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8.2 Strategies for Greening Spectrophotometric Methods 159
From the point of view of GAC, the development of advanced spectrophotometric meth-
ods differs only in the type of solvent used to complete the analysis as well as the amount
of waste generated by the method. Using meagre amounts of renewable and environmen-
tally friendly solvents is the main key to increase the method’s greenness. Fortunately, the
criteria for selecting an appropriate solvent for spectroscopic analysis are its ability to dis-
solve the drug, stability, safety, availability, and low cost. Therefore, the use of distilled
water and ethanol as solvents is very common, and both are green solvents, as well as green
methanol or bio-methanol being produced during biological pathways [3, 4], making it a
safe, biodegradable, and environmentally friendly solvent [5]. More than 90% of the sol-
vents used are considered green (water, ethanol, and methanol), which increases the
importance of spectrophotometric methods since they are safe, green, and used in very
small quantities compared to other analytical methods such as chromatography. Several
greenness metrics were developed for the assessment of analytical methods, as shown in
Table 8.2.
Absorption spectroscopy is a commonly applied tool for quantitation of analytes of inter-
est in different matrices. The basic principle of molecular spectroscopy is the interaction
between light and molecules. In normal conditions, the molecule acquires the ground
state, which is its lowest electronic energy state.
In the ultraviolet–visible (UV–VIS) region, the interaction of a molecule with photons
leads to the displacement of the valence electrons due to absorbing energy. The transition
of the molecule goes from its ground state energy level (Eg) to an excited state energy level
(Es) [15]. The Beer–Lambert law can be expressed as A = abc, where A is absorbance, a is
absorptivity, b is path length, and c is concentration. The law entails that absorbance will
be equal to zero (A = 0) at zero concentration (c = 0) at path length = 1 [16]. Thus, the
linear regression equation relating absorbance (y-axis) and concentration (x-axis) is
expressed as y = mx + b, where m and b represent the slope and the intercept values,
respectively. The Beer–Lambert law is valid for describing dilute solutions only. Deviations
from the law occur at higher concentrations and linearity is lost. Spectrophotometric anal-
ysis transcends molecular spectroscopic techniques because of its simplicity, precision,
accuracy, and low time consumption [17].
Spectrophotometric analysis of multicomponents should be applied where the compo-
nents’ spectra show partial or complete overlapping. Determination of the multicompo-
nents can be done using simultaneous equations to calculate the concentration of each
component; otherwise, resolution of the overlapped spectra into individual ones is carried
out [18]. To resolve the spectrum of one component from a mixture, the spectrum of the
second component should be subtracted from the total solution absorbance. The methods
are classified according to the extent of overlapping into resolving either severely over-
lapped or partially overlapped spectra [19]. The spectrophotometric techniques developed
in the last few decades for the analysis of multicomponent mixtures are classified based on
manipulation steps into four windows [20]:
● Window 1: Spectrophotometric methods based on zero-order (D
0
) absorption spectra.
● Window 2: Spectrophotometric methods based on derivative spectra.
● Window 3: Spectrophotometric methods based on ratio spectra.
● Window 4: Spectrophotometric methods manipulating ratio spectra.
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Table 8.2 A summary of greenness metrics for assessment of analytical sample preparation.
