Dell’Anna et al., 2009 - Google Patents
Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learningDell’Anna et al., 2009
View PDF- Document ID
- 17481728272355854127
- Author
- Dell’Anna R
- Lazzeri P
- Frisanco M
- Monti F
- Malvezzi Campeggi F
- Gottardini E
- Bersani M
- Publication year
- Publication venue
- Analytical and bioanalytical chemistry
External Links
Snippet
The discrimination and classification of allergy-relevant pollen was studied for the first time by mid-infrared Fourier transform infrared (FT-IR) microspectroscopy together with unsupervised and supervised multivariate statistical methods. Pollen samples of 11 different …
- 238000005033 Fourier transform infrared spectroscopy 0 title abstract description 31
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light using near infra-red light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N2021/653—Coherent methods [CARS]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/02—Devices for withdrawing samples
- G01N1/22—Devices for withdrawing samples in the gaseous state
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/02—Details
- G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
- G01J3/024—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using means for illuminating a slit efficiently (e.g. entrance slit of a spectrometer or entrance face of fiber)
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Dell’Anna et al. | Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning | |
Holt et al. | Principles and methods for automated palynology | |
Feilhauer et al. | Multi-method ensemble selection of spectral bands related to leaf biochemistry | |
Li et al. | Estimation of area-and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis | |
Dong et al. | Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics? | |
Zimmerman et al. | Analysis of allergenic pollen by FTIR microspectroscopy | |
García-Timermans et al. | Label-free Raman characterization of bacteria calls for standardized procedures | |
Hespanhol et al. | Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling | |
Song et al. | Chlorophyll content estimation based on cascade spectral optimizations of interval and wavelength characteristics | |
Acosta et al. | Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value | |
Raj et al. | Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra | |
Xu et al. | Monitoring ratio of carbon to nitrogen (C/N) in wheat and barley leaves by using spectral slope features with branch-and-bound algorithm | |
Äijälä et al. | Resolving anthropogenic aerosol pollution types–deconvolution and exploratory classification of pollution events | |
Asri et al. | Discrimination and source correspondence of black gel inks using Raman spectroscopy and chemometric analysis with UMAP and PLS-DA | |
Melucci et al. | Rapid in situ repeatable analysis of drugs in powder form using reflectance near‐infrared spectroscopy and multivariate calibration | |
Cooman et al. | Implementing machine learning for the identification and classification of compound and mixtures in portable Raman instruments | |
Sha et al. | Evaluation of sample pretreatment method for geographic authentication of rice using Raman spectroscopy | |
Yiyu et al. | Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs | |
John et al. | Overview of cocaine identification by vibrational spectroscopy and chemometrics | |
Shi et al. | Classification of rice varieties using sIMCA applied to NIR spectroscopic data | |
Asri et al. | Raman spectroscopy with self-organizing feature maps and partial least squares discriminant analysis for discrimination and source correspondence of red gel ink pens | |
Noshad et al. | Volatilomic with chemometrics: a toward authentication approach for food authenticity control | |
Zhai et al. | Application of visible/near-infrared spectroscopy and hyperspectral imaging with machine learning for high-throughput plant heavy metal stress phenotyping: A review | |
Qin et al. | Non-destructive recognition of copy paper based on advanced spectral fusion and feature optimization | |
Ryckewaert et al. | Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress |