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Manufacturers and consumers are increasingly concerned about high-quality cigar products with clear geographical sources. To characterize the chemical profiles of different geographical origins of cigar leaf samples, headspace solid phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC–MS) combined with a chemometrics method was used in this study. The chromatography and chemical composition of cigar leaf samples from 16 geographical origins were acquired and profiled. The chemical markers contributing to the differentiation of cigar leaf samples were observed and characterized by a supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) method of chemometrics. Eight chemical markers were tentatively identified to be used as specific chemical markers for the differentiation of geographical cigar leaf samples. HS-SPME-GC–MS method coupled with chemometrics analysis has potential to be used for discriminating different geographical origins for cigar leaf samples.

) is widely planted and consumed all over the world. By manipulating its leaves, different products can be prepared for smoking or chewing. Today, cigars and rope tobacco (tobacco mincing and rolling) are popular.

The quality of smoke produced in the normal combustion process of cigars depends largely on the chemical composition of tobacco (1–3). The chemical composition of tobacco is affected by many factors, among which the origin of the tobacco is the most important. To obtain the best quality cigar tobacco, we must recognize the source of cigar tobacco. Cuba, Indonesia, Dominica, Brazil, Nicaragua, and China all have famous cigar leaf planting locations.

Nowadays, manufacturers and consumers are increasingly concerned about high-quality cigar products with clear geographical sources. Flavor is one of the important distinguishing feature of cigar quality and consumer affection. The taste of a cigar is very complex, depending on the compounds it contains, such as ketones, esters, alcohols, nitrogen compounds, acids, aldehydes, and lactones. More than 1000 aromatic compounds have been funded, covering a wide range of polarity and volatility (4–7).

Analytical techniques and separation techniques, such as high performance liquid chromatography (HPLC) and matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI–TOF-MS), have been used to determine the geographic origin of food (8–10). This multifactor method requires chemometric analysis of the data provided by analytical instruments.

Headspace solid phase microextraction (HS-SPME) is particularly beneficial for volatile and “semi-volatile” substances because of the low chromatographic background. This method is especially suitable for the detection of volatile and flavor substances. Taking vegetable oil as an example, Yang and Peppard (11) introduced flavor analysis of juice drinks and butter. SPME was used to analyze the varietal characteristics by using different types of fibers. There are many types of SPME fibers, which can be used to analyze many kinds of compounds. Among the existing SPME fibers, SPME fibers containing liquid and solid components have high sensitivity. The SPME program is simple to use, with a duration of less than 1 h without solvent extraction. This method is also cheaper and allows the feature of headspace analysis, making it an optimized method for analyzing aroma in cigar leaves (12–14).

Traditional methods to separate aroma and flavor from tobacco, such as solvent extraction, adsorbent capture, simultaneous distillation extraction, or the combination of these methods, is labor-intensive because of the very low concentration of relevant compounds in a complex matrix, and often the sensitivity is not sufficient (15,16).

Vas and Vekey (17) recently published a comprehensive review on the subject of SPME. HS-SPME is particularly suitable for the analysis of tobacco leaf components because most of these compounds are volatile and can be adsorbed by SPME fiber materials. Therefore, this technology has been used to analyze tobacco constituents many times. In most cases, these methods have been limited to substances such as organic acids like alkenylbenzene (18–22).

HS-SPME and gas chromatography (GC) are the most suitable techniques for determining the origins of tobacco leaves used for cigar-production. In this study, a quick and simple method of cigar leaf screening is introduced, and some of the cigar leaves are quantitatively analyzed by HS-SPME-GC–MS before principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) is used to analyze the aroma spectrum of 16 cigar leaves.

Phenylethyl acetate, which served as the internal standard, was obtained from Sigma–Aldrich. The chemicals and reagents used in analytical grade purity were obtained from Merck; ultrapure water was purified by a Milli-Q system.

For this study, 16 species of cigar leaf samples were collected from five countries. Packs of 16 different common cigar leaves were provided by the Sichuan tobacco company (Sichuan, China), the information of which is displayed in Table I. The 3R4F reference cigarette was purchased from the University of Kentucky. The cigar leaf samples were stored at −40°C prior to analysis.

Instruments and Accessories For GC–MS and HS-SPME

An Agilent gas chromatograph coupled to a mass selective detector (MSD) (7890- 5977B) was used for the GC–MS analysis. A multipurpose sampler called MPS2 (Gerstel) for automatic performance of the HS-SPME measurements was also used.

A DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm) from Agilent was used for the gas chromatographic analysis with helium as the carrier gas at a flow rate of 1.0 mL/min.

The SPME fibers tested in this work were 100 μm of polydimethyl-siloxane (PDMS), 85 μm carboxen/polydimethylsiloxane (CAR/PDMS), 65 μm polydimethylsiloxane/divinylbenzene (PDMS/DVB), and 50 μm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). All SPME fibers were purchased from Supelco.

The optimal analyses were performed with the 50 μm DVB/CAR/PDMS fiber. All GC–MS analysis were performed using the enhanced ChemStation G1701 DA, version F.01.01.2317 software from Agilent Technologies.

Sample Preparation For Qualitative Screening Method and HS-SPME

In the optimized qualitative screening method, each cigar sample was analyzed under three different conditions. An 0.1 g sample of the cigar leaf was placed in a 22 mL headspace vial. Then, 50 μL of the phenylethyl acetate methanol solvent with a concentration of 100 μg/mL was added as the internal standard. Then, the 22 mL headspace vials were tightly closed and placed into the vial rack of the sampler. The samples were extracted for 40 min at 80 °C and desorpted at 280 °C for 3 min.