Greenness metric Merits Demerits Pictogram References
AGREE (Analytical
GREEnness) is an
algorithmic green
assessment tool that
incorporates the 12
green analytical
chemistry (GAC)
principles
Covers all the principles of
GAC
Explainable
Rich with green information
Gives qualitative and
quantitative assessment
Comprehensive input
Simplicity and clarity of
output
Type of hazard is not
clear
Need special software
to calculate
Does not consider
analytical efficiency
[10]
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Greenness metric Merits Demerits Pictogram References
GAPI (Green
Analytical
Procedure Index) is
based on the
analytical methods
and procedures
used as well as
providing the main
information related
to the prepared
samples, solvents
used, and
instrumentation
types
Semi-quantitative and
qualitative tool
Used to compare the
greenness of two methods
Does not need any special
software to calculate the result
Evaluates the analytical
method in different steps
Information about
hazards is not available
Structure of the
hazardous solvent is
not clear
Absence of sensitivity
Analytical efficacy is
not included
2
5
6
7
8
9
10
11
12
13
14
15
4
1
3
The rules for coloring pictogram
Environmentally
friendly
1-Sample collection
2-Sample preservation
4-Sample stotage
5-Type of method
6-Scale of extraction
7-Nature of solvents
10-Hazard of used solvents
11-Solvent hazard
12-Energy consumption
13-Occupational hazar
ds
14-Waste amount
15-Waste treatment
9-Volume of solvents
8-Sample is collected in-line, off line..etc.
3-Sample transportation
Slightly
harmful
Harmful to the
environment
[11]
(Continued)
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Greenness metric Merits Demerits Pictogram References
AES (Analytical
Eco-Scale) depends
on calculating the
penalty points (PPs)
for reagents,
hazards, energy,
and waste, and then
subtracting the total
from 100
The outcome is
graded on a scale
and the greening of
the analytical
method is classified
into four types
Semi-quantitative tool
Easy to use
Does not require any software
to calculate the results
Used to compare the
greenness of two methods
Gives information about the
hazard and amount of the
solvent used
Evaluates the analytical
method in different steps
No information
provided in the final
result (if the final score
is 42, this does not
reveal whether the
waste is high or the
solvent is hazardous)
Not all of the GAC
principles are relied on
to calculate the final
score
Analytical efficacy is
not ensured
010
20 30
40
50
60
70
80
90
100
• Ideal green analysis if the score of (AES) = 100
• Excellent green analysis if the score of (AES) > 75
• Acceptable green analysis if the score of (AES) > 50
• Inadequate green analysis if the score of (AES) < 50
Analytical Eco-Scale (AES)
(AES) = 100 − (PPs)
Inadequate Acceptable Excellent Ideal
[12]
Table 8.2 (Continued)
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Greenness metric Merits Demerits Pictogram References
AGP (Assessment of
Green Profile) is a
semi-quantitative
greenness tool
depending on five
risk potentials:
health, safety,
environmental,
energy, and waste
Outcomes are in
pictogram form
divided into five
parts, each one
coloured based on
the sort of
environmental
effect
Semi-quantitative and
qualitative tool
Does not require any special
software to calculate the result
Easily compares two or more
analytical greenness methods
Information about
sample preparation is
not available
Structure of the
hazardous solvent is
not clear
Absence of sensitivity
Analytical efficacy is
not ensured
12 principles of GAC
are omitted
The rules for coloring pictogram
Environmentally
friendly
Slightly
harmful
Harmful to the
environment
Environmental
Health
Safety
Waste
Energy
[13]
NEMI (National
Environmental
Methods Index) is
considered as the
first and oldest
greenness tool and
depends on getting
information about
where a sample fits
in the following
categories:
● PBT (persistent,
bio-cumulative,
toxic)
● Hazard
● Corrosive
● Waste amount
Easy to understand
Qualitative tool
Gives a general idea about the
method’s greenness in just
one look
No special software required
Does not cover all
green analytical
principles
Not quantitative
Little and general
information
Search for each
chemical used in
official lists is time
consuming
Energy use
Green is lled when the
reagent does not belong
to the category of
per
sistent, bio-accumulative,
and toxic substances
Green is lled when the
reagent is not hazardous
Green is lled when
the pH of the sample
falls within the range
of 2 to 12
Green is lled if
the amount of
generated wast
e does
not exceed 50 g
PBT Hazard
corrosive waste
[14]
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8 Advanced Approaches in Green Univariate Spectrophotometric Methods164
8.2.1 Window 1
Group A includes methods that depend on calculating the zero-order absorbance differ-
ence (
ΔA) between two points (dual wavelength), which is in direct proportion to the con-
centration of the component of interest. This group includes dual wavelength (DW) [21–26]
and induced dual wavelength (IDW) [23–25, 27, 28], where the interfering substance
shown (
ΔA) does not equal zero. Therefore, the absorbance of the interfering substance(s)
will be equalised using a factor at the two selected wavelengths, while the component of
interest will show different absorbance. The dual wavelength resolution technique (DWRT)
is coupled with DW or IDW for solving the overlapping spectra [18, 23]. The absorption
correction method (ACM) is applied for partial overlapped spectra where the absorbance of
Y can be related at the two wavelengths using a factor and the X concentration can be esti-
mated by subtracting the Y absorbance from the total absorbance [25, 29–31]. The H-point
standard addition method (HPSAM) [32–34] is based on constructing a plot between the
absorbance of mixed components at two assigned wavelengths against the concentration of
X (the added analyte), where the difference in absorbance for Y (the interfering substance)
equals zero, and thus two straight lines are plotted that have a common point.
