Teaching
Objectives
-
To introduce students to Qualitative Analysis and underscore its present and future significance.
-
To adapt classical analytical properties to the specificities of Qualitative Analysis.
-
To define and characterize binary responses, and potential errors in them (false positives and false negatives).
-
To describe the most salient classical and instrumental methods of qualitative analysis.
6.1 Explanation of the Slides
Slide 6.1
This slide places in Part II (The
Analytical Process) and shows the other two parts. This is the
third chapter in Part II and deals with analytical processes that
produce qualitative results in the form of YES/NO binary
responses.
Slide 6.2
6.2.1. This is an outline of the contents
of this chapter. The first section places Qualitative Analysis in
context and is followed by a description of screening systems in
the second. There follow the most salient features of the binary
response in the third and various classifications of Qualitative
Analysis in the fourth. The last two sections exemplify Classical
and Instrumental Qualitative Analysis.
6.2.2. As can be seen, the primary
teaching objectives of this chapter are to introduce students to
Qualitative Analysis; and to describe the YES/NO binary response,
its features and potential errors (false positives and false
negatives) through examples of classical and instrumental
qualitative methods.
6.1.1 Introduction to Qualitative Analysis (2 Slides)
Slide 6.3
Qualitative Analysis is the branch of
analysis encompassing those analytical processes that yield a
YES/NO binary response.
Qualitative Analysis is also the first
step in the hierarchy of goals of Analytical Chemistry in Slide
1.9, where it is followed by Quantitative and Structural Analysis.
It makes no sense to apply a quantitative analytical process to a
sample before the sample is checked to contain the target
analyte.
Qualitative Analysis can be described
in terms of the verbs to
detect (vs. “to determine” in Quantitative Analysis) and
to identify (or, in other
words, “to recognize”). Also, it is associated to the verb
to classify (samples
according to qualitative content).
Most qualitative chemical measurement
processes (CMPs) are much simpler and faster than quantitative
CMPs. As a result, the former are typically designated “tests” or
“assays” rather than “methods”.
Not long ago, Qualitative Analysis was
considered to be the “lesser child” of Analytical Chemistry. As
recently as the last quarter of the XX century, Qualitative
Analysis was even used to undervalue the significance of this
scientific discipline. At present, however, it is gaining
increasing recognition as a means of fulfilling clients’
information requirements—the ultimate goal as increasingly
recognized by many. Very often, such requirements are in the form
of binary information (see Slide 6.8) and fulfilled with an
expanding array of commercially available qualitative analytical
means such as screening systems, portable test kits and biosensors.
Some (e.g., planar chromatography on paper) are semi-quantitative
and afford estimating the concentration of an analyte in addition
to identifying it.
Slide 6.4
Qualitative analytical information
therefore takes the form of a YES/NO binary response. As shown in
Sect. 6.1.3.2, however, it almost invariably possesses
some quantitative connotation. This type of information is rarely
needed in Classical (Physical) Metrology; also, it is associated to
special analytical properties and usually easier to obtain than
other types of analytical information.
6.1.2 Analytical Screening Systems (3 Slides)
Slide 6.5
6.5.1. An analytical screening system is
usually a simple analytical process used to classify samples into
two groups according to whether they give a positive (YES) or
negative (NO) response to the binary question posed.
This slide summarizes the operation of
a screening system: each sample in a set is independently subjected
to the screening process in order to rapidly classify it according
to whether it gives a positive (black) or negative result
(white).
6.5.2. The process is finished when the
response is NO. On the other hand, a YES response requires
confirmation with a conventional analytical process (e.g., sample
treatment followed by gas or liquid chromatography). This process
additionally allows the binary information initially obtained to be
expanded. Thus, a sample of imported dried fruits testing positive
for mycotoxins in an immunochemical test will be confirmed to
contain them and assigned relative concentrations of the different
toxins. Also, the results of screening analyses are frequently
subjected to confirmation analyses for quality control
purposes.
6.5.3. The combination of a screening
system and a conventional analytical process constitutes a
vanguard–rearguard analytical strategy. The two can be connected
off-line (that is, operate independently) or on-line.
Slide 6.6
There are two types of screening in
Qualitative Analysis, namely:
-
Analyte screening, which is used to identify the presence of an analyte or analyte family in a sample and corresponds to Type I Qualitative Analysis in the next slide.
-
Sample screening, which is used to classify samples according to the presence or absence of a particular analyte (e.g., benzene) or analyte family (e.g., BTEX, which comprises benzene, toluene, ethylbenzene and xylene organic molecules). This is the most common type of Qualitative Analysis (Type II in Slide 6.10).
Slide 6.7
It is not uncommon to refer to
Qualitative Analysis as Classification Analysis (particularly in
chemometric contexts).