Gas Chromatography–Mass Spectrometry (GC–MS)

Using helium as the carrier gas, the flow rate was 1.0 mL/min, and the injection mode was splitless at 280 °C. The optimized method was as follows: 2 min at 60 °C, then the temperature was increased 10 °C/min up to 280 °C, and then it remained for 10 min at 280 °C. The temperature of quadrupole and the ion source were 150 °C and 230 °C, respectively. Analytes were detected in scan mode from 

 = 31 to 550. Peak recognition in scanning mode was performed using the software MS Spectral Library NIST17 (National Institute of Standards and Technology).

By comparing the retention time and mass spectra of the samples with the mass spectra present in the National Institute of Standards and Technology (NIST) MS 17 spectral database, the compounds were qualitative. Quantitative analysis was carried out by the internal standard method.

All data of the peaks were exported into the Simca software (v. 13.0) for OPLS-DA analysis. One-way analysis of variance (ANOVA) was used to analyze the significant difference of markers in different samples. The differences were considered statistically significant with the analysis of variance, 

 ≤ 0.05, maximum folding change ≥2, and the valuable importance in the projection (VIP) value was >1 as the limiting conditions.

Volatiles present in cigar leaves were extracted using a 50 μm DVB/CAR/PDMS fiber at 80 °C for 40 min. For this study, 95 compounds that were identified and quantified are presented. Because of the different structure, polarity, and volatility of the target compounds, a medium polarity fiber material was needed. Four fibers (PDMS, PDMS/DVB, CAR/PDMS, DVB/CAR/PDMS) were compared for extracting volatile compounds. The experiment was performed using a China cigar leaf that contained the volatile compounds of esters, acids, and alcohols (23–25). The performance of each fiber was compared by the number of compounds identified and the response area, in which a group of 50 volatile compounds were considered (25 esters, 14 terpenes, 5 alcohols, and 6 acids).

Compared with other fibers, the extraction rate of DVB/CAR/PDMS and PDMS/DVB coatings is higher for volatiles. The results of the DVB/CAR/PDMS fibers were significantly different from the other three fibers, and showed that the total area of esters, alcohols, and acids was the highest, and that more volatile compounds (95) were detected in these functional groups. In addition, the repeatability of DVB/CAR/PDMS was less than 14%. The extraction efficiency of benzyl alcohol, pyridine, and benzaldehyde was slightly better than that of PDMS/DVB fiber, as shown in Table II. This is the most commonly used fiber in the literature (26–29).

Although DVB/CAR/PDMS and PDMS/DVB are bipolar fibers covered with porous solid coating, which indicates that the extraction of analytes is carried out by adsorption, the qualitative and quantitative behavior of PDMS/DVB fiber is worse than that of DVB/CAR/PDMS, and 63 compounds are detected. The extraction efficiency of the PDMS/DVB fiber for neophytadiene, one of the most important compounds in cigar leaves, is also very low. This choice could be confirmed by comparing the peak areas of alkaline, neutral, and acidic compounds in SIM mode.

Nonpolar-coating PDMS fibers could only identify 39 volatile compounds. However, the peak area of these compounds was low, which indicates that PDMS fibers are not suitable for quantitative analysis. The combination of DVB and CAR increases the porosity distribution and polarity of the fiber while improving the retention rate of analyte on the fiber compared with the coating only composed of PDMS.

The extraction efficiency of DVB/CAR/PDMS fiber for volatile components from cigar leaves were the highest, and the repeatability was better (RSD 10%). Previous studies indicated that the DVB/CAR/PDMS fiber is the most suitable because of its extraction ability for compounds (molecular weight 40–275) (30–34). As an example, the total ion chromatograms obtained after basic preparation are shown in Figure 1.

FIGURE 1: Total ion chromatograms of a cigar sample measured by HS-SPME and GC–MS with the 50 μm DVB/CAR/PDMS SPME fiber after basic sample preparation.

All results of 16 cigar leaf samples were exported into the Simca software (v 13.0) for OPLS-DA analysis. Outliers and classification trends of 16 cigar leaf samples were observed in the OPLS-DA results (Figure 2). The score plot obtained by OPLS-DA showed that there was a clear distinguish between the S1 and S2 groups and the S3 to S16 group, indicating that the cigar leaf sample from China Sichuan was very different from other samples in Indonesia, Dominica, Brazil, and Nicaragua. Cigar leaf samples from China Sichuan (S1 and S2 groups) clustered together and separated from other samples in Indonesia, Dominica, Brazil, and Nicaragua (Figure 3). R2X of the OPLS-DA model (0.554) showed that the model had good adaptability and predictability. S1 and S2 samples from China Sichuan province clustered together respectively, which shows that there are significant differences between domestic and foreign samples; the separation between S3–S10, S11 and S12, S13–S15, and S16 indicated that samples in Indonesia, Dominica, Brazil, and Nicaragua also had significant differences.

FIGURE 2: Orthogonal partial least squares discriminant analysis (OPLS-DA) scores plot of 16 cigar samples from different geographical origins: (CI green) domestic samples from China; FO (blue) foreign samples from outside of China.

FIGURE 3: Clustering of 16 samples showed that there are significant differences between group 1 (green), domestic samples from China; and group 2 (blue), foreign samples from outside of China.

Based on the retention behavior and mass distribution, potential chemical markers in cigar leaves from different geographical origins were identified. First, the OPLS-DA model was constructed from the Simca software because potential biomarkers of interest can be extracted from loading plots and VIP plots of that model.

In addition, with the analysis of variance of 

 ≤ 0.05, maximum folding change of ≥2, and the VIP value >1 as the limiting conditions, the compounds with significant changes were screened out to reduce the “false discovery rate (FDR)”.