Group B includes methods based on the isoabsorptive point, which is the point at which
several components can act as a single one due to exhibiting equal absorptivity values, as in
the conventional isoabsorptive point method [35–37]. The absorbance subtraction (AS)
method is similar to the ACM, but using the isoabsorptive point regression equation for the
calculation of each component concentration [38–41]. The advanced absorbance subtraction
(AAS) method depends on calculating ΔA (the absorbance difference) between two selected
wavelengths including the isoabsorptive point [22, 42–44]. The Q-absorbance ratio method
[45–47] and the absorbance ratio method (ARM) [35, 48] apply an absorbance ratio at two
selected wavelengths (isoabsorptive point and λ
max
of one of the two components), followed
by the application of mathematical equations. The absorptivity factor (a-factor) method is
applied where there is a huge difference between the absorptivity of the components, so that
the isoabsorptive point does not occur naturally but it is created by the a-factor [49–51].
Group C includes methods based on area under the curve (AUC-D
0
) measurements
instead of absorbance [52–54]. The AUC correction method (AUC-CM) was introduced to
calculate the AUC of overlapped spectra instead of Cramer’s rule [55, 56].
Group D includes methods that depend on mathematical calculation of an absorbance
vector such as Vierordt’s method, which uses two simultaneous equations and a bivariate
procedure [57–59]. The spectrum subtraction method depends on the fact that the light
absorption of a sample solution is additive [Mixture (X + Y) − X = Y]. It usually represents
a complementary step to other methods [60–63].
8.2.2 Window 2
Derivative spectrophotometry follows the same laws as D
0
absorption, where the derivative
amplitude is additive and dependent on the analyte concentration. The direct derivative
method, in its four orders, has been performed for the quantitation of several drug mixtures
and in the presence of impurities or degradation products. In spectroscopy software,
numerous functions are utilised to achieve preliminary overlapped peak separation and
noise filtering. The derivative function with different orders, such as the first derivative,
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8.2 Strategies for Greening Spectrophotometric Methods 165
second derivative, third derivative and fourth derivative, is commonly used for this pur-
pose. On the other hand, Fourier self-deconvolution is not as widespread, but it serves as a
simple mathematical technique to effectively eliminate broadening in the resulting spectra
[37, 64–66]. Amplitude subtraction (PS) and its modification, modified amplitude subtrac-
tion (MPS), act by recording the mixture’s peak of amplitude and then calculating the pos-
tulated value at the same wavelength. The drugs’ maximum amplitude can be obtained
after subtraction from the mixture’s amplitude [24, 67, 68]. The amplitude correction
(P-correction ) method uses an experimentally tested factor [67–69]. The amplitude sum-
mation method (A-Sum) is similar to PS but shows an isoabsorptive point shift in derivative
order rather than zero order [20, 36, 40, 62, 70]. The compensated area under the curve
(CAUC) method involves calculating AUC for a mixture containing X + Y against different
concentrations of pure drug positioned in the reference cell [71].