In Binary Classification Analysis, samples
yielding a positive response (yellow circles) are separated from
the rest (see Slide 6.5). One example is the classification of
atmospheric samples into two groups depending on whether they
contain acid rain gases (SO x, N x O y ) at levels exceeding the
tolerated limits of the United States Environmental Protection
Agency (US-EPA).
Multiple Classifying Analysis is more
ambitious: it classifies samples into more than two groups, which
usually requires using computers, chemometric software,
sophisticated equipment and multiple information (e.g., the
presence and concentration of several analytes) for each
sample.
6.1.3 The YES/NO Binary Response (18 Slides)
6.1.3.1 Types (4 Slides)
Slide 6.8
Qualitative Analysis can be classified
in various ways. The classification in this slide is based on the
type of information sought. The questions to be answered are shown
here in increasing order of demand regarding the type of binary
information involved. As can be seen, the types of response to be
obtained range from simple identification (first level) through
identification and quantitative estimation (second level), and
chemically discriminate information (third level), to spatially and
temporally discriminate information (fourth level).
Slide 6.9
These examples illustrate the four
qualitative information levels in the previous slide. As noted
earlier, these information requirements are becoming increasingly
common, so Analytical Chemistry must gradually aim at their
fulfilment.
Slide 6.10
There are two major types of
Qualitative Analysis according to primary purpose.
In Type I Qualitative Analysis the aim is
to identify an analyte (e.g., phenol) or analyte family (several
phenols including polyphenols).
In Type II Qualitative Analysis the target
is the sample and the aim to qualify or classify it (e.g., to find
whether it is edible on the basis of its toxin levels). Type II
qualitative analyses can be performed in two ways.
-
The simpler way involves using a straightforward, fast direct analysis system such as a reagent strip or test kit to obtain binary responses. One example is the pregnancy test kit for urine.
-
Alternatively, a conventional analytical system such as a photometer, fluorimeter or chromatograph can be used to obtain instrumental signals for conversion into binary responses in accordance with a preset scheme. These modes of qualitative analysis is illustrated in the next slide.
Slide 6.11
This slide highlights the differences
between sample and analyte screening (that is, Type II and Type I
Qualitative Analysis).
-
Sample screening is more simple and expeditious than analyte screening—and so is the equipment needed by the former.
-
Sample treatment is also more simple in sample screening than it is in analyte screening.
-
Global information (e.g., total hydrocarbon levels in water) is more frequently managed in sample screening. On the other hand, specific information about individual analytes (that is, discriminate information such as hydrocarbon types in water) is more common in analyte screening.
Slide 6.12
Converting primary instrumental data
such as absorbances, fluorescence intensities, current intensities,
volumes or potentials into binary responses is no easy task. In
fact, it requires considering convergent and divergent criteria
that are called “filters” here. The analytical criterion is
intrinsic whereas the client’s criterion, when applicable, is
extrinsic.
In converting an absorbance datum for
an analyte in a screened sample, the laboratory criterion
materializes, for example, in constructing a calibration curve (see
Slide 2.36) to derive a YES/NO binary response from the datum or
using the limit of detection (LOD). The client’s criterion,
however, may differ depending on how strict the conversion is to
be. Obviously, laboratory and client’s criteria should always be
reconciled when needed to solve an analytical problem (see Slides
7.8 and 7.23).
6.1.3.2 Quantitative Connotations (1 Slide)
Slide 6.13
6.13.1. As can be inferred from the
previous slides, Qualitative Analysis frequently has quantitative
connotations, especially when quantitative primary data are to be
converted into binary responses (see Slide 6.12).
The conversion involves using a scale
of absolute amounts (A) or concentrations (C A) of analyte. The scale
includes the limit of
detection (C
LOD, Slide 2.40), which is typical of the analytical
process, and the limiting
or threshold concentration
or amount imposed by legislation or the client (C L). In some cases, the
laboratory adopts a cut-off
concentration C
C as a stricter limit than the threshold concentration
for internal quality assurance—to avoid errors. Note that the limit
of quantification (C
LOQ, Slide 2.41) has been excluded from the scale
because it pertains to Quantitative Analysis.
6.13.2. The following two limits in the
relative concentration scale define key zones in Qualitative
Analysis.
-
The limit of detection sets the analyte concentration level above and below which the analyte is detected and not detected, respectively. This limit is inherent in the particular test or qualitative method.
-
The cut-off concentration sets the level above which detection with a given probability will occur. This limit is self-imposed by the laboratory.
6.13.3. The cut-off and threshold
concentrations also define the zones for the binary responses YES
(right) and NO (left).
