A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material

Food Chemistry 120 (2010) 940–944

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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Analytical Methods

A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material Ilze Vermaak, Josias H. Hamman, Alvaro M. Viljoen * Department of Pharmaceutical Sciences, Faculty of Science, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa

a r t i c l e

i n f o

Article history: Received 1 June 2009 Received in revised form 19 September 2009 Accepted 7 November 2009

Keywords: FT-NIR spectroscopy FT-MIR spectroscopy Hoodia gordonii OPLS P57 PLS

a b s t r a c t The feasibility of quantifying the perceived active ingredient (P57) in Hoodia gordonii raw material using Fourier transform near- and mid-infrared spectroscopy combined with chemometric techniques was investigated. The concentration of P57 (a triterpene glycoside) was determined in 146 plant samples with liquid chromatography coupled to mass spectrometry and these values were used to develop a calibration model based on the partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression algorithms. The performance of each calibration model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R2). The PLS model with 2nd derivative pre-processing predicted P57 content based on the FT-NIR spectra with the best accuracy and a correlation coefficient (R2) value of 0.9629 and the lowest RMSEP of 0.03%. These results demonstrated that FT-NIR spectroscopy can be used to rapidly quantify P57 in H. gordonii raw material with high accuracy. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Hoodia gordonii, a succulent plant belonging to the Apocynaceae family, is indigenous to South Africa and Namibia. It is most wellknown for its appetite-suppressing properties which have been widely advertised (Lee & Balick, 2007). H. gordonii is marketed as a functional food and products are available in many different dosage forms including tablets, capsules, powders, sprays, tea, fruit and chocolate bars, patches, topical gel formulations and even lollipops. The pioneering group of scientists who investigated Hoodia species, Van Heerden et al. (1998), identified two steroidal glycosides from the extracts prepared from Hoodia species, one of which was named P57 (12-O-Tigloyl-3ß, 12ß, 14ß-pregn-5-en-20-one-3O-ß-D-thevetopyranosyl-(1?4)-ß-D-cymaropyranosyl-(1?4)-ß-Dcymaropyranoside) (Fig. 1). Since then several other researchers have identified compounds from H. gordonii, including Dall’Acqua and Innocenti (2007) who isolated ten C21-steroidal derivatives named gordonosides, Pawar, Shukla, and Khan (2007) isolated ten calogenin glycosides and Shukla et al. (2009) isolated seven pregnane glycosides. Despite the isolation of these potentially appetite-suppressing compounds, the quality of raw H. gordonii plant material purchased by manufacturers is still exclusively determined by quantifying P57. The preferred analytical method currently used to quantify P57 is high performance liquid * Corresponding author. Tel.: +27 12 382 6360; fax: +27 12 382 6243. E-mail address: [email protected] (A.M. Viljoen). 0308-8146/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2009.11.017

chromatography (HPLC) coupled to a mass spectrometer (LC– MS) although more recently Janssen et al. (2008) quantified steroidal glycosides by using HPLC coupled to an ultraviolet detector (LC-UV). However, chromatographic analysis of raw material requires skilled personnel, and in addition is both laborious and costly. Infrared (IR) spectroscopy has been used with success in the food industry for quantifying various components such as peptides in cheeses (González-Martín, Hernández-Hierro, VivarQuintana, Revilla, & González-Pérez, 2009) adulterants in Mexican honeys (Gallardo-Velázquez, Osorio-Revilla, Zuñiga-de Loa, & Rivera-Espinoza, 2009), caffeine in green tea powder and granules (Sinija & Mishra, 2009), the acid value of peanut oil (Rao et al., 2009) and aflatoxin B1 in chilli powder (Tripathi & Mishra, 2009). It has also found application as an analytical technique in the agricultural (Camps & Christen, 2009; Xie, Ying, & Ying, 2009), petrochemical (Balabin & Safieva, 2008; Zhan, Yin, Shang, & Liu, 2008) and pharmaceutical (Mantanus et al., 2009; Qu, Mingchao, Mi, Dou, & Ren, 2008) industries amongst others. Fourier transform near-infrared (FT-NIR) and Fourier transform mid-infrared (FT-MIR) spectroscopy have the advantages of being environmentally friendly since no chemicals are needed, minimal or no sample preparation is required, and they are rapid, simple and it is easy to use the equipment (Janssen et al., 2008; Mantanus et al., 2009; Tripathi & Mishra, 2009). For both FT-NIR and FTMIR the samples only need to be powdered and can be measured in the solid state, while more extensive and time consuming sample preparation methods such as solvent extraction is required for

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O O

O

OH HO H3CO

O OH

O

O

O

O

OCH3

O OCH3

Fig. 1. Chemical structure of P57 (12-O-Tigloyl-3ß, 12ß, 14ß-pregn-5-en-20-one-3-O-ß-D-thevetopyranosyl-(1?4)-ß-D-cymaropyranosyl-(1?4)-ß-D-cymaropyranoside).

LC–MS analysis. The objective of this study was to evaluate the potential of near-infrared and mid-infrared spectroscopy to rapidly determine the levels of P57 in H. gordonii raw material samples. 2. Methods and materials 2.1. Plant material and reagents H. gordonii stem samples (n = 146) were collected at various locations throughout South Africa and Namibia from natural populations as well as cultivated sites. Plant samples were handpicked, sliced and air-dried. The dried plant material was ground with a RetschÒ 400 MM ball mill at a frequency of 30.0 Hz for 90 s and thereafter passed through a 500 lm sieve to ensure a relatively narrow particle size distribution. The P57 reference standard was purchased from Chromadex Inc (California, USA). 2.2. FT-NIR spectroscopy analysis The powdered H. gordonii samples were placed in ChromacolÒ glass vials. Near-infrared spectra of the powdered samples were collected from 10,000–4000 cm 1 on a Büchi NIRFlex N500 FTNIR spectrophotometer with NIR measurement software (NIRWare version 1.2.3000 advanced edition) in reflectance mode. Spectra were collected at a spectral resolution of 4 cm 1 with 32 scans per sample. 2.3. FT-MIR spectroscopy analysis FT-MIR spectra were generated with a Bruker Alpha-P FT-MIR spectrophotometer in absorbance mode and recorded with OPUS (version 6.5) software. Spectra were collected from 4000– 375 cm 1 at intervals of 4 cm 1 and the data for each spectrum was the average spectrum of 32 scans. The mean of three spectra collected for each sample was used for further analysis. A blank background measurement was recorded after every three samples. 2.4. LC–MS analysis The samples (0.2 g) were extracted in an ultrasonic bath for 2 h with 4 ml of 50% acetonitrile and 0.1% formic acid. The detection and quantification of P57 in the 146 H. gordonii samples was performed using a Waters API Q-TOF Ultima system (Waters BEH C18 column, 1.7 lm, 2.1  50 mm). The MS detector was operated in electron impact mode with the capillary voltage at 3.5 kV and the cone voltage at 35 kV (positive switching – ES+). The flow rate of the HPLC was 0.4 ml/min, the cone gas flow rate was 50 l/h and the desolvation gas flow rate was 350 l/h. The source temperature was 100 °C and the desolvation temperature 350 °C. Accurate mass (861.5 m/z) was used to quantify P57 in ES+ mode within a mass

range of 200–1800 m/z. The injection volume was 2 ll. Gradient elution was used with 0.1% formic acid in water (Solvent A) and acetonitrile (Solvent B) as mobile phases. The gradient started with 100% solvent A, changed to 20% solvent B over 0.5 min, to 100% solvent B over 11.5 min, followed by a 2 min isocratic step and a return to initial conditions for 1 min for a total run time of 15 min. The LC–MS data was analysed using Masslynx (version 4.2) software. The specific mass of 861.5 (m/z) is used for quantification. 2.5. Data analysis The spectral data was exported to MicrosoftÒ Excel (2003) to enable analysis using Simca-P chemometric analysis software (version 11.0.0.0). Chemometrics is the science of relating measurements made on a chemical system to the state of the system via the application of mathematical or statistical methods. The power of chemometrics is that it can be used to model systems that are both largely unknown and complex. Chemometrics has a holistic, exploratory approach to data and process modelling. No assumptions about the data are made prior to analysis because in chemometrics the process or data structures reveal the relations themselves. Chemometric methods are nearly always multivariate methods of which many are based on modelling and visualising covariance structure among variables (Swanepoel & Esbensen, 2007). The spectral data were analysed using partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression methods and various mathematical pre-processing methods were investigated to develop a mathematical calibration model (Thermo Scientific, 2009). Partial least squares regression (PLS-R) represents an extremely successful new extension of traditional statistical regression methodology, developed over the last 35 years within the discipline of chemometrics. In PLS the independent data (X) is related to the dependent data (Y) and PLS attempts to capture variance and achieve correlation between X and Y-data. O-PLS is based on PLS but includes an orthogonalisation step. Strong systematic variance present in X may be unrelated to Y and is therefore irrelevant for the prediction of Y. Removing the information in X which is orthogonal to Y, termed structured noise, will not influence the variation between X and Y. Therefore the interpretation of the model is improved while the correlation remains the same (Swanepoel & Esbensen, 2007). To establish a test set and a work set, 30% of the Y-values (P57 concentration determined by LC–MS) were chosen randomly by the software. Due to this randomised approach there will be slight differences in the results but this gives and indication of the robustness of a calibration model. Several repetitions were done for each pre-processing method to determine whether the results remained consistent and differences were not significant. The standard normal variate (SNV) and multiplicative scatter/signal correction (MSC) pre-processing methods