According to the condition detailed above, eight chemical markers (1-hexanol, 2-ethyl-; megastigmatrienone; 2-pyrrolidinone, 1-methyl-; 3-hexene, 3-methyl-, (Z)-; ethanone,1-(3,4,5-trimethoxyphenyl)-; 1-hexene- 3,5-dione; cyclopropane, 1-methyl- 2-pentyl-; and 1H-naphtho[2,1-b] pyran, 4a,5,6,6a,7,8,9; short named H1–H8) in cigar leafs from different origins were identified (Table III).

Six repeated QC samples were analyzed to evaluate the repeatability of SPME-GC–MS. The relative standard deviations of peak areas and retention time were 2.3–5.7 and 0–0.14%, respectively. Quality control (QC) samples were kept in the automatic sampler for 5, 10, 20, and 40 h to evaluate the stability of the instrument. The relative error of peak area is less than 10.5%, which has good repeatability and stability.

To more clearly describe the differences of chemical markers in samples from different geographical origins, the relative intensity of chemical markers are shown in Table III and Figure 4. The max fold changes of H1–H8 were 8.99, 8.64, 5.63, 3.64, 3.38, 3.13, 2.34, and 2.25, respectively. The results of Figure 4 and Table II showed that the content of H1 in the 16 samples had the biggest difference, and the content of H1 in S1 was the highest, which was about 45.08 more times than S10; the content of H5 in S1 and S2 were similar, which was higher than other 14 samples; and the content of H2 and H3 in all samples were similar.

FIGURE 4: Orthogonal projections to latent structures discriminant analysis (OPLS-DA) showing the variable importance in the projection (VIP) value of chemical markers.

The content of H6 in S16 was the highest; the content of S2 and S13 were higher than S1, S3–S12, S14, and S15. The content of H7 in S1 and S2 was similar, which was higher than the other 14 samples.

Manufacturers and consumers are increasingly focused on high-quality cigar products with well-defined geographical origins. Therefore, the research of the source of cigar leaves is an important subject. Flavor is one of the important indicators of cigar quality and consumer acceptance. In this study, 16 cigar leaf samples were collected from five countries. Eight representative chemical markers related to the differences were made on 16 cigar leaf samples. The contents of H1 and H5 were the highest in S1 and S2. The results showed that H1 and H5 could be used as specific chemical markers of S1 and S2; H3 could be used as specific chemical markers of S16; H4 and H6 could be used as specific chemical markers of S6–S8; and H8 could be used as a unique chemical marker of S13.

The OPLS-DA results of different samples showed that the cigar leaf from the Sichuan provinces in China were different from the foreign samples, indicating that the China samples from the Sichuan province had some similarity. The samples from Indonesia, Dominica, Brazil, and Nicaragua were also clustered together, respectively.

Cigar leaves contain a variety of alcohols, terpenes, ketones, esters, acids, aldehydes. 1-hexanol 2-ethyl-(H1) contains a slight floral fragrance, which increases the floral scent when smoking cigars.

Megastigmatrienone (H2) is a key flavor compound in tobacco. As the most abundant substance in neutral aroma components, megastigmatrienone was found and its chemical structure was identified. Scientists also found that burley tobacco concentrate contains nearly 10% of the megastigmatrienone chemicals, and it exists more or less in flue-cured tobacco. It is a degradation product of carotenoids and formed by the degradation of lutein, which has a very important contribution to the aroma of tobacco leaves.

The 16 cigar leaf samples from different geographical origins were identified by HS-SPME-GC–MS and chemometrics. These chemical markers with significant aroma can be used to discriminate the geographical origin of cigar leaf samples. Chemometrics technology has potential application prospects in the discovery and quality evaluation of flavor components in different geographical environment, which helps us to find natural substitutes for geographical origins more quickly.

Classification and differentiation of cigar leaves according to their producing areas is an important basis to ensure the quality of tobacco leaves. The chemical constituents of cigar leaves from different areas were characterized by HS-SPME-GC–MS. HS-SPME combined with GC–MS is a fast, simple and solvent-free method. The chromatographic results of adsorption with PDMS, PDMS/DVB, DVB/CAR/PDMS, and CAR/PDMS coated fibers showed that DVB/CAR/PDMS was the best one for analyzing alcohols, esters and acids in cigar leaves.

The OPLS-DA method was used to distinguish the cigar leaf samples from different geographical origins. The marker compounds used to differentiate cigar leaves were discovered. There were eight chemical markers that were preliminarily identified and semi-quantitated in cigar leaf samples from several countries. The chemical markers results showed that there were significant differences between different regions. These chemical markers can be used to distinguish the origin of samples from different countries. The results show that chemometrics technology may be used to find aroma components related to complex components and different geographical environments of cigar leaves, which will help us find natural substances for geographical sources quicker.

The study was supported by Key Laboratory of Chinese cigar fermentation of China Tobacco Sichuan Industrial Co., Ltd. (ctx201901).

