Original Papers
1023
Authors
Meta Kokalj, Karmen Štih, Samo Kreft
Affiliation
Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
Key words " herbal teas l " mid‑infrared spectroscopy l " chemometrics l " quality control l
Abstract !
Herbal teas and other herbal preparations are becoming more and more popular, and it is essential to ensure their quality. Quality control methods that are simple, fast, and of low cost are needed by the producers and by inspections. Infrared spectroscopy coupled with multivariate mathematical methods has been shown to be useful for the identification and characterization of plant samples. In this work, we developed a method for the identification of herbal drugs in different herbal teas. 100 one-component herbal teas were first used to build an identification algorithm,
Introduction !
received revised accepted
Nov. 21, 2013 April 25, 2014 June 30, 2014
Bibliography DOI http://dx.doi.org/ 10.1055/s-0034-1382904 Published online August 6, 2014 Planta Med 2014; 80: 1023–1028 © Georg Thieme Verlag KG Stuttgart · New York · ISSN 0032‑0943 Correspondence Dr. Meta Kokalj Faculty of Pharmacy University of Ljubljana Aškerčeva 7 SI-1000 Ljubljana Slovenia Phone: + 38 6 14 76 97 04 Fax: + 38 6 14 25 80 31 [email protected]
Herbal medicinal products have been used as remedies since antiquity. In recent years, the public interest in natural products has increased significantly. The most common reasons for the use of herbal preparations are self-medication in the belief of their greater safety compared to synthetic drugs. Because these preparations are used by people already in poor health, their quality assurance is even more important. The outcome of using a product of poor quality can be the absence of therapeutic effect as well as poisoning, allergic reactions, and even death [1–3]. Some of the main areas of concern for quality control are contamination with microorganisms, heavy metals, pesticides, etc., as well as misidentification of the herb [4, 5]. The identification of the herbal drug is an extremely important control step for safety and efficacy reasons. The dried crushed medicinal plant can be adulterated with inert plant material, which lowers the efficacy of such preparations. Or it can be adulterated or mistaken for poisonous plants, which has even more serious consequences.
which showed 100 % correct classification. In the next validation step, 13 samples from 7 herbal mixtures were analyzed, confirming high accurate results for classification. The influence of using different number of components in the principal component analysis is also explored. Infrared spectroscopy coupled with analysis of variance, principal component analysis, and discriminant analysis was shown to be highly applicable for quality control procedures. Supporting information available online at http://www.thieme-connect.de/products
The confirmation of identity can be achieved by macroscopic and microscopic examination [6] of the product, which is time-consuming and requires specific knowledge; thus, confirmation cannot be automated and in some cases, when dealing with a heterogeneous crushed sample, can prove to be impossible. Confirmation of identity can also be obtained by DNA analysis, which is laborious, complicated, and expensive [7], or by identifying and qualifying some of the main constituents by time-consuming chromatographic methods, requiring specialized methods for each plant species and the use of organic solvents. Mid-infrared (IR) spectroscopy has proven to be effective in the classification of plant material and in the detection of adulteration [8–11]. IR spectroscopy is fast, easy to implement, requires a very low quantity of the sample, does not use organic solvents and can be implemented automatically. In our previous work, it has been shown that in some samples, mid-IR spectroscopy is more appropriate comparing to near IR (NIR) spectroscopy. Mid-attenuated total reflectance (ATR) and mid-potassium bromide (KBr) methods showed to be the most suitable [12]. Also papers
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
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Herbal Tea Identification Using Mid-Infrared Spectroscopy
Original Papers
Table 1 Highest percentages of correctly classified samples for different spectral measurements and pretreatments.
All spectra Averaged spectra
Spectral
Whole drug ATR cor-
Ground drug ATR cor-
Ground dried drug ATR
KBr pellet correctly
pretreatment
rectly classified (%)
rectly classified (%)
correctly classified (%)
classified (%)
original normalization derivative original normalization derivative
93.3 96.0 90.7 92 96 95
100 100 100 100 100 100
100 100 100 100 100 100
97.3 98.3 99.3 100 99 100
True sample
Predicted
No. of mistakes
Comment
Calendula flower Chamomile flower Chamomile flower Equisetum stem Equisetum stem Linden flower Melissa leaf Mint leaf Mint leaf Thyme Thyme
equisetum stem calendula flower thyme calendula flower thyme mint leaf thyme melissa leaf thyme melissa leaf mint leaf
2 1 1 1 1 1 3 2 2 3 1 total: 18
Asteraceae – Equisetaceae same family Asteraceae – Lamiaceae Equisetaceae – Asteraceae Equisetaceae – Lamiaceae Tiliaceae – Lamiaceae same family same family same family same family same family same family: 12
comparing NIR and mid-IR spectroscopies do not conclude that one is better than the other [13]. In our previous research, we found that different statistical approaches for analyzing the IR spectra of leaves can be employed successfully to discriminate among six species of the genera Epilobium and Hypericum [12, 14, 15]. Mid-IR and NIR spectroscopies have successfully been used for qualitative and quantitative analysis of tea (Camelia sinensis), however, studies on herbal teas are lacking in the field of IR spectroscopy [16–18]. Herbal teas are some of the most widespread drinks in the world; they are used for their taste and beneficial effects. In this study, herbal teas composed of ten different herbal drugs were used: chamomile, nettle, linden, thyme, lemon balm, mint, marigold, horsetail, rosehip, and sage. The results show that IR spectroscopy can be used successfully and with high accuracy for herbal tea identification.