The coupling of successive derivative subtraction with constant multiplication (SDS-CM)
is a resolution technique for mixtures showing severely overlapped D
0
spectra but partially
overlapped derivative spectra [70, 72, 73]. Derivative transformation (DT) is applied by
converting the derivative spectrum into its original zero-order spectrum using the normal-
ised spectrum of the analyte [44, 61, 74, 75].
8.2.3 Window 3
Group A includes methods that depend on subtracting the ratio spectra amplitudes. The
ratio subtraction method (RSM) deals with the extension of the D
0
spectrum of one compo-
nent over the other(s) where the constant is measured [76–79]. The extended ratio subtrac-
tion method (EXRSM) is applied as a complementary step to RSM for quantitation of the
less extended component, followed by the modified method to determine the extended one
[78, 80–84]. Another modification to RSM is simultaneous ratio subtraction (SRS) [39, 85],
where the two components (extended and less extended) can be recovered using their cor-
responding divisors.
Group B includes methods calculating the difference of ratio spectra amplitudes. The
ratio difference spectrophotometric method (RDSM) [21, 78, 86–88] depends on calculat-
ing the difference in amplitudes at two selected wavelengths on the ratio spectra, which is
directly proportional to the concentration of the component of interest, and not sensitive to
the interfering component. The constant centre spectrophotometric method (CCSM) [89–
93] includes two complementary steps, namely constant calculation via the amplitude dif-
ference method followed by constant multiplication where the components are determined
via zero-order curves at λ
max
. CCSM can be coupled with spectrum subtraction [43]. The
amplitude centre method [94] is a progressive manipulating approach applied to a ternary
mixture using a single divisor.
Group C includes methods that deal with modulation of ratio spectra amplitudes.
Amplitude modulation method (AMM) [39, 40]. Differential amplitude modulation [75]
uses the normalised spectrum of the divisor where one component is more extended than
the other in the presence of the isoabsorptive point [41, 95–99]. Its extension, advanced
amplitude modulation (AAM) [22, 23, 42], is appropriate for binary mixtures with severely
overlapping spectra showing the isoabsorptive point either by merging the CCSM with the
AMM or calculating the difference between the isosbestic point and another point in the
wavelength. Induced amplitude modulation (IAM) [23, 27, 68] can be applied in case of a
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8 Advanced Approaches in Green Univariate Spectrophotometric Methods166
lack of isoabsorptive point or for an isoabsorptive point with low absorptivity, in addition
to the concentration value method that modulates the amplitude to concentration [75].
Group D includes methods featuring a geometrical representation of ratio spectra ampli-
tudes. The geometrical amplitude modulation method (GAM) [100, 101] offers a geometric
representation of the standard addition effect of X on the binary mixture response of X + Y
and shows it as a regression equation using a normalised divisor spectrum. A modification
of this method, geometrically induced amplitude modulation (GIAM) [100, 101], was
introduced for a binary mixture of X + Y showing absorptivities with large differences, and
hence the zero spectrum exhibits no isoabsorptive point. The ratio H-point standard addi-
tion method (RHSAM) is an extension to the conventional HPSAM [100, 102, 103] using a
normalised divisor spectrum.
8.2.4 Window 4
Ratio spectra are further manipulated using different approaches to estimate each compo-
nent independently. Salinas et al. [21, 104, 105] introduced derivative ratio spectrophotome-
try (DR), which is done by calculating the derivative of the ratio spectrum of the mixture,
which will be independent on the divisor. Double divisor-ratio spectra derivative spectropho-
tometry (DD-DR) is applied for ternary mixtures using a first derivative single divisor of two
components [55, 106, 107]. Zero-crossing derivative spectrophotometry [66, 108] is applied for
ternary mixtures where the derivative spectrum is measured at the zero crossing of the inter-
ferants. Simultaneous derivative ratio spectrophotometry (S
1
DD) [103, 109] is transformed
into simultaneous mode by adjusting with amplitude modulation. Mean centring [78, 110–
112] involves data transformation of ratio spectra by calculating the geometric mean rather
than the arithmetic mean to obtain a less biased central tendency. The pure component con-
tribution algorithm (PCCA) is applied for extraction of components’ signals [113–115] by cod-
ing a function that eliminates interfering components’ signals using the tool of mean centring.
