Spectroscopy and Analytical Chemistry File Formats Reference

Spectroscopy and Analytical Chemistry File Formats Reference

This reference covers file formats used in various spectroscopic techniques and analytical chemistry instrumentation.

NMR Spectroscopy

.fid - NMR Free Induction Decay

Description: Raw time-domain NMR data from Bruker, Agilent, JEOL Typical Data: Complex time-domain signal Use Cases: NMR spectroscopy, structure elucidation Python Libraries:

• nmrglue: nmrglue.bruker.read_fid('fid') or nmrglue.varian.read_fid('fid')
• nmrstarlib: NMR data handling EDA Approach:
• Time-domain signal decay
• Sampling rate and acquisition time
• Number of data points
• Signal-to-noise ratio estimation
• Baseline drift assessment
• Digital filter effects
• Acquisition parameter validation
• Apodization function selection

.ft / .ft1 / .ft2 - NMR Frequency Domain

Description: Fourier-transformed NMR spectrum Typical Data: Processed frequency-domain data Use Cases: NMR analysis, peak integration Python Libraries:

• nmrglue: Frequency domain reading
• Custom processing pipelines EDA Approach:
• Peak picking and integration
• Chemical shift range
• Baseline correction quality
• Phase correction assessment
• Reference peak identification
• Spectral resolution
• Artifacts detection
• Multiplicity analysis

.1r / .2rr - Bruker NMR Processed Data

Description: Bruker processed spectrum (real part) Typical Data: 1D or 2D processed NMR spectra Use Cases: NMR data analysis with Bruker software Python Libraries:

• nmrglue: Bruker format support EDA Approach:
• Processing parameters review
• Window function effects
• Zero-filling assessment
• Linear prediction validation
• Spectral artifacts

.dx - NMR JCAMP-DX

Description: JCAMP-DX format for NMR Typical Data: Standardized NMR spectrum Use Cases: Data exchange between software Python Libraries:

• jcamp: JCAMP reader
• nmrglue: Can import JCAMP EDA Approach:
• Format compliance
• Metadata completeness
• Peak table validation
• Integration values
• Compound identification info

.mnova - Mnova Format

Description: Mestrelab Research Mnova format Typical Data: NMR data with processing info Use Cases: Mnova software workflows Python Libraries:

• nmrglue: Limited Mnova support
• Conversion tools to standard formats EDA Approach:
• Multi-spectrum handling
• Processing pipeline review
• Quantification data
• Structure assignment

Mass Spectrometry

.mzML - Mass Spectrometry Markup Language

Description: Standard XML-based MS format Typical Data: MS spectra, chromatograms, metadata Use Cases: Proteomics, metabolomics, lipidomics Python Libraries:

• pymzml: pymzml.run.Reader('file.mzML')
• pyteomics.mzml: pyteomics.mzml.read('file.mzML')
• MSFileReader: Various wrappers EDA Approach:
• Scan count and MS level distribution
• Retention time range and TIC
• m/z range and resolution
• Precursor ion selection
• Fragmentation patterns
• Instrument configuration
• Quality control metrics
• Data completeness

.mzXML - Mass Spectrometry XML

Description: Legacy XML MS format Typical Data: Mass spectra and chromatograms Use Cases: Proteomics workflows (older) Python Libraries:

• pyteomics.mzxml
• pymzml: Can read mzXML EDA Approach:
• Similar to mzML
• Version compatibility
• Conversion quality assessment

.mzData - mzData Format

Description: Legacy PSI MS format Typical Data: Mass spectrometry data Use Cases: Legacy data archives Python Libraries:

• pyteomics: Limited support
• Conversion to mzML recommended EDA Approach:
• Format conversion validation
• Data completeness
• Metadata extraction

.raw - Vendor Raw Files (Thermo, Agilent, Bruker)

Description: Proprietary instrument data Typical Data: Raw mass spectra and metadata Use Cases: Direct instrument output Python Libraries:

• pymsfilereader: Thermo RAW files
• ThermoRawFileParser: CLI wrapper
• Vendor-specific APIs EDA Approach:
• Method parameter extraction
• Instrument performance metrics
• Calibration status
• Scan function analysis
• MS/MS quality metrics
• Dynamic exclusion evaluation

.d - Agilent Data Directory

Description: Agilent MS data folder Typical Data: LC-MS, GC-MS with methods Use Cases: Agilent MassHunter workflows Python Libraries:

