Quality performance metrics in multivariate classification methods for qualitative analysis

2016 ◽  
Vol 80 ◽  
pp. 612-624 ◽  
Author(s):  
Luis Cuadros-Rodríguez ◽  
Estefanía Pérez-Castaño ◽  
Cristina Ruiz-Samblás
Keyword(s):  
Qualitative Analysis ◽  
Performance Metrics ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Quality Performance

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

scholarly journals What Is Performance? A Scoping Review of Performance Outcomes as Study Endpoints in Athletics

Sports ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 66 ◽  
Author(s):  
Benjamin Raysmith ◽  
Jenny Jacobsson ◽  
Michael Drew ◽  
Toomas Timpka
Keyword(s):  
Qualitative Analysis ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Selection Procedure ◽  
Performance Outcomes ◽  
Practical Application ◽  
Performance Metric ◽  
The Common ◽  
Future Direction

Purpose: This review set out to summarise, define, and provide future direction towards the use of performance outcome measures as endpoints in research performed at international benchmark events in athletics. Methods: Scoping review methodology was applied through a search of the PubMed and Sports Discus databases and a systematic article selection procedure. Articles that met the inclusion criteria underwent triage for further quantitative and qualitative analysis. A concept chart was generated to describe the methods by which performance had been measured and introduce descriptive labels for theoretical and practical application. Results: None of 2972 articles primarily identified from the database search met the triage standards for quantitative data extraction. Eleven articles were included in a qualitative analysis. The analysis identified the common methods by which performance has been measured, reported and analysed. The resulting concept chart collates labels from the qualitative analysis (categories, themes, and constructs) with sports practice labels (performance metrics, framework, and analysis). Conclusions: The state of knowledge concerning methods to employ performance metrics as endpoints in studies performed at major competitions in athletics has been summarised. Constructing a methodology that combines the performance metric variables (continuous and ordinal) that are currently utilised as endpoints remains a challenge.

How Do We (Actually) Know Our Quality Is Improving?

2018 ◽  
Author(s):  
Justin R. Papp ◽  
William H. Forbes ◽  
Matthew A. R. Yarmuch
Keyword(s):  
Performance Metrics ◽  
Cost Model ◽  
Quality Improvements ◽  
Quality Model ◽  
Quality Performance ◽  
Cost Of Quality ◽  
Business Objectives ◽  
Operational Activities ◽  
The Right ◽  
Operational Excellence

We have all dealt with performance metrics in the pipeline industry. How do we measure operational excellence? Are we prioritizing the right corrective actions? Are our existing metrics fair and driving the right behaviors? Will they recognize success and actually show us and our clients that we are improving? This paper describes how Enbridge Major Projects measures and knows our Quality is improving; how we prioritize, focus, and monitor Quality improvement. Using our roadmap, your organization can transform existing data streams from anecdotal to well established facts that produce actionable results and drive business objectives. To reach this outcome, Enbridge Major Projects quickly matured our Quality Culture by leveraging our strong Safety Culture and habits. On our journey to meaningful overall Quality metrics, Enbridge built a foundation through non-punitive incident reporting using incident resolution tools and a Cost of Quality model. Cost of Quality models can be designed and executed in a variety of ways. This paper will focus on applying a model specifically suited for pipeline construction and operational activities. Key topics to be addressed include: • basic common principles of an overall Cost of Quality model, • various data collection methods to suit the model’s design, and • how a Documented Defects Quality cost model allows Enbridge to identify, prioritize, and monitor Quality improvements focused on preventing recurrence and occurrence of Quality issues. Examples will be provided for common pipeline applications, including valves, pipe, and other commodities and services. This approach has enabled Enbridge Major Projects to prioritize improvement actions and meet business objectives. Applying a Cost of Quality model will enhance your operational excellence and greater adoption would provide the foundation for industry-wide Quality performance metrics that will recognize success and validate that Quality is improving in the pipeline industry.

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