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:
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:
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:
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:
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:
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:
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:
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:
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:
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.