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## Über dieses Buch

This is the first book to show how to apply the principles of quality assurance to the identification of analytes (qualitative chemical analysis). After presenting the principles of identification and metrological basics, the author focuses on the reliability and the errors of chemical identification. This is then applied to practical examples such as EPA methods, EU, FDA, or WADA regulations. Two whole chapters are devoted to the analysis of unknowns and identification of samples such as foodstuffs or oil pollutions. Essential reading for researchers and professionals dealing with the identification of chemical compounds and the reliability of chemical analysis.

## Inhaltsverzeichnis

### Chapter 1. Principles of Identification

In this initial chapter, concepts and terms related to qualitative chemical analysis are outlined and discussed. Chemical identification is defined as assigning an analyte to one from known chemical compounds or a group/class of compounds. General principles for identification through the use of chemical tests and instrumental measurements are formulated. Qualitative analytical procedures and approaches to implement them are classified. Components of identification procedures are further described. Objects for identification such as compounds, substances, and analyzed samples are discussed in great detail, including identifiers of the objects. Known chemical substances, which amount to more than 110 million entities, are statistically reviewed. Finally, two key metrological issues, traceability in identification operations and qualitative scale of measurements, are discussed.
Boris L. Milman

### Chapter 2. Techniques and Methods of Identification

In this chapter, techniques and method of chemical analysis are discussed, with the focus on their potential for use in identification procedures. It is demonstrated that analytical techniques providing more information, in particular molecular spectrometry, are preferred for identification. Other techniques are just briefly considered, with for the exception of chromatography, whose combination with spectrometric techniques sharply increases possibilities and trueness of identification. As a whole, mass spectrometry is superior to other spectral techniques in such features as sensitivity, selectivity, generation possibility of molecular mass/formula, and combinability with chromatography. Different types of mass spectrometric instruments are outlined, with many performances tabulated. Experimental conditions for identification of volatile, non-volatile, and high-molecule compounds are discussed. Next, classification of chemical methodologies is given where screening and confirmatory methods are noted. Related procedures, sample treatment, and quantitative determination are also considered as ones affecting qualitative analysis.
Boris L. Milman

### Chapter 3. Probability, Statistics, and Related Methods

The probability/statistical methods used for identification purposes are briefly considered. The basic statement is that many phenomena and procedures included in qualitative analysis are of a probabilistic nature. The probability of yes/no responses in target detection is described by binomial distribution. Values of quantities required for identification, such as retention times in chromatography, wavelengths and frequencies in optical spectroscopy, masses in mass spectrometry, intensities (heights, areas) of any analytical signals, are considered as normally distributed (including t-distributed) ones over probabilities. Parameters of the distributions are used in calculations incorporated into procedures of detection and identification. Multivariate statistics connected with chemometrics is essential for classification/authentication of samples, i.e., qualitative analysis II. Bayesian statistics takes into account a prior probability that an analyte is present in a sample.
In the second part of this chapter, operations of setting up, testing, and screening of hypotheses as the core processes of qualitative analysis, are considered. The simplest are hypotheses for a detection operation, e.g., ‘$${H_0}$$: an analyte is absent in the sample’. In identification, analogous hypotheses: ‘$${H_0}$$: the analyte is compound A’, and ‘$${\overline H_0}$$: the analyte is not compound A’ are set up and tested. Identification hypotheses are transformed into experimental and statistical ones to be accepted or rejected on the basis of corresponding criteria, both range/tolerance and statistical criteria. False acceptance or rejection of hypotheses leads to false positive/negative results of identification or detection, the probability of which can be estimated.
Boris L. Milman

### Chapter 4. Reliability and Errors of Identification

In this chapter, approaches to estimating reliability and errors of detection and identification are considered. Related terminology is presented; reliability of identification is defined as a probability of its true result. False results are demonstrated to be attributes of determination of low analyte amounts by screening methods. Formulas for calculating rates of true and false, positive and negative results are given. The rates are derived both from tests using analytical standards (blank samples) and upon verification of screening results by confirmatory methods/techniques. A replication of analytical determinations is also considered, including Bayesian statistics. Limit characteristics of detection and identification are treated.
It is noted that confirmatory methods based on spectrometry must be free of identification errors. Nevertheless, errors occur if methods are non-targeted, invalidated, or ad hoc. True and false results obtained with use of spectral techniques are discussed in terms of matching spectra. A best/good or poor matching resulting in a high or low match factor means a good/fair or poor chance respectively of accepting an identification hypothesis. Different match factors calculated in mass spectrometry and also NMR, IR-, and UV–V is spectroscopy are outlined, with many details with regard to searches in reference spectral libraries. Further, a probability interpretation of match factors is considered, which is essential for identification of peptides and proteins in proteomics. Other approaches to deriving a probability of identification from analytical/spectral data are also noted. This kind of probability, as well as the reported result of identification, can be expressed in words.
Boris L. Milman

