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新生儿足底血片17α-羟孕酮含量近红外光谱模型的构建 被引量:1

Construction of near infrared spectroscopic model for content of 17α-hydroxyprogesterone in neonatal blood tablets
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摘要 目的依据近红外光谱技术建立新生儿足底血片17α-羟孕酮快速无损的检测手段。方法本研究选用来自于山东省妇幼保健院新生儿疾病筛查中心的121份足底血样本进行光谱采集,随后采用时间分辨荧光免疫分析法测定17α-羟孕酮的含量,利用偏最小二乘法(PLS)构建17α-羟孕酮近红外定标模型,该模型的内部验证均方差(RMSEC)为0.9098,相关系数R^(2)=0.9916。选用未参与建模的21份足底血样本对该模型进行外部验证,模型预测值和化学测定值的决定系数R2=0.9833,表明该模型可适用于足底血17α-羟孕酮的检测。结论近红外光谱法对足底血中17α-羟孕酮含量预测结果较好,为足底血样本中17α-羟孕酮实验室在线、无损定量分析提供了依据。 Objective To establish a rapid and noninvasive method for detection of 17α-hydroxyprogesterone in neonatal blood plantar tablets.Methods 121 blood samples from the Neonatal Disease Screening Center of Maternal and Child Health Care Hospital of Shandong Province were used for spectral collection.Then 17α-hydroxyprogesterone was determined by time-resolved fluorescence immunoassay.A 17-α-hydroxyprogesterone near-infrared scaling model was constructed by partial least squares(PLS).The mean square error of the model was 0.9098,and the correlation coefficient R=0.9916.Twenty-one blood samples were used to validate the model.The predictive coefficient of the model was R^(2)=0.9833,which indicated that the model was suitable for the detection of 17α-hydroxyprogesterone in blood.Conclusion NIRS is a good method for predicting the content of 17α-hydroxyprogesterone in blood samples of plantar,which provides a basis for on-line and nondestructive quantitative analysis.
作者 孙岱 张娟 闫浩田 孙茂 马正 周玉侠 SUN Dai;ZHANG Juan;YAN Haotian;SUN Mao;MA Zheng;ZHOU Yuxia(Maternal and Child Health Care Hospital of Shandong Province,Clinical Laboratory,Jinan,Shandong 250ooo,China;Shandong Provincial Hospital Department of Neurology,Jinan,Shandong 250ooo,China;Shandong Jinzhang Longxiang Intelligent Technology Co.,Ltd.,Jinan,Shandong 250ooo,China)
出处 《中国优生与遗传杂志》 2022年第8期1458-1461,共4页 Chinese Journal of Birth Health & Heredity
基金 山东省医药卫生科技发展计划项目(2013WS0126)。
关键词 近红外光谱技术 17α-羟孕酮 偏最小二乘法(PLS) 内部验证均方差 near infrared spectroscopic technology 17α-hydroxyprogesterone partial least squares(PLS) internally validated mean square deviation
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