Characterization of Organic Compounds

Peck, Robert L.

doi:10.1021/ac60037a027

Cited by 4 publications

(5 citation statements)

References 37 publications

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“…Functional groups describe and define the physical and chemical properties of compounds. , Identification of many organic groups is accomplished via FTIR due to the associated unique vibrational frequencies. , Large numbers of spectra are time consuming to analyze and require expert chemist analysis to determine present composition. This limits the application of FTIR spectral techniques as a sampling method for functional group elucidation.…”

Section: Introductionmentioning

confidence: 99%

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

et al. 2021

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Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method. File list (2) download file view on ChemRxiv ML functional group ID in FTIR spectra.docx (545.84 KiB) download file view on ChemRxiv ML functional group ID in FTIR spectra.pdf (645.08 KiB) Functional group identification for FTIR spectra using image-based machine learning models

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Section: Introductionmentioning

confidence: 99%

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

et al. 2021

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“…Infrared spectroscopy has continued to find wide application in characterization W'ork (64,65). Most organic compounds yield infrared spectra which provide unique reference data for comparative purposes.…”

Section: Physical Propertiesmentioning

confidence: 99%

“…The development by Craig (16,64) of a machine for carrying out many-plate countercurrent distributions, and the recent introduction of a convenient glass apparatus for larger numbers of separation stages (19,65) are now' widely recognized. These methods have been introduced in many laboratories and have proved their W'orth.…”

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“…The importance of caution in the interpretation of paper strip chromatograms for identification purposes (64,65) is again indicated by observations of Carter, Hearn, and Taylor (11), who isolated from streptothricin a substance (Compound A) isomeric with lysine. The tw'o compounds behaved in substantially identical fashion in paper strip chromatograms, but the isomeric compound w'as shown to be a new diamino-Ce acid.…”

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Characterization of Organic Compounds

Peck¹

1952

Anal. Chem.

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HE accepted requirements for full characterization of an T organic compound include establishment of purity of sample, determination, of physical properties, determination of elementary composition, functional groups, and empirical formula, and elucidation of structural formula and spatial relationships. It is evident that identification of a known molecule will involve less extensive investigation than full characterization of a complex new compound. The characterization of simple or complex compounds may be accomplished by any of a Tide variety of techniques and methods, some of which are more effective than others. The choice of criteria and procedural steps is often dependent on availability of special equipment. Infrared absorption spectra and x-ray diffraction patterns alone, for example, are sufficient to identify many known substances; identification carried out by chemical methods, while sometimes more timeconsuming, is equally effective.A consequence of the recent increase in number and scope of chemical studies of naturally occurring organic substances is seen in the widely held view that thorough training in principles of characterization is of fundamental importance for students majoring in organic chemistry. A comprehensive treatise on detailed methods and techniques of characterization may some day be available. I t is probably true, however, that after successfully completing the characterization of a complex, new organic molecule the chemist enjoys a thrill of satisfaction derived in large part from the realization that the sequence of steps used was not given to him in a single reference work but was the result of his own experience, insight, and skill (and perhaps some luck).The present review of recent contributions in this realm of characterization is concerned with methods for determining purity of samples, procedures for establishing physical properties, and chemical methods of characterization. PURITYIt is essential for clear-cut determination of properties and for interpretable degradative work that the samples employed be substantially pure. Procedures which are now found most useful for establishment of degree of purity of samples were reviewed recently (64, 65). No fundamentally new methods of these types have been introduced during the past year, but there have appeared a number of papers in which extensions or new applications of method are described.The development by Craig (16, 64) of a machine for carrying out many-plate countercurrent distributions, and the recent introduction of a convenient glass apparatus for larger numbers of separation stages (19, 65) are now widely recognized. These methods have been introduced in many laboratories and have proved their worth. Continued studies by Craig and his coworkers have culminated in the development of an automatic control device for use with the glass apparatus (17). A strictly discontinuous extraction train containing 220 glass equilibration cells may now be operated automatically, so that 800 equilibration stages (about 150,000 ext...

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“…Developments in micro and semimicro techniques, applications of organic reactions to small scale operation, introduction of new procedures for evaluation of purity, adaptation of instruments for small scale physical determinations, and the ingenuity of analytical chemists and those engaged in characterization work are all in part responsible for the present scope of the field (98). Insight into the current trend of thinking is provided by a recent article (40) which surveys the course of analytical chemistry over the past quarter of a century.…”

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Characterization of Organic Compounds

Peck¹

1951

Anal. Chem.

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of pure organic compounds may be achieved today with rapidity and certainty. The continued introduction and development of new reactions and techniques have so extended the scope of the organic chemist that only the most complex substances are at present beyond the possibility of direct structural elucidation.The chemical and physical properties which constitute the criteria for recognition, identification, and proof of structure of organic compounds may be obtained at the present time with relative rapidity and with relatively small quantities of sample.

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Characterization of Organic Compounds

Cited by 4 publications

References 37 publications

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Characterization of Organic Compounds

Characterization of Organic Compounds

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