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Mismatching of sample results

For obvious safety reasons, sample Identification systems are mandatory In this field. Keller [11] defined the term sample Identification as all Instructions, equipment and processes for the recognition and allocation of a specimen or sample, and an analytical result to a distinct source of specimen. The identification Involves three essential aspects (a) allocation of a complete identification statement consisting of information about the source [Pg.79]

There are a variety of systems for direct Identification (labelling) of the tubes and other vessels used to hold samples. The Eppendorf labelling system is based on reflection marks Imprinted on the process vessels, which are read automatically by the machine they are supplemented by visual characters so that visual checking by the operator is possible at any time. The marks can be printed on labels or punched Into perforated cards. Bar codes are a recent alternative now in widespread use. They offer a series of advantages such as the simplicity and rapidity of the reading or Identification (based on laser technology). Even more recent and promising are optical character rec- [Pg.80]

The concept of sample treatment Is normally used to refer to the series of operations aimed to render the collected sample ready for introduction into the analyser or instrument. Although this should be a distinct stage, very often It Is not separable from the analytical reaction development, either because both are carried out simultaneously (e.g. liquid-liquid extraction with formation of a coloured chelate) or because they take place in a sequential manner within the analyser. [Pg.82]

As put forward In the preceding chapter, it is difficult to establish a clear-cut distinction between the automation of sample collection and treatment in many configurations, both commercial and non-commercial. Thus, in collecting air pollutants by means of sorption tubes, the analytes are simultaneously preconcentrated from the collected samples. [Pg.82]

The objectives of the automation of the sample treatment stage are as varied as the nature of the sample and the number and type of analytes it may contain. The commonest, summarized in Table 4.1, are as follows  [Pg.82]


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