CN100458409C - 表征频谱仪并提供定标模型的方法 - Google Patents
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Abstract
频谱仪仪器是通过把它们的频谱归类到先前定义的群集内来表征的。该频谱被映射到群集,并且根据经提取的频谱特征与先前定义的群集之一的相似性而进行归类(12)。对每个群集提供定标模型来补偿仪器的偏差。定标模型既可以通过把主定标(21)传输到从属定标(23)来提供,又可以通过对每个群集计算独立的定标来提供。定标传输的简化方法互相映射这些群集,所以在群集间传输的定标只模拟这两个群集间的差异,大大地降低了该模型的复杂度。
Description
发明背景
发明领域
本发明涉及频谱仪仪器中的偏差。本发明尤其涉及通过把频谱仪仪器的响应归类到有限数量的群集中并且发展补偿仪器偏差的群集间的定标转移模型来表征频谱仪仪器。
现有技术描述
频谱仪的许多分析的应用需要创建起来耗时并昂贵的定标数据集。一般地,这些定标具有高度的特殊性。例如,由相同制造商生产的同一仪器显然应显示微小的仪器偏差;当用与另一台仪器内的相同部件有轻微变化的部件建立一台仪器时,可以看见该偏差。此外,由一个制造商生产的仪器的定标集一般不适合由另一个制造商生产的类似仪器。而且,对单一仪器的修补会引起仪器的频谱响应的变化。当仪器老化后,它的频谱响应可能发生变化。仪器的频谱响应可以根据操作环境的波动而变化。在需要例如极低浓度的分析物、非侵入的血糖预测的应用中,甚至很小的仪器偏差都能造成无法接受的程度的分析误差。倘若另一个考虑到该仪器当前的频谱响应的定标模型可以补偿仪器偏差。然而,新定标模型的发展是耗时的、劳动密集型的并且昂贵的。
在生物医学应用的基于频谱学的分析仪的发展中,对特定的应用需要生产几千到几百万台分析仪。不存在迅速并廉价地为大量仪器提供定标的方法。
因此,试验针对把定标从一台分析仪转移到另一台。例如,可见E.Bouveresse,C.Hartmann,D.Massart,I.Last,Prebble所著的“Standardization ofnear-infrared spectrometric instruments”,(Anal.Chem.,vol.68,pp.982-990,1996)和M.Defernez,R.Wilson所著的“Infrared spectroscopy:instrumentalfactors affecting the long-term validity of chemometric models”,(Anal.Chem.,vol.69,pp.1288-1294,1997),以及E.Bouveresse,D.Massart,P.Dardenne所著的“Calibration transfer across near-infrared spectrometricinstruments using Shenk’s algorithm:effects of different standardizationsamples”,(Analytica Chimica Acta,vol.297,pp.405-416,1994)和Y.Wang,D.Veltkamp,B.Kowalski所著的“Multivariate instrument calibration”,(Anal.Chem.,vol.63,pp.2750-2756,1991)。
大多数已公布的定标转移的方法已被应用在包含高浓度分析物的情况中,其中的信噪比很高。因为这些当前已知的方法在转移定标时起到平滑的功能,所以它们降低了可观察到的信噪比,从而阻碍了对低浓度分析物的分析。尚未提出其它随着时间或在仪器间的分辨率或带宽的改变问题。
此外,当前已知的方法仅仅成功地被用于包括少量仪器的情况下。当包括大量仪器时,已公布的方法并不能模拟所遇到的复杂度。
存在对大量分析物的定标的一种需要。对大量分析物提供一种降低传输定标时固有的复杂度是合乎需求的。提供一种无需信噪比的大幅下降而传输定标、并使定标传输实际用于低浓度分析物的分析中的装置也是有利的。
发明摘要
本发明提供了一种表征大量频谱仪仪器的方法,它根据频谱特性和性能特征的相似性,通过把仪器的频谱归类到先前定义的有限数量的群集中而实现。该方法也可用来跟踪单一仪器内随着时间的偏差。用于归类的频谱特性可能涉及已知的仪器参数,或者它们可以是用多种计算方法得到的抽象特性。这些群集预先根据范例数据组用被监督或未被监督的方法来定义。每个群集的定标模型补偿多台仪器间或一台仪器内随着时间产生的仪器偏差。
