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样品粒度对近红外分析结果的影响 总被引:3,自引:0,他引:3
探讨了样品粉碎粒度对近红外光谱测定结果的影响。在近红外分析仪的定标和测定中,要特别注意对粉碎机和筛网的选择,筛孔直径不能大于1.0mm,同时定标样品和测试样品的粉碎条件应当完全一致,才能减少误差 相似文献
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人参是我国东北三宝之首,具有较高的药用价值。目前对其主要成份人参总糖、皂苷等成份检测多采用繁琐的化学分析方法,耗时、成本高,同时不可避免的对样品造成损耗。采用近红外光谱分析技术对29份人参粉末样品成份进行快速、无损定量分析,获得了较高的分析精度。建立总糖、总皂苷、水分模型的预测值与化学标准值相关系数(R)分别为0.93、0.90、0.96,交叉检验标准差(RMSECV)分别为1.78、0.28、0.45。进一步深入研究有望实现珍贵的野山参样品营养成份近红外光谱无损检测。 相似文献
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以在MPA型和WQF-400N傅立叶近红外光谱仪上测量的茶叶和奶粉光谱为原始光谱,利用积分法选择不同的积分区间对原始光谱进行积分,模拟不同分辨率下的光谱,建立了茶叶中咖啡碱和奶粉中脂肪的定量分析模型,研究了不同分辨率对模型精度的影响。结果显示,对茶叶和奶粉,MPA型仪器分别是分辨率为32cm^-1和16cm^-1最好,相关系数(R)、预测均方差(RMSEP)分别为0.9400、0.2900和0.8879、1.0835;WQF-400N型仪器均是分辨率为64cm^-1时最好,R,RMSEP分别为0.9258、0.3311和0.9190、0.9365。试验结果表明分辨率对模型精度有很大影响,但并不是分辨率越高越好,对具体样品和仪器存在一个最为合适的分辨率。 相似文献
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《Measurement》2016
The development of an experimental mathematical model describing temperature state of the sample during high temperature spectral emissivity measurement is introduced. Dimensional analysis of the measurement process gives the physical dimensionless quantities and sensitivity analysis of the measurement process provides the large set of performed model experiments. Evaluated experimental mathematical models are presented including their accordance with model experiments. Established equations are generalization of sensitivity analysis of high temperature spectral emissivity measurement method and can be used for computation of spectral emissivity total uncertainty. 相似文献
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利用衰减全反射-傅立叶变换红外光谱法(ATR-FTIR)和OMNI采样器附件对乙二醇水溶液进行定量分析的研究。吸光度测量值有较宽的线性范围,重复性好。这一分析方法方便快捷、分析结果准确。 相似文献
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基于多体动力学理论和拉格朗日方程,将煤炭采样机械臂中的大臂、小臂、伸缩臂考虑为柔性臂,结合Pro/E,ANSYS和ADAMS分别建立刚性和刚柔耦合模型,在相同驱动函数下对两种运动模型进行分析,仿真得出两种模型变化曲线,对比仿真结果表明,在研究臂架系统时,考虑各个臂杆柔性变形是非常必要的,刚柔耦合建模更符合工程实际,所得到的研究结果为采样臂动力学分析和结构优化奠定了理论基础. 相似文献
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针对棒材生产中断面变化的几何特点,以Fourie热传导微分方程式为基础,用差商代替微分得到了棒材热传导微分方程的差分形式方程的数学模型,该模型可以为棒材温度计算提供理论指导。 相似文献
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The modeling accuracy of feed drive is mainly affected by two factors: the accuracy of model form and the completeness of sample data which is used to identify the parameters of the established model form. One of the main works of this study is to construct the experimental trajectories which are used to obtain the sample data. Different from the trajectories selected based on the researcher's experience in previous literatures which lacks theoretical evidence to confirm the completeness of the sample data, in this paper, a reverse construction method of experimental trajectory is proposed based on the required sample data. First, the completeness of sample data is analyzed, and the samples are generated by using the Hammersley sequence method. Then, the experimental trajectories are derived. In addition, considering that the data of the feed system has temporal correlations between the adjacent motion states in time sequence, this study made some improvements on the modeling framework composed of the first-principle model and machine learning model. The experiments conducted in the actual feed drive system showed that the maximum prediction error of the tracking error is 7.8%, which confirmed the effectiveness of the designed trajectories and the high accuracy of the proposed model. Furthermore, the advantage of the proposed trajectory is confirmed by the comparison experiment. At the same time, the same trajectories are used to train different model forms to verify the advantage of the proposed model. The proposed feed drive modeling method can be used for designing a high-performance feed drive control system or compensating errors when generating motion commands, thereby improving the machining accuracy of machine tool. 相似文献
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Study of sample temperature compensation in the measurement of soil moisture content 总被引:1,自引:0,他引:1
Since the near-infrared (NIR) spectrum is susceptible to sample temperature fluctuations, we investigate the influence of sample temperature on the predictive power of calibration model for soil moisture content (MC) and propose the multi-source information fusion technology based on back propagation neural network (BPNN) to compensate for sample temperature effect. With the discrete wavelet transform (DWT) as the pre-processing method and the least squares support vector machine (LS-SVM) regression as the modeling method, a model at 20 °C to predict MC of the soil samples at other temperatures was established. The results show that except for 20 °C, the root mean square error of prediction (RMSEP) are large. We analyze the predicted results with the dual-factor analysis of variance without duplication and the result shows that the effect of sample temperature on the prediction model for soil MC is significant. A temperature compensation model was then established with combining of soil MC and sample temperature based on BPNN. The predicted results showed that the prediction precision of the model was improved significantly. 相似文献
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