独立分量分析预处理法提高苹果糖度模型预测精度研究

Improving of Prediction Model for Apple Sugar Content Using Independent Component Analysis

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。

To improve the prediction model of sugar content, independent component analysis （ICA） was used to preprocess the near infrared （NIR） spectra of apples. Compared with those original spectra, apple spectra after ICA pretreatment were smoother, but their shape showed not much difference. This indicated that the major information in apple spectra could be reserved while noise was removed by ICA method. The partial least square （PLS） was used to establish the calibration models of sugar content against apple spectra after ICA and averaging pretreatment. Compared with just being averaged, the results show that the number of factors used in PLS model against the spectra pretreated by ICA decreased, and the precision was also improved. The optimum PLS calibration model was obtained with 6 factors, the correlation coefficient （rc） of 0. 9549 with the standard error of calibration （SEC） of 0. 3361 and the standard error of prediction （SEP） of 0. 4355. This result shows that the ICA preprocessing NIR spectra can not only improve precision, but also simplify the model.