Interestingly, the proportion of
errors in qualitative detection decreases with increasing analyte
concentration. A new analytical property called “reliability” is
defined in Slide 6.16.
6.1.3.3 Analytical Properties (7 Slides)
Slide 6.14
The overview of analytical properties
in Slide 2.4 is directly applicable to Quantitative Analysis but
requires some adaption for use in Qualitative Analysis. Although
the three types of properties (namely, capital properties for the
binary response, and basic and productivity-related properties for
the analytical process) remain, they differ in two respects from
those pertaining to Quantitative Analysis. Thus,
- (1)
accuracy and precision are not applicable to Qualitative Analysis; and
- (2)
a new capital property called reliability, which rests on the three basic properties (robustness, sensitivity and selectivity), is required.
There next slide discusses other, more
specific differences.
Slide 6.15
This slide summarizes again the
similarities and differences between the analytical properties
applicable to Quantitative and Qualitative Analysis. As can be
seen, the limit of detection (LOD, Slide 2.40) is the only
sensitivity-related parameter applicable to Qualitative
Analysis.
Slide 6.16
The capital
property reliability
pertains to Qualitative Analysis even though it is a combination of
two classical properties pertaining to Quantitative Analysis,
namely: accuracy and precision.
Slide 6.17
This is a brief summary of the capital
(uncertainty and accuracy) and basic properties (precision and two
sensitivity measures) not applicable to Qualitative Analysis.
Slide 6.18
The specific uncertainty of a
numerical result R,
U R, which is
described in detail in connection with Quantitative Analysis in
Slides 2.29 and 2.31, cannot be used in Qualitative Analysis. How
can a YES/NO binary response thus be assigned an uncertainty
interval? By approaching uncertainty in a novel, unorthodox manner
that is described in the next slide.
Slide 6.19
Specific uncertainty, which is typical
of Quantitative Analysis (see Slide 2.29), should be converted into
an uncertainty or
unreliability interval in
Qualitative Analysis.
The uncertainty or unreliability
interval is defined as the —generally symmetric—range around the
cut-off (C C) or
threshold concentration (C
L) where errors—whether false positives or false
negatives—can be expected to occur at a given probability level
(e.g., 95%). The interval is experimental established as follows:
- (1)
A set of standard samples containing variable concentrations of analyte is prepared. Because the analyte concentration in each sample is known, the result (response) can directly be deemed correct or incorrect and a distinction between false positives and false negatives, which are explained in Slide 6.21, be made. Each sample in the set is subjected to the qualitative analytical process and the results recorded.
- (2)
The results are plotted on an analyte concentration scale including the threshold or limiting concentration. Around such a concentration is the uncertainty interval (C 1–C 2), which includes a zone of dubious responses, another of false positives at the low-concentration end (near C 1) and a third of false positives at the high-concentration end (near C 2). Outside the uncertainty interval are two zones where
-
a negative response at low concentrations (<C 1) will be correct; and
-
a positive response at high concentrations (>C 2) will also be correct.
-
Therefore, the width of the
uncertainty interval determines the reliability or confidence of a
qualitative result.
Slide 6.20
This slide compares unreliability in
Qualitative Analysis to specific uncertainty in Quantitative
Analysis. Both concepts represent a concentration interval which,
however, differs between the two types of analysis. Thus,
-
in Qualitative Analysis, the unreliability interval is the zone where errors (false positives or false negatives) arise;
-
in Quantitative Analysis, the specific uncertainty interval is the concentration range where the result for another aliquot of the same sample subjected to the same analytical process can be expected to fall (see Slide 2.7).
Both intervals share a common trait:
their width depends on the selected probability level—albeit in a
different manner. Thus, the unreliability interval is a range of
values where the result is incorrect (that is, a range where
trueness in a qualitative result cannot be assured). As a
consequence, the higher is the statistical probability in
Qualitative Analysis, the narrower will be interval. Conversely,
the higher is the probability (confidence) in Quantitative
Analysis, the higher will be the specific uncertainty and the wider
the interval as a result (see Slides 2.29 and 2.31).
This difference arises from the way
the intervals are conceived in Qualitative and Quantitative
Analysis. Thus, the unreliability interval is a range of errors—an
unwanted outcome—whereas the uncertainty interval is a range where
the result can be expected to fall—a desirable outcome.
6.1.3.4 Errors: False Positives and False Negatives (3 Slides)
Slide 6.21
6.21.1. This slide defines “reliability”,
which appears in the overview of analytical properties applicable
to Qualitative Analysis (see Slide 6.14).
Subjecting n aliquots of a standard sample with
known binary responses to a qualitative test provides n binary responses the reliability of
which will be given by the ratio of accurately identified aliquots
to the total number of aliquots.
