Infrared sample preparation and interpretation using a knowledge based system

Moore, Denise S.; White, Jennifer; Harbin, B. Allen

doi:10.1016/0003-2670(94)85049-6

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…A figura 3 ilustra a sistematização e o número de artigos encontrados e incluídos. A expansão da técnica é atribuída ao desenvolvimento tecnológico e de obtenção dos dados, associados aos programas para este tipo de analise como aos programas de estatística, possibilitando seu uso em grande escala na área da química analítica [9]. Durante a seleção dos artigos mediante os critérios de inclusão e exclusão, foi observado que a maioria das publicações descartadas utilizava NIRS para determinar o tamanho de partículas em produtos farmacêuticos e matérias primas.…”

Section: Methodsunclassified

Espectroscopia óptica no infravermelho próximo (NIRS)

Santos

Gomes

Araújo

et al. 2020

BJD

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Section: Methodsunclassified

Espectroscopia óptica no infravermelho próximo (NIRS)

Santos

Gomes

Araújo

et al. 2020

BJD

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“…Presently, DENDRAL is one of the most widely used expert systems that has helped in thousands of chemical structure analyses. 1 In fact, structure elucidation and data interpretation have been very active research areas in which application of expert system technology covers various spectral and chromatographic methods or a combina- [7][8][9][10][11][12][13]27 IR, [14][15][16][17][18]28 NMR, [19][20][21][22][23] UV, 24 Raman, 25 X-ray, 26 GC-MS, 29,30 MS-MS, 32 chromatography [33][34][35] DENDRAL, 9,10,27 MYCIN, 13 Meta-DENDRAL, 11,12 HIPS, 21 MAXMASS, 7,8 EXPIRS, 28 PEPTO, 23 AUTARG, 29 ESSESA, 20 PAIRS, 15,18 ESESOC, 31 RASTR, 24 Cathie 35 computer assisted syntheses synthesis, retrosynthesis, starting material selection [36][37][38][39] LHASA, [40][41][42] SYNLIB, 43 SYN-GEN,…”

Section: Expert System Applications In the Chemistry Domainmentioning

confidence: 99%

Expert Systems and Analytical Chemistry: Recent Progress in the ACexpert Project

Zhu

Stillman

1996

J. Chem. Inf. Comput. Sci.

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In this paper we discuss the application of expert system technology in the analytical chemistry domain and summarizes the progress made in the implementation of the ACexpert project. We start with a brief description of the software tools that are commonly used in construction of expert system applications as well as the inference mechanisms employed to search the knowledge base. A brief review of the achievements made world wide during the past five years in the development of expert systems for use in the chemistry domain follows. This paper particularly addresses the difficulties found in the knowledge acquisition process. An effective knowledge encoding scheme, named the knowledge domain matrix process (KDM), that has been developed in our laboratory as an knowledge encoding alternative, is described. In the final section, we describe the development of several stand-alone expert system modules based on implementation of the KDM scheme within the framework of the ACexpert project. The importance of the user interface in an expert system program is also emphasized, and graphical user interfaces featuring icons, pull-down menus, and graphical descriptions have been developed for the different modules in the ACexpert project to ensure a user-friendly and self-explanatory environment.

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“…The technique can be performed upon optically thick specimens in a noncontact, backscattering geometry, requiring minimal sample preparation compared to Fourier transform infrared spectroscopy. 1 In the biomedical sciences, Raman spectroscopy has been used to study various diseases and cancers in many different tissues. [2][3][4][5] Such work typically uses near-infrared (NIR) excitation in the 780-840 nm range in order to increase the Raman/autofluorescence ratio.…”

Section: Introductionmentioning

confidence: 99%

Calibration Technique for Suppressing Residual Etalon Artifacts in Slit-Averaged Raman Spectroscopy

2021

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Back-illuminated charged-coupled device (BI-CCD) arrays increase quantum efficiency but also amplify etaloning, a multiplicative, wavelength-dependent fixed-pattern effect. When spectral data from hundreds of BI-CCD rows are combined, the averaged spectrum will generally appear etalon-free. This can mask substantial etaloning at the row level, even if the BI-CCD has been treated to suppress the effect. This paper compares two methods of etalon correction, one with simple averaging and one with row-by-row calibration using a fluorescence standard. Two BI-CCD arrays, both roughened by the supplier to reduce etaloning, were used to acquire Raman spectra of murine bone specimens. For one array, etaloning was the dominant source of noise under the exposure conditions chosen, even for the averaged spectrum across all rows; near-infrared-excited Raman peaks were noticeably affected. In this case, row-by-row calibration improved the spectral quality of the average spectrum. The other CCD’s performance was shot-noise limited and therefore received no benefit from the extra calibration. The different results highlight the importance of checking for and correcting row-level fixed pattern when measuring weak Raman signals in the presence of a large fluorescence background.

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Infrared sample preparation and interpretation using a knowledge based system

Cited by 10 publications

References 17 publications

Espectroscopia óptica no infravermelho próximo (NIRS)

Espectroscopia óptica no infravermelho próximo (NIRS)

Expert Systems and Analytical Chemistry: Recent Progress in the ACexpert Project

Calibration Technique for Suppressing Residual Etalon Artifacts in Slit-Averaged Raman Spectroscopy

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