Herbal Drugs and Fingerprints || FTIR Spectroscopy: Herbal Drugs and Fingerprints

121D.D. Joshi, Herbal Drugs and Fingerprints: Evidence Based Herbal Drugs,DOI 10.1007/978-81-322-0804-4_7, © Springer India 2012

In infrared (IR) spectroscopy, IR radiations are passed through a sample, where some of the infrared radiations are absorbed by the sample. When the radiant energy matches to the energy of speci fi c molecular vibration, absorption occurs. In IR spectrum, wave number (sometimes called as frequency) is plotted on the x -axis and is pro-portional to the energy. The band intensity can be expressed as absorbance ( A ) or percentage trans-mittance (% T ) as plotted on y -axis (zero trans-mittance corresponds to 100% absorption of light at that wave number) (Fig. 7.1 ). Absorbance ( A ) is logarithms, to the base 10, of the reciprocal to the transmittance ( T ).

(7.1)

The absorbance or transmittance with cor-responding wave numbers as a peak in spectra gives information about the molecule to the analyst, by matching it with spectrum of known compound, peak-by-peak correlation. The spec-trum represents a fi ngerprint of a sample, unique characteristic, as no two different molecular structures can produce the same spectra. This makes infrared spectroscopy useful for several types of analysis as each different material is a unique combination of atoms. Therefore, infrared spectroscopy results are used in molecu-lar identi fi cation (qualitative analysis) of every different kind of material, and the size of peaks in the spectrum is a direct indication of the amount of material present (i.e., purity) in the sample.

The original infrared instruments were of the dispersive type (i.e., the separation of individual frequencies of energy emitted from the infrared source, by a prism or grating). Grating is a more modern dispersive element, used to separate the frequencies of infrared energy. The detector measures the amount of energy at each frequency which has passed through the sample. This results in a spectrum which is a plot of intensity versus frequency. The slow scanning process is a main dif fi culty in this technique. In order to overcome this limitation, the Fourier transform infrared (FTIR) spectrometry is developed on the disper-sive instruments.

To measure all infrared frequencies simultane-ously, rather than individually, a very simple optical device known as interferometer is added with the instrument. The interferometer produces a unique type of signal which has all of the infrared frequencies encoded into it. The signal, known as interferogram, is measured very quickly, usually on the order of 1 s. The interferogram is decoded to individual frequencies, via a well-known mathematical technique called the Fourier transformation, to have a frequency spectrum (a plot of the intensity at each individual wave number/frequency). This transformation is per-formed by the computer with modern algorithms software as the spectrum. All the spectral features in the spectrum are only strictly due to the sample. A single background measurement is suf fi cient for many sample measurements because the background spectrum is characteristic of the instrument itself. Thus, the time span per sample

10log (1 / )A T=

7 FTIR Spectroscopy: Herbal Drugs and Fingerprints

122 7 FTIR Spectroscopy: Herbal Drugs and Fingerprints

is reduced to a few seconds rather than several minutes, as in the case of IR. The normal instru-mental process (Fig. 7.2 ) may be described as a black body source of radiations which passes through an aperture, into the interferometer (where spectral encoding results into the inter-ferogram), to the surface of the sample, fi nally to the detector, specially designed to measure the special interferogram signals. The measured signals are digitized and sent to the computer where the Fourier transformation takes place, as infrared spectrum.

The uniqueness of IR spectroscopy is that any sample virtually at any state may be studied [ 1, 2 ] . Infrared region starts immediately after the visible region at 700 nm. The classical infra-

red region extends from 2,500 to 50,000 nm. This spectral region encompasses three subdivi-sions: the far-infrared (FIR: 400–33 cm −1 or 30–300 m m), mid-infrared (MIR: 4,000–400 cm −1 or 3–30 m m), and near-infrared (NIR: 12,820–4,000 cm −1 or 0.78–3 m m), named in relation to the visible region. Infrared analysts often use wave numbers to describe the infrared spectral region. The energies of infrared radiations range from 48 kJ/mol at 2,500 nm to 2.4 kJ/mol at 50,000 nm. These low energies are not suf fi cient to cause electron transitions, but they are suf fi cient to cause vibrational changes within molecules, so IR spectroscopy is also known as vibrational spectroscopy. As spectrum generates through the incident radiation that is absorbed at a particular

Fig. 7.1 IR spectrum plotted using transmittance ( left ) and absorbance ( right )

Source Interferometer

Interferogram Computer IR Spectrum

Sample Detector

Fig. 7.2 A schematic presentation of IR spectroscopy

123Interpretation of IR Spectra

frequency (i.e., wave number), so it is based on the absorption of electromagnetic radiation by a molecular system. In other words, IR spectra provide images of vibrations of the atoms of a compound. FTIR spectroscopy has developed quickly due to its low noise, rapid speed, high repeatability, easy operation, low expense, and so on. It is also associated with other sciences just like mathematics or computers, or with other techniques such as two-dimensional cor-relation analysis (2D-IR); FTIR has become increasingly useful in the fi eld of evaluating herbal qualities. The vast majority of molecules exhibit infrared peaks in the mid-infrared region (4,000–400 cm −1 ). The position and intensity of a vibrational peaks are characteristics of the underlying molecular motion and consequently of the atoms participating in the chemical bond, their conformation, and their immediate environ-ment. Thus, a certain submolecular group pro-duces peaks in a characteristic spectral region. These characteristic peaks form the empirical basis for the interpretation of vibrational spectra. Moreover, characteristic absorption peaks are used for a speci fi c compound detection. FTIR spectrometers have almost entirely replaced dis-persive instruments because of their better per-formance in nearly all respects. The application of this technique has improved the acquisition of IR spectra dramatically. The major advantage of IR over other spectroscopic techniques is that practically all compounds show absorption and can thus be analyzed both qualitatively and quantitatively. FTIR spectroscopy is nonde-structive and allows in situ and remote measure-ments of almost any sample, irrespective of the physical state and without elaborate sample preparation [ 3, 4 ] , applied to identify the func-tional groups of chemical constituents but has been widely used and applied in recent years for the identi fi cation, quality control, and manu-facturing process supervision of pharmaceutical drugs.

Two units are used in vibrational spectroscopy: cm −1 (wave numbers) or nm. The choice of one of the units depends either on the type of spectrom-eter [dispersive vs. Fourier transform (FT)] or to avoid too large numbers in the NIR range where

nm is more often used. The relationship between the two units is as follows [ 5 ] :

(7.2)

The basic principle of IR spectroscopy is the measurement of the amount of IR radiations absorbed by a sample has a high potential for the elucidation of molecular structures [ 6 ] . The regions of an IR spectrum where bond-stretching vibrations are seen depend primarily on whether the bonds are single, double, or triple or bonds to hydrogen, etc., as [ 7 ] :

Bond Absorption region, cm −1

C–C, C–O, C–N 800–1,300 C=C, C=O, C=N, N=O 1,500–1,900 C=C, C≡N 2,000–2,300 C–H, N–H, O–H 2,700–3,800

Interpretation of IR Spectra

IR spectroscopy is an extremely effective method for determining the presence or absence of a wide variety of functional groups in a molecule. One of the most preferred methods to decipher IR spec-trum is to start at the high wave number end of the spectrum (typically 4,000 cm −1 ) and look for the presence and absence of characteristic absorptions (Table 7.1 ) as move toward lower wave numbers.

