Identifiability problem checking methods

The previous chapters of this book have discussed the many activities which laboratories undertake to help ensure the quality of the analytical results that are produced. There are many aspects of quality assurance and quality control that analysts carry out on a day-to-day basis to help them produce reliable results. Control charts are used to monitor method performance and identify when problems have arisen, and Certified Reference Materials are used to evaluate any bias in the results produced. These activities are sometimes referred to as internal quality control (IQC). In addition to all of these activities, it is extremely useful for laboratories to obtain an independent check of their performance and to be able to compare their performance with that of other laboratories carrying out similar types of analyses. This is achieved by taking part in interlaboratory studies. There are two main types of interlaboratory studies, namely proficiency testing (PT) schemes and collaborative studies (also known as collaborative trials). 

How then can an exact match be made In this case there must be some outside knowledge supplied and an examination of the X-ray fluorescence spectra would indicate the presence of only Co and O in the unknown powder. When this information is used, the unknown is identified as Co304, card 9-418. However, when the card is examined the weak line 2.13 A remains unidentified in the unknown pattern. X-ray intensity is proportional to the amount of the diffracting material present, so it is natural to suspect that a contaminant phase will only display its most intense lines. The problem in this case is that there is only one line remaining so that the Hanawalt manual is useless to help in the identification. The intuitive method must be used. The key question to be answered is, what contaminants might be found in a sample containing only Co and O The two possibilities that come to mind are elemental Co or another oxide of Co. When these two possibilities are checked, it is quickly found that the most intense line of CoO, card 9-402, is indeed 2.13 A, and the identification is complete. 

Correct identification of patients and specimens is a major concern for laboratories. The highest frequency of errors occurs with the use of handwritten labels and request forms. One method for checking identification is to compare identifiers such as the patient s name and his or her unique hospital number. The identification on the specimen label should also correspond with the identification on the requisition form. The use of plastic embossed patient identification cards to imprint the patient s name on test request forms and on blood collection labels can eliminate transcription and identification errors but does not guarantee that tiie patient name on the labels correctly identifies the donor of the specimen. The integration of bar code technology into the analytical systems that are used by clinical laboratories has significantly reduced identification problems (see Chapter 11). 

For very simple problems, such as the two examples in section II, one can often determine identifiability by inspection of the observation function. However, as soon as the models get more complex, that is no longer possible. A number of methods are available for checking identifiabUity. Here, I want to give brief descriptions of them. For more details see the books by Carson et al. (1983), Godfrey (1983), Jacquez (1985), and Walter (1982). 

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