(2) Z.Y. Chen, J.L. Tan, G.Y. Yang, M.M. Miao, Z.Y. Chen, and T.F. Li, 

(5) J.D. Heck, C.L. Gaworski, N. Rajendran, and R.L. Morrissey, 

(6) E. Roemer, R. Stabbert, K. Rustemeier, D.J. Veltel, T.J. Meisgen, W. Reininghaus, R.A. Carchman, C.L. Gaworski, and K.F. Podraza, 

(7) K. Rustemeier, R. Stabbert, H.J. Haussmann, E. Roemer, and E.L. Carmines, 

(8) M.L. Chen, W.Q. Chang, J.L. Zhou, Y.H. Yin, W.R. Xia, J.Q. Liu, L.F. Liu, and G.Z. Xin, 

(12) P. Guedes de Pinho, R.F. Goncalves, P. Valentao, D.M. Pereira, R.M. Seabra, P.B. Andrade, and M. Sottomayor, 

(14) L. Tat, P. Comuzzom, I. Stolfo, and F. Battistutta, 

(16) F. Peng, L. Sheng, B. Liu, H. Tong , and S. Liu, 

(19) S.B. Stanfill, A.M. Calafat, C.R. Brown, G.M. Polzin, J.M. Chiang, C.H. Watson, and D.L. Ashley, 

(21) S.S. Yang, C.B. Huang, and I. Smetena, 

(22) Z. Li, L. Wang, G. Yang, H. Shi, C. Jiang, W. Liu, and Y. Zhang, 

(23) R.R. Baker, J.R. Pereira da Silva, and G. Smith, 

(24) R.R. Baker, J.R. Pereira da Silva, and G. Smith, 

(25) R.R. Baker, E.D. Massey, and G. Smith, 

(26) R. Perestrelo, M. Caldeira, F. Rodrigues, and J.S. Camara, 

(27) L. Ferreira, R. Perestrelo, M. Caldeira, and J.S. Camara, 

(28) J. Oliveira, I. Araujo, O. Pereira, J. Maia, A. Amaral, and M.O. Maia, 

(29) J. Oliveira, M. Faria, F. Sa, F. Barros, and I. Araujo, 

(31) R.M. Pena, J. Barciela, C. Herrero, and S. Garcia-Martin, 

(32) L. Rebiere, A.C. Clark, L.M. Schmidtke, P.D. Prenzler, and G.R. Scollary, 

(33) L. Setkova, S. Risticevic, and J. Pawliszyn, 

(34) A. Zander, A.G. Bishop, and P.D. Prenzler, 

 are with the China National Tobacco Quality Supervision and Test Center in Zhengzhou, China. 

 are with the Technology Center at the China Tobacco Sichuan Industrial Co., Ltd., in Chengdu, China. Direct correspondence to Dongliang Li at: 

Articles

HS-SPME-GC–MS was combined with OPLS-DA data analysis to tentatively identify eight chemical markers to differentiate the geographical origins of cigar leaf samples.

Headspace Solid-Phase Microextraction Coupled with Gas Chromatography–Mass Spectrometry (HS-SPME-GC–MS) for the Characterization of Cigar Leaves

 is a commonly used antihyperglycemic in Egyptian folk medicine. The objective of the present study is to develop two chromatographic methods for standardizing the 

 extract by quantification of its main component, stigmasterol 3-O-β-D-glucopyranoside (S3G). The first method is gas chromatography (GC) where nitrogen was adopted as the carrier gas. The second method was thin layer chromatography (TLC) densitometry where chromatographic separation was established on aluminum plates using chloroform; methanol was used as the mobile phase followed by a densitometric measurement at 254 nm. Validation of the proposed methods was applied per International Council for Harmonization (ICH) guidelines. Both methods were adopted for quantification of S3G in 

 capsules. The standard calibration curve was established for S3G in the concentration range of 10–130 μg/mL and 0.5–7.5 μg/mL for the GC and TLC methods, respectively. A good linearity with a correlation coefficient of 0.999 was obtained for both methods. Furthermore, the mean percentage recovery was found to be 99.46% for GC and 100.67% for TLC. Good precision was achieved, with relative standard deviation values <1%, and finally, the limits of detection and quantification were 2.51 and 7.59 μg/mL for GC and 0.13 and 0.40 μg/mL for TLC, respectively.

, known as the desert date, is a spiny evergreen tree, classified as a member of the Zygophyllaceae family (1). It has been cultivated in Egypt for more than 4000 years, and produces a yellow, single-seeded fruit that is edible but bitter. These fruits are commonly used to treat diabetes in Egyptian folk medicine, and herbalists in the Egyptian market sell the fruits as an antidiabetic drug (2–8). The saponin-rich fraction of 

 and its content of furostanol saponin derivatives have shown significant in vitro aldose reductase inhibitory activities (9). Several saponins were isolated from the mesocarp of the 

 fruit (10). Therefore, stigmasterol 3-O-β-D-glucopyranoside (S3G) as a steroid saponin isolated from Symplocos lancifolia is used to standardize the 

 extract in the present study (Figure 1).

FIGURE 1: Chemical structure of stigmasterol 3-O-β-D-glucopyranoside (stigmast-5, 22-dien-3-O-β-D-glucopyranoside).

The objective of the present study is to develop simple, reliable, and validated analytical procedures for the quantification of S3G by applying gas chromatography (GC) and thin-layer chromatography (TLC). These methods could be used efficiently for routine quality control and for the analysis of 

 extract both in pure form and in a pharmaceutical formulations. Both GC and TLC densitometric methods were adopted for the first time for quantification of S3G. The GC method was found to be rapid, with minimum sample preparation without the need of any derivatization steps. The TLC densitometry method has the advantage of being relatively inexpensive compared to the GC method used in this study. The literature survey reveals that only spectroscopic analyses, including two-dimensional nuclear magnetic resonance (2D-NMR) techniques and infrared (IR), were previously reported for characterizing the mentioned saponin (10,11).