The intensity of an absorption peak in the IR spectrum is related to the change in dipole that occurs during the vibration. Consequently, vibrations that produce a large change in dipole (e.g., C=O stretch) result in a more intense absorp-tion than those that result in a relatively modest change in dipole (e.g., C=C). Vibrations that do not result in a change in dipole moment (e.g., a symmetrical alkyne C≡C bond stretch) have little or no absorption. The application of IR spectros-copy in herbal analysis is still very limited com-pared to its applications in other areas (food and beverage industry, microbiology, pharmaceutical, etc.), but infrared spectroscopy is certainly one of the most important analytical techniques available to scientists in herbal sciences [ 8 ] . The utility of FTIR fi ngerprints may be highlighted as follows.

−−=

×1

7

1[cm ]

[nm] 10


Table 7.1 Important regions of the IR spectrum

Wave number (cm −1 ) Functional group Characteristic features of the peak

3,600–2,700 cm −1 (X–H stretch region) 3,600–3,300 Alcohol O–H The alcohol OH stretch is usually a broad and strong absorption

near 3,400. The NH stretch is typically not as broad or strong as the OH, and in the case of an NH

2 , it may appear as two peaks.

The terminal alkyne C–H may be con fi rmed by a weak C≡C bond stretch near 2,150 cm −1

Amine or Amide N–H Alkyne C–H

3,300–2,500 Acid O–H This is normally a very broad signal centered near 3,000 cm −1 3,200–3,000 Aromatic (sp 2 ) =C–H The aromatic CH character usually appears as a number of weak

absorptions, while the alkene C–H is one or a couple stronger absorptions

Alkene (sp 2 ) =C–H

3,000–2,800 Alkyl (sp 3 ) C–H Almost all organic compounds have alkyl CH, so this is not usually too informative, but the intensity of these peaks relative to other peaks gives indication as to the size of the alkyl group

2,850 and 2,750 Aldehyde C–H Two medium-intensity peaks on the right-hand shoulder of the alkyl C–H. The response for the presence of carbonyl C=O peak in spectrum to con fi rm

2,300–2,100 cm − 1 (C≡X stretched region) 2,260–2,210 Nitrile C≡N A sharp, medium intensity peak. Carbon dioxide in the atmo-

sphere may also result in an absorption in this area if not subtracted/pursed-out

2,260–2,100 Alkyne C≡C The peak intensity varies from medium to nothing; since the intensity is related to the change in dipole moment, symmetrical alkynes will show little or no absorption here

1,850–1,500 cm −1 (C=X stretch region) 1,850–1,750 Anhydride C=O Anhydrides have two absorptions, one near 1,830–1,800 and one

near 1,775–1,740. The absorption frequency increases as the ring size decreases, e.g., cyclohexanone = 1,715, cyclopen-tanone = 1,745, cyclobutanone = 1,780, cyclopropanone = 1,850

3–4 member ring C=O

1,750–1,700 Aldehyde C=O It is usually the most intense absorption in the entire spectrum Ketone C=O Ester C=O Acid C=O

1,700–1,640 Amide C=O Due to resonance, amides, and conjugated carbonyl, there comes slightly lower response than normal C=O. In general, conjugation lowers the absorption by 20–50 cm −1

Conjugated C=O

1,680–1,620 Alkene C=C Absorption is not as intense as for C=O. It is variable and may be fairly small in symmetrical or nearly symmetrical cases. For con fi rming alkene response of C–H peaks above 3,000 cm −1

1,600–1,400 Aromatic C=C Multiple sharp, medium peaks appear. The pattern of peaks varies depending upon the substitution pattern. Usually there is one peak around 1,600 cm −1 and several others at lower wave numbers

1,500–400 cm −1 ( fi ngerprint region) 1,300–1,000 C–O A strong peak 1,500–400 Various functional

groups Interpretation of peaks in the fi ngerprint region is complicated due to the large number of different vibrations that occur here. These include single bond stretches and a wide variety of bending vibrations. This region gets its name since nearly all molecules (even very similar ones) have a unique pattern of absorptions in this region

125Authentication of Herbal and Herbal Drugs

Authentication of Herbal and Herbal Drugs

During the previous two decades, there have been much more emphasis on the use of FTIR for herbal authentication, as data from literature search indicate that most of the papers are related to qualitative assays of an active compound, but there are also many papers dedicated to quantita-tive methods. Authors have priority to proposing new methods for qualitative and quantitative determinations, but a few are also concern with establishing new methods for analytical estima-tion of geographic origin. Examples may be cited as follows:

Radix Aconiti kusnezof fi i has been analyzed from various processed products and from their ether extracts by using FTIR spectroscopy and 2D-IR [ 9 ] . There are distinctive differences in the absorption peaks in the range of 1,800–1,500 cm −1 in the second derivative spectra of different pro-cessed products. The authors used the second-derivative spectra because of their better resolution. With the use of high resolution, high speed, and convenience FTIR, quickly and precisely distin-guish various processed products such as Radix A. kusnezof fi i can be applied to predict the ten-dency of transformation of the complicated chemical mixture systems under heat perturbation.

The root of Angelica sinensis (olive) is well known in traditional Chinese medicine for common use. Modern pharmacological research indicates that it could be used to treat anemia, apoplexy, hypertension, coronary heart disease, thromboangii-tis obliterans, super fi cial thrombosed phlebitis, etc. FTIR associated with second-derivative infrared spectroscopy and 2D-IR has been used to study the main constituents in traditional Chinese formula-tion made from Angelica and its different extracts (extracted by petroleum ether, ethanol, and water in turn). This method to use macroscopic fi ngerprint characters of FTIR and 2D-IR spectrum to identify the main chemical constituents of plant in their different extracts and compare the components for differences among the similar samples is highly rapid, effective, visual, and accurate for pharmaceutical research [ 9 ] .

The quality of buchu oil obtained from two South African species, Agathosma betulina and Agathosma crenulata (family Rutaceae, both), has been studied using FTIR spectroscopy. Samples of A. betulina and A. crenulata are col-lected from different natural localities and culti-vation sites in South Africa. The essential oil was extracted by hydro-distillation and scanned using three spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), and Raman. A comparison of the three spectroscopy tech-niques indicates that MIR together with PLS (partial least squares) algorithms produces the best model for the quanti fi cation of six of the seven major oil constituents [ 9 ] .

FTIR has been used to simultaneously analyze the main chemical constituents in different sol-vent extracts of several kinds of Chrysanthemum samples of different regions [ 9 ] . The fi ndings indicate that different Chrysanthemum samples have dissimilar fi ngerprint characters in FTIR spectra. These spectral fi ngerprints provide struc-tural information for complicated test samples. Liu et al. have reported that they are able to iden-tify the main components of different extracts and distinguish the origins of the Chrysanthemum samples from different regions easily [ 10 ] . Multistep infrared spectroscopic methods, including conventional FTIR, second-derivative spectros-copy, and 2D-IR correlation spectroscopy, have been proved to be effective methods to examine complicated mixture system such as the inves-tigation on the effect of fl owering on the phar-maceutical components of Cistanche tubulosa . The results provide a scienti fi c explanation for the traditional experience that fl owering con-sumes the pharmaceutical components in stem and the seeds absorb some nutrients of stem after fl owering. A new method using single-re fl ection Fourier transform infrared spectroscopy was proposed for direct and fast determination of Corydalis yanhusuo W. T. Wang and of tradi-tional Chinese herbal medicines and its confus-able varieties. FTIR spectroscopy and 2D-IR have been employed to propose a new method for analysis of Cordyceps . Their second-derivative spectra can amplify the differences and con fi rm the potential characteristic IR absorption bands.