在本发明的优选实施例中,用定标传输的方法来提供定标模型,其中群集被彼此映射,从而从一个群集传输到另一个的定标只需要模拟两个群集间的差异。在另一个实施例中,对每个群集独立地计算不同的定标。在又一个实施例中,由于每个群集表示许多仪器,所以定标传输的数量大大降低。
附图简要说明
根据本发明,图1提供了通过根据频谱特性的分组来表征频谱仪仪器的方法的示意图。
根据本发明,图2提供了通过把主定标传输到从属定标模型来产生定标模型的方法的流程图。
图3A和3B说明了与频谱仪仪器内检测器温度的降低有关的频谱截止点的降低;以及
图4说明了与频谱仪仪器内源温度的改变有关的光通量的改变。
详细描述
本发明提供了一种表征大量频谱仪仪器的方法,它通过把来自许多频谱仪(分析仪)的频谱响应归类到先前定义的有限数量的群集中而实现。组成这些群集的群组根据特定特性的相似性而定义。根据频谱特性和性能特征把仪器分组能减少在给定的组内的仪器间的偏差。因此,对应于单个群集的频谱量比那些来自整组分析仪的频谱量更均匀。然后为每个群集创建的定标用于后续的分析。为每个群集创建的定标将具有包含在模型内的仪器偏差,它反映由随后的分析中使用的特定分析物所产生的偏差。理想情况下,群集特有的定标模型最好更简单并且具有改进的准确性水平。因此,多元分析将需要较少的因素来模拟仪器偏差,导致较早的模拟采样的因素以及较早的模拟小的吸收分析物的因素。最终结果是对采样分析需要较少的因素并且创建更加强的算法。
在本发明的一个实施例中,每个群集需要一个独立的定标数据组,每个群集的定标组中需要大量采样。在优选的实施例中,仅在一个群集中需要定标模型的足够采样。定义该群集和其它群集的主要特性被确定,并且这些群集被彼此映射,显示出从一个群集到另一个的特定差异。随后,根据群集间的特定差异,来自最初群集的定标被传输到另一个群集。
S.Malin和T.Ruchti提出的、美国专利申请序列号为09/359191(1999年7月22日)、题为“An intelligent system for noninvasive blood analyteprediction”的当前申请的原始申请中提供了对通过在吸收率频谱中观察到的相似性把频谱量归类到先前定义的群集的方法的详细说明。该题目的样本组织容量的归类系统在这里被扩展到包括仪器偏差的归类。
现在参考图1,它表示了把频谱量归类到先前定义的群集中的一般方法。该方法的简要步骤为:
1.测量。(11)
2.归类(12),其中经测量的频谱成为若干预定群集13中的一员。
3.定标(14),其中提供适合每个群集的定标模型。
4.标识逸出值(15)。
测量
一般地,仪器偏差可能通过产生信号强度变化、带宽变化、波长变化或它们的组合来影响频谱响应。这些仪器偏差可包括:
1.波长偏移;
2.非线性波长偏移;
3.波长扩展;
4.波长收缩;
5.非线性波长扩展;
6.源强漂移;
7.黑体外形变化;
8.带宽变化;
9.分辨率变化;
10.基线偏离;
11.随时间的变化;
12.温度效应;
13.检测器响应;
14.光学元件内的差异(例如,长通道的滤波器或光纤);
15.关于装配坐标的偏差;
16.光学界面到采样的差异(光纤间隔);
17.线性度;
18.检测器截止点;
及许多其它偏差,它们对于本领域的技术人员来说是显而易见的。用来分类的频谱一般是那些通常已知标准的频谱。用来归类在波长轴中观察到的偏移的特别有效的标准包括聚苯乙烯、稀土元素氧化物:例如氧化钬、氧化铒或氧化镝;或它们的组合。Labsphere,Inc.(North Sutton NH)公司提供的像漫反射率标准这样的标准可被用来归类强度轴上的偏移。样本的频谱也可被用来归类。本领域的技术人员将得知其它的强度和波长标准。
在近-IR的非侵入葡萄糖确定的特定情况下,这些标准覆盖该近-IR的频谱区域。其它像AA(原子吸收频谱学)或GC-MS(气体色谱法质谱学)这样的的频谱学和色谱-频谱的技术都需要各自自身的标准,这些标准是本领域的技术人员所熟知的。此外,在仪器上采集的组织幻象的频谱也可被用来表征该仪器。因为这些幻象的频谱仿真活动组织的非侵入频谱,所以组织幻象在表征仪器时是有帮助的。一些幻象仿真各种皮肤组织的吸收系数;而其它的幻象则仿真身体的散射系数。共同的模拟装置包括:
1.乳和乳制品;
2.结合墨汁的乳制品,用来调节吸收系数;