An Agilent 7890A series GC apparatus (Agilent Technologies) connected with an autosampler was used in this experiment. The output signal was monitored and processed using Chemstation software version B.04.03. An Agilent HP-5 (30 m length × 0.32 mm i.d., 0.25-μm film thickness) column and flame ionization detector (FID) were used. A Camag TLC scanner with winCATS computer software was used in the densitometric method. The TLC plates were pre-coated with silica gel 60 F254 (20 × 20 cm), and a Hamilton 100-μL microsyringe, an ultraviolet (UV) lamp with a short wavelength of 254 nm, and a chromatographic tank (25 cm × 25 cm × 9 cm) were also used.

Pure S3G was kindly supplied by the Pharmacognosy Department at Cairo University, and the S3G was previously isolated from 

 extract with 99.9% purity, assessed by a high performance liquid chromatography (HPLC) method.

 dry extract, in addition to the hard gelatin 

 capsules (Batch No. Bal0009), were manufactured by Atos for production of medicinal herbs. Each capsule was labeled to contain 400 mg of the 

 extract (standardized to contain 8.63% of S3G).

For the GC method, chromatographic HPLC grade methanol and acetonitrile (Fischer Scientific) and analytical grade chloroform (Honeywell) were purchased from the local market. An Agilent PTFE 0.45 μm syringe filter was also used.

For the TLC method, analytical grade methanol and ethanol (Adwic), chloroform (Honeywell), sulfuric acid (Sigma Aldrich), and vanillin powder (El-Nasr) were purchased from local market.

A vanillin-sulfuric acid spraying reagent was prepared from 1% vanillin in ethanol (solution I) and 10% sulfuric acid in ethanol (solution II) (12). The plate was sprayed with 10 mL solution I, followed immediately by spraying it with 10 mL of solution II. After heating the plate at 110 °C for 5–10 min under observation, it was evaluated in visible light or daylight.

Stock standard solution of the drug (0.14 mg/mL) was prepared by dissolving 14.0 mg of S3G in 100 mL 20% methanol in chloroform.

The column oven was programmed as follows: The initial column oven temperature was 600 °C and increased to 2800 °C at the rate of 500 °C/min. The injector and detector temperature was kept at 2500 °C and 3000 °C, respectively. Nitrogen was used as carrier gas at a flow rate of 3.35 mL/min and a split ratio at 1:10. The injection volume was 2.0 μL. Methanol was used as the syringe cleaning solvent between injections.

Aliquots of stock solutions of the standard drug (1.0 mg/mL) equivalent to 10.0–130.0 μg/mL of S3G were introduced into a series of 10-mL volumetric flasks. The volume was adjusted using 20% methanol in chloroform, and 2 μL were injected in triplicate for each concentration then chromatographed under the specified chromatographic conditions described previously. The calibration graph of S3G was obtained by plotting the peak area of each concentration against the corresponding concentration.

Application to the Pharmaceutical Dosage Form

 capsules were mixed well. Then, an aliquot of 593 mg was accurately weighed, dissolved in 100 m of methanol-chloroform mixture in a ratio of (20:80 by volume), and sonicated for 30 min, after which 1 mL from the stock solution was transferred into a 10 mL volumetric flask then the volume was adjusted with the same solvent. The obtained solution was filtered via the syringe filter, and then injected following the specified chromatographic conditions previously described. Three replicate determinations were then made; the corresponding drug concentrations were calculated from the appropriate regression parameters.

The recovery of the procedure was assessed by applying the standard addition technique and the mean percentage recovery (±%RSD) was calculated.

Analysis was performed on precoated 20 × 20 cm TLC aluminum silica gel 60 F254 plates. Samples were applied to the plates using a Hamilton micro syringe (100 μL). The plates were placed 2 cm apart from each other and 2 cm apart from the bottom edge. The chromatographic tank was presaturated with the mobile phase for 15 min before developing by ascending chromatography using chloroform with methanol (50:50, by volume) as a mobile phase. The plates were then air-dried and sprayed with the vanillin-sulfuric spraying reagent before being heated in the oven at 1100 °C for 5 min. Finally, once detected at 565 nm, all plates were scanned under the following conditions:

Result output: chromatogram and integrated peak area.

Aliquots of drug stock solutions (1.0 mg/mL) equivalent to 0.5–7.5 μg of S3G were introduced into a series of 10 mL volumetric flasks and the volume was adjusted using acetonitrile. Then, 20 μL of each solution were applied to the TLC plates following the specified chromatographic conditions described previously.

Calibration graph of S3G was constructed by plotting the peak area against the corresponding drug concentration in μg/mL.

The Application to the Pharmaceutical Dosage Form

 capsules were mixed well, and an aliquot of 100 mg of the mixed sample was accurately weighed before being dissolved in 100 mL of 20% methanol in chloroform; this stock solution was sonicated for 30 min. Next, 1 mL of the stock solution was transferred into a 25 mL volumetric flask and completed to volume with acetonitrile This solution was filtered via a syringe filter polytetrafluoroethylene (PTFE) 0.45 μm, and the obtained solution was analyzed following the specified chromatographic conditions described previously. Three replicate determinations were made for each sample. The corresponding drug concentrations were calculated from the appropriate regression model parameters.

The recovery of the procedure was assessed by applying the standard addition technique and the mean percentage recovery (±%RSD) of the added solution was calculated.

In the present study, a simple, rapid, and accurate GC method in addition to a sensitive and validated TLC method were developed for the determination of S3G.

The literature revealed that there is actually no validated GC or densitometric method concerning the estimation of S3G or any other quantification methods. Only a structure elucidation spectroscopic method has been reported.

The described methods in the study could be successfully applied for the analysis of S3G in crude plant sample and future commercial capsules containing 

The proposed methods required minimal sample preparation and an uncomplicated mobile phase.

