近红外光谱法在桂利嗪片快速分析中的应用
|
摘要
目的利用近红外光谱(NIR)技术建立快速鉴别桂利嗪片的一致性检验模型和特征谱段相关系数模型。方法以桂利嗪片为对象,采用光纤探头测定样品近红外光谱,通过一致性指数(CI)值和一致性指数限度比较法建立一致性检验模型;通过在特征谱段选择合适的相关系数(r)值为阈值建立相关系数模型。结果建立的一致性检验模型和特征谱段相关系数模型均可快速鉴别并准确区分桂利嗪片(辰欣药业股份有限公司)与其他生产厂家的同类产品;验证样品的一致性指数值均>7.0,相关系数均<97%。结论方法快速、简便,可用于桂利嗪片的现场快速筛查。
Objective To establish the conformity test model and characteristic spectral correlation coefficient model for rapid identification of Cinnarizine Tablets by near-infrared spectra( NIR).Methods NIR spectrum of Cinnarizine Tablets were measured by a fiber optic probe.Conformity test model was performed by comparing consistency index( CI) value and CI limit.Characteristic spectral correlation coefficient model was established by choosing the characteristic spectral section and setting appropriate threshold value( r).Results Conformity test model and characteristic spectral correlation coefficient model can accurately distinguish Cinnarizine Tablets( manufactured by Cisen Pharmaceutical Co.,Ltd.) from similar products of other manufacturers.The CI value of the validation samples were beyond 7. 0,the threshold value( r) were less than 97%.Conclusion The method was simple and rapid,which can be applied to fast screening of Cinnarizine Tablets in mobile lab.
Objective To establish the conformity test model and characteristic spectral correlation coefficient model for rapid identification of Cinnarizine Tablets by near-infrared spectra( NIR).Methods NIR spectrum of Cinnarizine Tablets were measured by a fiber optic probe.Conformity test model was performed by comparing consistency index( CI) value and CI limit.Characteristic spectral correlation coefficient model was established by choosing the characteristic spectral section and setting appropriate threshold value( r).Results Conformity test model and characteristic spectral correlation coefficient model can accurately distinguish Cinnarizine Tablets( manufactured by Cisen Pharmaceutical Co.,Ltd.) from similar products of other manufacturers.The CI value of the validation samples were beyond 7. 0,the threshold value( r) were less than 97%.Conclusion The method was simple and rapid,which can be applied to fast screening of Cinnarizine Tablets in mobile lab.
引文
[1]国家药典委员会.中华人民共和国药典2015年版(二部)[S].北京:中国医药科技出版社,2015:1116-1117.
[2]王海波,朱荣,朱建萍,等.头孢克肟片含量的近红外漫反射光谱法测定[J].中国药师,2013,16(8):1166-1168.
[3]武洋,杨永刚,王跃卿,等.近红外一致性检验模型快速鉴别拉米夫定片[J].中国药师,2013,16(9):1437-1439.
[4]朱静毅,应忠良,闻琍毓.近红外光谱分析技术在药品质量控制中的应用[J].中国药房,2009,20(7):550-551.
[5]臧恒昌,臧立轩,张惠,等.近红外光谱分析技术在制药领域中的应用研究进展[J].药学研究,2014,33(3):125-128.
[6]闫研,殷果,刘凯双,等.利用近红外光谱技术快速鉴别厄贝沙坦分散片[J].药学研究,2015,34(7):384-386.
[7]张慧,胡甜,臧恒昌.近红外光谱分析技术在白芍水提过程中的在线控制研究[J].药学研究,2015,34(5):272-275.
[8]马晓青,蔡皓,刘晓,等.近红外光谱在中药鉴别和质量控制中的应用[J].中国药房,2012,23(7):661-663.
[9]侯少瑞,冯艳春,胡昌勤.近红外光谱法快速分析注射用头孢曲松钠及其水分的含量[J].药物分析杂志,2008,28(6):936-941.
[10]阮治纲,李彬.近红外光谱分析技术的原理及在中药材中的应用[J].药物分析杂志,2011,31(2):408-417.
[11]陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2006.
[12]张学博,尹利辉.近红外光谱一致性检验方法用于快速判断药品质量的研究[J].药物分析杂志,2011,31(3):603-608.
[13]耿仲乐,王静,张瑞.利用近红外图谱比对法及一致性检验模型鉴别健胃消食片[J].中国药事,2012,26(3):279-281,287.
[14]马金金,尹力辉,丁永辉,等.注射用单唾液酸四己糖神经节苷脂钠近红外特征谱段相关系数模型[J].中国药师,2014(4):537-540.
[15]黄必胜,袁明洋,余池,等.近红外特征谱段相关系数法快速鉴别真伪龙骨[J].中药材,2014,37(9):1577-1582.
[2]王海波,朱荣,朱建萍,等.头孢克肟片含量的近红外漫反射光谱法测定[J].中国药师,2013,16(8):1166-1168.
[3]武洋,杨永刚,王跃卿,等.近红外一致性检验模型快速鉴别拉米夫定片[J].中国药师,2013,16(9):1437-1439.
[4]朱静毅,应忠良,闻琍毓.近红外光谱分析技术在药品质量控制中的应用[J].中国药房,2009,20(7):550-551.
[5]臧恒昌,臧立轩,张惠,等.近红外光谱分析技术在制药领域中的应用研究进展[J].药学研究,2014,33(3):125-128.
[6]闫研,殷果,刘凯双,等.利用近红外光谱技术快速鉴别厄贝沙坦分散片[J].药学研究,2015,34(7):384-386.
[7]张慧,胡甜,臧恒昌.近红外光谱分析技术在白芍水提过程中的在线控制研究[J].药学研究,2015,34(5):272-275.
[8]马晓青,蔡皓,刘晓,等.近红外光谱在中药鉴别和质量控制中的应用[J].中国药房,2012,23(7):661-663.
[9]侯少瑞,冯艳春,胡昌勤.近红外光谱法快速分析注射用头孢曲松钠及其水分的含量[J].药物分析杂志,2008,28(6):936-941.
[10]阮治纲,李彬.近红外光谱分析技术的原理及在中药材中的应用[J].药物分析杂志,2011,31(2):408-417.
[11]陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2006.
[12]张学博,尹利辉.近红外光谱一致性检验方法用于快速判断药品质量的研究[J].药物分析杂志,2011,31(3):603-608.
[13]耿仲乐,王静,张瑞.利用近红外图谱比对法及一致性检验模型鉴别健胃消食片[J].中国药事,2012,26(3):279-281,287.
[14]马金金,尹力辉,丁永辉,等.注射用单唾液酸四己糖神经节苷脂钠近红外特征谱段相关系数模型[J].中国药师,2014(4):537-540.
[15]黄必胜,袁明洋,余池,等.近红外特征谱段相关系数法快速鉴别真伪龙骨[J].中药材,2014,37(9):1577-1582.