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公开(公告)号：US07409299B2
公开(公告)日：2008-08-05
申请号：US11407392
申请日：2006-04-18
IPC分类号： G01N31/00
CPC分类号： G01J3/28 , G01N21/3577 , G01N21/359 , G01N33/02 , G01N2021/3129 , G01N2201/1293 , G06F19/703
摘要： Spectra data collected from a mixture defines an n-dimensional data space (n is the number of data points), and application of PCA techniques yields a subset of m-eigenvectors that effectively describe all variance in that data space. Bach member of a library of known components is examined based by representing each library spectrum as a vector in the m-dimensional space. Target factor testing techniques yield an angle between this vector and the data space. Those library members that have the smallest angles are considered to be potential mixture members and are ranked accordingly. Every combination of the top y library members is considered as a potential solution and a multivariate least-squares solution is calculated using the mixture spectra for each of the potential solutions. A ranking algorithm is then applied and used to select the combination that is most likely the set of pure components in the mixture.
摘要翻译： 从混合物收集的光谱数据定义了n维数据空间（n是数据点的数量），并且PCA技术的应用产生有效描述该数据空间中的所有方差的m个特征向量的子集。 通过将每个库谱表示为m维空间中的向量来检查已知组件的库的Bach成员。 目标因子测试技术在该矢量与数据空间之间产生一个角度。 具有最小角度的那些图书馆成员被认为是潜在的混合成员并被相应排名。 顶级图书馆成员的每个组合都被认为是潜在的解决方案，并且使用每个潜在解决方案的混合谱来计算多变量最小二乘解。 然后应用排序算法并用于选择混合中最可能的纯组分集合的组合。
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公开(公告)号：US07072770B1
公开(公告)日：2006-07-04
申请号：US10812233
申请日：2004-03-29
IPC分类号： G01N31/00
CPC分类号： G01J3/28 , G01N21/3577 , G01N21/359 , G01N33/02 , G01N2021/3129 , G01N2201/1293 , G06F19/703
摘要： The present invention is directed generally toward the field of spectral analysis and, more particularly, toward an improved method of identifying unknown components of a mixture from a set of spectra collected from the mixture using a spectral library including potential candidates. For example, the present method is directed to identifying components of a mixture by the steps which comprise obtaining a set of spectral data for the mixture that defines a mixture data space; ranking a plurality of library spectra of known elements according to their angle of projection into the mixture data space; calculating a corrected correlation coefficient for each combination of the top y ranked library spectra; and selecting the combination having the highest corrected correlation coefficient, wherein the known elements of the selected combination are identified as the components of the mixture.
摘要翻译： 本发明一般涉及光谱分析领域，更具体地说，涉及使用包括潜在候选物的光谱库从混合物中收集的一组光谱识别混合物的未知组分的改进方法。 例如，本方法旨在通过包括获得用于限定混合数据空间的混合物的一组光谱数据的步骤来识别混合物的组分; 根据其投影到混合数据空间的角度对已知元素的多个库光谱进行排序; 计算顶级排名图书馆光谱的每个组合的校正相关系数; 以及选择具有最高校正相关系数的组合，其中所选组合的已知元素被识别为混合物的组分。
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公开(公告)号：US20070061091A1
公开(公告)日：2007-03-15
申请号：US11407392
申请日：2006-04-18
申请人： Robert Schweitzer , Willem Windig , Patrick Treado
发明人： Robert Schweitzer , Willem Windig , Patrick Treado
IPC分类号： G01R23/16
CPC分类号： G01J3/28 , G01N21/3577 , G01N21/359 , G01N33/02 , G01N2021/3129 , G01N2201/1293 , G06F19/703
摘要： A set of spectral data is collected from a mixture and corrected to remove instrumental artifacts. The collected mixture spectra define an n-dimensional data space, where n is the number of data points in the spectra. Principal component analysis (PCA) techniques are applied to the n-dimensional data space to generate and select a subset of m eigenvectors that effectively describe all variance in the original data space. The members of a spectral library of known, pure components are examined based on this reduced dimensionality data space using target factor testing techniques. Each library spectrum is represented as a vector in the m-dimensional reduced data space, and target factor testing results in an angle between the library vector and the data space for each spectral library member. Those spectral library members that have the smallest angles with the data space are considered to be potential members, or candidates, of the mixture and are submitted for further testing. The spectral library members are ranked and every combination of the top y members is considered as a potential solution to the composition of the mixture. A multivariate least-squares solution is then calculated using the mixture spectra for each of the candidate combinations. Finally, a ranking algorithm is applied to each combination and is used to select the combination that is most likely the set of pure components in the mixture.
摘要翻译： 从混合物中收集一组光谱数据，并进行校正以去除仪器伪像。 收集的混合谱定义了n维数据空间，其中n是光谱中数据点的数量。 主成分分析（PCA）技术应用于n维数据空间，以生成和选择有效描述原始数据空间中所有方差的m个特征向量的子集。 基于使用目标因子测试技术的这种减小的维度数据空间来检查已知的纯组件的光谱库的成员。 每个库谱表示为m维缩减数据空间中的向量，目标因子测试导致库向量与每个谱库成员的数据空间之间的角度。 与数据空间具有最小角度的那些光谱库成员被认为是混合物的潜在成员或候选者，并被提交进一步测试。 光谱库成员被排名，并且顶部成员的每个组合被认为是混合物组成的潜在解决方案。 然后使用每个候选组合的混合谱计算多变量最小二乘解。 最后，将排序算法应用于每个组合，并用于选择混合中最可能的纯组分集合的组合。
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4.发明申请公开(公告)号：US20090012723A1
公开(公告)日：2009-01-08
申请号：US12196921
申请日：2008-08-22
摘要： A method for analyzing data from an unknown substance, whereby target data representative of an unknown substance is received and compared to reference data associated with one or more known substances. Such comparison determines one or more candidate substances. After determining candidate substances, it is determined if the target data is unique to a candidate substance. If the target data is unique to one of the candidate substances, then this determination is confirmed with fusion. If the target data is not unique, then the target data may be subjected to fusion and unmixing with fusion. If analysis of the target data determines that an outlier is present, then this target data is added to a pool of unassigned data. The addition of this new data to the pool of unassigned data may result in clustering of enough of the previously unassigned data to form a new candidate class. If analysis of the target data does not detect an outlier, but cannot be matched to an existing candidate class, the target data in this case can also be added to the pool of unassigned data. If no outlier is detected, and the Matching Existing Class step is successful, then the target data is added to the matched class. If this candidate class is confirmed, then it can be added to the list of existing classes.
摘要翻译： 用于分析来自未知物质的数据的方法，由此接收表示未知物质的目标数据并与与一种或多种已知物质相关联的参考数据进行比较。 这种比较确定一种或多种候选物质。 在确定候选物质之后，确定目标数据是否对于候选物质是唯一的。 如果目标数据对于候选物质之一是唯一的，则通过融合确认该确定。 如果目标数据不是唯一的，则可以对目标数据进行融合和解混合。 如果目标数据的分析确定存在异常值，则该目标数据被添加到未分配数据的池中。 将此新数据添加到未分配数据池可能会导致足够的以前未分配数据的聚类，以形成新的候选类。 如果目标数据的分析没有检测到异常值，但是不能与现有候选类别匹配，则在这种情况下的目标数据也可以被添加到未分配数据的池中。 如果没有检测到异常值，并且匹配现有类步骤成功，则将目标数据添加到匹配的类中。 如果这个候选类被确认，那么它可以被添加到现有类的列表中。
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公开(公告)号：US20120105838A1
公开(公告)日：2012-05-03
申请号：US13374703
申请日：2012-01-09
CPC分类号： G01N21/65 , G01N21/64 , G01N21/6402 , G01N21/8483 , G01N2021/6417
摘要： The invention relates to apparatus and methods for assessing occurrence of a hazardous agent in a sample by performing multipoint spectral analysis of the sample. Methods of employing Raman spectroscopy and other spectrophotometric methods are disclosed. Devices and systems suitable for performing such multipoint methods are also disclosed.
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公开(公告)号：US20080084553A1
公开(公告)日：2008-04-10
申请号：US11632471
申请日：2005-07-14
CPC分类号： G01J3/44 , G01J3/28 , G01J3/2823 , G01N21/6486 , G01N21/65 , G01N2201/1293
摘要： In one embodiment, the disclosure relates to a method for detecting and classifying an unknown substance in a sample. The method including the steps of (a) providing a spectrum for each of a predetermined number of reference substances; (b) detecting an area of interest on said unknown substance; (c) targeting said area of interest; (d) determining a spectrum from said area of interest; (e) comparing the determined spectrum with the spectrum of one of the reference substances; and (f) classifying said unknown substance based on the comparison of spectra.
摘要翻译： 在一个实施方案中，本公开涉及用于检测和分类样品中未知物质的方法。 该方法包括以下步骤：（a）为预定数量的参考物质中的每一个提供光谱; （b）检测所述未知物质的感兴趣区域; （c）针对该地区的目标; （d）从所述感兴趣区域确定频谱; （e）将所确定的光谱与参考物质之一的光谱进行比较; 和（f）基于光谱的比较对所述未知物质进行分类。
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7.发明申请System and method for spectral unmixing in a fiber array spectral translator based polymorph screening system 有权基于光纤阵列光谱变换器的多态性筛选系统中光谱解混合的系统和方法公开(公告)号：US20070201022A1
公开(公告)日：2007-08-30
申请号：US11679112
申请日：2007-02-26
IPC分类号： G01J3/00
CPC分类号： G01J3/2803 , G01J3/02 , G01J3/0218 , G01J3/0221 , G01J3/024 , G01J3/0291 , G01J3/0294 , G01J3/04 , G01J3/2823 , G01J3/44 , G01N21/65 , G01N2201/0833 , G01N2201/1293
摘要： The disclosure relates generally to methods and apparatus for using a fiber array spectral translator-based (“FAST”) spectroscopic system for performing spectral unmixing of a mixture containing multiple polymorphs. In an embodiment, a first spectrum of a mixture containing polymorphs of a compound is obtained using a photon detector and a fiber array spectral translator having plural fibers. A set of second spectra is provided where each spectrum of the set of second spectra may be representative of a different polymorph of the compound. The first spectrum and the set of second spectra may be compared, and based on the comparison, the presence of one or more polymorphs in the mixture may be determined.