The different fi ngerprints display different chem-ical constitutes. Chestnut ( Castanea sativa ) shell and eucalyptus ( Eucalyptus globulus ) bark, waste products of the food and wood industries, respec-tively, have been analyzed as potential sources of antioxidant compounds. The antioxidant activity and the total phenols content of the extracts had positive linear correlations. FTIR spectroscopy con fi rmed the higher content of phenolic com-pounds in chestnut shell extracts compared to eucalyptus bark extracts [ 9 ] .

The determination of sweet cherry anthocya-nins in crude material of three varieties using dif-fuse re fl ectance infrared Fourier transform spectroscopy (DRIFTS) and the curve- fi tting deconvolution method has been described. A lin-ear relationship between the sweet anthocyanins content and the peak area at 1,640–1,630 cm −1 was established with a high correlation coef fi cient (0.990). The deconvolution analysis using the curve- fi tting method allowed the elimination of spectral interferences from other cell wall com-ponents. The proposed method is simple, rapid, and nondestructive and could be applicable to any cherry varieties [ 9 ] .

A systematic study for the effect of the com-mercially available, ultra-diluted drug from Digitalis purpurea (extract of foxglove leaves) with aqueous ethanol has been conducted to mon-itor changes in its chemical structure/functional group arrangements using vibrational (FTIR and Raman) spectroscopy. These changes suggest a signi fi cant effect of ultra-dilution (micro-vol-umes) in the spectrum pro fi le of Digitalis bands in the fi ngerprint region. The technique is useful in the detection and identi fi cation of compounds/chemical groups present at levels lower than micro-volume in drugs used in alternative/com-plementary medicine [ 9 ] .

The composition of fenugreek seeds in the form of powder, ash, and oil has been investigated through FTIR and Raman spectra measurements. The results indicate that fenugreek seeds (in pow-der form) are rich in proteins, but lipids and starch are in small amounts. The fenugreek oil FTIR absorbance ratios A

3009 / A

2924 , A

3009 / A

2854 , and

A 3009

/ A 1740

have been studied for iodine values, and data reveals that the iodine value of fenugreek oil

is higher than that of other oils. On the other hand, the ash of fenugreek is very rich in phos-phate compounds. The spectra showed some absorption bands that are due to phosphate compounds. It could be concluded that the inor-ganic part of fenugreek consists mainly of phosphate compounds [ 9 ] .

Ginseng, an expensive herb of high therapeu-tic value, is mostly adulterated with other cheaper products, so quality assurance is required as its commercial products such as capsules, powder, soft gels, and teas are available globally with high demand. Yap et al. have discussed a rapid means of distinguishing American and Asian ginsengs from two morphological fakes, namely, sawdust and Platycodon grandi fl orum , via pattern differ-ences and principal component analysis of their infrared spectra [ 8 ] . The results showed that ginseng can be distinguished from both sawdust and Platycodon grandi fl orum ; hence, there is a potential for the use of infrared spectroscopy as a novel analytical technique in the authentication of ginseng. A simple and rapid protocol based on infrared wavelengths and principal component analysis for identi fi cation and categorization of ginseng has been elaborated. The advantage of this protocol is that it is able to provide rapid identi fi cation of natural products because it avoids tedious extraction or puri fi cation proce-dures. The results showed that this protocol was able to discriminate not only raw ginseng roots but also different types of ginseng in three com-mercial ginseng products [ 9 ] .

The misidenti fi cation of ginseng using tradi-tional methods of authentication via morphology has been overcome by infrared spectroscopy. Molecular spectroscopy (including near-infrared diffuse re fl ection spectroscopy, Raman spectros-copy, and infrared spectroscopy) with OPUS/Ident software (Thermo Scienti fi c-Nicolet, Waltham, MA) has been applied to clustering ginseng according to species and processing methods. The results demonstrate that molecular spectro-scopic analysis provides a rapid, nondestructive, and reliable method for identi fi cation of this Chinese traditional herbal drug. The traditional methods, which are laborious and time consuming, have been replaced by molecular spectroscopic


analysis, as more effective method. The herbal materials of Asian ginseng (the root of Panax gin-seng ), American ginseng (the root of Panax quinquefolius ), and notoginseng (the root of Panax notoginseng ) have been differentiated by conven-tional Fourier transform infrared spectroscopy (1D-FTIR) (Fig. 7.3 ) and 2D correlation FTIR applying a thermal perturbation. These species of herbs were further identi fi ed based on the positions and intensities of relatively strong auto-peaks and positive or negative cross-peaks in their 2D-FTIR spectra. The fi ndings provide a rapid and new operational procedure for the differentiation of these notable herbs.

Huanglongbing (HLB), also known as citrus greening, has greatly affected citrus orchards in Florida and has been studied for the detection using FTIR. Leaf samples of healthy, nutrient-de fi cient, and HLB-infected trees were processed and analyzed using a rugged, portable mid-infrared spectrometer. The spectral peak in the region of 952–1,112 cm −1 was found to be dis-tinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrated the applicability of mid-infrared spectroscopy for HLB detection in citrus [ 9 ] .

FTIR spectroscopy has been emphasized as a widespread technique in the quick assess of food components. In one such work, procyanidins have been extracted with methanol and acetone/water from the seeds of white and red grape vari-eties. FTIR spectroscopy allowed the creation of a partial least squares (PLS1) regression model with eight latent variables (LVs) for the estima-tion of the degree of polymerization (DPn), giv-ing a root mean square error of cross-validation (RMSECV) of 11.7%. The application of orthog-onal projection to latent structures (O-PLS1) clari fi es the interpretation of the regression model vectors. Moreover, the O-PLS procedure removed 88% of non-correlated variations with the DPn [ 9 ] . Kava has been used as a folk medicine and well-known traditional beverage. A study focused on quantitative analyses of kava lactones in kava samples using FTIR data results is similar to values with GC (R2 = 0.75–0.98), indicating that the FTIR method is suitable for determination of the contents of the six kava lactones in kava samples and thereby the chemotypes [ 9 ] .

Studies for relative abundance of active ingre-dients in many deciduous perennial fruit crops reveal requirement of winter chilling for adequate bud break and fl owering. Recent research has shown that changes in sugar and amino acid pro fi les are

Fig. 7.3 FTIR spectra of ( a ) Asian ginseng, ( b ) American ginseng, ( c ) notoginseng [ 9 ]


associated with the release of buds from dormancy. Judd et al. applied FTIR spectrometry to provide an alternative mechanism for tracking metabolic changes in the meristems of kiwi fruit buds during winter dormancy [ 11 ] ; results suggested that the application of multivariate analysis to FTIR spec-tra has the potential to be a reliable and fast method for detecting structural and compositional changes in fruit crops. These wave numbers appear to be associated with carbohydrate, pectin, and cellulose levels in the meristems. It is expected that this FTIR signature can be used to advance our understanding of the in fl uence of the various environmental and physiological factors on the breaking of bud dormancy and shoot out-growth, including the optimum timing and con-centrations of applications of bud break regulators, such as hydrogen cyanamide. FTIR for herbal analysis is used to decipher the geographical ori-gin of the raw herb. In addition to the applications already mentioned related to Agathosma betulina and Agathosma crenulata and ginseng analysis, there are other reports where FTIR was used as discrimination technique for Epimedium Korean Nakai from Jilin province, China, and patchoulis ( Pogostemon cablin Blanco) from different Chinese provinces (Nanxiang, Paixiang, Zhanxiang, and Zhaoxiang) (Fig. 7.4 ).