Continuous wavelet transform (CWT) is a powerful signal processing tool close to Fourier
transform in addition to trigonometric functions (sine and cosine) systems [116–120].
8.3 Advanced Ultraviolet Spectrophotometric Methods and
Outcomes
8.3.1 Window 1
8.3.1.1 Absorptivity Centring [121
● Spectral features: D
0
spectra of binary mixture, X + Y, with partially (POS) or completely
overlapped spectra (COS) intersecting in an isoabsorptive point (λ
iso
).
● Manipulation tools: Preparation of a factorised spectrum employing the spectrophotometer’s
software via the division of the D
o
spectrum of Y in its range of linearity by the recorded
absorbance at the isoabsorptive point. The absorptivity factor between the two chosen wave-
lengths is calculated using the average of various concentrations of Y in its pure form (in
POS) or via substitution in computing a statistical equation expressing the absorbance differ-
ence’s relationship at two selected wavelengths
[]λλ
iso
/
2
, where X shows equal absorbance
values versus the recorded absorbance at λ
iso
for different concentrations of pure Y (in COS).
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8.3 Advanced Ultraviolet Spectrophotometric Methods and Outcomes 167
● Mathematical resolution: The actual absorbance of Y at λ
iso
can be obtained either via
multiplying the recorded absorbance by the absorptivity factor or via substituting in
computing a regression equation expressing the absorbance difference’s relationship at
two selected wavelengths
iso
/
2
, where X shows equal absorbance values versus the
recorded absorbance at λ
iso
for different concentrations of pure Y (in COS). The calcu-
lated authentic absorbance of Y at λ
iso
is multiplied by the previously prepared factorised
spectrum of Y to obtain the D
0
of Y. Finally, subtracting the recovered D
0
spectrum of Y
from the corresponding gross mixture’s D
0
spectrum via the spectrum subtraction
method will successfully recover the D
0
spectrum of X.
● Quantification: Applying the regression equations demonstrating the D
0
spectral absorb-
ance of pure X and Y at their λ
max
against the corresponding concentrations will enable
the amount of each component in the mixture to be calculated.
8.3.1.2 Response Correlation [42]
● Spectral features: D
0
spectra of the binary mixture, X + Y, with POS or COS intersecting
in an isoabsorptive point (λ
iso
) as A
iso
and retained as P
iso
in the ratio spectra using pure
X as the divisor.
● Manipulation tools: A statistical equation (SE
1
) representing the relationship between
the absorbance difference at two selected wavelengths
iso
/
2
, where X shows equal
absorbance values versus the recorded absorbance at λ
iso
for various amounts of pure Y.
A second statistical equation (SE
2
) expressing the relation between A
iso
and P
iso
(using X′
as a divisor) for various pure Y concentrations.
● Mathematical resolution: The actual Y absorbance at λ
iso
could be obtained for analysing
the mixture by substituting the recorded absorbance difference in SE
1
. The mixture’s
amplitude corresponding to Y at λ
iso
is calculated via SE
2
. The noted amplitude at λ
iso
of
the mixture’s ratio spectrum is subtracted from the calculated mixture’s amplitude cor-
responding to Y to obtain the constant value of X in the mixture. For each mixture, mul-
tiplication of the calculated amplitude corresponding to X by the divisor (X′) will
successfully obtain the D
0
spectrum of X, while subtraction of the D
0
spectrum X from
the gross D
0
spectrum of the mixture will attain the D
0
spectrum of Y.
● Quantification: Exploiting the corresponding regression equation representing the
absorbance values of pure X or Y in their D
0
spectra at their λ
max
against their parallel
amounts enables calculation of the X and Y concentrations in laboratory mixtures.
