Ruggedness testing of an analytical method for pesticide residues in potato

Maestroni, Britt; Vazquez, Alan R.; Avossa, Valeria; Goos, Peter; Cesio, V.; Heinzen, Horacio; Riener, Joerg; Cannavan, Andrew

doi:10.1007/s00769-018-1335-7

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Núñez Ares et al (2023) extended the initial set of OMARS designs with a set of mixed-level OMARS designs, involving two-level categorical and three-level quantitative factors and possessing the same orthogonality properties as the initial set of pure three-level OMARS designs. Due to the recent nature of the introduction of OMARS designs in the literature, except for the design used by Maestroni et al (2018), there are no published applications of OMARS designs other than DSDs yet. The OMARS design in Maestroni et al (2018) was obtained by concatenating two DSDs and used for studying pesticide residue in potato.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the recent nature of the introduction of OMARS designs in the literature, except for the design used by Maestroni et al (2018), there are no published applications of OMARS designs other than DSDs yet. The OMARS design in Maestroni et al (2018) was obtained by concatenating two DSDs and used for studying pesticide residue in potato. Given that OMARS designs are at least as attractive as DSDs, we expect many successful applications to appear in the literature in the next few years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of data from orthogonal minimally aliased response surface designs

Hameed

Ares

Goos

2023

Journal of Quality Technology

Self Cite

View full text Add to dashboard Cite

Experimental data are often highly structured due to the use of experimental designs. This does not only simplify the analysis, but it allows for tailored methods of analysis that extract more information from the data than generic methods. One group of three-level experimental designs that are suitable for such tailored methods are orthogonal minimally aliased response surface (OMARS) designs (Núñez Ares and Goos, 2020), where all main effects are orthogonal to each other and to all second-order effects. The design based analysis method of Jones and Nachtsheim (2017) has shown significant improvement over existing methods in terms of powers to detect active effects. However, the application of their method is limited to only a small subgroup of OMARS designs known as definitive screening designs (DSDs). In our work, we not only improve upon the Jones and Nachtsheim method for DSDs, but we also generalize their analysis framework to the entire family of OMARS designs. Using extensive simulations, we show that our customized method for analyzing data from OMARS designs is highly effective in identifying active effects when compared to other modern (non-design based) analysis methods, especially in cases where the true model is complex and involves many second-order effects.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%