• Community parsers
• Chemstation integration EDA Approach:
• Directory structure validation
• Method parameters
• Calibration curves
• Sequence metadata
• Signal quality metrics

.wiff - AB SCIEX Data

Description: AB SCIEX/SCIEX instrument format Typical Data: Mass spectrometry data Use Cases: SCIEX instrument workflows Python Libraries:

• Vendor SDKs (limited Python support)
• Conversion tools EDA Approach:
• Experiment type identification
• Scan properties
• Quantitation data
• Multi-experiment structure

.mgf - Mascot Generic Format

Description: Peak list format for MS/MS Typical Data: Precursor and fragment masses Use Cases: Peptide identification, database searches Python Libraries:

• pyteomics.mgf: pyteomics.mgf.read('file.mgf')
• pyopenms: MGF support EDA Approach:
• Spectrum count
• Charge state distribution
• Precursor m/z and intensity
• Fragment peak count
• Mass accuracy
• Title and metadata parsing

.pkl - Peak List (Binary)

Description: Binary peak list format Typical Data: Serialized MS/MS spectra Use Cases: Software-specific storage Python Libraries:

• pickle: Standard deserialization
• pyteomics: PKL support EDA Approach:
• Data structure inspection
• Conversion to standard formats
• Metadata preservation

.ms1 / .ms2 - MS1/MS2 Formats

Description: Simple text format for MS data Typical Data: MS1 and MS2 scans Use Cases: Database searching, proteomics Python Libraries:

• pyteomics.ms1 and ms2
• Simple text parsing EDA Approach:
• Scan count by level
• Retention time series
• Charge state analysis
• m/z range coverage

.pepXML - Peptide XML

Description: TPP peptide identification format Typical Data: Peptide-spectrum matches Use Cases: Proteomics search results Python Libraries:

• pyteomics.pepxml EDA Approach:
• Search result statistics
• Score distribution
• Modification analysis
• FDR assessment
• Enzyme specificity

.protXML - Protein XML

Description: TPP protein inference format Typical Data: Protein identifications Use Cases: Proteomics protein-level results Python Libraries:

• pyteomics.protxml EDA Approach:
• Protein group analysis
• Coverage statistics
• Confidence scoring
• Parsimony analysis

.msp - NIST MS Search Format

Description: NIST spectral library format Typical Data: Reference mass spectra Use Cases: Spectral library searching Python Libraries:

• matchms: Spectral library handling
• Custom parsers EDA Approach:
• Library size and coverage
• Metadata completeness
• Peak count statistics
• Compound annotation quality

Infrared and Raman Spectroscopy

.spc - Galactic SPC

Description: Thermo Galactic spectroscopy format Typical Data: IR, Raman, UV-Vis spectra Use Cases: Various spectroscopy instruments Python Libraries:

• spc: spc.File('file.spc')
• specio: Multi-format reader EDA Approach:
• Wavenumber/wavelength range
• Data point density
• Multi-spectrum handling
• Baseline characteristics
• Peak identification
• Absorbance/transmittance mode
• Instrument information

.spa - Thermo Nicolet

Description: Thermo Fisher FTIR format Typical Data: FTIR spectra Use Cases: OMNIC software data Python Libraries:

• Custom binary parsers
• Conversion to JCAMP or SPC EDA Approach:
• Interferogram vs spectrum
• Background spectrum validation
• Atmospheric compensation
• Resolution and scan number
• Sample information

.0 - Bruker OPUS

Description: Bruker OPUS FTIR format (numbered files) Typical Data: FTIR spectra and metadata Use Cases: Bruker FTIR instruments Python Libraries:

• brukeropusreader: OPUS format parser
• specio: OPUS support EDA Approach:
• Multiple block types (AB, ScSm, etc.)
• Sample and reference spectra
• Instrument parameters
• Optical path configuration
• Beam splitter and detector info

.dpt - Data Point Table

Description: Simple XY data format Typical Data: Generic spectroscopic data Use Cases: Renishaw Raman, generic exports Python Libraries:

• pandas: CSV-like reading
• Text parsing EDA Approach:
• X-axis type (wavelength, wavenumber, Raman shift)
• Y-axis units (intensity, absorbance, etc.)
• Data point spacing
• Header information
• Multi-column data handling

.wdf - Renishaw Raman

Description: Renishaw WiRE data format Typical Data: Raman spectra and maps Use Cases: Renishaw Raman microscopy Python Libraries:

• renishawWiRE: WDF reader
• Custom parsers for WDF format EDA Approach:
• Spectral vs mapping data
• Laser wavelength
• Accumulation and exposure time
• Spatial coordinates (mapping)
• Z-scan data
• Baseline and cosmic ray correction

.txt (Spectroscopy)

Description: Generic text export from instruments Typical Data: Wavelength/wavenumber and intensity Use Cases: Universal data exchange Python Libraries:

• pandas: Text file reading
• numpy: Simple array loading EDA Approach:
• Delimiter and format detection
• Header parsing
• Units identification
• Multiple spectrum handling
• Metadata extraction from comments

UV-Visible Spectroscopy

.asd / .asc - ASD Binary/ASCII

Description: ASD FieldSpec spectroradiometer Typical Data: Hyperspectral UV-Vis-NIR data Use Cases: Remote sensing, reflectance spectroscopy Python Libraries:

• spectral.io.asd: ASD format support
• Custom parsers EDA Approach:
• Wavelength range (UV to NIR)
• Reference spectrum validation
• Dark current correction
• Integration time
• GPS metadata (if present)
• Reflectance vs radiance

.sp - Perkin Elmer

Description: Perkin Elmer UV/Vis format Typical Data: UV-Vis spectrophotometer data Use Cases: PE Lambda instruments Python Libraries:

• Custom parsers
• Conversion to standard formats EDA Approach:
• Scan parameters
• Baseline correction
• Multi-wavelength scans
• Time-based measurements
• Sample/reference handling

.csv (Spectroscopy)

Description: CSV export from UV-Vis instruments Typical Data: Wavelength and absorbance/transmittance Use Cases: Universal format for UV-Vis data Python Libraries:

• pandas: Native CSV support EDA Approach:
• Lambda max identification
• Beer's law compliance
• Baseline offset
• Path length correction
• Concentration calculations

X-ray and Diffraction

.cif - Crystallographic Information File

Description: Crystal structure and diffraction data Typical Data: Unit cell, atomic positions, structure factors Use Cases: Crystallography, materials science Python Libraries:

• gemmi: gemmi.cif.read_file('file.cif')
• PyCifRW: CIF reading/writing
• pymatgen: Materials structure analysis EDA Approach:
• Crystal system and space group
• Unit cell parameters
• Atomic positions and occupancy
• Thermal parameters
• R-factors and refinement quality
• Completeness and redundancy
• Structure validation

.hkl - Reflection Data

Description: Miller indices and intensities Typical Data: Integrated diffraction intensities Use Cases: Crystallographic refinement Python Libraries:

• Custom parsers (format dependent)
• Crystallography packages (CCP4, etc.) EDA Approach:
• Resolution range
• Completeness by shell
• I/sigma distribution
• Systematic absences
• Twinning detection
• Wilson plot

.mtz - MTZ Format (CCP4)

Description: Binary crystallographic data Typical Data: Reflections, phases, structure factors Use Cases: Macromolecular crystallography Python Libraries:

• gemmi: MTZ support
• cctbx: Comprehensive crystallography EDA Approach:
• Column types and data
• Resolution limits
• R-factors (Rwork, Rfree)
• Phase probability distribution
• Map coefficients
• Batch information

.xy / .xye - Powder Diffraction

Description: 2-theta vs intensity data Typical Data: Powder X-ray diffraction patterns Use Cases: Phase identification, Rietveld refinement Python Libraries:

• pandas: Simple XY reading
• pymatgen: XRD pattern analysis EDA Approach:
• 2-theta range
• Peak positions and intensities
• Background modeling
• Peak width analysis (strain/size)
• Phase identification via matching
• Preferred orientation effects

.raw (XRD)

Description: Vendor-specific XRD raw data Typical Data: XRD patterns with metadata Use Cases: Bruker, PANalytical, Rigaku instruments Python Libraries:

• Vendor-specific parsers
• Conversion tools EDA Approach:
• Scan parameters (step size, time)
• Sample alignment
• Incident beam setup
• Detector configuration
• Background scan validation

.gsa / .gsas - GSAS Format

Description: General Structure Analysis System Typical Data: Powder diffraction for Rietveld Use Cases: Rietveld refinement Python Libraries:

• GSAS-II Python interface
• Custom parsers EDA Approach:
• Histogram data
• Instrument parameters
• Phase information
• Refinement constraints
• Profile function parameters