3.水中脂肪物质的乳胶,它保存在像卵磷脂这样的乳化剂溶液中。一种这样的商业产物是INTRALIPID,它是由Kabivitrum AB(Stockholm,Sweden)公司提供的;
4.内血清和内凝胶。见K.Hazen,J.Welch,S.Malin,T.Ruchti,A Lorenz,T.Troy,S.Thenadil,T.Blank所著的“Intra-serum and intra-gel formodeling human skin tissue”,其美国专利申请序列号为09/502877(2000年2月10日)。
其它的散射和吸收媒质对本领域的技术人员是已知的。这些组织模拟装置的浓度可被调节以匹配像皮肤或内部器官这样的身体组织的散射和吸收系数。
同样显而易见的是,除了用于归类的实际频谱量之外,群集定义和定标模型的形成还需要一组范例测量。
归类
新的频谱量被传送到模式归类系统,该系统把这些测量归类到先前定义的群集中,这些群集在关于仪器偏差的频谱特性中具有高度的内部一致性。
该归类本身包括下列步骤:
1.特征提取;以及
2.按照归类模型的特性的归类。
特征提取
本申请的原始申请中更为详细地描述了特征提取的过程,该申请的美国专利申请序列号为09/359191,supra。特征提取是能加强对信号译释有帮助的数据的特定方面或质量的任何数学变换。特征可以是两种类型的:
1.抽象的,以及
2.简单的。
仪器可用抽象的特征归类,意味着它们可以用计算方法来归类。这些方法可以是被监督的或者未被监督的。例子包括彼此绘制最初的主要元件并且表示产生的群集;判别式分析,譬如Mahalanobis距离的测量,以及K平均群集。其它方法对本领域的技术人员是显而易见的。
值得注意的是,上面列出的群集技术并非互斥的。可以通过一种或多种这些方法以及用这些方法的组合来实现把未加工的频谱群集到一组或多组中。
简单的特征可以从系统的先验理解得到,并且可以直接关于一个仪器参数或元件(或多个仪器参数和元件)。例如,经测量的带宽、噪声特征、或线性度和检测器截止点。
群集定义
正如上面指出的,群集必须在事先已用范例频谱量的数据组定义。群集定义是把探查数据组内的测量值分配到各群集。在群集定义之后,这些测量值和类别分配被用来确定从特征到群集分配的映射。
群集定义既可以用被监督的方法又可以用未被监督的方法来进行。在被监督的情况下,可以用系统的特定指示来定义群集。例如,如下面进一步说明的那样,源强和检测器温度对频谱有特定效应。该方式中先验信息的使用是被监督的模式识别中的第一步骤,当该类别分配已知时形成归类模型。
另外,在未被监督的方法中群集可以用抽象特征来定义,譬如在主要元件得分“x”对频谱负载“y”的图内分成群集。结果是在给定的群集内,所有的频谱都具有相同的特征(干扰、仪器偏差或样本组织)。可以根据已知的下面的在特征空间内产生偏差的现象来译释从具有物理意义的特征形成的群集。
归类
在类别定义之后,通过被监督的模式识别设计一个归类器。根据把被测的特征组转换成被估计的类别的群集定义来创建一个模型。该类别模型是一种用预定的群集来确定一组相似性度量的方法。决策规则根据一组由决策机计算的度量值来分配成员。
定标
一旦频谱被分成许多群集,则每个群集就需要定标模型14。群集是通过两种方法之一提供的。在优选的实施例中,对第一群集形成主定标,随后该主定标被传输到从属定标,每个剩余的群集都有一个从属定标。下面进一步描述的另一个实施例为每个群集分别计算定标。
在用主从仪器采集频谱的情况下,本领域中通常使用的术语“定标传输”可能具有不同的含义。定标传输可以指把从属频谱传输到像是主频谱上,或者反之亦然。另外,主频谱和从属频谱都可以被传输到不存在于主数据组或从数据组内的共同的标准频谱中。此外,定标传输可以指基于标准的预处理步骤、采样频谱的多元调节、或者预定分析浓度的调节。
现在参考图2,它示出定标传输的过程。n个群集的每一个都需要定标14。对第一群集计算主定标21;接着,为了对每个剩余的n个群集提供定标,该主定标被传输到从属定标。在这种情况下,定标传输是指在从属仪器上传输频谱来匹配主定标的特征从而使该主定标能被应用于从属频谱上的过程。对于群集来说,定标传输是指在从属仪器上传输频谱来匹配主定标的特征从而产生新的定标的过程,该新的定标满足除了主定标为之形成的群集之外的群集的规定。这些群集也可被组织成群集组,从而该主定标被传输到从属定标23,该从属定标23依次被传输到每个组内各群集的从属定标。
