A Standard For Exchange Of Infrared Spectra In Computer Readable Form

McDonald, Robert; Wilks, Paul A.

doi:10.1117/12.970853

Cited by 3 publications

(6 citation statements)

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“…The use of proprietary data formats often caused compatibility issues because it was difficult to share data with other applications. To store and interchange result data between applications, several common interchange and archiving formats such as JCAMP-DX, 1 –5 GRAMS SPC, 6 and ANDI/NetCDF 7 –12 have been developed. These formats enable the interchange process, but this requires instrument software or other software tools to support import and export functions.…”

Section: Ultraviolet/visible Data Transfer Background and Problemmentioning

confidence: 99%

“…This new Web-aware language is being called the Analytical Information Markup Language (AnIML). AnIML is being based on SpectroML and Thermo LabSystems' GAML (Generalized Analytical Markup Language) with ingredients from JCAMP-DX, 1 –5 Andi, 7 –12 and other existing, well-established data dictionaries and ontologies. Information about the AnIML project can be found on the http://animl.sourceforge.net Website.…”

Section: Conclusion and Futurementioning

confidence: 99%

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Molecular Spectrometry Data Interchange Applications for NIST's SpectroML

Nguyen

Arslan

Travis

et al. 2004

JALA: Journal of the Association for Laboratory Automation

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S pectroML, a markup language for ultraviolet-visible spectroscopy data, has been developed as a ''Web-aware'' mechanism for instrument-to-instrument, instrument-to-application, and application-to-application data interchange and archiving. This article documents the application of SpectroML to the interchange and archiving of measurement data from three spectrophotometers that are used in the NIST optical filter standards program. It describes how result data from the NIST national reference spectrophotometer and two commercial spectrophotometers are converted into SpectroML format and how SpectroML-formatted data and metadata are imported into the optical filter standards database.

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Section: Ultraviolet/visible Data Transfer Background and Problemmentioning

confidence: 99%

Section: Conclusion and Futurementioning

confidence: 99%

Molecular Spectrometry Data Interchange Applications for NIST's SpectroML

Nguyen

Arslan

Travis

et al. 2004

JALA: Journal of the Association for Laboratory Automation

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“…Currently, many standardized file formats for result data are readily available. Formats like SpectroML, 1 –3 ANDI, 4 –8 and JCAMP-DX 9 –12 are excellent choices for their application domains. But the use of such current formats becomes difficult when multiple techniques are used or if vendor-specific data need to be stored.…”

Section: Introductionmentioning

confidence: 99%

“…This will happen in two phases: The techniques chosen for Phase 1 all have agreed upon ontologies, documented data dictionaries, and existing standardized file formats for their result data. [1][2][3][4][5][6][7][8][9][10][11][12] Domain expertise for many of these techniques exists within the other subcommittees of ASTM E-13 and within the IUPAC Subcommittee on Electronic Data Standards. However, we are seeking collaborations with other groups of domain experts to augment this list.…”

Section: Standards Development Processmentioning

confidence: 99%

“…Formats like SpectroML, [1][2][3] ANDI, [4][5][6][7][8] and JCAMP-DX [9][10][11][12] are excellent choices for their application domains. But the use of such current formats becomes difficult Keywords: result data management, experiment documentation, audit trail, electronic signatures, XML when multiple techniques are used or if vendor-specific data need to be stored.…”

Section: Introductionmentioning

confidence: 99%

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Documenting Laboratory Workflows Using the Analytical Information Markup Language

Schäfer

Poetz

Kramer

2004

JALA: Journal of the Association for Laboratory Automation

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T he Analytical Information Markup Language (AnIML) is a standardization effort of ASTM (formerly American Society for Testing and Materials) Subcommittee E13.15 on Analytical Data. AnIML provides an XML-based format for analytical data. It is designed specifically for spectroscopy and chromatography data, but is suitable for use with many different analytical measurement techniques. AnIML consists of a generic core structure that permits the storage of arbitrary analytical data. These include multi-dimensional data, name-value pairs, and hierarchies. The concept of technique definitions permits the formal specification of constraints for usage of the core. This way, a definition can prescribe how the data for specific measurement techniques should be captured in the data file. To address changing requirements, AnIML supports an extension concept that allows vendors or end users to specify additional data that should be stored for a technique. These extensions can also be formally documented so that they do not break compatibility with existing software. This article presents an overview of AnIML and demonstrates how AnIML can be used to record data from everyday experimental workflows in a laboratory environment. Issues related to the usage of AnIML in regulated environments are also discussed, including the use of digital signatures and audit trail functionality to ensure data integrity. ( JALA 2004;9:375-81) This project is funded (in part) by NIST's Systems Integration for Manufacturing Applications (SIMA) Program.

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Detecting and Quantifying Sunflower Oil Adulteration in Extra Virgin Olive Oils from the Eastern Mediterranean by Visible and Near-Infrared Spectroscopy

Downey

McIntyre

Davies

2002

J. Agric. Food Chem.

162

121

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One hundred and thirty-eight oil samples have been analyzed by visible and near-infrared transflectance spectroscopy. These comprised 46 pure extra virgin olive oils and the same oils adulterated with 1% (w/w) and 5% (w/w) sunflower oil. A number of multivariate mathematical approaches were investigated to detect and quantify the sunflower oil adulterant. These included hierarchical cluster analysis, soft independent modeling of class analogy (SIMCA method), and partial least squares regression (PLS). A number of wavelength ranges and data pretreatments were explored. The accuracy of these mathematical models was compared, and the most successful models were identified. Complete classification accuracy was achieved using 1st derivative spectral data in the 400-2498 nm range. Prediction of adulterant content was possible with a standard error equal to 0.8% using 1st derivative data between 1100 and 2498 nm. Spectral features and chemical literature were studied to isolate the structural basis for these models.

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A Standard For Exchange Of Infrared Spectra In Computer Readable Form

Cited by 3 publications

References 0 publications

Molecular Spectrometry Data Interchange Applications for NIST's SpectroML

Molecular Spectrometry Data Interchange Applications for NIST's SpectroML

Documenting Laboratory Workflows Using the Analytical Information Markup Language

Detecting and Quantifying Sunflower Oil Adulteration in Extra Virgin Olive Oils from the Eastern Mediterranean by Visible and Near-Infrared Spectroscopy

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