Results and Discussion !
The percentage of correctly identified samples with different sample preparations, spectral measurements, and spectral pre" Table 1. Slightly better results treatment methods is shown in l were obtained when three measurements of the same sample were averaged. Because all percentages are very high, it is difficult to state anything about which pretreatment of the spectrum is most successful. When the samples of drugs were ground, a 100% correct classification was obtained with ATR spectral measurement. Slightly worse results were obtained with the KBr pellet mode. A completely homogeneous KBr pellet was impossible to make; therefore, the spectra were not reproducible. The results obtained from ATR spectra of the whole unprocessed drug gave the worst results in this comparison, but a correct classification was still above 90%.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
Table 2 Mistakenly classified samples.
Among the 18 mistakes in averaged spectra, 13 include thyme " Table 2). These results are due mostly herb or equisetum stem (l to diversity among thyme samples and difficulty in obtaining a reproducible spectrum of equisetum stem. Thyme samples differed in macroscopic botanical morphology, some containing smaller leaves and stems and some larger leaves and flowers. " Table 2 shows that among 18 mistakenly classified samples, l 12 substitutions (67 %) were between members of the same botanical family (by chance only 14 out of 90 (15%) possible substitutions would be within the same family). For statistical analysis, different numbers of variables (spectral points) and components in principal component analysis (PCA) were used. Variables were first sorted according to F-values from ANOVA. The best variables according to ANOVA were included in the analysis. Analyses with different numbers of best variables were carried out. The number of variables varied from 1 to 500 in steps of 10. In the next step, PCA components were computed from those variables. Discriminant analysis (DA) was then computed using a different number of PCA components. The number of PCA components that enter DA is limited to the maximum number of samples in the training set minus the number of groups. This subtraction yielded a number of 89 for averaged spectra and 287 for all spectra. Different percentages of correctly identified samples were obtained when different numbers of variables were used and different numbers of PCA components entered the DA. In whole samples, where results were the worst, we observed how the percentage of correct classifications was related to the number of variables used and the number of PCA components that entered " Fig. 1). For the maximum number of PCA components that DA (l can enter DA, the percentage of correct classification first rises proportionally to the number of variables used, then starts to fall. At the point where the number of variables used exceeds the number of PCA components, the percentage of correct classification starts to rise again. When the number of variables that enter
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1024
Fig. 1 Percentage of correctly classified samples versus number of variables and PCA components. a Original averaged spectra of whole samples, b first
the analysis is low, variables contain useful information (ANOVA takes into account the herbal species and selects the information useful for identification). PCA transforms these variables into another form, and the variables all enter DA. When the number of variables rises, these variables start to contain some information that is not useful for sample classification. The variables are transformed to PCA, and all these variables enter DA, which cannot make a good model because it contains too much information that is not useful. When the number of variables exceeds the maximum number of PCA components that can enter DA, the information is extracted to some degree by PCA before entering DA, and the model improves. The obtained results showed that selecting the number of PCA components used is not a strait forward procedure and should be further explored. In the case of the best results (dried ground samples measured with ATR), we observed the whole range of optimal combinations " Fig. 2 of the numbers of variables and PCA components used. l shows that there are regions in this plot where 100 % or 99% correct classifications were obtained (black and grey). Thus, the optimal number of PCA components is not their maximum number but somewhere in the middle. In this case, where the number of all variables was approximately 1800, the optimal number of variables used in the first step was between 70 and 200 in averaged spectra (100 objects) and between 250 and 400 in all spectra (300 objects). The model used on herbal tea mixtures samples was built on 100 one-component herbal tea samples. The number of variables and PCA components used was selected from those results. In cases where less than 100 % correctness was achieved, these numbers were mostly well defined since only one (or few) combination(s) of numbers of variables and PCA components gave the best result.
derivative averaged spectra of whole samples, c all 300 original spectra of whole samples, d all 300 first derivative spectra of whole samples.
