Search results
Results from the WOW.Com Content Network
Cleaning validation is the methodology used to assure that a cleaning process removes chemical and microbial residues of the active, inactive or detergent ingredients of the product manufactured in a piece of equipment, the cleaning aids utilized in the cleaning process and the microbial attributes.
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Process validation is the analysis of data gathered throughout the design and manufacturing of a product in order to confirm that the process can reliably output products of a determined standard. Regulatory authorities like EMA and FDA have published guidelines relating to process validation. [ 1 ]
Contact time of the cleaning solution. The longer the detergent contact period, the higher the cleaning efficiency. After some time, the detergent eventually dissolves the hard stains/soil from the dirty surface. Pressure exerted by the cleaning solution (or turbulence). The turbulence creates an abrasive force that dislodges stubborn soil from ...
The desired results are established in terms of specifications for outcome of the process. Qualification of systems and equipment is therefore a part of the process of validation. Validation is a requirement of food, drug and pharmaceutical regulating agencies such as the US FDA and their good manufacturing practices guidelines. Since a wide ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
The pipetting and washing systems in an automated analyzer are designed to continuously cycle between the aspiration of patient specimens and cleaning. An obvious concern is a potential for carryover of analyte from one patient specimen into one or more following patient specimens, which can falsely increase or decrease the measured analyte ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]