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reversed-phase analytical methods, Talanta 147 (2016) 261-270.
11. D.R. Baker, L. Fages, E. Capodanno, N. Loftus, Expanding capabilities in multi-residue pesticide analysis using the LCMS-8060, Shimadzu Corporation, Application News Document No. C136, 2016.
12. X. Li, Y. Wang, Q. Zhou, Y. Yu, L. Chen, J. Zheng, A sensitive method for digoxin determination using formate- adduct ion based on the effect of ionization enhancement in liquid chromatograph-mass spectrometer, Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 978-979 (2015) 138-144.
Figure 6. PCA of descriptor data for all 653 compounds showing the score plots for principal component 1 and 2 (a) and the associated loading plots (b).
Zhao, R.D. Smith, Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses, Analytical Chemistry 75(5) (2003) 1039-1048.
6. R. Bade, L. Bijlsma, T.H. Miller, L.P. Barron, J.V. Sancho, F. Hernández, 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, Science of the Total Environment 538 (2015) 934-941.
7. T.H. Miller, A. Musenga, D.A. Cowan, L.P. Barron, Prediction of chromatographic retention time in high-resolution anti- doping screening data using artificial neural networks, Analytical Chemistry 85(21) (2013) 10330-10337.
8. C.B. Mollerup, M. Mardal, P.W. Dalsgaard, K. Linnet, L.P. Barron, Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry, Journal of Chromatography A 1542 (2018) 82-88.
9. R. Aalizadeh, M.C. Nika, N.S. Thomaidis, Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants, Journal of Hazardous Materials 363 (2019) 277-285.
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15. T.H. Miller, M.D. Gallidabino, J.R. MacRae, S.F. Owen, N.R. Bury, L.P. Barron, Prediction of bioconcentration factors in fish and invertebrates using machine learning, Science of the Total Environment 648 (2019) 80-89.
First High Flow Rate, In-line Degasser for Organic Solvents Announced
Biotech in co-operation with IDEX H&S are proud to present the world’s first in-line, membrane degasser ready to use with aggressive media and organic solvents, while maintaining flow-rates up to 150ml/min and above.
No troubles with bubbles anymore - the degasser will improve the performance of the fluidic pump as well as stabilising the detector baseline.
The new DEGASi Prep+ secures your analysis and ensures you will always get a perfect result whether you are working with preparative chromatography, dispensing systems or other high through-put systems.
Available configurations: Standalone (DEGASi Prep+), OEM open frame and 1-4 channels.
More information online:
ilmt.co/PL/jpLO
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