为了研究适合激光诱导击穿光谱(LIBS)检测猪肉中重金属铅(Pb)元素含量的光谱预处理方法,将配制的84个猪肉腿肌样品分为校正集和预测集,以相关系数(R)、内部交叉验证均方差(RMSECV)和预测均方根误差(RMSEP)作为评价指标,比较了5种光谱预处理方法对偏最小二乘法(PLS)建模预测效果的影响。结果表明,多元散射校正(MSC)预处理效果最好,定标模型预测值与实验室分析元素检测值的相关系数(R)达到0.9908,RMSECV为0.302,RMSEP为0.282,主成分数为16,18个预测集样品的验证结果的平均相对预测误差(ARPE)为7.8%。说明MSC是LIBS检测猪肉Pb含量的有效光谱预处理方法,该研究为进一步实现食品中重金属快速定量分析提供了方法和数据参考。
To search an optimum spectral pretreatment method to measure Pb content in the leg muscle of pork with laser-induced breakdown spectroscopy, a total of 84 pork samples were split into calibration and prediction sets. With correlation coefficient (R), root-mean-square error of cross-validation (RMSECV) and root-mean-square error of predictionas (RMSEP) as the evaluation index, the effect of five pretreatment methods to the model based on partial least square (PLS) were compared. The results showed that the multiplicative scatter correction (MSC) was most effective and its R, RMSECV, RMSEP and principal element were 0.9908, 0.302, 0.282 and 16, respectively. The average relative error of prediction sets of 18 samples was 7.8%. The MSC is an efficient method to measure Pb in pork with laser-induced breakdown spectroscopy. The research provides an approach for further improving the accuracy of LIBS quantitative analysis of heavy metals in food.
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