E.P.P.A. Derks

626 total citations
21 papers, 489 citations indexed

About

E.P.P.A. Derks is a scholar working on Analytical Chemistry, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, E.P.P.A. Derks has authored 21 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Analytical Chemistry, 8 papers in Control and Systems Engineering and 6 papers in Artificial Intelligence. Recurrent topics in E.P.P.A. Derks's work include Spectroscopy and Chemometric Analyses (9 papers), Neural Networks and Applications (6 papers) and Fault Detection and Control Systems (6 papers). E.P.P.A. Derks is often cited by papers focused on Spectroscopy and Chemometric Analyses (9 papers), Neural Networks and Applications (6 papers) and Fault Detection and Control Systems (6 papers). E.P.P.A. Derks collaborates with scholars based in Netherlands, Spain and Switzerland. E.P.P.A. Derks's co-authors include L.M.C. Buydens, Age K. Smilde, M.S. Sánchez, Carina M. Rubingh, I. Bobeldijk, Sunil Kochhar, Sabina Bijlsma, Elwin Verheij, L.M.C. Buydens and W.J. Melssen and has published in prestigious journals such as Analytical Chemistry, European Respiratory Journal and Analytica Chimica Acta.

In The Last Decade

E.P.P.A. Derks

21 papers receiving 467 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
E.P.P.A. Derks Netherlands	12	180	167	120	109	52	21	489
Karin Kjeldahl Denmark	5	216 1.2×	156 0.9×	95 0.8×	41 0.4×	56 1.1×	6	507
Qian Guo China	11	205 1.1×	143 0.9×	103 0.9×	73 0.7×	66 1.3×	31	538
Wen Wu United Kingdom	10	128 0.7×	165 1.0×	91 0.8×	85 0.8×	29 0.6×	16	433
Marianne Dyrby Denmark	6	217 1.2×	132 0.8×	66 0.6×	48 0.4×	99 1.9×	6	572
Alejandro García‐Reiriz Argentina	10	231 1.3×	113 0.7×	97 0.8×	118 1.1×	60 1.2×	17	459
Tong Wu China	18	290 1.6×	271 1.6×	189 1.6×	60 0.6×	149 2.9×	49	820
W. Penninckx Belgium	9	296 1.6×	50 0.3×	61 0.5×	80 0.7×	74 1.4×	16	482
Bernadette Govaerts Belgium	21	206 1.1×	254 1.5×	126 1.1×	137 1.3×	20 0.4×	59	900
C. Boucon Netherlands	12	161 0.9×	83 0.5×	110 0.9×	57 0.5×	17 0.3×	15	463
Nelson Lee Afanador United States	8	169 0.9×	98 0.6×	65 0.5×	24 0.2×	45 0.9×	14	396

Countries citing papers authored by E.P.P.A. Derks

Since Specialization

Citations

This map shows the geographic impact of E.P.P.A. Derks's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by E.P.P.A. Derks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E.P.P.A. Derks more than expected).

Fields of papers citing papers by E.P.P.A. Derks

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E.P.P.A. Derks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by E.P.P.A. Derks. The network helps show where E.P.P.A. Derks may publish in the future.

Co-authorship network of co-authors of E.P.P.A. Derks

This figure shows the co-authorship network connecting the top 25 collaborators of E.P.P.A. Derks. A scholar is included among the top collaborators of E.P.P.A. Derks based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with E.P.P.A. Derks. E.P.P.A. Derks is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Derks, E.P.P.A., et al.. (2022). Visualizing dynamic (after)taste effects by means of time‐discrete TCATA data analysis. Journal of Sensory Studies. 37(3). 5 indexed citations

Derks, E.P.P.A., et al.. (2016). Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry. Analytical Chemistry. 89(2). 1212–1221. 21 indexed citations

Barcaru, Andrei, E.P.P.A. Derks, & Gabriel Vivó‐Truyols. (2016). Bayesian peak tracking: A novel probabilistic approach to match GCxGC chromatograms. Analytica Chimica Acta. 940. 46–55. 10 indexed citations