摘要翻译： 本公开总体上涉及使用用于执行含有多个多晶型物的混合物的光谱解混合的光纤阵列光谱转换器（“FAST”）光谱系统的方法和装置。 在一个实施方案中，使用具有多个纤维的光子检测器和光纤阵列光谱转换器获得含有化合物多晶型物的混合物的第一光谱。 提供了一组第二光谱，其中该组第二光谱的每个光谱可以代表该化合物的不同多晶型物。 可以比较第一光谱和第二光谱集，并且基于比较，可以确定混合物中一个或多个多晶型物的存在。
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公开(公告)号：US08094294B2
公开(公告)日：2012-01-10
申请号：US12422360
申请日：2009-04-13
CPC分类号： G01N21/65 , G01N21/64 , G01N21/6402 , G01N21/8483 , G01N2021/6417
摘要： The invention relates to apparatus and methods for assessing occurrence of a hazardous agent in a sample by performing multipoint spectral analysis of the sample. Methods of employing Raman spectroscopy and other spectrophotometric methods are disclosed. Devices and systems suitable for performing such multipoint methods are also disclosed.
摘要翻译： 本发明涉及通过对样品进行多点光谱分析来评估样品中危险剂的发生的装置和方法。 公开了采用拉曼光谱法和其他分光光度法的方法。 还公开了适于执行这种多点方法的设备和系统。
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公开(公告)号：US20090097020A1
公开(公告)日：2009-04-16
申请号：US11000683
申请日：2004-11-30
IPC分类号： G01N21/25
CPC分类号： G01N21/65 , G01N21/64 , G01N21/6402 , G01N21/8483 , G01N2021/6417
摘要： The invention relates to apparatus and methods for assessing occurrence of a hazardous agent in a sample by performing multipoint spectral analysis of the sample. Methods of employing Raman spectroscopy and other spectrophotometric methods are disclosed. Devices and systems suitable for performing such multipoint methods are also disclosed.
摘要翻译： 本发明涉及通过对样品进行多点光谱分析来评估样品中危险剂的发生的装置和方法。 公开了采用拉曼光谱法和其他分光光度法的方法。 还公开了适于执行这种多点方法的设备和系统。
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10.发明授权Forensic integrated search technology with instrument weight factor determination 有权法医综合检索技术与仪器重量因子测定公开(公告)号：US08112248B2
公开(公告)日：2012-02-07
申请号：US12017445
申请日：2008-01-22
CPC分类号： G06K9/00536 , G06F19/703 , G06F19/709 , H01J49/0036
摘要： A system and method to search spectral databases and to identify unknown materials from multiple spectroscopic data in the databases. The methodology may be substantially automated and is configurable to determine weights to be accorded to spectroscopic data from different spectroscopic data generating instruments for improved identification of unknown materials. Library spectra from known materials are divided into training and validation sets. Initial, instrument-specific weighting factors are determined using a weight grid or weight scale. The training and validation spectra are weighted with the weighting factors and indicator probabilities for various sets of “coarse” weighting factors are determined through an iterative process. The finally-selected set of coarse weighting factors is further “fine tuned” using a weight grid with finer values of weights. The instrument-specific finer weight values may be applied to test data sets (or spectra) of an unknown material as well as to the library spectra from corresponding spectroscopic instruments. Instrument-specific weights for each class of samples may also be computed for additional customization and accuracy.
摘要翻译： 一种用于搜索光谱数据库并从数据库中的多个光谱数据中识别未知物质的系统和方法。 该方法可以基本上是自动化的，并且可配置为确定要与来自不同光谱数据生成装置的光谱数据一致的权重，以改进未知材料的识别。 已知材料的谱图谱分为训练和验证集。 使用权重网格或权重量表确定初始的仪器特定加权因子。 训练和验证光谱通过加权因子加权，并且通过迭代过程确定各组“粗”加权因子的指标概率。 最终选择的粗加权系数集合使用具有更精确的权重值的权重网格进一步“精细调整”。 仪器特定的更精细的重量值可以应用于未知材料的测试数据集（或光谱）以及来自相应光谱仪的文库光谱。 也可以计算每类样品的仪器特定重量，以获得额外的定制和准确性。
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