【作者】 王晓燕； 韩鲁佳； 黄光群；

【Author】 WANG Xiao-yan1,2,HUANG Guang-qun1,2, HAN Lu-jia1,2* (1.College of Engineering,China Agricultural University,Beijing 100083,China; 2.Chinese Academy of Agricultural Mechanization Science,Beijing 100083,China)

【机构】 中国农业大学工学院； 中国农业机械化科学研究院；

【摘要】 利用近红外光谱技术对鸡粪和菊花渣工厂化好氧堆肥过程中重要理化指标进行了速测分析研究。采集堆肥过程样品148份,基于偏最小二乘法建立了含水率、酸碱度、电导率、挥发性固体、总碳、总氮和碳氮比的近红外定量分析模型,并对所建模型进行了评价分析。研究发现,近红外光谱分析方法测定鸡粪和菊花渣工厂化堆肥过程含水率、挥发性固体和总碳含量具有较好的精度;也可测定总氮含量,但精度有待于进一步提高;对于酸碱度和电导率,目前只能进行含量大小的定性分析;测定碳氮比不成功。更多还原

【Abstract】 This study explored the efficiency of quantitative analysis for the contents of moisture (Moist), pH, electrical conductivity (EC), volatile solid (VS), total carbon (TC), total nitrogen (TN), and the ratio of carbon and nitrogen (C:N) during chicken manure composting with chrysanthemum residue in a plant field using a Thermo Nicolet Antaris near-infrared reflectance spectral apparatus equipped with InGaAs detectors (Thermo Nicolet Corporation, Chicago, USA). The total of 148 samples were collected from different and stages and different positions and the spectra were recorded from 10000 to 4000 cm-1. Regression models were developed by spectral data and partial least-squares (PLS). Based on the values of determination coefficient (r2) and relative prediction deviate (RPD) in validation sets, the prediction results were evaluated as excellent for Moist, VS, TC and approximate for TN. It needs more research to analyze pH, EC and C:N accurately.更多还原