### Chapter 5. Target Identification in Methods

Target identification is considered in detail. A qualitative analysis of this type is mostly performed according to validated methods which are screening and confirmatory. An identification result is the conclusion based on criteria. Those for screening identification are not very rigorous and not numerous. An example is the presence of a particular mass chromatographic peak in a rather wide range of the retention parameter. Most chromatographic techniques are suitable for screening. For confirmation of identity, more analytical data are required, e.g., three or four mass peaks and matching tolerance/range criteria for peak intensities. Any such value is named an identification point. An analyst should gather the required number of points. Chromatography and mass spectrometry and their combinations are the most appropriate techniques for the purpose. Different versions of the techniques, as well as other types of spectroscopy, are considered. The requirements and guidelines for setting up identification criteria presented in a number of laboratory guidances which have been issued by various organizations and agencies are outlined in detail. These are not the same in different documents; that is the reason for criticizing them. The system of identification points itself and the evident or suspected invalidity of tolerance criteria has also been criticized. The criticism is partly accepted, and some objections are also presented here. In general, the guidelines are regularly tested through a global analytical practice, and new improvements of identification criteria are reported.
Boris L. Milman

### Chapter 6. Prior Data for Non-target Identification

This chapter is devoted to prior information required to set up and test identification hypotheses. According to its type, the relevant information is divided into meaning and statistical data. Knowledge with regard to the origin, properties, and use of chemical compounds is very essential in order to be able to propose and reject candidate compounds for identification. Prior information about samples analyzed is important in order to gather full evidence of the trueness of an identification result. Plausibility of qualitative analytical results is also taken into account to confirm conclusions made by analysts. Much of such data are extracted from chemical databases outlined in this chapter. These data sources are also used to calculate statistical rates of occurrence and co-occurrence of chemical compounds in the literature. The occurrence rate is the direct measure of the abundance of chemical compounds, and the related possibility of presenting in samples to be analyzed. Rare compounds are filtered out by means of this rate, and further excluded from consideration for identification purposes. Most known compounds are rare ones, as proved by respective statistical data. Facts and rates of the co-occurrence of chemical compounds in the literature provide the possibility of a priori prediction of a group of compounds available in the same samples analyzed. Different methods of estimating these rates are described; examples of their use for identification are given.
Boris L. Milman

### Chapter 7. Non-target Identification. Chromatography and Spectrometry

The content of this chapter are focused on unknown analysis when a chemist answers the question of what compounds are present in the sample. The true result of identification is provided by at least two independent (orthogonal) methods. The most general approach to the identification of non-targets is based on chromatography mass spectrometry. Gas chromatographic parameters, widely used for identification, are retention indices. To a lesser degree, retention indices are applicable in liquid chromatography. Now, retention parameters are required in proteomics. In mass spectrometry, volatile analytes are preferably identified by means of reference libraries of electron ionization mass spectra. For identification of nonvolatile compounds, libraries of tandem/product mass spectra have been built. Their use is especially effective when combined with high-resolution mass spectrometry which provides candidate molecular formulas. Interpretation of mass spectra is also possible but not widely applied. NMR and IR spectroscopy are comparable to MS in identification potential if there are a relatively large amount of analytes and a simple composition of a sample under analysis. In NMR, algorithms of spectral prediction as well as respective spectral databases have been rapidly developed. Analytical metabolomics and proteomics are individually discussed, with the focus on approaches to identification, identification criteria, the problems arising due to a great complexity of analytes and unavailability of analytical standards, and interlaboratory comparisons. For all the techniques, information about reference spectral libraries/databases is tabled. Quality assurance of identification is widely covered in the chapter.
Boris L. Milman

### Chapter 8. Chemical Qualitative Analysis II

Qualitative analysis II is identification/classification/authentication of such objects as foodstuffs, products, specimens, materials, pollutions, living organisms, and others. Typical procedures of this sort are authentication of food, determination of its adulteration, oil spill identification, and that of microorganisms. Identification of an object is based on recognition of its indicative component(s), measuring ratios between several components of a sample, or fingerprinting overall sample signals. Almost all analytical techniques are applicable for the purpose, with an indispensable role for chemometrics/multivariate statistics in processing of analytical data. In the same way as in identification of individual chemical compounds, quality of identification II is assured by validation of methods, the use of reference materials, and availability of standard/valid reference data. Examples of qualitative analysis of vegetable oils, honey, wine, and some non-food samples are given.
Boris L. Milman

### Chapter 9. Good Identification Practice

Good identification practice is considered as an underlying system of particular requirements and guidelines with regard to laboratories, personnel, instruments, and methods directed to quality assurance and quality control of chemical identification (qualitative analysis). Terminological standardization and “metrologization” of qualitative analysis are stated to be general prerequisites for consistency and comparability of identification results between analytical/bioanalytical chemists and laboratories. Requirements and guidelines concerning quality assurance and control of identification procedures which are contained in official laboratory guidances are considered. According to principles of good identification practice, criteria for detection and identification in target methods, screening and confirmatory ones, should be formulated and validated. Accepted levels of false result rates are established. In non-target/unknown analysis, approaches to identification should be validated, which include evaluation of pertinent databases, spectral libraries, predictor programs, identification/classification algorithms, and so on. Interlaboratory studies provide assessment of laboratory performances and evaluation (validation) of identification methods/approaches.
Boris L. Milman

### Backmatter

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