6.21.2. The opposite of “reliability” is
“error”, which is defined here as the proportion of incorrect
responses. In Qualitative Analysis, errors can be of two types,
namely:
-
False positives, which occur when the result is YES but should have been NO and are especially likely at analyte concentrations slightly below the limiting concentration (C L).
-
False negatives, which arise when the result is NO but should have been YES and are therefore more likely to occur at concentrations slightly above C L.
A sample containing an analyte
concentration near C
L may give a dubious
result (see Slide 6.19).
6.21.3. The percent reliability of a
qualitative analysis is calculated by subtracting the proportion of
errors (that is false positives and false negatives, which can be
easily discriminated) from 100.
Slide 6.22
6.22.1. Whether a binary response is
correct, a false positive or a false negative can be ascertained by
relating the analyte concentration (C A) to the limit of
detection, cut-off concentration or threshold concentration.
-
Thus, when C A is lower than the previous limits, the correct binary response is NO, so YES is incorrect and constitutes a false positive.
-
On the other hand, when C A exceeds the previous limits, the correct response is YES, so NO is incorrect and a false negative.
The table is highly illustrative. As
can be seen, the concentration level to be used in order to
classify a response as YES or NO depends on the particular limit
chosen.
6.22.2. It is extremely important to
understand that the two types of error in Qualitative Analysis
differ strongly in their practical consequences. Thus, as can be
seen in Slide 6.5, if the response of a screening system is NO, the
analysis is finished; on the other hand, if the response is YES,
the samples testing positive are almost invariably subjected to a
confirmatory analysis with a conventional analytical process.
Consequently, false negatives should
be avoided at any rate because they are not routinely confirmed.
For example, a customs laboratory passing an imported batch of
peanuts which has incorrectly tested negative for mycotoxins in a
screening analysis can have serious adverse consequences on
consumers’ health because mycotoxins are carcinogenic. The ensuing
risk should be avoided by assuring the complete absence of false
negatives in the analysis.
Slide 6.23
6.23.1. These are two examples of errors
(false positives and false negatives) in Qualitative Analysis
providing for the concepts explained in the previous slide.
6.23.2. The red boxes represent the
correct way of labelling the ensuing errors.
Slide 6.24
The experimental procedure for
calculating the proportion of false positives and false negatives,
and the unreliability interval around the threshold concentration
(C L) for a
screening system, is as follows:
- (1)
A set of, for example, 60 standard samples containing 6 different analyte concentrations C 1–C 6 (that is, 10 replicates per concentration) is prepared. Three of the chosen concentrations (C 1–C 3) should be higher than the threshold concentration (C L) and the other three (C 4–C 6) lower.
- (2)
The samples are subjected to the screening process in a random sequence and a binary response for each is obtained.
- (3)
Since the correct binary responses for the whose sample set are known, each sample can be classified as “correct”, “false positive” or “false negative” by comparison with the actual (experimental) result.
The table shows the results for the
60 samples according to analyte concentration (C 1–C 6).
Although the 30 samples with
C
A < C L should have yielded a NO
response, not all did—particularly those containing the analyte at
concentrations near the threshold (C 2 and C 3), which gave 2 (20%) and
5 (50%) false positives, respectively. Note that errors increased
with increasing nearness of the analyte concentration to
C L.
Likewise, all 30 samples with
C
A > C L should have tested
positive (YES) for the analyte; however, 4 samples with
C 4 and 2 with
C 5 led to false
negatives. Again, the number of errors increased with increasing
nearness of the analyte concentration to C L.
Slide 6.25
This graph shows the two types of
errors in Qualitative Analysis (false positives and false
negatives) on two y-axes as
a function of the analyte concentration on the x-axis. The graph focuses on the zone
around the threshold concentration, where the three concentrations
below C L and
the three above it in the example of the previous slide are placed.
As can be seen, the errors exhibit a Gaussian distribution on the
analyte concentration scale, which encompasses the zones shown in
that slide —that of dubious results excluded.
Both types of errors increase near
the threshold concentration. At the two ends of the scale are the
zones of total reliability, that is, of error-free classification
of samples as YES (left end) or NO (right end).
6.1.4 Types of Qualitative Identification (1 Slide)
Slide 6.26
This is an overview of Qualitative
Analysis in the form of non-mutually exclusive classifications
according to various, complementary criteria, namely:
- (1)
Nature of the response, which can be binary (YES/NO) or multiple (see Slide 6.7).
- (2)
Number of analytes, which can be one (e.g., phenol) or several (e.g., an analyte family such as phenols).
- (3)
Analytical technique, which can be a classical or instrumental qualitative test or screening system. This criterion is used to describe Qualitative Analysis in Sects. 6.1.5 and 6.1.6.
