近红外光谱分析技术定标和预测中的相似样品剔除算法

The Algorithm of Eliminating the Similar Samples in the Process of Calibration and Prediction

在近红外仪器的研制和调试过程中 ,需要对实验仪器进行最终的定标分析来验证近红外分析仪器是否正常。然而近红外定标过程中传统的方法往往将所有的样品对仪器进行定标分析 ,定标样品集在采集时为了能够覆盖将来所出现的样品范围 ,其数量往往是很大的 ,对于定标实验人员来说 ,其工作量是巨大的。文章通过采用给出的相似样品剔除算法成功地从 1 78个玉米粉样品中提取出 94个优选样品 ,通过定标实验分析发现 ,经过该算法优选的样品不仅保持了原始样品集的代表性 ,而且达到了和原始样品集参与定标相近的定标精度。经由此算法进行筛选得到的优选定标样品集数量上有了很大程度的减少 ,极大地减轻了实验人员的工作强度 ,而且为进行更多重复的实验提供了条件。

During the manufacture and debugging of the NIR instruments it is compulsory to make final calibration analysis to verify whether the instruments operate correctly. However, the conventional method of NIR calibration needs to make calibration analysis for the instruments with all the samples at hand. In fact in order to cover the samples that will be encountered in the future the amount of the samples set and the labor that the personnel need to offer are enormous. In this paper a new algorithm is presented which can be used to effectively eliminate the similar samples in the original sample set. By using this algorithm we have chosen 94 optimal samples in the original 178 sample set successfully. After performing calibration experiment we found that the sample set chosen by this algorithm are equally representative to the original sample set and obtained almost the same precision compared to the original sample set when the two sample sets were individually calibrated. This algorithm brings great relief to the labor of the workers, presents the possibility of performing more experiments and greatly improves the efficiency of performing calibration experiment. As a result, the amount of calibration sets and the labor of the personnel are reduced remarkably.