Ruggedness

Ruggedness refers to the ability of an analytical method to withstand minor procedural changes without affecting the overall performance of the assay. The ruggedness of an assay 

QUALITY ASSURANCE, QUALITY CONTROL AND METHOD VALIDATION 

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Small size, ruggedness, simple cabling and the ability to operate the equipment under adverse conditions in the field has also been design goals. The system should also conform with the regulations necessary for the CE-marking (i. e. standards and directives for EMC, Electrical Safety and Machine Safety). 

The requirements of the analysis determine the best method. In choosing a method, consideration is given to some or all the following design criteria accuracy, precision, sensitivity, selectivity, robustness, ruggedness, scale of operation, analysis time, availability of equipment, and cost. Each of these criteria is considered in more detail in the following sections. 

An important step in developing a standard method is to determine which factors have a pronounced effect on the quality of the analytical method s result. The procedure can then be written to specify the degree to which these factors must be controlled. A procedure that, when carefully followed, produces high-quality results in different laboratories is considered rugged. The method by which the critical factors are discovered is called ruggedness testing. ... 

Ruggedness testing is often performed by the laboratory developing the standard method. Potential factors are identified and their effects evaluated by performing the analysis while the factors are held at two levels. Normally one level for each factor is that given in the procedure, and the other is a level likely to be encountered when the procedure is used by other laboratories. 

The experimental design for ruggedness testing is balanced in that each factor level is paired an equal number of times with the upper case and lower case levels... 

Experimental Design for a Ruggedness Test Involving Seven Factors... 

A proposed gravimetric method was evaluated for its ruggedness by varying the following factors... 

The most popiilar form of motor speed control for adjustable-speed pumping is the voltage-controlled pulse-width-modulated (PWM) frequency synthesizer and AC squirrel-cage induction motor combination. The flexibility of apphcation of the PWM motor drive and its 90 percent- - electric efficiency along with the proven ruggedness of the traditional AC induction motor makes this combination popular. 

Ionic liquids have already been demonstrated to be effective membrane materials for gas separation when supported within a porous polymer support. However, supported ionic liquid membranes offer another versatile approach by which to perform two-phase catalysis. This technology combines some of the advantages of the ionic liquid as a catalyst solvent with the ruggedness of the ionic liquid-polymer gels. Transition metal complexes based on palladium or rhodium have been incorporated into gas-permeable polymer gels composed of [BMIM][PFg] and poly(vinyli-dene fluoride)-hexafluoropropylene copolymer and have been used to investigate the hydrogenation of propene [21]. 

Widespread concern with the significance of the results and their interpretation Ruggedness and reliability problems with equipment Concern with intrinsic safety of equipment... 

As K increases from 0 (where T x) = fi xi)) to N — 1 (where F becomes the sum of N independent random numbers), the NK-landscape goes from having a single maximum to having more and more maxima that become less and less correlated to, finally, being essentially totally random. The parameter K can therefore be used to tune the degree of ruggedness of the landscape. 

Because the number of data points is low, many of the statistical techniques that are today being discussed in the literature caimot be used. While this is true for the vast majority of control work that is being done in industrial labs, where acceptability and ruggedness of an evaluation scheme are major concerns, this need not be so in R D situations or exploratory or optimization work, where statisticians could well be involved. For products going to clinical trials or the market, the liability question automatically enforces the tried-and-true sort of solution that can at least be made palatable to lawyers on account of the reams of precedents, even if they do not understand the math involved. 

Of all the requirements that have to be fulfilled by a manufacturer, starting with responsibilities and reporting relationships, warehousing practices, service contract policies, airhandUng equipment, etc., only a few of those will be touched upon here that directly relate to the analytical laboratory. Key phrases are underlined or are in italics Acceptance Criteria, Accuracy, Baseline, Calibration, Concentration range. Control samples. Data Clean-Up, Deviation, Error propagation. Error recovery. Interference, Linearity, Noise, Numerical artifact. Precision, Recovery, Reliability, Repeatability, Reproducibility, Ruggedness, Selectivity, Specifications, System Suitability, Validation. 

In the following, an example from Chapter 4 will be used to demonstrate the concept of statistical ruggedness, by applying the chosen fitting model to data purposely corrupted by the Monte Carlo technique. The data are normalized TLC peak heights from densitometer scans. (See Section 4.2) ... 

For the purpose of making the concept of ruggedness clear, a polynomial... 

Figure 3.9. Demonstration of ruggedness. Ten series of data points were simulated that all are statistically similar to those given in Table 4.5. (See program SIMILAR.) A quadratic parabola was fitted to each set and plotted. The width of the resulting band shows in what ar-range the regression is reliable, higher where the band is narrow, and lower where it is wide. The bars depict the data spread for the ten statistically similar synthetic data sets.

ND 60.dat Fifteen columns that each contain 160 random numbers. To be used with MSD, HISTO, CORREL, SMOOTH to obtain a baseline, against which to compare real data sets the ruggedness of evaluations can be checked through comparisons with sets of random numbers. 

Vander Heyden, Y., Khots, M. S., and Massart, D. L., Three-Level Screening Designs for the Optimization or the Ruggedness Testing of Analytical Procedures, Analytica Chimica Acta 276, 1993, 189-195. 

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