Automatic Generation of Knowledge Base from Infrared Spectral Database for Substructure Recognition

Dębska, B.; Guzowska-Świder, † and Barbara; Cabrol‐Bass, Daniel

doi:10.1021/ci990271x

Cited by 11 publications

(2 citation statements)

References 61 publications

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“…Spectral database systems (SDBSs) have been considered as effective tools for the management of spectral information. [1][2][3][4] It has also been found that unknown samples can be recognised and analysed by means of searching a special SDBS. 5,6 Different from other database systems, SDBSs have to be supported by more algorithms, in which spectral matching algorithms (SMAs) and spectral feature extraction algorithms are the most important ones.…”

Section: Introductionmentioning

confidence: 99%

Algorithms Research of near Infrared Spectral Database of Apples

Zhou

Zhang

2015

Journal of Near Infrared Spectroscopy

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A near infrared spectral database system of apples was studied. A new spectral matching algorithm based on Jaccard similarity coefficient (SMA-JSC) is proposed for higher spectral matching accuracy. A total of 840 apples of seven different varieties were used to evaluate the performance of this method. At the same time, a comparison was done among the existing spectral matching algorithms with spectral peak information (SMA-P) and the existing spectral matching algorithms with full spectra (SMA-FS). The highest accuracies of these existing SMA-P and SMA-FS were 48.9% and 72.57%, both of which were quite low mainly because of noise. For SMA-JSC, the firstorder derivative was calculated and transformed into binary values (with only 0 or 1) to eliminate the influence of noise. The accuracy of our proposed algorithm was 94.1% for the calibration samples and 94.3% for the validation samples. Also, using linear discriminate analysis (LDA), we compared the existing SMA-P, the existing SMA-FS and our new proposed algorithm. The best result obtained using LDA was 88.0% (with the raw spectra) which was larger than the accuracies of the existing SMA-P and SMA-FS but less than the accuracy of our proposed SMA-JSC. In addition, several important spectral data parameters were optimised for automatic spectral peak detection without manually setting parameters (optimised parameters: spectral resolution was 32 cm −1 , threshold of peak width was 29 spectral data points and threshold of peak shape index was 0.005). A selective spectral smoothing algorithm is also proposed to protect peak bands. With these methods, spectral peaks could be detected 100% correctly without manually setting parameters.

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Section: Introductionmentioning

confidence: 99%

Algorithms Research of near Infrared Spectral Database of Apples

Zhou

Zhang

2015

Journal of Near Infrared Spectroscopy

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“…AI currently encompasses a huge variety of subfields, from general-purpose areas such as perception and logical reasoning, to specific tasks such as constructing teams of robots able to play soccer [1], managing information for bankruptcy support systems [2], investigating drug crimes [3], coping with air traffic [4], and recognizing molecular substructures from infrared spectra [5]. A useful classification of AI approaches is: (a) systems that reason (and/or act) like humans; and (b) systems that reason (and/or act) rationally.…”

Section: Introductionmentioning

confidence: 99%

AI and SAR approaches for predicting chemical carcinogenicity: Survey and status report

Benigni

2002

SAR and QSAR in Environmental Research

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A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

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Correlations between Chemical Structure and Infrared Spectra

Selzer¹

2003

Handbook of Chemoinformatics

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Automatic Generation of Knowledge Base from Infrared Spectral Database for Substructure Recognition

Cited by 11 publications

References 61 publications

Algorithms Research of near Infrared Spectral Database of Apples

Algorithms Research of near Infrared Spectral Database of Apples

AI and SAR approaches for predicting chemical carcinogenicity: Survey and status report

Correlations between Chemical Structure and Infrared Spectra

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