In cases where 100% correct classifications were achieved, there was an area of 100% correct classification. The number of variables and PCA components was chosen to be somewhere in the " Fig. 1 a, the combination middle of this area. For example, from l " Fig. 1 b, used was 140 variables and 50 PCA components; from l " 160 variables and 50 PCA components; from l Fig. 1 c, 340 vari" Fig. 1 d, 300 variables ables and 70 PCA components; and from l " Table 3. In and 120 PCA components. The results are shown in l this new independent set, the results from previous analyses were confirmed: the best results were obtained with the ATR method for samples that were ground or dried and ground. " Fig. 3 shows results for ground dried drug measured with ATR. l There is a whole range of optimal combinations of the numbers of variables and PCA components used. There are regions in this plot where 100 % correct classification was obtained (black). IR spectroscopy was used to differentiate among different herbal tea samples. Among four different combinations of sample preparation and spectral measurement methods, two performed best: ground and ground dried samples measured by the ATR technique. The results achieved 100 % correct classification. Then, we tested these methods in practice by manually isolating individual compounds from herbal tea mixtures. On these samples, the high accuracy of the method was confirmed. A 100% correct classification was obtained with ground and dried ground samples measured by the ATR technique. These results imply high applicability of these methods in quality control of herbal tea.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
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Original Papers
Original Papers
Fig. 2 Areas of different percentages of correctly classified samples. a Original averaged spectra of ground dried samples, b first derivative averaged spectra of ground dried samples, c all 300 original spectra of ground dried samples, d all 300 first derivative spectra of ground dried samples.
Fig. 3 Areas of different numbers of correctly classified samples. a Original averaged spectra of ground dried samples of herbal mixtures, b first derivative averaged spectra of ground dried samples of herbal mixtures, c all 300 original spectra of ground dried samples of herbal mixtures, d all 300 first derivative spectra of ground dried samples of herbal mixtures.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
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1026
Original Papers
1027
All spectra Averaged spectra
Spectral
Whole drug ATR
Ground drug ATR
Ground dried drug ATR
KBr pellet correctly
pretreatment
correctly classified (%)
correctly classified (%)
correctly classified (%)
classified (%)
(no. variables/no. PCA)
(no. variables/no. PCA)
(no. variables/no. PCA)
(no. variables/no. PCA)
94.9 (360/60) 97.4 (410/100) 89.7 (320/120) 84.6 (240/50) 92.3 (290/40) 76.9 (150/60)
100 (340/70) 100 (300/120) 100 (370/80) 100 (140/50) 100 (250/50) 100 (160/50)
100 (320/70) 100 (270/90) 100 (330/90) 100 (200/50) 100 (230/60) 100 (160/50)
97.4 (420/90) 100 (470/30) 100 (480/120) 100 (350/70) 100 (110/40) 100 (350/30)
original normalization derivative original normalization derivative
Table 4 Samples of mixtures of herbal teas. Mixture
Herbs contained in the mixture
Herbs collected from the mixture and sample
number 1 2 3 4 5 6 7
number nettle leaf, calendula flower, equisetum stem rose hips, hibiscus flower, blueberry fruit, blackberry leaf, mint leaf ribwort plantain herb, licorice, fennel fruit, thyme, primrose root, linden flower hawthorn flower and leaf, mistletoe herb, Equisetum stem, valerian root, melissa leaf chamomile flower, fennel fruit, aniseed, marjoram herb, mint leaf, melissa leaf milk thistle fruit, dandelion root, mint leaf, caraway fruit black elder flower, linden flower, chamomile flower
Materials and Methods !
Samples Herbal tea samples were purchased at local stores and pharmacies. Plant material has been macroscopically examined and identified according to European Pharmacopoeia 8.0. Two sample sets were used. The first sample set consisted of 100 one-component herbal teas: 10 samples of chamomile flower, 10 samples of nettle leaf, 10 samples of linden flower, 10 samples of thyme herb, 10 samples of melissa leaf, 10 samples of mint leaf, 10 samples of calendula flower, 10 samples of equisetum stem, 10 samples of rose hips, and 10 samples of sage leaf (Tables 1S–10S, Supporting Information). The second sample set consisted of mix" Table 4; Table 11S, Supporting Informatures of herbal teas (l tion). The selected herbs were manually separated from the mixture. The voucher samples are deposited at the Faculty of Pharmacy, University of Ljubljana, Department of Pharmaceutical Biology.
Sample preparation Three different sample preparation techniques were compared. The first technique was the whole drug without any preparation; the second technique used finely ground drug; and in the third method, the drug was first dried over silica gel and then finely ground.
IR spectral measurement Two methods of collecting the spectra were used: ATR and KBr pellet mode. The ATR mode was used on all three differently prepared samples: whole drug, ground drug, and ground dried drug. KBr pellet mode was used only on samples of ground dried drug. Attenuated total reflectance: A diamond ATR accessory (Dura SampllR Technologies) on a Nicolet Nexus 470 FT‑IR spectrometer (Nicolet Instrument Co.) equipped with a deuterated triglycine sulfate (DTGS) detector was used. The diamond was 1 mm
nettle leaf, calendula flower, equisetum stem 1 rose hips, mint leaf 1 thyme, linden flower 1 equisetum stem 2, melissa leaf chamomile flower 1 mint leaf 2 linden flower 2, chamomile flower 2
in diameter and mounted flat with the plate for optimal sample contact. The appropriate drug sample was pressed against the diamond crystal of the ATR device with a pressure applicator. The torque knob on the pressure applicator ensured that the same pressure was applied to all the samples. Spectra were recorded between 600 and 4000 cm−1 with a resolution of 1.93 cm−1. The number of scans was set to 50. For each sample, spectra were collected from three different pieces of a whole drug or three different aliquots of ground drug. KBr transmittance: A Perkin Elmer FTIR 1600 (Perkin Elmer) spectrometer equipped with a deuterated triglycine sulfate detector was used. An accessory for the KBr transmittance mode of spectrum acquisition was used. For the KBr transmittance spectra, the KBr pellet with sample was placed in the IR beam path. Spectra were recorded between 450 and 4000 cm−1 with a resolution of 2 cm−1. The number of scans was set to 10. For each KBr pellet, spectra were collected from three different locations on the pellet. For the KBr pellet, 2.0 ± 0.5 mg of dried leaf was powdered and mixed with 160 mg of KBr. The mixture was homogenized and pressed to form a pellet through which the beam was able to pass.
