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本文以纤维滤膜富集大米中的微量农药残留,提高近红外光谱技术的检测限。向阴性大米样本中喷洒不同浓度毒死蜱标准溶液,制备含农药残留大米样品,以乙腈为溶剂提取大米中的毒死蜱农药,用氮吹仪将提取液浓缩后,使用滤纸富集提取液中的农药,真空冷冻干燥,采集滤纸的近红外漫反射光谱。运用特征波长筛选方法优选特征变量,建立大米中毒死蜱农药残留的近红外光谱分析模型。结果表明,利用联合区间偏最小二乘法方法从全光谱区优选出子区间[3 4 5 10],进一步用遗传算法从子区间中优选80个变量时,所建模型性能最好。在0.46~11.20 mg/kg浓度范围内,模型对预测集样本的相关系数为0.9798,预测均方根误差为0.604 mg/kg,将该模型预测4个未知农药含量的大米样本,其预测值与实际测量值具有较好的一致性。研究表明该方法能较好地快速检测大米中微量农药残留。
In this paper, microfiltration membrane enrichment of trace pesticide residues in rice to improve the detection limit of near infrared spectroscopy. Spraying different concentrations of chlorpyrifos standard solution on negative rice samples to prepare pesticide residues rice samples, extracting chlorpyrifos pesticides from rice with acetonitrile as solvent, enriching the extract with nitrogen blowing instrument, enriching the pesticide in the extract by using filter paper, Vacuum freeze-drying, collecting filter paper near-infrared diffuse reflectance spectrum. By using characteristic wavelength screening method to optimize characteristic variables, a near-infrared spectral analysis model of pesticide residues in rice was established. The results show that the best performance of the proposed model is obtained by using the joint interval partial least-squares method to select the sub-interval [34 5 10] from the full spectral range and further optimizing 80 variables from the sub-interval by genetic algorithm. In the range of 0.46 ~ 11.20 mg / kg, the correlation coefficient between the model and the predictive set was 0.9798 and the root mean square error of prediction was 0.604 mg / kg. The model predicts the rice samples with four unknown pesticide contents, The actual measured values have good consistency. The results show that this method can detect trace pesticide residues in rice quickly and conveniently.