8.3.1.3 Advanced Balance Point-Spectrum Subtraction via Zero-Order Spectrum [42]
● Spectral features: D
0
spectra of the binary mixture, X + Y, with POS or COS where X is the
minor component.
● Manipulation tools: A value for the pure Y response ratio (RR), which is the absorbance
ratio (AR) at two maximum points. A statistical equation is representative of the direct
relationship of the mixture’s RR at the two selected wavelengths, λ
1
, λ
2
, of each different
mixture’s spectrum versus the corresponding pure X concentration, C
X
.
● Resolution: Analysing a mix of two components, X and Y, the method observes the change
in the AR of the mixture (on subtracting various concentrations of the pure minor com-
ponent X above and below that expected to be in the mixture solution. The difference
spectrum for each concentration of X is calculated using spectrum subtraction. As C
X
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8 Advanced Approaches in Green Univariate Spectrophotometric Methods168
increases, the characteristic equilibrium point of the combination gradually approaches
the pure drug (Y) and finally accords with the mixture’s RR of the pure drug (Y). The
balance point can be identified using the computed statistical equation as discussed pre-
viously, where C
X
in the combination is equivalent to the subtracted C
X
.
● Quantification: Calculating the minor component X’s concentration in the combination
is achieved by substituting the calculated value of RR for pure Y at the two selected wave-
lengths in the linear regression equation.
8.3.1.4 Induced Concentration Subtraction [27]
● Spectral features: D
0
spectra of the binary mixture, X + Y, with POS or COS where Y is
more extended than X.
● Manipulation tools: Two absorptivity factors, the first (F
λF
) expressing the proportion of the
absorptivity of X to the absorptivity of Y at λ
F
. The second factor (F
Y
) is calculated as the mean
ratio between the two values of absorbance [abs (λ
F
)/abs of various concentrations of pure Y].
● Resolution: For analysing the mixture comprising X and Y, multiplication of the formerly
determined F
Y
of Y by the recorded mixture’s absorbance at λ
1
could successfully attain
component Y’s absorbance alone.
● Quantification: C
Y
in the mixture is assessed by the unified regression equation (URE),
expressing a direct relationship between the absorbance of pure Y at λ
F
versus its correspond-
ing concentrations, while the total mixture’s concentration could be computed by the replace-
ment of the recorded absorbance (A
m
) in the URE at λ
F
. Finally, the Y concentration is
subtracted from the complete mixture, and the outcome will represent F
λF
C
x
, which accord-
ingly will be multiplied by 1/F
λF
to get the amount of component X in the mixture.
8.3.2 Window 2
8.3.2.1 Advanced Balance Point-Spectrum Subtraction via Derivative Spectrum [42]
● Spectral features: Two analytes, X and Y, in their binary mixture with POS or COS where
X is a minor component in any derivative order, D
n
.
● Manipulation tools: A value of the pure Y RR, which is the AR at the selected two ampli-
tude maxima. A statistical equation is generated that demonstrates the linear relation-
ship of the mixture’s RR at the two selected wavelengths, λ
1
, λ
2
, of each different mixture’s
spectrum versus the corresponding pure X concentration, C
X
.
● Resolution: To analyse a mixture of two components, X and Y, the method monitors the
variation in AR for the mixture (on subtracting various concentrations of the pure minor
component X above and below that expected to be in the mixture solution. The differ-
ence spectrum for each concentration of X is calculated using spectrum subtraction. As
C
X
increases, the characteristic equilibrium point gradually approaches the pure drug
(Y) and finally accords with the mixture’s RR of pure drug (Y). The equilibrium point can
be identified using the computed statistical equation as discussed previously, where C
X
in the combination is equivalent to the subtracted C
X
.
● Quantification: Calculating the minor component X’s concentration in the combination
is achieved by substituting the calculated value of the RR of pure Y at the two wave-
lengths in the linear regression equation.
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