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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration
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Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration

机译:近红外光谱法测定黄热病疫苗中蛋白质氮的非破坏性方法的发展,近红外光谱和多变量校准

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摘要

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the dassical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm(-1) region. (C) 2018 Elsevier B.V. All rights reserved.
机译:近红外光谱(NIR)与多变量校准相关的漫反射率,具有主要优点,通过其信号的数学分离更换干扰物的物理分离,不需要试剂消耗,化学废物生产或样品操纵。寻求优化质量控制分析,该光谱分析方法被证明是一种可行的Dassical KjeldaHL方法,用于测定黄热病疫苗中蛋白质氮的方法。通过乘法信号校正(MSC)处理和数据平均定心(MC),使用等于1的最小数量,实现最合适的多变量校准(MSC)处理和数据平均值(MC)来实现。与最高百分比值(91%)样品相关的平均平均预测误差(0.00330)的平方根。在4000-5184厘米(-1)区域中,精度范围为95至105%恢复。 (c)2018年elestvier b.v.保留所有权利。

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