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为了研究中煤的发热量,采集了100个中煤样品的近红外漫反射光谱,采用主成分分析(PCA)对数据进行降维,建立定量的数学模型并与工业检测对比。分析结果表明,PC1的累计方差贡献率为92.13%,PC2的累计方差贡献率为91.35%;校正集和预测集的相关系数(R2)分别为0.961 54和0.880 64,校正集的均方根误差(RMSEC)和预测集的均方根误差(RMSEP)分别为0.173和0.300。实验结果表明:模型具有较高的相关性、稳定性和预测精度,为中煤发热量的近红外光谱定量检测奠定了基础。
In order to study the calorific value of coal, the near-infrared diffuse reflectance spectra of 100 Chinese coal samples were collected. The principal component analysis (PCA) was used to reduce the dimension of the data, and a quantitative mathematical model was established and compared with the industrial test. The results showed that the cumulative variance contribution rate of PC1 was 92.13%, and the cumulative variance of PC2 contribution rate was 91.35%. The correlation coefficient (R2) between the calibration set and the prediction set was 0.961 54 and 0.880 64 respectively. The root mean square error (RMSEC) and RMSEP of the prediction set are 0.173 and 0.300 respectively. The experimental results show that the model has high correlation, stability and prediction accuracy, which lays the foundation for the quantitative detection of calorific value of coal in near-infrared spectroscopy.