Electron Spectroscopy

.vms - VG Scienta

Description: VG Scienta spectrometer format Typical Data: XPS, UPS, ARPES spectra Use Cases: Photoelectron spectroscopy Python Libraries:

• Custom parsers for VMS
• specio: Multi-format support EDA Approach:
• Binding energy calibration
• Pass energy and resolution
• Photoelectron line identification
• Satellite peak analysis
• Background subtraction quality
• Fermi edge position

.spe - WinSpec/SPE Format

Description: Princeton Instruments/Roper Scientific Typical Data: CCD spectra, Raman, PL Use Cases: Spectroscopy with CCD detectors Python Libraries:

• spe2py: SPE file reader
• spe_loader: Alternative parser EDA Approach:
• CCD frame analysis
• Wavelength calibration
• Dark frame subtraction
• Cosmic ray identification
• Readout noise
• Accumulation statistics

.pxt - Princeton PTI

Description: Photon Technology International Typical Data: Fluorescence, phosphorescence spectra Use Cases: Fluorescence spectroscopy Python Libraries:

• Custom parsers
• Text-based format variants EDA Approach:
• Excitation and emission spectra
• Quantum yield calculations
• Time-resolved measurements
• Temperature-dependent data
• Correction factors applied

.dat (Spectroscopy Generic)

Description: Generic binary or text spectroscopy data Typical Data: Various spectroscopic measurements Use Cases: Many instruments use .dat extension Python Libraries:

• Format-specific identification needed
• numpy, pandas for known formats EDA Approach:
• Format detection (binary vs text)
• Header identification
• Data structure inference
• Units and axis labels
• Instrument signature detection

Chromatography

.chrom - Chromatogram Data

Description: Generic chromatography format Typical Data: Retention time vs signal Use Cases: HPLC, GC, LC-MS Python Libraries:

• Vendor-specific parsers
• pandas for text exports EDA Approach:
• Retention time range
• Peak detection and integration
• Baseline drift
• Resolution between peaks
• Signal-to-noise ratio
• Tailing factor

.ch - ChemStation

Description: Agilent ChemStation format Typical Data: Chromatograms and method parameters Use Cases: Agilent HPLC and GC systems Python Libraries:

• agilent-chemstation: Community tools
• Binary format parsers EDA Approach:
• Method validation
• Integration parameters
• Calibration curve
• Sample sequence information
• Instrument status

.arw - Empower (Waters)

Description: Waters Empower format Typical Data: UPLC/HPLC chromatograms Use Cases: Waters instrument data Python Libraries:

• Vendor tools (limited Python access)
• Database extraction tools EDA Approach:
• Audit trail information
• Processing methods
• Compound identification
• Quantitation results
• System suitability tests

.lcd - Shimadzu LabSolutions

Description: Shimadzu chromatography format Typical Data: GC/HPLC data Use Cases: Shimadzu instruments Python Libraries:

• Vendor-specific parsers EDA Approach:
• Method parameters
• Peak purity analysis
• Spectral data (if PDA)
• Quantitative results

Other Analytical Techniques

.dta - DSC/TGA Data

Description: Thermal analysis data (TA Instruments) Typical Data: Temperature vs heat flow or mass Use Cases: Differential scanning calorimetry, thermogravimetry Python Libraries:

• Custom parsers for TA formats
• pandas for exported data EDA Approach:
• Transition temperature identification
• Enthalpy calculations
• Mass loss steps
• Heating rate effects
• Baseline determination
• Purity assessment

.run - ICP-MS/ICP-OES

Description: Elemental analysis data Typical Data: Element concentrations or counts Use Cases: Inductively coupled plasma MS/OES Python Libraries:

• Vendor-specific tools
• Custom parsers EDA Approach:
• Element detection and quantitation
• Internal standard performance
• Spike recovery
• Dilution factor corrections
• Isotope ratios
• LOD/LOQ calculations

.exp - Electrochemistry Data

Description: Electrochemical experiment data Typical Data: Potential vs current or charge Use Cases: Cyclic voltammetry, chronoamperometry Python Libraries:

• Custom parsers per instrument (CHI, Gamry, etc.)
• galvani: Biologic EC-Lab files EDA Approach:
• Redox peak identification
• Peak potential and current
• Scan rate effects
• Electron transfer kinetics
• Background subtraction
• Capacitance calculations