For the GC method, during the method development, different chromatographic parameters were optimized to obtain an acceptable peak shape and resolution between the peak of solvent and peak of drug with acceptable recoveries to satisfy the GC system suitability.

By trying different flow rates (1–5 mL/min, at constant pressure), it was found that a flow rate less than 3 mL/min causes significant observed peak tailing and that more than 4 mL/min leads to bad resolution between the peaks of the solvent and drug, so a flow rate of 3.35 mL/min was determined to be the best choice.

The split ratio factor was also studied, and it was observed that a high split ratio leads to poor sensitivity, so variable ratios were tried. A 1:10 ratio was selected because it gave good sensitivity and acceptable peak shape.

One of the most important studied factors in this research was the change in column oven heating rate because it was found that low heating rates lead to a peak tailing problem and higher rates (more than 500 °C/min) lead to bad resolution between the peaks of solvent and drug. After studying different heating rates, it was found that 500 °C/min was the best choice.

Satisfactory selectivity, sensitivity, resolution, and speed of chromatographic separations with stable baselines were only achieved on a HP-5 column (30 m length × 0.32 mm i.d., 0.25 μm film thickness) using nitrogen as a carrier gas before following the procedure mentioned previously. Applied analysis time was 3 min, as shown in Figures 2 and 3.

FIGURE 2: GC chromatogram of standard S3G.

FIGURE 3: GC chromatogram of blank solvent mixture.

For the TLC method, different mobile phases in different ratios were tried, namely hexane:ethyl acetate in a 83:17 ratio, hexane:acetone in a 3:1 ratio (13,14), chloroform:ethanol in a 9.8:0.2 ratio (15), and chloroform:methanol in a 8:0.6 ratio (16).

The best peak shape of S3G was obtained upon using a mobile phase consisting of chloroform:methanol (50:50 by volume).

Different scanning wavelengths were tried, including 205, 254, 274, 365, and 565 nm. It was found that the most suitable detection wavelength for S3G was 565 nm after spraying and the spots appeared at 

FIGURE 4: TLC densitometry chromatogram of a standard S3G at 565 nm.

The developed method was validated according to the guidelines of the International Council for Harmonization (ICH) for validation of analytical procedures (17).

Under the optimized conditions described previously, the analytical curve was obtained by plotting the peak area and the corresponding S3G concentrations over the range of 10–130 μg/mL for GC and 0.5–7.5 μg/mL for TLC. It showed a significant linear relationship and yielded correlation coefficient, 

, of 0.999; the regression parameters were computed and presented in Table I.

Limits of Detection (LOD) and Limits of Quantification (LOQ)

The limits of detection (LOD) and limits of quantification (LOQ) were determined based on the standard deviation of the response (σ) and the slope of the calibration curve (S) according to the ICH guidelines, as follows: LOD = 3.3 (σ/S) and LOQ = 10 (σ/S). The LOD and LOQ for GC and TLC were 2.51 and 7.59 μg/mL for the GC method and 0.13 and 0.40 μg/mL for TLC method, respectively, as shown in Table I.

The accuracy and precision of the procedure was determined using three different concentrations of pure samples of the drug covering the specified range, each in triplicate, within one day for intraday analysis and three days for interday analysis.

To determine the intra- and inter-day precision of the method, S3G was assayed at three different concentrations: (20, 60, and 120 μg/mL) for GC and (1.5, 3.5, and 6.5 μg/mL) for TLC, respectively. The experiment was performed using nine replicates (

= 9) for intra-day and on three separate days in triplicates (

 = 9) for inter-day. The %RSD was taken as a measure of precision, and the accuracy of the method was expressed as R%. The results were summarized in Table I.

Repeatability intraday precision of the proposed method for S3G was found to be reliable based on %RSD (<2%).

Intermediate precision (interday precision) was demonstrated on three successive days by two analysts. The %RSD values were less than 2%, confirming that the method is sufficiently precise, as shown in Table I.

The selectivity was assured by studying the possibility of interference of the inactive ingredients in 

 capsules (placebo) with S3G standard by GC and TLC method. The obtained chromatograms of standard S3G and placebo did not show any peak interference which revealed high selectivity of the proposed method.

For the GC method, it was evaluated by studying the effect of changing the injector and detector temperature by ±2 °C. It was found that these deliberate variations did not affect the system suitability parameters, confirming robustness of the method and the %RSD found to be <2%, which was also in favor of the developed GC method, as shown in Table II.

For the TLC method, it was evaluated by studying the influence of small variations in the mobile phase ratios (49:51 mL by volume) then observing 

 values upon introduction of these deliberate variations in chloroform and methanol volume.

It was found that these variations cause no significant difference in 

 values. Also, neither tailing of the separated bands or interference were observed, confirming robustness of the method, and the %RSD was found to be 1.41 and 1.53 (<2%) which was also in favor of the developed TLC method.

The stability of the S3G solution in 80% chloroform and 20% methanol was evaluated by analyzing two different solutions: One of them was kept at room temperature, and the other was kept in the refrigerator at 40 °C and compared against the freshly prepared standards. It was found that the drug solution was stable for one week at room temperature and for two weeks in a refrigerator.

Application to Pharmaceutical Dosage Form

The proposed GC and TLC methods were applied for determining S3G in newly formulated 

 capsules. Three replicate determinations were performed; the obtained results were in good agreement with the label claim, where no interference from excipients and additives was observed, as demonstrated in Tables III and IV.

The recovery of the procedure was assessed by applying standard addition technique and the mean percentage recovery ±%RSD of pure added was shown in Tables III and IV.