定标传输的现有技术方法在模拟为大量仪器提供定标模型包含的复杂度时是不成功的。把频谱测量归类到具有高度内部一致性的群集中会把问题的复杂度降低到一定水平从而可以把定标传输应用于大量仪器。把所获的频谱分组到有限数量的子群允许该定标传输组织被分裂成多个子组,从而只有所有组织的一个子组需要被定位在任何两个群集之间。结果是给定的群集内,所有的频谱具有相同的特征。用包含有限量的该群集特征的采样的频谱产生给定群集的定标。该排出需要处理可能在未加工的测量中的所有偏差,大大地降低了需由定标来模拟的复杂度。由此,用于给定群集的定标会具有包含在模型内的仪器偏差,它们类似于由所使用的分析仪产生的偏差。由于每个群集具有其自身的规定,该定标传输技术只需要处理两个群集间的差异。例如,如果群集间的仅有差异是线性x轴偏移,那么该定标传输技术只需要处理该参数。这允许使用更特定的定标传输技术,它是加强的,产生较少的因素,因为只需要模拟较少仪器偏差。由于在定标传输步骤信噪比的保持,这将导致对较低浓度的分析物的分析。
一般地,定标传输技术必须提出仪器和仪器间的偏差,譬如波长偏移、非线性波长偏移、波长扩展(收缩)、非线性波长扩展、源强漂移、黑体外形变化、带宽(分辨率)变化、基线偏离、随时间的变化、温度效应及其它本领域的技术人员已知的偏差。
然而,除了定标传输中的仪器组织之外,采样组成和采样处理根据在定标传输中也非常重要。因此,每个用来预测结果的被分析的采样应该是该定标数据组的矩阵空间的插值;否则该定标则不能计算分析物浓度的准确预测。例如,如果该定标数据组包含范围在40到400mg/dL内的葡萄糖采样,那么对带有该范围外的葡萄糖的采样的预测是令人怀疑的。
由此,如图1所示,被发明的归类算法的重要附加优点在于可以标识逸出的频谱。每个群集具有其自身的一组归类要求。如果频谱未落在给定群集的参数内,则可找到另一个群集,其参数允许该频谱的分析。如果未找到任何群集,则把采样的值公布为一个逸出值,而非说明该分析物可能错误的值。在该方式中避免对未形成令人满意的定标模型的采样或仪器进行分析。
被归类为逸出值的频谱可采用两种形式。在一些情况下,该逸出频谱接近给定的群集。在这种情况下,常规定标传输技术可应用于该频谱以变换该频谱,从而使它落在存在定标的群集中的一个内。若失败,该频谱仍被归类为逸出值,正如将被表征为总逸出值的频谱一样。
归类和定标传输无须限于仪器间的差异;它也可以用于用来预测分析物的采样频谱中。例如,可以对健康的18到30岁间的人定义一个群集。可以对该有限的人口统计建立一个非侵入的葡萄糖模型。18到30岁的未怀孕妇女可形成一个独立的群集。可以提出这两个群集间的差异,譬如身体脂肪和采样的体积,并且定标传输技术对于那些基于相对少的频谱的差异可能是最佳的,从而使最初为有限人口统计产生的葡萄糖模型渐渐被扩展。
上述的实施例无须对每个群集建立定标,并且从而提供了能节约为每个群集建立独立定标所需的相当多的时间、金钱和努力的重要优点。可以从本领域技术人员已知的许多算法中选择所使用的实际定标传输技术。
另一个实施例像在优选实施例中那样把频谱归类到群集,并且为每个群集建立各自的定标。在需要更多频谱的代价下,定标传输的需求被消除。然而,通过带有明确的并有限的参数的群集可以获得其它的可靠性的测量。如在前面的实施例中,需要模拟较少的仪器偏差,所以较早的因素能集中在分析物信息上,这产生加强了的模型。
虽然本发明的上述描述针对表征不同的仪器,然而被发明的方法也可以用于根据已知的仪器元件内的偏差或已知的环境偏差来归类来自单个仪器的频谱。下面描述两个例子。
1.使用在许多近-IR的分析物中的2.6μm的InGaAs检测器的截止点随着检测器温度的降低而发生蓝移。图3表示在NICOLET 860上采集的空气频谱,该NICOLET 860是由Nicolet Instrument Corporation(Madison WI)公司用0到-20℃范围内的InGaAs检测器提供的。该2400到2600的频谱截止点区域在图3B内得到扩展。该截止点随着检测器温度的降低而降低。像确定在观察到10%的峰值强度处的波长这样的简单分析使用基准或空气频谱,允许该仪器根据检测器是否被适当地冷却以及被冷却到什么程度而被归类。
2.随着卤素钨源温度的增加,黑体发射的幅度增加并且观察到附加的光通量。例如,如图4所示,在5%Labsphere漫反射率标准的漫反射率光谱中,观察到总强度随着源温度的增加而增加。该效应可被用来根据源温度和总的光通量归类仪器。
相对简单的工作是根据附加的元件表征该仪器。例子有影响带宽的缝隙宽度或者影响总的光通量的室温。然而,频谱仪具有有限数量的元件;已观察到这些仪器组成相对少量的群集。