- (4)
Type of qualitative test or screening system used, which can involve
-
one or several (bio)chemical reactions; and
-
With and without a chromatographic or non-chromatographic analytical separation system.
-
- (5)
Binary and multiple classification of samples entail using a given number of primary data (signals) obtained in a discriminate manner from a single or several instrumental parameters (that is, specific information dimensions).
-
Classical Instrumental Analysis generally uses a single “instrument” (e.g., human sight) to observe a single signal (e.g., formation of a precipitate).
-
Identifying quinine in tonic water by its native fluorescence entails using two instrumental parameters (the excitation and emission wavelengths) to produce a signal (fluorescence intensity).
-
Identifying a substance by infrared (IR) absorption spectroscopy requires using a whole IR spectrum, which entails acquiring a large number of absorbance signals at many different wavenumbers. Only in that way can the target substance be identified (see, for example, benzene in Slide 6.37).
-
In summary, Qualitative Analysis
possesses a variety of nuances that substantially enrich it
conceptually.
6.1.5 Classical Qualitative Analysis (8 Slides)
6.1.5.1 Generalities (2 Slides)
Slide 6.27
This brief description of
Classical Qualitative
Analysis is started with its definition, which comprises the
use of
-
human senses as “instruments” and the brain as a “computer”; and
-
(bio)chemical or immunological reactions between the analyte and a reagent to obtain a product that can be easily seen or smelt.
Qualitative
identification additionally involves comparing the signal for the analyte—or
its absence—with that for a standard. The two are compared by the
brain to produce a result: YES or NO. Thus, if an unknown liquid
smells of acetic acid (the standard), the liquid can be identified
as vinegar.
Although Qualitative Analysis
possesses doubtless advantages (e.g. expeditiousness and
simplicity), it also has major limitations such as the following:
-
a low selectivity arising from little variety in the information that can be obtained and the fact that the reactions used—immunoassays excepted—are typically subject to many interferences; and
-
an also low sensitivity resulting from the limited ability of human senses to detect small changes.
As a consequence, reliability in
Classical Qualitative Analysis is usually modest because it rests
on the basic properties sensitivity and selectivity (see Slide 6.14
for a general scheme of analytical properties in Quantitative
Analysis).
The previous limitations preclude the
use of Classical Qualitative Analysis for multiple classification
and restrict it to binary (YES/NO) classification (see Slide
6.7).
Slide 6.28
This slide classifies Classical
Qualitative Analysis according to the following criteria:
- (1)
The experimental procedure used, which depends on whether one, several or many analytes are to be identified in the same sample. Sensitive, selective reagents, which are very scant, afford direct analyses (that is, analyses without separation); most often, however, some analytical separation into groups of species (that is, an analytical scheme) is needed to improve the sensitivity and selectivity of the identification.
- (2)
The nature of the analytes, which will require a different type of procedure depending on whether they are inorganic, organic or biochemical.
- (3)
The nature and purpose of the reagents, which is very important (see Slides 6.29 and 6.30).
6.1.5.2 Types of Reagents (3 Slides)
Slide 6.29
This slide expands on the
classifications of Classical Qualitative Analysis according to the
nature and purpose of the reagents used for identification.
The first
classification, based on the nature of the reagents, distinguishes
between biochemical and immunochemical reagents—the latter are
especially useful by virtue of their high selectivity.
The second
classification is based on the function of the reagents, namely:
separating a group of analytes, identifying an analyte or masking
it to facilitate the identification of others.
Both classifications are discussed in
greater detail in the next two slides.
Slide 6.30
Based on their nature, analytes and
reagents —largely identification reagents— can be related in eleven
different ways for purposes the most common among which are as
follows:
-
detection of inorganic analytes with inorganic or organic reagents;
-
detection of organic analytes with organic, biochemical or immunochemical reagents; and
-
detection of biochemical analytes with biochemical or immunochemical reagents.
Slide 6.31
These are several examples of
identification reactions.
-
The first table exemplifies the detection of inorganic analytes according to the effect to be detected by the human senses (formation of precipitate, a colour change, bubbling of a gas). The analyte, the reagent and the substance producing the effect are stated, and so is the supplementary confirmation reaction to be used.
-
The second table shows three examples of identification of organic species (or species families). The effect shown on the first column in each row is due to the product on the last.
6.1.5.3 Analytical Schemes (3 Slides)
Slide 6.32
Analytical schemes are classical
qualitative analytical processes that are used to detect many
analytes (A, B, C, D, E, F, etc.) in the same sample. There are
three different types of systematic detection processes depending
on the characteristics of the sample matrix and the number of
analytes to be identified, namely:
- (A)
Direct detection processes, which can only be used in ideal situations and involve subjecting n aliquots of sample to n direct, independent tests for each analyte. The low sensitivity and selectivity of Classical Qualitative Analysis make direct detection highly desirable but unfeasible unless only a few analytes contained in a simple sample matrix (e.g., water) are to be identified—which, however, usually requires using highly specific, expensive reagents.
