食醋中总酸含量测定的不确定度评定

[目的]评定食醋中总酸含量测定的不确定度。[方法]对食醋中总酸含量测定的不确定度进行评定,找出影响不确定度的因素并对各个不确定主分量进行评估f确定测量结果的置信区间和置信水平。[结果]分析得出,影响食醋中总酸测定结果的主要不确定度分量是测试过程的重复性带来的不确定度,主要是指样品从取样、定容、滴定及试验使用的标准溶液制备过程的重复性试验。[结论]在食醋中总酸含量检测过程中,应对取样、定容、滴定等过程进行严格控制,规范操作,使不确定度尽可能降低。     

基于不确定的模糊机会约束优化的农业水质管理模型研究

以模糊机会约束规划（FCCP）框架为基础,结合区间线性规划方法（ILP）,最终形成基于区间模糊机会约束优化的农业水质管理模型（IFAWQMM）。IFAWQMM有效地改进FCCP和ILP,可以同时处理农业水质管理系统中的模糊和离散区间形式的变量;另外,它允许一些模糊约束只在特定的置信水平条件下满足即可。因此,求解IFAWQMM模型,可以获得不同置信水平条件下的更优解。利用模型处理假定的农业水质管理问题。结果表明,它可以帮助决策者生成高效益的农业生产模式和分析系统经济性和稳定性之间的妥协。     

GUM法评定菊粉菌落总数的测量不确定度

[目的]采用JJF1059.1—2012《测量不确定度评定与表示》规定的GUM法对实验室菊粉菌落总数的测量进行不确定度评定。[方法]依据GB4789.2—2010《食品安全国家标准食品微生物学检验菌落总数测定》来进行测定和结果判定,结合对测定过程中引入的不确定度分量进行评定,最后采用合成的方法来计算和评定菌落总数的不确定度。[结果]菊粉菌落总数为1.5×104CFU/g,扩展不确定度为0.4×104。[结论]此方法可用于对菊粉样品的菌落总数进行不确定度评定,适用于复现检测条件下菊粉及其他粉状食品菌落总数不确定度的评定。     

茶叶中铜含量测定的不确定度评定

[目的]评定茶叶中铜含量测量结果的不确定度,为原子吸收光谱法测定其他金属元素的不确定度分析提供参考。[方法]利用火焰原子吸收法对测定茶叶中铜含量的测量不确定度进行评定。[结果]在茶叶样品铜含量的测定中,铜含量为18.2μg/g,其扩展不确定度为0.95μg/g（置信度95%,k=2）。[结论]影响茶叶中铜含量测量结果不确定度的最大因素是拟合曲线引入的不确定度。     

Building a climate resilient farm: A risk based approach for understanding water, energy and emissions in irrigated agriculture

The links between water application, energy consumption and emissions are complex in irrigated agriculture. There is a need to ensure that water and energy use is closely considered in future industry planning and development to provide practical options for adaptation and to build resilience at the farm level. There is currently limited data available regarding the uncertainty and sensitivity associated with water application and energy consumption in irrigated crop production in Australia. This paper examines water application and energy consumption relationships for different irrigation systems, and the ways in which the uncertainty of different parameters impacts on these relationships and associated emissions for actual farms. This analysis was undertaken by examining the current water and energy patterns of crop production at actual farms in two irrigated areas of Australia (one using surface water and the other groundwater), and then modelling the risk/uncertainty and sensitivity associated with the link between water and energy consumption at the farm scale. Results showed that conversions from gravity to pressurised irrigation methods reduced water application, but there was a simultaneous increase in energy consumption in surface irrigation areas. In groundwater irrigated areas, the opposite is true; the use of pressurised irrigation methods can reduce water application and energy consumption by enhancing water use efficiency. Risk and uncertainty analysis quantified the range of water and energy use that might be expected for a given irrigation method for each farm. Sensitivity analysis revealed the contribution of climatic (evapotranspiration and rainfall) and technical factors (irrigation system efficiency, pump efficiency, suction and discharge head) impacting the uncertainty and the model output and water-energy system performance in general. Flood irrigation systems were generally associated with greater uncertainty than pressurised systems. To enhance resilience at the farm level, the optimum situation envisaged an irrigation system that minimises water and energy consumption and greenhouse gas emissions. Where surface water is used, well designed and managed flood irrigation systems will minimise the operating energy and carbon equivalent emissions. Where groundwater is the dominant use, the optimum system is a well designed and managed pressurised system operating at the lowest discharge pressure possible that will still allow for efficient irrigation. The findings might be useful for farm level risk mitigation strategies in surface and groundwater systems, and for aiding adaptation to climate change.