Data analysis Data analyses were carried out in MATLAB (R2008a). Original spectra were compared to the first derivative and normalized spectra. A comparison was made with the use of all measured spectra (100 samples×3 measurements = 300 spectra) and with the averaged spectral measurement from the same sample (100 spectra). The first derivative and normalization were calculated as shown below. Because the difference between neighboring wavenumber values is constant, the first derivative was simplified and calculated as xi* = xi + 1 –xi xi* is a first derivative value, and xi and xi + 1 are neighboring original spectrum values.
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Planta Med 2014; 80: 1023–1028
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Table 3 Percentages of correctly classified samples with different spectral measurements and pretreatments of samples isolated from mixtures. The number of variables used and PCA components were determined from results obtained from one-component sample results and are added in brackets.
Original Papers
Conflict of Interest
Normalization calculation: xnor i
!
x xmin ¼ i xmax xmin
None of the authors has any conflict of interests.
xinor is a normalization value, xi is an original spectrum value, xmin is a minimum of the original spectrum value, and xmax is a maximum of the original spectrum value. The chemometric analysis consisted of first lowering the number of variables (spectrum values) by selection of the most informative variables with ANOVA, followed by PCA and DA. ANOVA: Each step of leave-one-out ANOVA was carried out on 99 samples. The F-value was calculated as a ratio of between-group variability (ten groups of one component teas) and within-group variability (all 99 samples). For each variable, the F-value was computed, and then variables were sorted according to the descending F-value. PCA: the number of variables entering the PCA was from 1 to 500 in steps of 10. The variables with the highest ANOVA F-value were selected. DA: The maximum number of principal components that can enter the discriminant analysis is restricted to the number of samples in the calibration set minus the number of groups. The number of PCA components that entered DA were 10, 20, 30, 40, 50, 60, 70, 80 for the data set containing 100 spectra and 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280 for the data set containing 300 spectra. Model building: The first part of the experiment was carried out on 100 samples, and the method of validation used was leaveone-out cross validation. In the case of the data set with all 300 spectra, all three spectra from same sample were left out in each cross validation step. In the case of the averaged spectra, one spectrum of one sample was left out in each step. In this part of the experiment, the optimal numbers of variables and PCA components used in the method were established. Validation: The method with the optimal number of variables and PCA components was then used in the second part of the experiment, where newly introduced 13 samples from 7 herbal " Table 4; Table 11S, Supporting Informamixtures were tested (l tion).
Supporting information Suppliers and serial numbers of samples and herbal mixtures are available as Supporting Information.
Acknowledgements !
We are grateful to the Slovenian Research Agency (grant P40127) for funds received to carry out this study.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
References 1 Butt PPH, Tomlinson B, Cheung KO, Yong SP, Szeto ML, Lee CK. Adulterants of herbal products can cause poisoning. BMJ 1996; 313: 117 2 Ernst E. Harmless herbs? A review of the recent literature. Am J Med 1998; 104: 170–178 3 Sperl W, Stuppner H, Gassner I, Judmaier W, Dietze O, Vogel W. Reversible hepatic veno-oclusive disease in an infant after consumption of pyrrolizidine-containing herbal tea. Eur J Pediatr 1995; 154: 112–116 4 Elvin-Levis M. Should we be concerned about herbal remedies. J Etnopharmacol 2001; 75: 141–164 5 Calixto JB. Efficacy, safety, quality control, marketing and regulatory guidelines for herbal medicines (phytotherapeutic agents). Braz J Med Biol Res 2000; 33: 179–189 6 European Directorate for the Quality of Medicines and Health Care. European pharmacopoeia, 8th edition. Strasbourg: Council of Europe; 2014: 1193–1421 7 Slanc P, Ravnikar M, Strukelj B. Identification of individual herbal drugs in tea mixtures using restriction analysis of ITS DNA and real-time PCR. Pharmazie 2006; 61: 912–915 8 Rohman A, Che Man YB. Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil. Food Res Int 2010; 43: 886–892 9 Quiñones-Islas N, Meza-Márquez OG, Osorio-Revilla G, Gallardo-Velazquez T. Detection of adulterants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis. Food Res Int 2013; 51: 148–154 10 Cozzolino A. An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals. Food Res Int 2014; 60: 262–265 11 Downey G. Food and ingredient authentication by mid infrared spectroscopy and chemometrics. Trends Anal Chem 1998; 17: 418–424 12 Kokalj M, Kolar J, Trafela T, Kreft S. Differences among Epilobium and Hypericum species revealed by four IR spectroscopy modes: transmission, KBr tablet, diffuse reflectance and ATR. Phytochem Anal 2011; 22: 541–546 13 Maree JE, Viljoen AM. Fourier transform near- and mid- infrared spectroscopy can distinguish between the commercially important Pelargonium sidoides and its close taxonomic ally P. reniforme. Vib Spectrosc 2011; 55: 146–152 14 Kokalj M, Rihtarič M, Kreft S. Commonly applied smoothing of IR spectra showed unappropriate for the identification of plant leaf samples. Chemometr Intell Lab Syst 2011; 108: 154–161 15 Kokalj M, Strgulc Krajšek S, Omahen Bratuša J, Kreft S. Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra. J Chemometr 2010; 23: 611– 616 16 Budínová G, Vláčil D, Mestek O, Volka K. Application of infrared spectroscopy to the assessment of authenticity of tea. Talanta 1998; 47: 255–260 17 Zhang MH, Luypaert J, Fernández Pierna JA, Xu QS, Massart DL. Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration. Talanta 2004; 62: 25–35 18 Chen Q, Zhao J, Zhang H, Wang X. Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration. Anal Chim Acta 2006; 572: 77–84
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1023
Authors
Meta Kokalj, Karmen Štih, Samo Kreft
Affiliation
Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
Key words " herbal teas l " mid‑infrared spectroscopy l " chemometrics l " quality control l
Abstract !
Herbal teas and other herbal preparations are becoming more and more popular, and it is essential to ensure their quality. Quality control methods that are simple, fast, and of low cost are needed by the producers and by inspections. Infrared spectroscopy coupled with multivariate mathematical methods has been shown to be useful for the identification and characterization of plant samples. In this work, we developed a method for the identification of herbal drugs in different herbal teas. 100 one-component herbal teas were first used to build an identification algorithm,
Introduction !
received revised accepted
Nov. 21, 2013 April 25, 2014 June 30, 2014
Bibliography DOI http://dx.doi.org/ 10.1055/s-0034-1382904 Published online August 6, 2014 Planta Med 2014; 80: 1023–1028 © Georg Thieme Verlag KG Stuttgart · New York · ISSN 0032‑0943 Correspondence Dr. Meta Kokalj Faculty of Pharmacy University of Ljubljana Aškerčeva 7 SI-1000 Ljubljana Slovenia Phone: + 38 6 14 76 97 04 Fax: + 38 6 14 25 80 31 [email protected]
Herbal medicinal products have been used as remedies since antiquity. In recent years, the public interest in natural products has increased significantly. The most common reasons for the use of herbal preparations are self-medication in the belief of their greater safety compared to synthetic drugs. Because these preparations are used by people already in poor health, their quality assurance is even more important. The outcome of using a product of poor quality can be the absence of therapeutic effect as well as poisoning, allergic reactions, and even death [1–3]. Some of the main areas of concern for quality control are contamination with microorganisms, heavy metals, pesticides, etc., as well as misidentification of the herb [4, 5]. The identification of the herbal drug is an extremely important control step for safety and efficacy reasons. The dried crushed medicinal plant can be adulterated with inert plant material, which lowers the efficacy of such preparations. Or it can be adulterated or mistaken for poisonous plants, which has even more serious consequences.
which showed 100 % correct classification. In the next validation step, 13 samples from 7 herbal mixtures were analyzed, confirming high accurate results for classification. The influence of using different number of components in the principal component analysis is also explored. Infrared spectroscopy coupled with analysis of variance, principal component analysis, and discriminant analysis was shown to be highly applicable for quality control procedures. Supporting information available online at http://www.thieme-connect.de/products
The confirmation of identity can be achieved by macroscopic and microscopic examination [6] of the product, which is time-consuming and requires specific knowledge; thus, confirmation cannot be automated and in some cases, when dealing with a heterogeneous crushed sample, can prove to be impossible. Confirmation of identity can also be obtained by DNA analysis, which is laborious, complicated, and expensive [7], or by identifying and qualifying some of the main constituents by time-consuming chromatographic methods, requiring specialized methods for each plant species and the use of organic solvents. Mid-infrared (IR) spectroscopy has proven to be effective in the classification of plant material and in the detection of adulteration [8–11]. IR spectroscopy is fast, easy to implement, requires a very low quantity of the sample, does not use organic solvents and can be implemented automatically. In our previous work, it has been shown that in some samples, mid-IR spectroscopy is more appropriate comparing to near IR (NIR) spectroscopy. Mid-attenuated total reflectance (ATR) and mid-potassium bromide (KBr) methods showed to be the most suitable [12]. Also papers
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited.