A novel, simple, rapid and accurate GC method with minimum sample preparation and a wide linearity range, in addition to a newly developed, cost-effective densitometric method, were developed and validated for the estimation of S3G in extracts of 

The newly proposed methods are applicable for the determination of S3G in pure form and in pharmaceutical formulations.

(2) H. Rashad F.M. Metwally S.M. Ezzat, M.M. Salama, A. Hasheesh, and A.A. Abdel Motaal, 

(3) A.A. Motaal, S. Shaker, and P.S. Haddad, 

(4) M.S. Kamel, K. Ohtani, T. Kurokawa, M.H. Assaf, M.A. El-Shanawany, A.A. Ali., R. Kasai, S. Ishibashi, and O.S. Tanaka, 

(5) A.E. Baragob, W.H. Almalki, I. Shahid, F.A. Bakhdhar, H.S. Bafhaid, and O.M. Eldeen, 

(6) N.S. Abou Khalil, A.S. Abou-Elhamd, S.I. Wasfy, I.M. El Mileegy, M.Y. Hamed, and H.M. Ageely, 

(7) N.H. Shafik, R.E. Shafek, H.N. Michael, and E.F. Eskander, 

(8) A.P. Singh, S. Das, A. Mazumder, M. Kumar, and N. Gautam, 

(9) A. Abdel Motaal, H. El-Askary, S. Crockett, O. Kunert, B. Sakr, S. Shaker, A. Grigore, R. Albulescu, and R. Bauer. 

(10) D. Staerk, B.P. Chapagain, T. Lindin, Z. Wiesman, and J.W. Jaroszewski, 

(11) Y.C. Teles, O.S. Chaves, M. de Fátima Agra, et al. 

Plant Drug Analysis: A Thin Layer Chromatography Atlas

 (Springer Science and Business Media, Berlin, Germany, 2nd Ed., 1996).

GC and TLC methods are demonstrated for quantification of stigmasterol 3-O-β-D-glucopyranoside (S3G), the main active component in the herbal nutraceutical Balanites aegyptiaca, an antihyperglycemic in Egyptian folk medicine.

Development and Validation of Novel Gas Chromatography (GC) and Thin Layer Chromatography (TLC) Densitometry Methods for the Quantification of Stigmasterol 3-O-β-D-Glucopyranoside (S3G) in Balanites Aegyptiaca Extract: Application to Newly Formulated Balanites Capsules

This article is the second of a series of four white papers on the bioanalysis of small-molecule drugs and metabolites by liquid chromatography–mass spectrometry (LC–MS). Part 1 was an overview of the fundamentals and regulations. This installment, part 2, is on sample preparation. Part 3 will cover method development, optimization, and best practices, and part 4 discusses method validation for regulated bioanalysis. This section provides an overview of sample preparation fundamentals, best practices, and modern trends. A sample preparation method development case study illustrates these practices and concepts.

Many factors need to be considered when developing sample preparation methods for bioanalysis. For a more in-depth discussion on sample preparation techniques, the reader is referred to textbooks on general applications in chromatography (1), pharmaceutical assays (2,3), and bioanalysis (4). Modern trends and practical aspects in sample preparation can also be found in numerous articles on “Sample Prep Perspectives” in the 

 column by Douglas Raynie (5). Sample matrix, compounds, chemical properties, instrumentation, and desired limits of quantitation (LOQs) all affect method development decisions for liquid chromatography–mass spectrometry (LC–MS) analysis of complex physiological samples. Multiple options exist for sample cleanup, from more simple methods like filtration, pass-through solid-phase extraction (SPE), and liquid-liquid or supported liquid extraction (LLE and SLE), to more complex methods like hydrophobic interaction and mixed-mode ion-exchange SPE with bonded silica or polymeric sorbents. This paper aims to review emerging trends in sample preparation of biological samples such as blood, urine, and tissues, and sample preparation options based on their characteristics. Chemical properties such as the octanol–water partition coefficient (log 

) that affect sample preparation and method development are discussed. Automation strategies are summarized. A case study on sample preparation method development and experimental design for a drug in plasma is presented.

First, the desired compounds, their required LOQs, and the instrumentation used for analysis should be determined. Will the method be a sensitive, quantitative method or a qualitative screen? Are there any relevant metabolites that need to be included? How much of the specimen is available? What is the stability of the analytes in and out of the matrix?

Knowing the properties of the biological matrix is important to determine the type of sample preparation required. For example, urine, blood, or tissues all have components that can cause ion suppression or enhancement, and can foul the LC column and LC–MS instrumentation (4).

Whole blood is a commonly analyzed matrix. It is about 80% water. The remaining 20% is primarily red blood cells (43%), plasma (50%), proteins, fats, and sugars. Plasma is mainly composed of proteins like albumin, lipids, and globulins. Lipids and phospholipids in plasma can cause ion suppression and build-up on LC columns, resulting in clogging and reduced column lifetimes. Analytes can be bound to red blood cells or plasma proteins. Many analytes are bound to red blood cells that must be lysed during sample preparation for accurate quantitation. Water, acidic or basic buffers (ammonium chloride, trichloroacetic acid, zinc sulfate, perchloric acid), freezing, or centrifuging can lyse red blood cells. Acidic buffers or organic solvents (trichloroacetic acid, phosphoric acid, formic acid, zinc sulfate, methanol, ethanol, isopropanol) can disrupt plasma proteins (4,6).