- (B)
Processes involving systematic separations to isolate individual analytes or analyte groups in order to increase the sensitivity and selectivity of the detection tests. Analytes can be separated in two different ways, namely:
-
Chromatographically. Each analyte is isolated in a given zone of the mobile phase to enable its interference-free detection.
-
By groups. This requires using so-called “group reagents” (see Slide 6.29), which are usually precipitants, in systematic separations. Analytical schemes with group separation are described in Slides 6.33 and 6.34.
-
- (C)
Mixed processes. These are combinations of the previous two involving the sequential detection of each analyte in a sample aliquot with or without application of a separation system. These processes constitute analytical schemes without group separation (see Slide 6.34), a dubious designation because they do involve separations—to identify individual analytes rather that to separate them in groups, however.
Slide 6.33
Analytes can be separated into
different groups (G) by using precipitating reagents (R) in two
different ways, namely:
- (1)
By sequentially adding the reagents (R1–R3 in the slide) to the sample solution in order to successively separate the analytes into groups (I–III), after which the sample will only contain a group of soluble analytes (IV) not reacting with the precipitants.
- (2)
By using a reagent R1 to split the analytes in the sample into two large groups: soluble and insoluble analytes. In parallel, the two groups are treated separately with two other reagents R2 and R3 in order to eventually obtain two soluble groups (I and III) and another two insoluble groups of analytes (II and IV).
The symbols at the bottom of the
slide make it easier to interpret the two types of schemes.
Slide 6.34
This slide explains analytical
schemes without group separation for the identification of a set of
analytes A1–A n in the same sample, which share
several common features, namely:
-
they involve performing n independents tests (one per analyte to be identified);
-
they use highly sensitive and selective—and usually expensive—reagents;
-
the identification tests must be conducted in a strictly controlled sequence—from the most sensitive or selective to the least;
-
the qualitative information obtained in each test is used to adjust the next—hence the need to use highly sensitive and selective tests first;
-
each test requires some separation, usually with a precipitating reagent or, less often, a masking (chelating) reagent (see Slide 6.29); and
-
the complexity (number of steps) of the tests increases as the identification process develops.
6.1.6 Instrumental Qualitative Analysis (7 Slides)
6.1.6.1 Generalities (1 Slide)
Slide 6.35
6.35.1. Instrumental Qualitative Analysis uses
instrumentally measured physico–chemical properties of analytes or
their reaction products for identification.
6.35.2. Identification in Instrumental
Qualitative Analysis relies on a triple comparison involving the
measurements (signals) for a blank (the sample matrix containing no
analyte), a standard of the analyte and the sample from which
analytical information is to be extracted.
For example, the fluorescence
intensity I F at
a given excitation and emission wavelength for the blank signal was
0.020; that for a sample standard containing an analyte
concentration C
AP was 0.210; and that for the target sample 0.280. The
signal (fluorescence intensity) corresponding to the analyte
concentration is thus 0.260 and a simple proportion allows
C A to be easily
calculated.
If the analyte concentration is equal
to or greater than the limiting concentration (C A ≥ C L), then the binary
response will be YES; otherwise (C
A < C L), the response will be
NO.
6.35.3. Reliability is much greater in
Instrumental Qualitative Analysis than it is in Classical
Qualitative Analysis because instruments are much more sensitive
and selective than the human senses.
6.35.4. Instrumental Qualitative Analysis
can be classified in many different ways. The most immediate way is
according to the type of signal (optical, electroanalytical,
thermal, mass, radiochemical) used for identification. The
following four slides describe an alternative classification based
on the time-dependence of the signal, according to which
instrumental qualitative analytical systems can be of the
static or dynamic type.
6.1.6.2 Static Systems (2 Slides)
Slide 6.36
In static systems, the signal remains
constant over time.
Not all measuring instruments are
equally capable of discriminating signals for different sample
components. Thus, UV–visible absorption spectroscopy has a low
discrimination potential. As can be seen in the slide, identifying
all three analytes (A, B, and C) in this example is impossible
because their absorption spectra (absorbance vs wavelength
recordings), in black, are very similar, so no wavelength zone
exists where each analyte can be detected in the presence the other
two. If no alternative detection equipment is available, the
analytes must be discriminated chemically. For example, if a
reagent R reacts selectively with only one of them (e.g., A) to
form a bluish red chelate AR and the chelate absorbs at 600 nm
without interference from the other two analytes, A can be reliably
identified.