Weusten, Jos, E.P.P.A. Derks, John Mommers, & Sjoerd van der Wal. (2012). Alignment and clustering strategies for GC×GC–MS features using a cylindrical mapping. Analytica Chimica Acta. 726. 9–21. 15 indexed citations

Kant, Kim D. G. van de, Joep J B N van Berkel, Quirijn Jöbsis, et al.. (2012). Exhaled breath profiling in diagnosing wheezy preschool children. European Respiratory Journal. 41(1). 183–188. 52 indexed citations

Derks, E.P.P.A.. (2009). PANCA: Panel concordance analysis. Food Quality and Preference. 21(3). 324–329. 5 indexed citations

Westerhuis, Johan A., E.P.P.A. Derks, Huub C. J. Hoefsloot, & Age K. Smilde. (2007). Grey component analysis. Journal of Chemometrics. 21(10-11). 474–485. 21 indexed citations

Rubingh, Carina M., Sabina Bijlsma, E.P.P.A. Derks, et al.. (2006). Assessing the performance of statistical validation tools for megavariate metabolomics data. Metabolomics. 2(2). 53–61. 131 indexed citations

Derks, E.P.P.A., Johan A. Westerhuis, Age K. Smilde, & Bonnie M. King. (2003). An introduction to Multi-block Component Analysis by means of a flavor language case study. Food Quality and Preference. 14(5-6). 497–506. 12 indexed citations

10.

Derks, E.P.P.A., C.A.A. Duineveld, Alexandra E. M. Boelrijk, et al.. (2003). Flavour perception and flavour release in fruit-flavoured yoghurt.. 164–169. 1 indexed citations

11.

Derks, E.P.P.A. & L.M.C. Buydens. (1998). Aspects of network training and validation on noisy data. Chemometrics and Intelligent Laboratory Systems. 41(2). 171–184. 24 indexed citations

12.

Derks, E.P.P.A., et al.. (1997). Adaptive noise cancellation on inductively coupled plasma spectroscopy. Chemometrics and Intelligent Laboratory Systems. 39(2). 143–159. 6 indexed citations

13.

Derks, E.P.P.A., et al.. (1996). Parallel processing of chemical information in a local area network—II. A parallel cross-validation procedure for artificial neural networks. Computers & Chemistry. 20(4). 439–448. 25 indexed citations

14.

Derks, E.P.P.A., M.S. Sánchez, & L.M.C. Buydens. (1996). Response to “Comment on a recent sensitivity analysis of radial base function and multi-layer feed-forward neural network models”. Chemometrics and Intelligent Laboratory Systems. 34(2). 299–301. 4 indexed citations

15.

Derks, E.P.P.A., et al.. (1996). Parallel processing of chemical information in a local area network—III. Using genetic algorithms for conformational analysis of biomacromolecules. Computers & Chemistry. 20(4). 449–457. 11 indexed citations

16.

Melssen, W.J., et al.. (1996). Parallel processing of chemical information in a local area network — I. HYDRA: Concept, configuration, and implementation of parallel applications. Computers & Chemistry. 20(4). 431–438. 2 indexed citations

17.

Broek, W.H.A.M. van den, et al.. (1996). Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS). Chemometrics and Intelligent Laboratory Systems. 35(2). 187–197. 35 indexed citations

18.

Sánchez, M.S., H. Swierenga, L.A. Sarabia, E.P.P.A. Derks, & L.M.C. Buydens. (1996). Performance of multi layer feedforward and radial base function neural networks in classification and modelling. Chemometrics and Intelligent Laboratory Systems. 33(2). 101–119. 21 indexed citations

19.

Derks, E.P.P.A., M.S. Sánchez, & L.M.C. Buydens. (1995). Robustness analysis of radial base function and multi-layered feed-forward neural network models. Chemometrics and Intelligent Laboratory Systems. 28(1). 49–60. 63 indexed citations

20.

Derks, E.P.P.A.. (1995). Robustness analysis of radial base function and multi-layered feed-forward neural network models. Chemometrics and Intelligent Laboratory Systems. 28(1). 49–60. 19 indexed citations

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