Urine, another commonly analyzed matrix, is about 95% water. The main interferences that should be removed from urine during extraction are salts, urea, pigments, and creatinine. Many drugs and metabolites are enzymatically glucuronidated to aid in excretion from the body. These glucuronides can be measured directly, but they usually do not ionize well, and are more polar than their non-conjugated “free” drug or metabolite counterparts. Hydrolyzing conjugated metabolites to the “free” drug or metabolite increases sensitivity and simplifies method development. Hydrolysis can be done with acid or base, but these are not very selective, and usually require a pH adjustment prior to extraction. Enzyme hydrolysis with a natural or recombinant beta-glucuronidase enzyme is more selective. However, using an enzyme introduces proteins to your urine sample that should be removed before analysis (7).

Tissues require extensive sample clean-up. Commonly analyzed tissues include liver, brain, heart, and umbilical cord. Tissues contain blood vessels, arteries, veins, muscles, and other solid components. Tissues cannot be analyzed directly. They need to be homogenized and the analytes of interest extracted followed by sample preparation to remove inferences. Homogenization can be done with a blender, bead mill, tissue grinder, or bead ruptor. Samples can be homogenized in the presence of aqueous or organic solvent until they are liquified. The homogenate is usually centrifuged, and the supernatant is extracted for additional clean-up prior to LC–MS analysis (8).

Knowing the physicochemical properties of the target analytes can help determine the sample preparation procedure. Information on the structure, functional groups, polarity, p

, and stability is beneficial for selecting the appropriate approach and extraction parameters.

 indicates an analyte’s hydrophobicity. Analytes with a negative log 

 are more soluble in an aqueous solvent or more hydrophilic. Conversely, analytes with a positive log 

 are more soluble in an organic solvent or more hydrophobic. This is an important consideration when developing LLE, SLE, or hydrophobic interaction SPE methods. The distribution coefficient, log 

, provides information on the solubility and hydrophobicity of ionized compounds (9).

Knowing the functional groups of the molecule can help determine whether it is an acid or a base. For instance, acidic drugs might have a sulfonic acid, carboxylic acid, or aromatic sulfonamide as part of their structures. Similarly, basic drugs might have phenols, imides, aliphatic amines, aromatic amines, or heterocyclic amines as part of their structures. Amphoteric compounds contain both acidic and basic functional groups, and have both a positive and a negative charge under certain pH conditions. Knowing a compound’s functional groups can aid in choosing possible sample preparation options (4).

The negative log of the acid dissociation constant, or p

, is the pH where a functional group of an analyte is 50% ionized and 50% neutral (non-ionized). This value is particularly important when developing sample preparation methods as the pH of the sample should not be near the analyte’s p

, because the compound would be in two different ionization states. An analyte is usually fully charged or fully neutral two units away from its p

. This phenomenon is defined by the Henderson-Hasselback equation (2). For example, for a weak base with a p

 of 8, the analyte would be 100% positively charged two units below the p

 (pH 6 or lower) and 100% neutral two units above the p

 (pH 10 or higher). Similarly, for a weak acid with a p

 of 6, the analyte would be fully ionized and negatively charged two units above the p

 (pH 8 or higher) and neutral two units below the p

 (pH 4 or lower). For LLE, SLE, and hydrophobic interaction SPE, compounds should be neutral. For mixed-mode ion-exchange SPE, compounds should be charged (10).

Many resources are available to research compound properties. Chemicalize (11) is a website that provides compound information like log 

, chemical structure, solubility, and other properties. It does require a paid subscription. The Human Metabolome Database (12) is a free service with information for many drugs and metabolites. Pubmed and Google Scholar are resources for peer-reviewed publications. Multiple reference books can also be used to research compound properties, for example, 

Disposition of Toxic Drugs and Chemicals in Man

Figure 1 shows sample preparation techniques organized by increasing selectivity and increasing sample cleanliness. Sample preparation options can be selected based on assay requirements and analyte properties. Table I summarizes SPE product offerings from several manufacturers categorized by sample preparation options and sorbent chemistries. This is not meant to be an all-inclusive list, merely a sampling of what is available. The reader is encouraged to visit the company’s website for updated and detailed information. An overview of sample preparation options is described briefly below.

FIGURE 1: Selectivity vs. sample cleanliness using different sample preparation techniques.

Dilute and shoot methods, simply diluting the sample and centrifuging prior to analysis, yield the dirtiest samples. There is no clean-up involved, and all matrix components are still present in the sample, which can cause ion suppression or enhancement. Particulates can clog the LC column and contaminate the MS source.

Filtration of dilute and shoot samples can remove particulates from the sample. Samples can be passed through a syringe or vial filter or a filter plate for particulate removal prior to LC–MS analysis. These are usually 0.2 μm filters made of polytetrafluoroethylene (PTFE) or polyvinylidene fluoride (PVDF). An inline filter is usually placed pre-column, and can capture any particulates that would otherwise build up in the LC system. These filters should be changed frequently, as retained particulates accumulate and can leach out due to increased backpressure. LC guard columns can be used before the analytical column to retain components and increase LC column lifetime.

Protein removal is used for blood or plasma samples. This can be done manually or in a 96-well plate or cartridge. A crash solvent (such as acetonitrile or methanol) is added to the sample for a manual crash. Samples are mixed and centrifuged, and the supernatant is collected. A protein removal plate or cartridge removes proteins and filters the sample. The crash solvent is usually added first, then the sample is added, mixed, and vacuum or positive pressure is applied to collect the crashed sample. The precipitated proteins are retained while the rest of the sample flows through. Protein removal, using manual, plate, or cartridge protocols, only removes proteins, which are not the only endogenous component that cause matrix effects or interferences when performing LC–MS/MS quantitation (4,14).

This concise yet comprehensive overview of sample preparation for bioanalysis looks at sample preparation fundamentals, best practices, and modern trends—all illustrated with a case study.

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