Studies on the identi fi cation of four species of Mongolian herbal medicine by ultraviolet (UV)/ fl uorescence/IR spectroscopy have been

carried out. The methanolic extracts of Mongolian herbal drug Rubus sachalinensis Leveille, its substitute materials Sambucus williamsii Hance and Uncaria rhynchophylla (Miq.) Jacks and its adulterant Cinnamomum cassia Presl showed clear differences in the IR spectra. IR spectros-copy provided more information through the fi ngerprints region of herbal medicines, render-ing the technique direct and simple. Lakshimi Prasuna et al. studied the FTIR spectra of Basella rubra and Moringa oleifera leaves and showed evidence for the protein matrix bands and those corresponding to carboxylic C–O bonds (Fig. 7.5 ) [ 12 ] . Phyllagathis praetermissa collected from Pasoh Forest Reserve (Negeri Sembilan), Ampang Forest Reserve (Selangor), and Bukit Lagong (Selangor) in Peninsular Malaysia were differenti-ated based on their chemical constituents by using multistep infrared macro- fi ngerprinting.

These data provided useful information for the best choice of soil, geographical location, and transplantation of herb culture. According to these spectral fi ngerprint features, we cannot only identify the main components of different herbal plants and extracts but can also distinguish the origins of samples from different regions easily, which is troublesome using existing analytical methods. Compared with traditional methods, which are laborious and time consuming, the molecular spectroscopic analysis is more effec-tive and ef fi cient.

3354

2929

1652

17371692

1659 16061500

1516 1468

1455

15151036

1319

782

A

A

wave number cm−1 wave number cm−1

A A

B

B

C

C

D

D

Fig. 7.4 Conventional IR spectra ( panel I ) and second derivative IR spectra ( panel II ) of four patchouli samples of different geographical origins at room temperature [ 9 ] .

(A) Nanxiang, (B) Paixiang, (C) Zhanxiang, and (D) Zhaoxiang


Interpretation of Herbal Drug FTIR Spectra by Chemometrics: The data interpretation with the help of personal computers, generated by modern analytical instruments, for the acquisi-tion and processing needs the education of chem-istry, mathematics, and statistics with numerical software. This computer-based chemical disci-pline that uses mathematical and statistical meth-ods, (a) to design or select optimal measurement procedures and experiments and (b) to provide maximum chemical information by analyzing chemical data, is known as chemometrics.

The FTIR spectral fi ngerprint provides the information about the source of pure ingredient either of natural or synthetic in origin. Due to the inherent complexity of the IR spectrum, the actual interpretation may be dif fi cult, and opera-tion requires much experience. Indeed, slight dif-ferences in the spectra within the same plant species may not be obvious and generally not vis-ible to the naked eye. The quality of plant sam-ples from different localities and growing conditions based on their geographical origin may vary. Thus, the identi fi cation of origin of crude herbs based on geographical origins is cru-cial in order to ensure authenticity, quality, safety, and ef fi cacy of the raw material before it is con-verted to the fi nal product. Herbal product manu-facturers are always looking for such faster and cost-effective veri fi cation method for traceability,

as wet chemistry analysis is too laborious and time consuming.

A case study was conducted by incorporating appropriate chemometric methods [principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA)] as tools for extracting relevant chemical information from the infrared spectra. The team of scientists under-took this effort to introduce and to develop a rapid quality veri fi cation method with the integration of statistical and mathematical modeling for extracting relevant information base on the infra-red spectroscopy data and extending the used of FTIR transmission spectroscopy, selecting Orthosiphon stamineus Benth (Java tea), used for treating infection of the urinary tract, kidney, and bladder stones disease based on its geographical origin and varieties. The dried leaves of O. sta-mineus from ten different geographical origins of two varieties (white and purple fl owers) were col-lected from Malaysia and Indonesia and coded (Table 7.2 ) [ 13 ] .

The dried leave samples were milled until fi ne powder and were fi ltrated with sieves 0.071 and 0.500 mm mesh size. KBr of spectroscopy grade was also fi ltrated with sieves 0.071 mm mesh size. 2-mg samples were mixed uniformly with 100 mg KBr (2% w/w) and homogenized by using stir vortex CENCO 34 (Breda, Netherlands). The FTIR spectra were recorded in the mid-IR

Fig. 7.5 FTIR spectrum of Basella rubra leaf (at room temperature) [ 9, 12 ]


region 4,000–400 cm −1 at resolution 4 cm −1 with 16 scans using Thermo Nicolet FTIR Nexus spectrometer coupled with DTGS (deuterated triglycine sulfate) detector. The interferometer and the detector chamber were purged with dry nitrogen to remove spectral interference due to atmospheric carbon dioxide and water vapor. Air background spectrum was recorded before each sample, and all experiment were performed in six triplicates (six pellets KBr with three scan each), and baseline corrected for each spectrum; absor-bance was normalized so that peak absorbance of the most intense band is set to unity. The spectra were transferred via a JCAMP.DX format into the statistical software program. As per Beer’s law, the absorption of light at a given wavelength is due entirely to the absorptivity of the constitu-ents in the sample. Therefore, any variations in the spectral due to spectrometer and sampling error should be eliminated prior to data analysis. So in this study, two chemometric preprocessing steps, namely, baseline correction and normaliza-tion, were used. Baseline effects introduced by the spectrometer (e.g., detector drift, changing environmental condition such as temperature, spectrometer purge, and sampling accessories) were removed by auto-correcting the spectrum baseline, and normalization of the absorbance

spectra to the most intense band (hydroxyl group, 3,406 cm −1 ) deletes the differences between spec-tra due to different amounts of sample or path length variation [ 13 ] .

Chemometric data analysis was done using the PCA algorithms in the study for reducing the high-dimensional spectroscopic data by con-structing a linear combination of the original variable into a few orthogonal principle compo-nents which contain most of the variability of the data set. This projection method allows (a) visu-alization of the natural clustering in the data, (b) primary evaluation of the between-class similarity, and fi nally (c) fi nding the reasons behind the observed pattern by making correlation with the chemical or physicochemical properties of the studied samples. SIMCA (soft independent modeling of class analogy), a popular classi fi cation, method was used to assign unknown samples in order to test the robustness of this study. The fi ve steps followed were as follows: (1) constructing separate PCA models for each class; (2) deter-mining the optimal number of PCs by validation; (3) fi tting the unknown samples to each prede fi ne model, provided that the class are distinct enough; (4) deciding whether the samples belong to the corresponding class by referring to the object-to-model distance and leverage (distance of the

Table 7.2 List of O. stamineus samples as per its geographical origin and varieties [ 13 ]

S. no. Code Location State Group ID

1 BLKBPM Bumbung Lima Penang A 2 SRKBPM Kepala Batas Penang B 3 SZBKAM Bota Kanan Perak C 4 MABTRM Bohor Temak Perlis D 5 ZARWBM Rawang Selangor E 6 STJGCM Jengka Pahang F 7 NNPPDM Pasir Puteh Kelantan G 8 MSSMSM Semonggok Sarawak H 9 USKGSM Kuching Sarawak I 10 NHPJI Pulau Jawa Jakarta J 11 BPPM_P Balik Pulau Penang K 12 NNPPDM_P Pasir Puteh Kelantan L 13 SABAH_P -b Sabah M 14 USKGSM_P Kuching Sarawak N

a Example BLKBPM: BL: distributor; KB: location; P: state; M: country (M: Malaysia; I: Indonesia); [ 13 ] P: purple fl owers (white fl owers not indicated) b Information not available


sample to the model center); and fi nally (5) vali-dating the classi fi cation results with statistical test called signi fi cance test [ 13 ] .

