连续投影算法在砂糖橘总酸无损检测中的应用

Successive projections algorithm for variable selection in nondestructive measurement of citrus total acidity

酸度是评价砂糖橘品质的重要指标之一,为了消除光谱变量间的共线性影响、减少建模变量以提高校正速度,该文应用连续投影算法(SPA)对砂糖橘总酸近红外光谱无损检测模型进行优化。利用连接点修正方法修正近红外光谱,结合学生化残差图和模型回归图剔除异常样本,利用SPXY(sample set partitioning based on joint x-y distances)方法划分样本集,最后利用SPA进行变量选择,比较SPA选择的变量建模和全光谱变量PLS模型的预测效果,并分析橘皮对总酸模型的预测精度的影响程度。结果表明,只用了全部2001个变量中的9个变量,整果测定酸度情况下的SPA-MLR模型和SPA-PLS模型的预测精度与全部变量PLS模型的预测精度相当,预测相关系数Rp分别为0.829470,0.837095和0.857299。去皮留果肉测定酸度情况下则优选了13个变量,其SPA-MLR模型和SPA-PLS模型的Rp分别为0.819430、0.825277,均比全光谱变量PLS模型的Rp(0.780146)高,SPA算法提高了去皮留果肉测定酸度情况下的模型预测精度。

The total acidity is an important index in the citrus internal quality assessment.In order to minimize variable collinearity effects in the calibration data set,reduce the modeling variables to alleviate the computation workload,a novel variable selection strategy-successive projections algorithm(SPA) was employed to optimize the near infrared spectrum testing model of citrus total acidity.The splice correction method was used to correct the original NIR spectra.The outlier samples were analyzed by studentized residual error and regression line.After outlier samples eliminated,The SPXY(sample set partitioning based on joint x-y distances) method was used to subset partitioning.Finally,the 'Successive Projections Algorithm'(SPA) was applied to select the optimal sets of variables for calibration,and then the prediction performance comparison between the model built by the selected variables and full-spectrum-PLS model,the influence of the orange peel to the total acidity model prediction accuracy is also analyzed in this paper.As can be seen,nine and thirteen optimal effective variables were selected from full-spectrum variables by SPA,for the total acidity determination with the whole fruit samples and the flesh samples,respectively.SPA-MLR,SPA-PLS and full-spectrum-PLS were comparable in terms of prediction performance for the total acidity determination with the whole fruit samples.The prediction set correlation coefficient(Rp) of the total acidity determination with the whole fruit samples was 0.829470,0.837095 and 0.857299,respectively.While SPA-MLR and SPA-PLS resulted in models with good prediction ability when compared to full-spectrum PLS model for the total acidity determination with the flesh samples.The Rp of the total acidity determination with the flesh samples was 0.819430,0.825277 and 0.780146,respectively.The SPA improves the prediction ability of the total acidity determination with the flesh samples effectively.