提出了一种基于多种群精英共享遗传算法的异常光谱识别方法.该方法应用于红外光谱数据的分析,并在删除异常光谱样本后使用偏最小二乘方法进行建模.与使用蒙特卡洛交叉验证、留一交叉检验、马氏距离以及传统遗传算法进行异常光谱识别的方法相比,所提方法将水分预测模型的预测误差平方和(PRESS)分别降低了72.4%,39.5%,39.5%和14.5%;将脂肪含量的预测模型的PRESS值分别降低了86.2,75.9%,84.9%和19.9%;将蛋白质含量的预测模型的PRESS值分别降低了56.5%,35.7%,35.7%和18.2%.实验表明,所提方法不仅能适应不同成分光谱数据的异常识别,而且删除异常光谱数据后所建立的模型具有较高的预测能力和较好的稳健性.%The present paper proposed an outlier detection method for spectral analysis based on multi-population elitists shared genetic algorithm. The method was exploited in the NIR data set analysis to remove the outliers from the data set, and partial least squares (PIS) was combined with the proposed method to build a prediction model. In contrast with Monte Carlo cross validation, leave-one-out cross validation, Mahalanobis-distance and traditional genetic algorithm for outlier detection, the prediction residual error sum of squares (PRESS) for moisture prediction model based on the proposed method decreases in the rate of 72.4％, 39.5％, 39.5％ and 14.5％; the PRESS value for fat prediction model decreases in the rate of 86.2％, 75.9％,84. 9％ and 19. 9％ ; and the PRESS value for protein prediction model decreases in the rate of 56. 5％, 35. 7％, 35. 7％ and 18. 2％ respectively. Results indicated that the method is applicable for spectral outlier detection for different species, and the model based on the data set without the removed outliers is more accurate and robust.
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