分级评价指标优化玉米近红外光谱定性分析模型研究

验证了种间的相对距离这一评价指标优化玉米近红外光谱定性分析性能的有效性。首先,对A、B两组实验数据使用相关性、欧氏距离和熵3种常用方法计算原始光谱、预处理后的光谱和特征提取后的光谱数据的种间相对距离,并与每一步得到的正确识别率进行对照分析,得到欧氏距离是一种有效的计算方法。最后,对实验数据C采用欧氏距离方法计算数据处理每一过程的种间相对距离,经过对预处理算法的调整,种间相对距离由0.6582增大到了1.2972,正确识别率由40.86%提高到了70.08%;通过对特征提取算法的优化,种间相对距离由1.3102增大到了2.4910,正确识别率由68.32%提高到了93.27%。通过该评价指标对数据分析过程的评价结果可以看出,正确识别率显著提高,使模型得到了优化。

Effect of Evaluation Index on Optimizing the Near-infrared Spectral Qualitative Analysis of Corn

Near-infrared spectrum analysis as a rapidly developing technique has been applied in recognition analysis because of their simplicity, promptness and low cost. It was used to build an effective model to qualitatively analyze the corn. To evaluate the analysis results, an innovative grading evaluation index, defined with the relative distance of inter-species, was proposed for optimizing the near-infrared spectrum analysis process. It was applied to analyze the effect on optimizing the performance of the near-infrared spectrum qualitative analysis of corn. Firstly, two group spectral data were measured including the transmittance of 6 corn species sampled in Beijing (group A) and the reflectance of 6 corn species sampled in Hainan province (group B). The sampling data were processed involving original spectral data, the spectral data after pre-processing, and the spectral data after feature extraction from the group A and B experimental data. The relative distances of inter-species were calculated by using correlation, Euclidean distance, and entropy respectively. The result of contrast analysis showed that Euclidean distance was an effective calculation method for varieties recognition with good performance both in group A and B. Secondly, the reflectance of 6 corn species sampled in Henan province (group C) was measured. The Euclidean distance method was used to calculate the inter-specific relative distance between process steps as mentioned above. As a result, after the adjustment of the pretreatment algorithm, the relative distance between species increased from 0.6582 to 1.2972, and the correct recognition rate increased from 40.86% to 70.08%. By optimizing the feature extraction algorithm, the relative distance between species increased from 1.3102 to 2.4910, and the correct recognition rate increased from 68.32% to 93.27%. It was indicated that the correct recognition rate could be improved by the evaluation of the data analysis process.