一旦仪器或给定的频谱被归类到群集,则可以使用适当的定标程序。由于仪器在其寿命期间可能发生改变,所以一个分析仪可以加载多个定标程序。实际上,环境因素可能造成该适当的定标随着每个采集的频谱而改变。
该仪器归类方法被设计以用在非侵入的葡萄糖分析物上,其中葡萄糖是用人体上皮肤的漫反射率频谱来测量的。然而,同样的技术可用于任何形式的非侵入分析中,它们包括、但不限于:白蛋白、球蛋白、尿素、肌酸酐、氧、血红蛋白A1C以及像Na+,K+,Cl-这样的电解质的非侵入分析。该技术也可被用于生物医学的应用中。
这里揭示的归类方法在所企图的各种领域内获得应用,例如,农业领域。示例性的农业应用有:牛奶里脂肪的分析、小麦内的蛋白质或湿度分析、或者水果内糖的分析。该归类方法也可以在完整药片的分析中或在原材料的表征中有益于制药公司。最终,制药公司可以在燃料、燃料副产品和原始燃料材料的归类中使用该方法。一般地,在使用大量分析仪来定量采样中的分析物时,该技术是有效的。
尽管这里参考优选实施例描述了本发明,本领域的技术人员可以理解,其它的应用可以替代这里提出的应用而无须背离本发明的精神和范围。从而,本发明仅受到所附的权利要求的限制。
Claims (24)
1.一种根据仪器偏差来表征频谱仪仪器的方法,其特征在于包括下列步骤:
从至少一台频谱仪仪器提供标准的频谱测量;以及
根据经提取的频谱特性把所述频谱测量归类到预定的群集中;以及
对每个所述预定的群集提供定标模型,其中所述定标模型补偿所述仪器偏差。
2.如权利要求1所述的方法,其特征在于,所述仪器偏差包括下述情况中的任一种:
波长偏移;
波长扩展;
波长收缩;
源强漂移;
黑体外形变化;
带宽变化;
分辨率变化;
基线偏离;
随时间的变化;
温度效应;
检测器响应;
光学元件内的差异;
关于装配坐标的偏差;
光学界面到采样的差异;
线性度;
检测器截止点;
3.如权利要求1所述的方法,其特征在于,所述标准频谱是在多个频谱仪仪器上测量的。
4.如权利要求1所述的方法,其特征在于,所述标准频谱是在一个频谱仪仪器的连续时间间隔内测量的。
5.如权利要求1所述的方法,其特征在于,所述归类步骤包括:
提取特性;以及
根据归类模型和决策规则归类所述特性。
6.如权利要求5所述的方法,其特征在于,所述的特征提取步骤包括加强有益于信号译释的所述预定群集的特定方面或质量的所述预定群集的任何数学变换。
7.如权利要求5所述的方法,其特征在于,所述归类模型包括用预定的类别确定一组相似性度量的装置。
8.如权利要求5所述的方法,其特征在于,所述决策规则包括根据由决策机计算的一组度量来分配类别成员的装置。
9.如权利要求4所述的方法,其特征在于,各个特性被分成两个类别,所述类别包括:
抽象特性,其中所述特性是用各种计算方法提取的;以及
简单特性,它从系统的先验理解得到,其中所述特性直接涉及仪器参数或元件。
10.如权利要求9所述的方法,其特征在于,所述抽象特性用以下的任何方法计算;
彼此绘制最初的主要元件并且标识产生的群集;
判别式分析;以及
K平均群集。
11.如权利要求5所述的方法,其特征在于,所述归类步骤还包括用基于因素的方法来建立能表示被测的频谱中的变化的模型,该变化与频谱响应的变化有关;
其中把被测的吸收频谱投射到所述模型上来构成表示关于仪器偏差的频谱偏差的特性。
12.如权利要求5所述的方法,其特征在于,所述归类步骤还包括下列步骤:
测量特性与预定群集的相似性;以及
在一个群集内分配成员。
13.如权利要求5所述的方法,其特征在于,还包括下述步骤:
把探查数据组内的测量分配到群集。
14.如权利要求13所述的方法,其特征在于,还包括下述步骤:
用度量和类别分配来确定从特性到群集分配的映射。
15.如权利要求1所述的方法,其特征在于,还包括为新样本测量的分析提供定标模型的步骤。
16.如权利要求15所述的方法,其特征在于,所述定标模型模拟所述预定群集间的差异。
17.如权利要求15所述的方法,其特征在于,从一组具有参考值和预分配的类别定义的范例频谱中形成第一个所述群集的主定标模型。
18.如权利要求17所述的方法,其特征在于,还包括把所述主定标模型传输到多个从属定标模型的步骤,其中对每个剩余的群集计算从属定标模型,其中根据定义每个类别的主要特性把所述主定标模型修改为从属定标模型。
19.如权利要求18所述的方法,其特征在于,所述传输步骤包括:
把所述主定标模型转换到第一从属定标模型;