Herbal Tea Identification Using Mid-Infrared Spectroscopy
Original Papers
Table 1 Highest percentages of correctly classified samples for different spectral measurements and pretreatments.
All spectra Averaged spectra
Spectral
Whole drug ATR cor-
Ground drug ATR cor-
Ground dried drug ATR
KBr pellet correctly
pretreatment
rectly classified (%)
rectly classified (%)
correctly classified (%)
classified (%)
original normalization derivative original normalization derivative
93.3 96.0 90.7 92 96 95
100 100 100 100 100 100
100 100 100 100 100 100
97.3 98.3 99.3 100 99 100
True sample
Predicted
No. of mistakes
Comment
Calendula flower Chamomile flower Chamomile flower Equisetum stem Equisetum stem Linden flower Melissa leaf Mint leaf Mint leaf Thyme Thyme
equisetum stem calendula flower thyme calendula flower thyme mint leaf thyme melissa leaf thyme melissa leaf mint leaf
2 1 1 1 1 1 3 2 2 3 1 total: 18
Asteraceae – Equisetaceae same family Asteraceae – Lamiaceae Equisetaceae – Asteraceae Equisetaceae – Lamiaceae Tiliaceae – Lamiaceae same family same family same family same family same family same family: 12
comparing NIR and mid-IR spectroscopies do not conclude that one is better than the other [13]. In our previous research, we found that different statistical approaches for analyzing the IR spectra of leaves can be employed successfully to discriminate among six species of the genera Epilobium and Hypericum [12, 14, 15]. Mid-IR and NIR spectroscopies have successfully been used for qualitative and quantitative analysis of tea (Camelia sinensis), however, studies on herbal teas are lacking in the field of IR spectroscopy [16–18]. Herbal teas are some of the most widespread drinks in the world; they are used for their taste and beneficial effects. In this study, herbal teas composed of ten different herbal drugs were used: chamomile, nettle, linden, thyme, lemon balm, mint, marigold, horsetail, rosehip, and sage. The results show that IR spectroscopy can be used successfully and with high accuracy for herbal tea identification.
Results and Discussion !
The percentage of correctly identified samples with different sample preparations, spectral measurements, and spectral pre" Table 1. Slightly better results treatment methods is shown in l were obtained when three measurements of the same sample were averaged. Because all percentages are very high, it is difficult to state anything about which pretreatment of the spectrum is most successful. When the samples of drugs were ground, a 100% correct classification was obtained with ATR spectral measurement. Slightly worse results were obtained with the KBr pellet mode. A completely homogeneous KBr pellet was impossible to make; therefore, the spectra were not reproducible. The results obtained from ATR spectra of the whole unprocessed drug gave the worst results in this comparison, but a correct classification was still above 90%.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
Table 2 Mistakenly classified samples.
Among the 18 mistakes in averaged spectra, 13 include thyme " Table 2). These results are due mostly herb or equisetum stem (l to diversity among thyme samples and difficulty in obtaining a reproducible spectrum of equisetum stem. Thyme samples differed in macroscopic botanical morphology, some containing smaller leaves and stems and some larger leaves and flowers. " Table 2 shows that among 18 mistakenly classified samples, l 12 substitutions (67 %) were between members of the same botanical family (by chance only 14 out of 90 (15%) possible substitutions would be within the same family). For statistical analysis, different numbers of variables (spectral points) and components in principal component analysis (PCA) were used. Variables were first sorted according to F-values from ANOVA. The best variables according to ANOVA were included in the analysis. Analyses with different numbers of best variables were carried out. The number of variables varied from 1 to 500 in steps of 10. In the next step, PCA components were computed from those variables. Discriminant analysis (DA) was then computed using a different number of PCA components. The number of PCA components that enter DA is limited to the maximum number of samples in the training set minus the number of groups. This subtraction yielded a number of 89 for averaged spectra and 287 for all spectra. Different percentages of correctly identified samples were obtained when different numbers of variables were used and different numbers of PCA components entered the DA. In whole samples, where results were the worst, we observed how the percentage of correct classifications was related to the number of variables used and the number of PCA components that entered " Fig. 1). For the maximum number of PCA components that DA (l can enter DA, the percentage of correct classification first rises proportionally to the number of variables used, then starts to fall. At the point where the number of variables used exceeds the number of PCA components, the percentage of correct classification starts to rise again. When the number of variables that enter
This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited.