Interpretation of Data

As per Malaysian Herbal Monograph, the main chemical constituents of O. stamineus are fl avonoids, caffeic acid derivatives, diterpene ester, triterpene saponins, and other minor compounds. Each of these compounds plays an indispensable role in the complicated system of mixture con-tributing to the ef fi cacy and potency due to the synergistic effect contributed from a number of constituents present in the herb. In FTIR spectra (4,000–400 cm −1 ) of ten different sample’s origin of (a) white fl owers and (b) purple fl owers variet-ies (Fig. 7.6 ) [ 13 ] , the sharp absorption peak at 1,600–1,760 cm −1 is assigned to C=O stretching vibration in carbonyl compounds which may be characterized by the presence of high content of terpenoids and fl avonoids in the complex mixture of O. stamineus . The presence of a narrow and sharp peak at ~2,925 and ~2,853 cm −1 was assigned to C–H and C–H (methoxy compounds) stretch-ing vibration, respectively. The presence of diter-penes was further proven with the absorption band

of hydroxyl (3,500–3,480 cm −1 ), ester carbonyl (1,270–1,150 cm −1 ), and phenyl (1,600, 1,420 cm −1 ) [ 13 ] .

By visual recognition, there is no signi fi cant difference in the characteristic absorption bands, but the intensity of certain wavelength does differ from each other especially at the fi ngerprint region (1,800–800 cm −1 ). This interprets the sim-ilarity of certain main chemical component observed among different O. stamineus sample origin. The presentation of spectrum in 3-D (Fig. 7.7 ) [ 13 ] further enhances the visualization of the variability in the intensity absorption bands between the respective origins. Differences between spectra are generally not visible to the naked eyes; it is more practical to incorporate sta-tistical method for the aid of interpreting the obtained measurement results from spectroscopic analysis. Since the discrimination of different geographical origin of herb based on the slight differences among particular absorption bands is too subjective, the results may vary between ana-lysts as reported [ 13 ] .

The PCA was carried out using 520 points (normalized absorbance) between the ranges of the selected spectral region 1,800–800 cm −1 . A total of 18 data sets from six triplicate measure-ments of each sample, 12 data sets were randomly

4000.0 40003000 30002000

BLKBPM

NNPPDM

MABTRM

ZARWBM

NHPJIUSKGSM_P

SABAH-P

NHPJI

NNPPDM-P

USKGSM

AASZBKAM

MSSMSM

SRKBPM

USKGSM

STJGCM

2000

wave number cm−1 wave number cm−11500 15001000 1000400.0 400

Fig. 7.6 The characteristic FTIR spectra of O. stamineus samples [ 13 ] . Where white fl owers ( left ), purple fl owers ( right ) from different origin


selected to represent the respective sample’s origin and varieties (as shown in Table 7.1 ). Natural grouping of O. stamineus from ten differ-ent geographical origins, the fi rst three principal components (PCs) represent only 61 % of the total variance (PC1 = 29 %, PC2 = 22 %, and PC3 = 10 %). This indicates that these three com-ponents are not suf fi cient to provide effective clustering of the sample’s origin with clear sepa-ration between the groups.

The rule of thumb to make a good classi fi cation is to make sure that the variation within different sample must be greater than the variance between individual samples. Thus, spectral reproducibil-ity is important for creating a robust classi fi cation model. The systematic variation between repli-cate spectra due to baseline effect is highlighted and removed after derivatization. Generally, the use of spectra derivatives with Savitzky–Golay algorithm as a chemometric preprocessing tech-niques is widely reported in most classi fi cation based on FTIR spectroscopy. The potential rela-tionship and pattern associate between different O. stamineus samples source with regard to its complex mixtures were further investigated by computing a PCA based on the fi rst-derivative spectral data. The fi rst three PCs represent almost

90 % of the total variance in the data set. The fi rst PCs consist 48 % of the total variability followed by the second PCs with 34 % variance and only 8 % variance carried by the third PCs. Overall, each sample was able to form distinct cluster in the two-dimensional plot. Examining the space de fi ned by the fi rst and second PCs, (a) samples from the same origin (state), for example, Kelantan (group G and L) and Sarawak (group H, I and N), tends to groups itself together in the fi rst PCs. Samples from Penang origin only observed this relationship when it is projected onto the third PCs, which contain only 8 % of the total data variability. From this inherent structure, we can make a sound assumption that the composition of complex chemical constituent does vary accord-ing to its origin. But there are no signi fi cant dif-ferences in the complex mixture observed between the different plant varieties as sample with purple fl ower (rectangular shape) forms a close cluster with the white fl ower samples (oval shape). The fi rst and second PCs which contain almost 60 % of the data variances separate the four respective sample’s origin into four well-de fi ned spaces.

The overall result from aforesaid studies pro-vides evidence that O. stamineus samples from

Fig. 7.7 3-D absorbance matrix spectra (1,800–800 cm −1 ) of different sample’s origin [ 13 ]

133Identification and Comparison of Biomolecules

different geographical origin and varieties have varied complex chemical mixture. Sample ori-gin seems to have more dominant effect to the chemical constituent of the plant compared to plant varieties. The good classi fi cation model obtained from both PCA and SIMCA further proved that classi fi cation of samples from vari-ous sourcing and varieties is possible with the incorporation of chemometric techniques and FTIR spectroscopy. Therefore, the obtained classi fi cation model may be of great use for quality inspection of raw herbal material on a continuous basis as new batches are produced. Chemometrics analysis of spectra data is rapid and simple since no chemical treatment of sam-ples is required [ 13 ] .

Identi fi cation and Comparison of Biomolecules

During the decade of 1940, IR spectroscopy has become an accepted tool for the characterization of biomolecules, and with technological advance-ment, the FTIR has been proven to be useful in studying compositional changes in plant cell walls. This property was utilized to determine changes in cell wall, if any, due to biotic/abiotic factors. In the beginning, use of IR spectroscopy was restricted only for structural elucidation of isolated compounds from the herbal matrices. Drug discovery from the plants continued to pro-vide new and important leads against various pharmacological targets including cancer, HIV/AIDS, Alzheimer’s, malaria, infections, and pain. A number of events for new drug discovery and interaction of a drug with biological target and pharmacological effects have to be considered, and these involve absorption, distribution, metab-olism, and elimination. In order to assess the importance of each of these factors on drug action, both structural and physicochemical prop-erties of the drug are taken into account. In bio-logical systems, properties such as electrostatic bonds, hydrogen bonds, van der Waals bonds, as well as effects related to electron transfer and hydrophobic effects, are of major importance. Although the hydrogen bond is fairly weak

compared to other interactions, it is of paramount importance in biological systems to investigate the drug metabolism, its biotransformation pathways, and toxicological, pharmacological, and biomedical interest. FTIR spectroscopy has been used for identi fi cation and comparison of biomolecules in two different species of medicinal plants, for example, Atylosia albicans and Tephrosia tinctoria [ 7 ] .

Plants Atylosia albicans and Tephrosia tincto-ria were collected from the western Ghats region of Hassan District, Karnataka, India. The leaf, fl ower, fruit, and stem were carefully excised from the plant, cleaned, shade dried, and placed in polythene bags (to avoid contamination, and reference sample was kept in the herbarium), and powdered. The plant powders were kept in a lyo-philizer to remove water. The samples were again ground in an agate mortar and pestle in order to obtain fi ne powder. Each powdered plant mate-rial was mixed with completely dried potassium bromide (99:1 ratio), and the mixture was sub-jected to a pressure of 5 × 106 pa in an evacuated die to produce a KBr pellet for use in an FTIR spectrometer. FTIR spectra were recorded with an FTIR 460 plus Jasco. The powdered samples of both Atylosia albicans and Tephrosia tinctoria were mixed with dried potassium bromide and prepared as pellets and scanned at room tempera-ture (25 ± 2 °C) at 4,000–400 cm −1 spectral range. To improve the signal to noise ratio for each spec-trum, 100 interferograms with a spectral resolu-tion of ±4 cm −1 were averaged. Background spectra, which were collected under identical conditions, were subtracted from the sample spectra. Each sample was scanned under the same conditions with six different pellets. Special care was taken to prepare the pellets at the same thick-ness by taking the same amount of sample and applying the same pressure. Therefore, in the present study, it was possible to directly relate the intensities of the absorption bands to the concen-tration of the corresponding functional groups. The results of functional group analysis (Table 7.3 ) using FTIR revealed the existence of various characteristic functional groups in leaves, stem, fl ower, and fruit of A. albicans and T. tinctoria (Fig. 7.8 , 7.9 , 7.10 , 7.11 and 7.12 ) [ 7 ] .


The dominant bands at 1,655 and 1,546 cm −1 were attributed to protein amide І and ІІ bands. The shoulder at about 1,750 cm −1 was attributed to lipid C=O stretching vibration. The band at 1,465 cm −1 was assigned to the =CH

2 bending

mode of the cell lipids. The band at 1,460 cm −1

represents asymmetric –CH 3 bending modes

of end ethyl group proteins. The band at 1,402 cm −1 represents C=O symmetric stretching of COO– and assigned to lipids. Band at 1,377 cm −1 rep-resents C–H bending mode of =CH

2 . The

very strong absorption band observed around

Table 7.3 General band assignments of the FTIR spectra of biological tissue [ 7 ]

S. no. Peak Assignment

1 521 cm −1 Torsion and ring torsion of phenyl 2 600–900 cm −1 CH out of plain bending vibrations 3 892 cm −1 C–C, C–O, deoxyribose 4 940 cm −1 Carotenoid 5 1,000–14 cm −1 Protein amide I absorption 6 1,000–200 cm −1 C–OH bond in oligosaccharides such as mannose and galactose 7 1,000–350 cm −1 Region of phosphate vibration, carbohydrate residues attached

to collagen and amide III vibration (in collagen) 8 1,020–50 cm −1 Glycogen 9 1,030 cm −1 Collagen 10 1,105 cm −1 Carbohydrates 11 1,145 cm −1 Phosphate and oligosaccharides 12 1,180–300 cm −1 Amide III band region 13 1,206 cm −1 Amide III collagen 14 1,244/5 cm −1 PO −2 asymmetric (phosphate –I) 15 1,255 cm −1 Amide III 16 1,312–131 cm −1 Amide III band components of protein collagen 17 1,456 cm −1 CH

3 bending vibration (lipid and protein)

18 1,482 cm −1 Benzene 19 1,504 cm −1 In plane CH bending vibration from the phenyl rings 20 2,800–3,000 cm −1 C–H lipid region 21 3,500–600 cm −1 OH bond 22 3,000–700 cm −1 O–H stretching (water)

Fig. 7.8 FTIR spectra of T. tinctoria leaves [ 7 ]

135Identification and Comparison of Biomolecules

Fig. 7.9 FTIR spectra of A. albicans leaves [ 7 ]

Fig. 7.10 FTIR spectra of T. tinctoria fl ower [ 7 ]

Fig. 7.11 FTIR spectra of A. albicans fruit [ 7 ]


3,373–3,422 cm −1 may be due to the presence of bonded N–H/C–H/O–H stretching of amines and amides. A very strong absorption at 3,400 cm −1 shows the presence of amino acids, and the very strong absorption band appearing in the region 2,933–2,922 cm −1 is due to N–H stretching. The lone C=O stretching vibration band correspond-ing to saturated aliphatic ester 1,743 cm −1 is present in all parts of the plants. The bands at 900–1,350 cm −1 , 1,020 cm −1 , 1,024 cm −1 , and 1,050–100 cm −1 are attributed to phosphordiester stretching bands region (for absorbance due to collagen and glycogen), DNA, glycogen (C–O stretch associated with glycogen, phosphate, and oligosaccharides PO −2 stretching modes), P–O–C antisymmetric stretching mode of phosphate ester, and C–OH stretching of oligosaccharides, respectively. A band at 1,051 cm −1 is attributed to C–O–C stretching of DNA and RNA. The more intense bands occurring at 3,419, 2,927, 2,853, 1,633,1,421, 1,260, 1,073, 816, and 635cm −1 corresponding to O–H/N–H, C–H, C–O, and C–Cl/C–S stretching/bending vibrations, respec-tively, indicate the presence of amino acids, alkenes, nitrates, ethers, organic halogen com-pounds, and carbohydrates in A. albicans and T. tinctoria [ 7 ] .

A symmetrical stretching of NO 2 − group

results into strong absorption in the region 1,660–1,625 cm −1 . The observed absorption band at 1,630 cm −1 indicates the presence of amines (protein). This gives the evidence that the plants A. albicans and T. tinctoria are rich

in proteins. The weak absorption band observed between 1,421 and 1,415 cm −1 in the plant parts of A. albicans and T. tinctoria may be due to the presence of bonded C–O/O–H bending. The medium absorption band of 620 cm −1 indicates the presence of sulfate. A strong absorption band occurs at 597 and 580 cm −1 in the stem, leaves, fruits, and fl ower of two species which is possibly due to aliphatic C–Cl absorption and brominated compounds. The brominate com-pounds show an infrared band region at 600–500 cm −1 . The weak absorption band at 539 cm −1 indicates the presence of phosphates in the leaves, fruits, fl owers, and stem in the examined plants. The very weak band occurring at 780 cm −1 in the fl owers, leaves, stem, and fruits of these plants can be attributed to out-of-plane N–H wagging, primary and secondary amide and nitrite group. Five major peaks 1,590, 1,348, 1,051, 3,385, 1,063, and 456 cm −1 were observed in the FTIR spectra. This is a signi fi cant observation made in A. albicans as it is not reported in any legumes so far. A weak absorption band at 940 cm −1 is attributed to car-otenoid being present only in the fruit of A. albicans . A weak absorption band at 965 cm −1 is attributed to C–O stretching of the phospho-diester and the ribose and is present only in A. albicans . A very weak peak at 892 cm −1 is attributed to C–C and C–O deoxyribose and seen only in A. albicans . A medium peak at 1,340 cm −1 is due to CH

2 wagging collagen

present in A. albicans. A very strong peak at

Fig. 7.12 FTIR spectra of A. albicans stem [ 7 ]

137Authenticity of Herbal and Herbal Drugs

1,581, 1,358, and 520 cm −1 is attributed to ring C–C stretch of phenyl, stretching C–O, defor-mation C–H, deformation N–H, and phenyl group respectively, are present in fruits, leaves, and stem of A. albicans and leaves and fl owers of T. tinctoria. A medium peak at 3,300 and 1,020–1,050 cm −1 is attributed to amide І bands stemming from N–H stretching modes in pro-teins, nucleic acids, and glycogen [ 7 ] .

From above investigations, it was concluded that A. albicans and T. tinctoria are rich in pheno-lic compounds and also show the presence of oligosaccharides, phosphates, proteins, carbohy-drates, and carotenoid. This work also indicates the presence of biomolecule concentration which is different in different parts of the plants. This work offers scope for further research in phy-tochemical analysis and biological activity of medicinal plants. Fourier transform infrared spectroscopy is proved to be a reliable and sensi-tive method for detection of biomolecular com-position of cells. From this study, we examined the potential of FTIR spectroscopy for easy and rapid discrimination and identi fi cation of various functional groups responsible for medicinal prop-erties. Spectral area ranged between 4,000 and 400 cm −1 is important for an easy and reliable dis-crimination between different plant species based on biomolecules due to unique fi ngerprint for a particular moiety.

Authenticity of Herbal and Herbal Drugs

The true and false identi fi cation of herbal drugs can be performed by the characteristic bend of their IR fi ngerprints using the common and variant peak ratio sequent analytical method to evaluate the quality of the same medicinal plant species. For this purpose, three methods are in practice for collecting IR fi ngerprints for quality control of herbal drugs. In method one, solvents of different polarities are used to extract compo-nents. After evaporating the solvent, the compo-nents are blended with KBr powder and pressured into a pellet, and then the IR fi ngerprints are gen-erated. The IR spectrum of samples enables us to

distinguish different herbal drugs effectively. In method two, powder of herbal drug is blended with KBr powder and pressured into a pellet; then, the IR spectra of the samples are collected, while in method three, the re fl ectance IR spectra are obtained directly from herbal materials.Although methods two and three are convenient methods, they provide lower resolution ability in identi fi cation of herbal drugs as compared to method one [ 14 ] .

The IR fi ngerprint spectra are an effective and convenient for the primary determination of true and false identi fi cation of herbals and herbal drugs. Under the same experimental conditions, the IR fi ngerprints of different herbal drugs have differences in their speci fi c bends. Herbal drugs can be identi fi ed clearly by speci fi c bends and their relative intensities. Since early 1987, the fi ngerprint spectra are in use for the identi fi cation of herbal drugs, by the extraction of components from the herb with certain solvents of different polarities on the order of petroleum ether (or cyclohexane), chloroform, ethanol, and water, and their UV fi ngerprint spectra are also measured [ 15 ] . Chinese herbal drugs, which are complex mix-tures of many herbals, can be easily identi fi ed for purity using their IR spectra. The speci fi c bends and the relative intensities of the IR spec-tra are the main criteria for authenticity.

In a study by Zou et al., the IR spectra of nine kinds of roots of Glycyrrhiza uralensis Fisch and Glycyrrhiza in fl ata Batal, including planted and wild samples from different regions, looked very similar (Fig. 7.13 ) [ 16 ] . The IR spectra of their water extract showed signi fi cant differences. The common peak ratios of the different species were minimum, while their variant peak ratios were maximum. The common peak ratio of the same species of the wild and the planted from near producing regions was of slight difference, but their variant peak ratios were obviously larger than that of planted samples. The common and variant peak ratios of the same species samples from the same region had nonsigni fi cant difference. On further investigation, they fi nd IR spectra having great differences once extracted with chloroform and with water [ 17 ] .


To Distinguish Herb from Its Morphological Fakes

The quality control of herbals and herbal prod-ucts is important for consumer’s safety and ef fi cacy, as traditional herbal drugs are not regu-lated, as considered dietary supplements and not drugs. At present, these commercial products are available in various formulations such as capsules, powder, softgels, and tea. The tradi-tional means of authentication via smell, taste, or physical appearance are hardly reliable for these high-value products, and adulteration with other cheaper products may occur. Example may be cited from ginseng, the famous Chinese herb, as it exhibits an adaptogenic effect and improves physical and mental performance. Different parts and species of ginseng are believed to have different medicinal properties. There are many species of ginseng, of which the two most common and widely used are Panax ginseng C.A. Meyer (Asian ginseng) and Panax quinquefolius L. (American ginseng). Panax ginseng was previously known as “ Panax schin-seng Nees” and is cultivated in China, Germany, Japan, Korea, and Russia, while Panax quinque-folius , previously known as Aralia canadensis , is found in the United States of America and Canada. Besides these two major varieties, there are 11 other ginseng species: Panax japonicus

C.A. Meyer (Japanese ginseng), Panax major Ting, Panax notoginseng (Burk.) F.H. Chen (Sanqi or Tianqi ginseng), Panax omeiensis J. Wen, Panax pseudoginseng Wallich (Himalayan ginseng), Panax sinensis J. Wen, Panax stipule-anatus H.T. Tsai and K.M. Feng, Panax trifolius L. (dwarf ginseng), Panax vietnamensis Ha et Grushv (Vietnamese ginseng), Panax wangianus Sun, and Panax zingiberensis C.Y. Wu and K.M. Feng [ 8 ] .

Asian ginseng is not a generic term and is used to refer to ginsengs which originate from Asian countries. Ginseng with other species such as Panax japonicus and Panax notoginseng , Panax ginseng , is also classi fi ed under Asian ginseng. The chemical composition of ginseng includes the saponins, naphtha class, carbohydrates, and starch. Its pharmacological activity is due to the mixture of its constituents and not the presence of a single compound. The triterpene saponins, called ginsenosides, are the major active ingredi-ents in ginseng, and there are more than 30 differ-ent ginsenosides. These triterpene glycosides are characterized by a four trans-ring steroid aglycone skeletons with attached sugar moieties, and they can be grouped into the 20-( S )-protopanaxadiols and 20-( S )-protopanaxatriols, except for the oleanolic acid-derived ginsenoside Ro. The major ginsenosides Rb1, Rb2, Rc, Rd, Re, and Rg1 are used as markers of ginseng

Fig. 7.13 The IR spectra of roots of planted G. uralensis Fisch, wild G. uralensis Fisch, and wild G. in fl ata Batal [ 16 ]

139To Distinguish Herb from Its Morphological Fakes

quality because they account for 90 % of the total ginsenosides [ 8 ] .

American and Asian ginsengs are distin-guished by their chemical pro fi les. The glucogin-senoside Rf is detectable in Asian ginseng but not in American ginseng, and the ocotillol-type trit-erpene 24-( R )-pseudoginsenoside F11 is present in American ginseng but absent in Asian ginseng. The relative abundances of ginsenosides can also be used to distinguish between the Asian and American species, as a higher amount of protopanaxadiol ginsenosides exists in American ginseng, in contrast to a higher amount of pro-topanaxatriol ginsenosides in Asian ginseng. In addition, ginsenoside ratios are also indicative of the types of ginseng. A higher Rb1/Rg1 value usually indicates P. quinquefolius , and a high Rf/F11 ratio of more than 700:1 distinguishes the Asian from the American varieties [ 8 ] .

Herbs like ginseng have traditionally been authenticated by morphological and histological means. In Asia, ginseng quality is based on the age, origin, as well as the physical characteristics of the root. The potency of the ginseng roots is determined by its shape and can be classi fi ed into three kinds based on the numbers and sizes of the lateral branches on the main root: “pencil” roots, “chunky” roots, and “complex” roots. These methods are hardly reliable at present hi-tech and commercial scenario, as many commercial prod-ucts are in the form of various formulations. Additional commercial aspect is that ginseng is expensive, and cultivation of ginseng is slow and dif fi cult, and it takes more than 4 years before it can be harvested. Under these circumstances, the possibility of adulterating it with other cheaper products is more expected [ 8 ] .

FTIR technique is based on the fact that when low-energy transitions occur in molecules of the sample material, sample absorbs the IR radiation, resulting as in absorbance spectra represent the quality of the major structural features/basic functional groups of the molecules, present in the sample. No two chemical structures can have iden-tical IR spectra, as each type of bond in different compounds has different vibrational frequencies. The IR spectra, which are usually plot of percent transmittance or absorbance versus frequency

expressed in wave numbers (cm −1 ), are used as a fi ngerprint for compound identi fi cation [ 8 ] .

In a case study, American ginseng, Asian gin-seng, and Platycodon grandi fl orum (jiegeng) root samples were cut into small pieces and ground into fi ne powder. Each sample was mixed uni-formly with spectroscopic grade potassium bro-mide (KBr) powder (1 % w/w) in an agate pestle and mortar and then pressed into a pellet. The spectra were recorded in the region of 4,000–400 cm −1 on a Shimadzu IRPrestige-21 FTIR spectrometer (Shimadzu Corporation Pte. Ltd., Asia Paci fi c) equipped with a KBr beam splitter and a deuterated L-alanine triglycine sulfate (DLATGS) detector. Each spectrum was an aver-age of 40 scans co-added at 4 cm −1 resolution, and pure KBr background spectra were recorded before analysis of the samples. Base line correc-tion was done for each spectrum, and their absor-bance normalized with the Shimadzu

IRsolution 1.10® software program, prior to data analysis, so that the peak absorbance of the most intense band was set to unity. The cor-rected spectra were then analyzed for pattern similarity and functional group identi fi cation (Fig. 7.14a ). With complex mathematical pro-cessing techniques, tiny differences in the IR spectrum were ampli fi ed via their derivative spectra, for the separation of overlapping bands and determination of exact peak locations (Fig. 7.14b ). The spectra were converted to their corresponding second derivatives with the same Shimadzu IR solution 1.10® software program (Creon Lab Control AG, Shimadzu Corporation Pte. Ltd., Asia Paci fi c) using the 23-point Savitzky and Golay algorithm. Principal com-ponent analysis (PCA) is a method of chemo-metric analysis which is widely used for reorganizing information and explaining causes of variance in a set of data, in this case IR spec-tra (Figs. 7.15 and 7.16 ) [ 8 ] .

In aforesaid studies, it is concluded that FTIR spectroscopy is a novel and interesting innova-tion for detecting adulteration in very short time before the utilization of raw herb for commercial production to distinguish between morphological and chemical fakes, speci fi cally cases similar to ginseng.


Fig. 7.14 Comparison of ( a ), general; ( b ), second-derivative MIR spectra of sawdust with American and Asian gin-sengs [ 8 ]

Fig. 7.15 Comparison of the ( a ) general MIR spectra, ( b ) magni fi ed fi ngerprint region (2,000–600 cm −1 ), and ( c ) second-derivative MIR spectra of American and Asian ginsengs with jiegeng [ 8 ]

141Standardization of Metal-Based Herbal Drugs

Standardization of Metal-Based Herbal Drugs

Metal-based herbal drugs have its own repute since ancient as gold and silver has been described bene fi cial in healing and anti-disease drugs. In olden days, people used silver bottles for storing water, wine, and milk to prevent from spoiling. In “Siddha” medicine, a form of south Indian medicine, apart from gold and silver, mercury, sulfur, mica, arsenic, zinc, and several other minerals are treated with indigenous herbs, and given as “Bhasma” and “Chendurams.” “Bhasma” means a fi ne ash obtained through

incineration. “Chendurams” are prepared by the process of sublimation, and they are much more potent. In “Siddha,” there is a faith for prolonga-tion of life through rejuvenation using mercurial drugs. In Indian market, Siddha drugs like Poorna Chandrodaya Chenduram (PCC), Kshya kulanthanga Chenduram (KsKc), Velli Parpam (SP), Naga Chenduram (NC), Naga Parpam (NP), and three different popular brands of Linga Chenduram (LC1, LC2, and LC3) are prepared from puri fi ed metal, triturated with decoction of herbal juices, generally prescribed in the dose of 100–200 mg/day, and recommended to be taken with suitable adjuvant [ 18 ] .

Fig. 7.15 (continued)


Fig. 7.16 2D plots of jiegeng, sawdust, and American and Asian ginsengs. ( a ) General spectra of sawdust and ginsengs, PC1 = 82 % versus PC2 = 10 %; ( b ) second-derivative spectra of sawdust and ginsengs, PC1 = 74 % versus PC2 = 11 %; ( c ) general spectra of jiegeng and gin-sengs, PC1 = 76 % versus PC2 = 14 %; ( d ) second-derivative

spectra of jiegeng and ginsengs, PC1 = 51% versus PC2 = 29%; ( e ) general spectra of jiegeng, sawdust, and ginsengs, PC1 = 80 % versus PC2 = 7 %; and ( f ) second-derivative spectra of jiegeng, sawdust, and ginsengs, PC1 = 57 % versus PC2 = 17 % [ 8 ]

143Standardization of Metal-Based Herbal Drugs

Fig. 7.16 (continued)


In a study for quality and quantity determina-tion for these formulations, about 0.1–0.2 g of the drug was digested with nitric acid: perchloric acid (2:1) and allowed to cool and transferred into beaker after complete digestion. The beaker was heated to remove acids; the resulting solu-tion was diluted to 50 ml, with deionized water (perchloric acid was used as it does not form complexes with metals and metalloids). IR spec-tra (4,000–450 cm −1 ) were recorded (Figs. 7.17 , 7.18 , 7.19 ), using Perkin Elmer FTIR spectro-photometer in KBr pellets [ 18 ] .

The spectra clearly indicate that drug sample does not have any organic compound, strong evidence of being “Bhasma.” The three different brands of LC showed a similar pattern (Fig. 7.16 ). Spectrum of PCC is similar to LC. The peak at 592 cm −1 in KsKc is due to Fe

3 O

4 , but the exact

form of silver in SP could not be concluded using IR spectrum. LC is used to treat fevers, skin dis-eases, and venereal diseases. PCC is used for treating tuberculosis, jaundice, fever, and bron-chitis. KsKc is given for all respiratory diseases including tuberculosis. SP is used to treat cough, tuberculosis, piles, leucorrhoea, gonorrhea and spermatorrhea. NC is used to treat piles, skin dis-eases, and white discharge. Thus, the fi ngerprints for purity of metal-based herbal drugs using FTIR are quite helpful to decipher the quality [ 18 ] .

Herbal drugs play an important role in modern human life and have signi fi cant effects on treat-ing diseases, but the quality and safety of these herbal products have now become a serious issue due to increasing pollution in air, water, soil, and modern life style. Using FTIR spectroscopy, the technique for liquid and solid samples, along with the statistical method of principal compo-nent analysis (PCA) to identify and discriminate herbal drugs, and traditional formulation may be scrutinized for quality purpose. Traditional drugs are widely accepted in China, India, Japan,

4000 3500 3000

LC1

LC2

LC3

2500 2000 1500 1000 500

Fig. 7.17 FTIR of LC (LC1, LC2, LC3)

4000 3500 3000

884981

NC

NP

2500 2000 1500 1000 500

Fig. 7.18 FTIR of NC and NP (zinc-based formulation)

4000 3500 3000

PCC

592

SP

KsKc

2500 2000 1500 1000 500

Fig. 7.19 FTIR of PCC, KsKc, SP (Au- and Ag-based formulation)

145Bibliography

Pakistan, Sri Lanka, and Thailand. In China, about 40 % of the total medicine consumption is attributed to traditional tribal medicines. In Japan, herbal medicinal preparations are more in demand than mainstream pharmaceutical products. Due to globalization, ethics are being substituted by commercial opportunities, so there are more chances of intentional adulteration and contami-nation in herbal drugs. The FTIR spectra are helpful in developing methods to test raw materi-als and fi nished products, identi fi cation and iso-lating active components, as provides clue for the functional groups of ingredients.

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Herbal Drugs and Fingerprints || FTIR Spectroscopy: Herbal Drugs and Fingerprints