把所述第一从属定标模型传输到第二从属定标模型;
以及重复从一个从属定标模型到另一个从属定标模型的所述传输,直到为每个预定的群集提供一个定标为止。
20.如权利要求17所述的方法,其特征在于,还包括把所述主定标模型传输到多个从属定标模型的步骤,其中对每个剩余的群集计算从属定标模型,并且其中根据定义每个类别的主要特性把所述从属定标模型修改为所述主定标模型。
21.如权利要求20所述的方法,其特征在于,所述传输步骤包括:
把所述主定标模型传输到第一从属定标模型;
把所述第一从属定标模型传输到第二从属定标模型;
以及重复从一个从属定标模型到另一个从属定标模型的所述传输,直到为每个预定的群集提供一个定标为止;
其中一变换根据定义每个所述群集的主要特性来修改所述被传输的定标模型。
22.如权利要求15所述的方法,其特征在于,对每个类别形成不同的定标模型,并且其中所述定标模型是从一组具有参考值和预分配的群集定义的范例频谱中形成的。
23.如权利要求22所述的方法,其特征在于,频谱被分配到许多预定的群集之一,定标模型为了这些预定群集而形成的。
24.如权利要求1所述的方法,其特征在于,还包括下列步骤:
提供新的频谱测量;
根据经提取的频谱特性比较所述新的频谱测量和每个所述的预定群集;
把未找到匹配群集的测量公布为逸出值。
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US09/664,973 | 2000-09-18 | ||
US09/664,973 US6864978B1 (en) | 1999-07-22 | 2000-09-18 | Method of characterizing spectrometer instruments and providing calibration models to compensate for instrument variation |
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CN100458409C true CN100458409C (zh) | 2009-02-04 |
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US (2) | US6864978B1 (zh) |
EP (2) | EP1319176B1 (zh) |
JP (1) | JP2004526938A (zh) |
CN (1) | CN100458409C (zh) |
AT (1) | ATE306656T1 (zh) |
AU (1) | AU2001283414A1 (zh) |
DE (1) | DE60114036T2 (zh) |
DK (1) | DK1319176T3 (zh) |
HK (1) | HK1058234A1 (zh) |
WO (1) | WO2002025233A2 (zh) |
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EP1442699A1 (en) | 2004-08-04 |
JP2004526938A (ja) | 2004-09-02 |
DE60114036D1 (de) | 2005-11-17 |
ATE306656T1 (de) | 2005-10-15 |
EP1319176B1 (en) | 2005-10-12 |
HK1058234A1 (en) | 2004-05-07 |
EP1319176A2 (en) | 2003-06-18 |
US6864978B1 (en) | 2005-03-08 |
DK1319176T3 (da) | 2006-02-13 |
CN1483141A (zh) | 2004-03-17 |
US7038774B2 (en) | 2006-05-02 |
AU2001283414A1 (en) | 2002-04-02 |
WO2002025233A2 (en) | 2002-03-28 |
DE60114036T2 (de) | 2006-06-22 |
WO2002025233A3 (en) | 2002-06-27 |
US20040223155A1 (en) | 2004-11-11 |
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