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Fig. 1 Percentage of correctly classified samples versus number of variables and PCA components. a Original averaged spectra of whole samples, b first
the analysis is low, variables contain useful information (ANOVA takes into account the herbal species and selects the information useful for identification). PCA transforms these variables into another form, and the variables all enter DA. When the number of variables rises, these variables start to contain some information that is not useful for sample classification. The variables are transformed to PCA, and all these variables enter DA, which cannot make a good model because it contains too much information that is not useful. When the number of variables exceeds the maximum number of PCA components that can enter DA, the information is extracted to some degree by PCA before entering DA, and the model improves. The obtained results showed that selecting the number of PCA components used is not a strait forward procedure and should be further explored. In the case of the best results (dried ground samples measured with ATR), we observed the whole range of optimal combinations " Fig. 2 of the numbers of variables and PCA components used. l shows that there are regions in this plot where 100 % or 99% correct classifications were obtained (black and grey). Thus, the optimal number of PCA components is not their maximum number but somewhere in the middle. In this case, where the number of all variables was approximately 1800, the optimal number of variables used in the first step was between 70 and 200 in averaged spectra (100 objects) and between 250 and 400 in all spectra (300 objects). The model used on herbal tea mixtures samples was built on 100 one-component herbal tea samples. The number of variables and PCA components used was selected from those results. In cases where less than 100 % correctness was achieved, these numbers were mostly well defined since only one (or few) combination(s) of numbers of variables and PCA components gave the best result.
derivative averaged spectra of whole samples, c all 300 original spectra of whole samples, d all 300 first derivative spectra of whole samples.
In cases where 100% correct classifications were achieved, there was an area of 100% correct classification. The number of variables and PCA components was chosen to be somewhere in the " Fig. 1 a, the combination middle of this area. For example, from l " Fig. 1 b, used was 140 variables and 50 PCA components; from l " 160 variables and 50 PCA components; from l Fig. 1 c, 340 vari" Fig. 1 d, 300 variables ables and 70 PCA components; and from l " Table 3. In and 120 PCA components. The results are shown in l this new independent set, the results from previous analyses were confirmed: the best results were obtained with the ATR method for samples that were ground or dried and ground. " Fig. 3 shows results for ground dried drug measured with ATR. l There is a whole range of optimal combinations of the numbers of variables and PCA components used. There are regions in this plot where 100 % correct classification was obtained (black). IR spectroscopy was used to differentiate among different herbal tea samples. Among four different combinations of sample preparation and spectral measurement methods, two performed best: ground and ground dried samples measured by the ATR technique. The results achieved 100 % correct classification. Then, we tested these methods in practice by manually isolating individual compounds from herbal tea mixtures. On these samples, the high accuracy of the method was confirmed. A 100% correct classification was obtained with ground and dried ground samples measured by the ATR technique. These results imply high applicability of these methods in quality control of herbal tea.
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Original Papers
Fig. 2 Areas of different percentages of correctly classified samples. a Original averaged spectra of ground dried samples, b first derivative averaged spectra of ground dried samples, c all 300 original spectra of ground dried samples, d all 300 first derivative spectra of ground dried samples.
Fig. 3 Areas of different numbers of correctly classified samples. a Original averaged spectra of ground dried samples of herbal mixtures, b first derivative averaged spectra of ground dried samples of herbal mixtures, c all 300 original spectra of ground dried samples of herbal mixtures, d all 300 first derivative spectra of ground dried samples of herbal mixtures.
Kokalj M et al. Herbal Tea Identification …
Planta Med 2014; 80: 1023–1028
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All spectra Averaged spectra
Spectral
Whole drug ATR
Ground drug ATR
Ground dried drug ATR
KBr pellet correctly
pretreatment
correctly classified (%)
correctly classified (%)
correctly classified (%)
classified (%)
(no. variables/no. PCA)
(no. variables/no. PCA)
(no. variables/no. PCA)
(no. variables/no. PCA)
94.9 (360/60) 97.4 (410/100) 89.7 (320/120) 84.6 (240/50) 92.3 (290/40) 76.9 (150/60)
100 (340/70) 100 (300/120) 100 (370/80) 100 (140/50) 100 (250/50) 100 (160/50)
100 (320/70) 100 (270/90) 100 (330/90) 100 (200/50) 100 (230/60) 100 (160/50)
97.4 (420/90) 100 (470/30) 100 (480/120) 100 (350/70) 100 (110/40) 100 (350/30)
original normalization derivative original normalization derivative
Table 4 Samples of mixtures of herbal teas. Mixture
Herbs contained in the mixture
Herbs collected from the mixture and sample
number 1 2 3 4 5 6 7
number nettle leaf, calendula flower, equisetum stem rose hips, hibiscus flower, blueberry fruit, blackberry leaf, mint leaf ribwort plantain herb, licorice, fennel fruit, thyme, primrose root, linden flower hawthorn flower and leaf, mistletoe herb, Equisetum stem, valerian root, melissa leaf chamomile flower, fennel fruit, aniseed, marjoram herb, mint leaf, melissa leaf milk thistle fruit, dandelion root, mint leaf, caraway fruit black elder flower, linden flower, chamomile flower
Materials and Methods !
Samples Herbal tea samples were purchased at local stores and pharmacies. Plant material has been macroscopically examined and identified according to European Pharmacopoeia 8.0. Two sample sets were used. The first sample set consisted of 100 one-component herbal teas: 10 samples of chamomile flower, 10 samples of nettle leaf, 10 samples of linden flower, 10 samples of thyme herb, 10 samples of melissa leaf, 10 samples of mint leaf, 10 samples of calendula flower, 10 samples of equisetum stem, 10 samples of rose hips, and 10 samples of sage leaf (Tables 1S–10S, Supporting Information). The second sample set consisted of mix" Table 4; Table 11S, Supporting Informatures of herbal teas (l tion). The selected herbs were manually separated from the mixture. The voucher samples are deposited at the Faculty of Pharmacy, University of Ljubljana, Department of Pharmaceutical Biology.