Slide 6.37
This slide illustrates the automatic
identification of benzene by infrared (IR) absorption spectroscopy,
which is a widely used technique for qualitative analytical
purposes.
The signals for the sample and the
standard (US000022) remain unchanged with time. Comparing a large
number of instrumental (absorbance, wavenumber) data ensures a high
reliability.
The first IR spectrum corresponds to
the sample and the second to an analyte standard as recorded in a
spectral database. A computer allows the sample spectrum to be
compared to more than 50,000 spectra in a database and, if a
coincidence is found, the analyte to be matched to a specific
compound with a given level of reliability (98% in our case).
Advances in miniaturization have allowed databases to be
incorporated into measuring instruments and enormously empowered
Instrumental Qualitative Analysis as a result.
A mass spectrometer (MS) is another
instrument with a high analyte identification potential.
6.1.6.3 Dynamic Systems (4 Slides)
Slide 6.38
In dynamic systems for Instrumental
Qualitative Analysis, the measured signal changes with time (it is
time-dependent). These systems typically use a detector coupled to
a GC or LC chromatographic column or electrophoretic capillary for
detection after separation. Each analyte is identified in terms of
a qualitative parameter called the “retention time”. If the
chromatogram exhibits a signal at the typical retention time for a
given analyte, then the binary response for the presence of the
analyte in the sample will be YES.
This slide depicts a gas
chromatograph (GC) and a liquid chromatograph (LC), which differ in
the way the chromatographic fluid is propelled and hence in the
nature of the mobile phase. The two use a similar, but not
identical, sample insertion (injection) system, column and
continuous detector.
Slide 6.39
This slide illustrates the potential
of liquid chromatography for multi-identification with the
separation and identification of 19 drugs in human serum. Although
some peaks in the chromatogram are overlapped, their retention
times are different enough for qualitative identification
purposes.
Slide 6.40
6.40.1. This is a real-life example: the
identification by liquid chromatography of an unknown compound in a
cola drink.
6.40.2. A sample of the drink gives a
chromatograph including three identifiable (expected) peaks and a
fourth corresponding to an unknown compound—possibly an
acidulant.
6.40.3. Samples of the drink spiked with
different preservatives are analysed in the same manner. The sample
to which benzoic acid is added gives a peak coinciding with the
fourth in the chromatogram for the initial sample, albeit much
higher, which confirms that the peak in the original chromatogram
corresponded to the acidulant benzoic acid.
Slide 6.41
So-called
instrumental hybridization
is the synergistic combination of two or more instrument systems in
order to boost their individual qualitative and quantitative
information potentials. The key to an effective connection here is
finding an appropriate interface with a view to maximizing analyte
separation and information production.
A large number of hybrid instrumental
systems are commercially available at present. Especially
interesting are those combining a dynamic system and a static
system.
Most hybrid systems use a gas or
liquid chromatograph, or a capillary electrophoresis system, in
combination with a mass spectrometer. The detector coupled to the
separation system should never be of the destructive type. Hybrid
systems produce vast amounts of information that require
computerized processing but afford extremely reliable qualitative
identifications.
6.2 Annotated Suggested Readings
BOOKS
Principles of Analytical Chemistry
M. Valcárcel
Springer-Verlag, Berlin, 2000
This was the first book to start the
teaching of Analytical Chemistry with its foundations before
dealing with methods and techniques in order to provide students
with an accurate notion of what Analytical Chemistry is and
means.
The contents of this chapter coincide
to a great extent with those of Chapter V in the book (“Qualitative
Aspects of Analytical Chemistry”). Although this chapter is more
synthetic, it contains more examples and elaborates on some topics
to better illustrate the state of the art in Qualitative Analysis
(particularly as regards screening systems and the adapted
description of the “uncertainty” concept). The book provides a
source for direct consultation. Few Analytical Chemistry textbooks
deal with Modern Qualitative Analysis.
Metrology of Qualitative Chemical
Analysis
M. Valcárcel et al.
European Commission, Brussels,
2002.
This 166-page document presents a
systematic approach to basic and applied developments in Metrology
in Qualitative Analysis. This chapter is based on it.
PAPERS
Qualitative Analysis
M. Valcárcel et al.
Encyclopaedia of Analytical Science,
Elsevier (Amsterdam), 2005, 405–411.
This paper expands on the contents of
the present chapter. The paper is structured similarly but contains
additional figures that complete the message of this chapter.
Analytical Features of Qualitative
Analysis
S. Cárdenas & M. Valcárcel
Trends in Analytical Chemistry, 2005,
24, 477–487.
