Quantitative structure–retention relationships for mycotoxins and fungal metabolites in LC‐MS/MS

Ji, Caihong; Li, Yanwei; Su, Li; Zhang, Xiaoyun; Chen, Xingguo

doi:10.1002/jssc.200900441

Cited by 14 publications

(13 citation statements)

References 29 publications

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“…Many different approaches to t R prediction exist and range from the simple (Kern et al, 2009;Nurmi et al, 2012) to the complex (Goryński et al, 2013;Ji et al, 2009;Kaliszan et al, 2003;Ukić et al, 2014a). For example, logK ow models can be derived using freely accessible data from chemical databases such as ChemSpider and PubChem, as well as freeware prediction sources such as VCCLABS.…”

Section: Introductionmentioning

confidence: 99%

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Bade

Bijlsma

Miller

et al. 2015

Science of The Total Environment

101

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Abstract:The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (t R ) and mass spectral comparisons. Chromatographic t R prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for t R prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound t R dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85 % of all compounds to within 2 minutes of their measured t R for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95 % prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.

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Section: Introductionmentioning

confidence: 99%

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Bade

Bijlsma

Miller

et al. 2015

Science of The Total Environment

101

View full text Add to dashboard Cite

show abstract

“…[20][21][22][23][24][25][26][27][28] The application of these techniques usually requires variable selection for building well-fitted models. [20][21][22][23][24][25][26][27][28] The application of these techniques usually requires variable selection for building well-fitted models.…”

Section: Introductionmentioning

confidence: 99%

Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression

et al. 2011

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The chemical composition of the volatile fraction obtained by head-space solid phase microextraction (HS-SPME), single drop microextraction (SDME) and the essential oil obtained by cold-press from the peels of C. sinensis cv. valencia were analyzed employing gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35 %, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and α-pinene (0 %, 6.65 %, 1.4 %) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative structure-retention relationship (QSRR) study for the prediction of retention indices (RI) of the compounds was developed by application of structural descriptors and the multiple linear regression (MLR) method. Principal components analysis was used to select the training set. A simple model with low standard errors and high correlation coefficients was obtained. The results illustrated that linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSRR model for prediction of the retention indices of different compounds. This model, with high statistical significance (R2 train = 0.983, R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be used adequately for the prediction and description of the retention indices of the volatile compounds

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“…Regarding the buffer concentration they showed an increase in relative retention by a factor of 2-4 when going from 10 to 100 mM acetate. Ji et al (2009) worked out a different approach. They used a quantitative structure-retention relationship (QSRR) model to describe the retention times of mycotoxins and fungal metabolites.…”

Section: Liquid Chromatographic Aspects Of Multi-mycotoxin Methodsmentioning

confidence: 99%

Mass spectrometry in multi-mycotoxin and fungal spore analysis

Spanjer

2011

Determining Mycotoxins and Mycotoxigenic Fungi in Food and Feed

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Quantitative structure–retention relationships for mycotoxins and fungal metabolites in LC‐MS/MS

Cited by 14 publications

References 29 publications

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression

Mass spectrometry in multi-mycotoxin and fungal spore analysis

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