Sample preparation Three different sample preparation techniques were compared. The first technique was the whole drug without any preparation; the second technique used finely ground drug; and in the third method, the drug was first dried over silica gel and then finely ground.
IR spectral measurement Two methods of collecting the spectra were used: ATR and KBr pellet mode. The ATR mode was used on all three differently prepared samples: whole drug, ground drug, and ground dried drug. KBr pellet mode was used only on samples of ground dried drug. Attenuated total reflectance: A diamond ATR accessory (Dura SampllR Technologies) on a Nicolet Nexus 470 FT‑IR spectrometer (Nicolet Instrument Co.) equipped with a deuterated triglycine sulfate (DTGS) detector was used. The diamond was 1 mm
nettle leaf, calendula flower, equisetum stem 1 rose hips, mint leaf 1 thyme, linden flower 1 equisetum stem 2, melissa leaf chamomile flower 1 mint leaf 2 linden flower 2, chamomile flower 2
in diameter and mounted flat with the plate for optimal sample contact. The appropriate drug sample was pressed against the diamond crystal of the ATR device with a pressure applicator. The torque knob on the pressure applicator ensured that the same pressure was applied to all the samples. Spectra were recorded between 600 and 4000 cm−1 with a resolution of 1.93 cm−1. The number of scans was set to 50. For each sample, spectra were collected from three different pieces of a whole drug or three different aliquots of ground drug. KBr transmittance: A Perkin Elmer FTIR 1600 (Perkin Elmer) spectrometer equipped with a deuterated triglycine sulfate detector was used. An accessory for the KBr transmittance mode of spectrum acquisition was used. For the KBr transmittance spectra, the KBr pellet with sample was placed in the IR beam path. Spectra were recorded between 450 and 4000 cm−1 with a resolution of 2 cm−1. The number of scans was set to 10. For each KBr pellet, spectra were collected from three different locations on the pellet. For the KBr pellet, 2.0 ± 0.5 mg of dried leaf was powdered and mixed with 160 mg of KBr. The mixture was homogenized and pressed to form a pellet through which the beam was able to pass.
Data analysis Data analyses were carried out in MATLAB (R2008a). Original spectra were compared to the first derivative and normalized spectra. A comparison was made with the use of all measured spectra (100 samples×3 measurements = 300 spectra) and with the averaged spectral measurement from the same sample (100 spectra). The first derivative and normalization were calculated as shown below. Because the difference between neighboring wavenumber values is constant, the first derivative was simplified and calculated as xi* = xi + 1 –xi xi* is a first derivative value, and xi and xi + 1 are neighboring original spectrum values.
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Table 3 Percentages of correctly classified samples with different spectral measurements and pretreatments of samples isolated from mixtures. The number of variables used and PCA components were determined from results obtained from one-component sample results and are added in brackets.
Original Papers
Conflict of Interest
Normalization calculation: xnor i
!
x xmin ¼ i xmax xmin
None of the authors has any conflict of interests.
xinor is a normalization value, xi is an original spectrum value, xmin is a minimum of the original spectrum value, and xmax is a maximum of the original spectrum value. The chemometric analysis consisted of first lowering the number of variables (spectrum values) by selection of the most informative variables with ANOVA, followed by PCA and DA. ANOVA: Each step of leave-one-out ANOVA was carried out on 99 samples. The F-value was calculated as a ratio of between-group variability (ten groups of one component teas) and within-group variability (all 99 samples). For each variable, the F-value was computed, and then variables were sorted according to the descending F-value. PCA: the number of variables entering the PCA was from 1 to 500 in steps of 10. The variables with the highest ANOVA F-value were selected. DA: The maximum number of principal components that can enter the discriminant analysis is restricted to the number of samples in the calibration set minus the number of groups. The number of PCA components that entered DA were 10, 20, 30, 40, 50, 60, 70, 80 for the data set containing 100 spectra and 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280 for the data set containing 300 spectra. Model building: The first part of the experiment was carried out on 100 samples, and the method of validation used was leaveone-out cross validation. In the case of the data set with all 300 spectra, all three spectra from same sample were left out in each cross validation step. In the case of the averaged spectra, one spectrum of one sample was left out in each step. In this part of the experiment, the optimal numbers of variables and PCA components used in the method were established. Validation: The method with the optimal number of variables and PCA components was then used in the second part of the experiment, where newly introduced 13 samples from 7 herbal " Table 4; Table 11S, Supporting Informamixtures were tested (l tion).
Supporting information Suppliers and serial numbers of samples and herbal mixtures are available as Supporting Information.
Acknowledgements !
We are grateful to the Slovenian Research Agency (grant P40127) for funds received to carry out this study.
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