This paper focuses on the
singularities of analytical properties as applied to Qualitative
Analysis. Thus, it provides firm support for the description of
analytical properties and errors here. The paper additionally
describes the validation of an analytical process for qualitative
purposes.
6.3 Questions on the Topic (Answered in Annex 2)
6.1. Does the qualitative analysis of
samples fit in Classification Analysis?
6.2. What name is usually given to
qualitative analytical processes?
6.3. Tick the analytical properties that
are not applicable to Qualitative Analysis.
-
[ ] Representativeness
-
[ ] Accuracy
-
[ ] Precision
-
[ ] Sensitivity
6.4. Two methods for the qualitative
analysis of milk samples possibly contaminated with pesticides
provide wrong information. Thus, method A gives false positives and
method B false negatives. Which would you use? Why?
6.5. What are the main differences
between Qualitative Analysis and Quantitative Analysis? Tick the
correct answers.
-
[ ] The binary response
-
[ ] A classical method of analysis
-
[ ] The use of analytical chemical standards
-
[ ] The analytical property “reliability”
-
[ ] Selectivity
6.6. What are the differences between
binary and multiple classification in Qualitative Analysis?
6.7. What are the factors dictating the
following parameters?
(a) Limit of detection
(b) Cut-off concentration
(c) Threshold concentration
6.8. What is a false positive in
Qualitative Analysis? Give an example.
6.9. What is a false negative in
Qualitative Analysis? Give an example.
6.10. An immunochemical test (method A)
and a chemical spot test (method B) are used to detect the same
analyte in the same sample. The results of analysing 100 samples
are as follows:
|
Reliability (%)
|
False positives (%)
|
False negatives (%)
|
|
|---|---|---|---|
|
Method A
|
95
|
2
|
3
|
|
Method B
|
94
|
6
|
0
|
Which method provides the better
results? Why?
6.11. What analytical properties are
applicable to quantitative determinations but not to qualitative
tests? Why?
6.12. What are “analytical systems with
group separation” in Classical Qualitative Analysis?
6.13. What are the differences between
group, identification and masking reagents in Classical Qualitative
Analysis?
6.14. Name two identification
(Qualitative Analysis) procedures used in dynamic instrumental
systems (e.g., chromatography).
6.15. Tick the words directly connected
with Qualitative Analysis:
-
[ ] Detection
-
[ ] Quantification
-
[ ] Identification
-
[ ] Qualification
6.16. How does a “white” sample differ
from a “black” sample?
6.17. Is Qualitative Analysis important
to modern Analytical Chemistry? Why?
6.18. What are the three quantitative
landmarks for the binary response in Qualitative Analysis?
6.19. One brand of canned tuna fish
contains 4 ppm tin. A qualitative test with C LOD = 1 ppm
for the metal gave a positive (YES) response. What type of error
was made?
-
[ ] None
-
[ ] A false positive
-
[ ] A false negative
6.20. What type of error is the more
crucial in Qualitative Analysis? Why? Give an example.
6.21. Is “specific uncertainty”
applicable to Qualitative Analysis? Why?
6.22. What are the three most important
limitations of Classical Qualitative Analysis in relation to
Instrumental Qualitative Analysis?
6.23. What are the three types of
reagents used in Qualitative Analysis? What is their purpose? Give
an example of each.
|
Name
|
Purpose
|
Example
|
|
|---|---|---|---|
|
Type 1
|
|||
|
Type 2
|
|||
|
Type 3
|
6.24. What are the three main features of
so-called “analytical schemes without group separation”?
6.25. What is the difference between a
dynamic and a static instrumental system in Qualitative
Analysis?
6.26. What analytical properties are
applicable to Qualitative Analysis?
6.27. Are both types of calibration
applicable to Qualitative Analysis?
![$$ \begin{array}{*{20}l} {{\text{Method}}\,{\text{calibration}}} \hfill & {[\;\;]{\text{ Yes}}} \hfill & {[\;\;]{\text{ No}}} \hfill \\ {{\text{Equipment}}\,{\text{calibration}}} \hfill & {[\;\;]{\text{ Yes}}} \hfill & {[\;\;]{\text{ No}}} \hfill \\ \end{array} $$](A431731_1_En_6_Chapter_Equa.gif)
6.28. What types of instruments does
Classical Qualitative Analysis use?
6.29. What are masking reagents? In what
context are they used?
6.30. Define “reliability” in Qualitative
Analysis. To which classical analytical properties does it
relate?
6.31. Instrumental Qualitative Analysis
relies on a triple comparison of signals to be subjected to the
analytical process. What do the three signals belong to?
6.4 An Abridged Version of the Chapter
The contents of this chapter can be
shortened by about 30% for teaching Analytical Chemistry to
students not majoring in Chemistry. The following 12 slides can be
omitted for this purpose: