, including 24 fresh manure samples, 28 mixed manure samples and 11 cushion samples, were used as calibration set to construct a calibration set. The partial least squares model for quantitative analysis of TN was established in the whole process of fecal treatment in large-scale dairy farms. 31 unknown samples, including 12 fresh manure samples, 9 mixed manure samples and 10 cushion samples, were predicted by the established global model. The correlation coefficient (R) between the predicted concentration and its actual concentration was 0.91, and the root means square error of prediction (RMSEP) was 0.151%. The research showed that it was fully feasible to determine the TN contents in the manure of large-scale dairy farms integrated the near infrared diffuse reflectance spectroscopy with chemometrics, not only providing the theoretical and experimental basis for the development and field application of a near-infrared instrument for rapid detection of TN in the manure, but also the support for quantitatively recycling the manure to the farmland.
Key words:Large-scale dairy farm; Manure; Total nitrogen (TN); Near-infrared diffuse reflectance spectroscopy; Partial least squares
王 鹏,孙 迪,牟美睿,刘海学,张克强,孟祥辉,杨仁杰,赵 润. 近红外漫反射光谱快速检测规模化奶牛场粪便运移全程中的全氮含量[J]. 光谱学与光谱分析, 2020, 40(10): 3287-3291.
WANG Peng, SUN Di, MU Mei-rui, LIU Hai-xue, ZHANG Ke-qiang, MENG Xiang-hui, YANG Ren-jie, ZHAO Run. Rapid Detection of Total Nitrogen Through the Manure Movement of in Large-Scale Dairy Farm by Near-Infrared Diffuse Reflectance Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(10): 3287-3291.
[1] Zhao-hai B, Lin M, Shu-qin J, et al. Environmental Science Technology, 2016, 50: 13409.
[2] CAO Yu-bo, XING Xiao-xu, BAI Zhao-hai, et al(曹玉博, 邢晓旭, 柏兆海, 等). Scientia Agricultura Sinica(中国农业科学), 2018, 51(3): 566.
[3] WANG Qin, LIU Ye-hai, GAO Min-guang, et al(王 琴, 刘业海, 高闽光, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(12): 3941.
[4] Amodi M L, Ceglie F, Chaudhry M M A, et al. Postharvest Biology and Technology, 2017, 125: 112.
[5] Dixon R M, Coates D B. Australian Journal of Experimental Agriculture, 2008, 48: 835.
[6] LI Wen-long, QU Hai-bin(李文龙,瞿海斌). China Journal of Chinese Materia Medica(中国中药杂志),2016,41(19):3511.
[7] Dematte J A M, de Castro Oliveira J, Tavares T R, et al. Environmental Earth Sciences, 2016, 75(18): 1277.
[8] Cascant M M, Sisouane M, Tahiri S, et al. Talanta, 2016, 153: 360.
[9] FAN Xia, HAN Lu-jia, HUANG Cai-jin, et al(樊 霞, 韩鲁佳, 皇才进, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2006,(3): 76.
[10] Reeves III J, Van K J S. Journal of Near Infrared Spectroscopy, 2000, 8(3): 151.
[11] Asai T, Shimizu S, Koga T. Japanese Journal of Soil Science and Plant Nutrition, 1993, 64(6): 669.
[12] YANG Zeng-ling, HAN Lu-jia, LI Qiong-fei, et al(杨增玲,韩鲁佳,李琼飞,等). NIRS Sensing of Nutrient Contents in Fatlening Pig Manure(利用NIRS测定肥育猪粪便中主要成分含量的研究). Proc. of 2005 Annual Meeting of the Chinese Society of Agricultural Engineering(2005年中国农业工程学会学术年会论文集),2005. 273.
[13] Saeys W, Mouazen A M, Ramon H. Biosystems Engineering, 2005, 91(4): 393.
[14] SHI Lu-zhen, ZHANG Jing-chuan, JIANG Xia, et al(石鲁珍, 张景川, 蒋 霞, 等). Food Science and Technology(食品科技), 2016, 41(1): 82.
[15] XU Hong-yu, ZHANG Jing-fang, HOU Li-xuan, et al(徐洪宇, 张京芳, 侯力璇, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2013, 13(11): 153.
[16] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立, 袁洪福, 陆婉珍). Progress in Chemistry(化学进展), 2004,(4): 528.