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yielded poor R2 and RMSEP values which implies that the accuracy of predicting P57 will be very low and these methods were consequently not further investigated. To avoid overfitting of the model it is important to choose a model with a small number of PLS factors which were satisfactory for all the models constructed.

3. Results and discussion

Table 1 R2, RMSEP values and PLS factors for determining P57 content in raw H. gordonii plant material with FT-NIR utilising different spectral pre-processing techniques. Pre-processing technique

R2

RMSEP (%)

PLS factor

None PLS model SNV PLS model MSC PLS model 2nd derivative PLS model 2nd derivative O-PLS model

0.2021 0.3424 0.2497 0.9629 0.9629

0.10 0.11 0.13 0.03 0.03

1 4 3 6 6

3.1. FT-NIR The general profile of the FT-NIR spectra of H. gordonii powdered raw material (A) and the reference standard P57 (B) is depicted in Fig. 2. From visual inspection the wavenumbers 10,000–7800 cm 1 does not contribute to the model evident from the low absorbance values. Between 5000–4000 cm 1 strong peaks from combination bands are present in the reference standard spectrum (B). These peaks are also present in the H. gordonii powdered raw material spectra (A) but overlapping of peaks occurs due to ‘‘masking” by other components. CH2 and CH3 absorption peaks are observed between 7000–5000 cm 1. Many different calibration models can be generated according to the method of pre-processing used. Criteria such as the correlation coefficient (R2), the number of PLS factors and the root mean square error of prediction (RMSEP) are used to assess the ability of these models to accurately predict the chosen quality parameter (Mantanus et al., 2009). Table 1 shows the results for these criteria for several models using different pre-processing techniques. It can be seen that the best model was constructed using the 2nd derivative pre-processing method. The T vs U plot, describing the intercorrelation between the X and Y illustrates the potential of a correlation that exists be-

tween these two variables. About 42% of the variance of X is explained by the PLS model after the exclusion of X-variables with low loading weights (wavenumbers 10,000–7800 cm 1) as indicated on the loadings line plot before and after exclusion (Fig. 3). This is a considerable improvement as before exclusion only 24% of the variance of X was explained. After construction of the PLS model six outliers were removed which improved the accuracy of prediction or the R2 value. The PLS model had an R2 value of 0.9629, RMSEP of 0.03% and 6 PLS factors (Fig. 4). The cumulative overall cross-validated R2X (Q2) for this model was 75.6%, indicating good prediction ability of this model. An O-PLS model separates the PLS components into two groups, those related to Y (predictive) and those orthogonal to Y. The Y-orthogonal component may be part of the model, but is not useful for the prediction of Y. The main benefit of OPLS is model interpretation, while maintaining the predictive ability of the PLS method. As for the PLS model the O-PLS model had an R2 value of 0.9629, RMSEP of 0.03% and 6 PLS factors and in addition the T vs U plot indicated good correlation of 0.9848 between the observed values (obtained from LC–MS measurements) and predicted values for P57 (obtained from the spectra

Fig. 2. Typical original FT-NIR spectra for H. gordonii powdered raw material (A) and the spectrum for the reference standard P57 (B).

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Fig. 3. Loadings line plot from the PLS calibration model using FT-NIR data before (A) and after (B) removal of X variables with low loading weights (10,000–7800 cm

y=0.9755*x-0.0004824 R2=0.9629

P57% predicted (FT-NIR)

0.3

0.2

0.1

0.0

0.0

0.1

0.2

0.3

P57% observed (LC-MS)

RMSEP = 0.0248354

SIMCA-P 11 - 9/15/2009 4:24:31 PM

Fig. 4. PLS model using the 2nd derivative pre-processing technique calculated from FT-NIR data.

1

).

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by FT-NIR measurements). The cumulative overall cross-validated R2X (Q2) for the O-PLS model was 56.5%. This indicated that the prediction ability of the O-PLS model is not as good as the prediction ability of the PLS model as some of the X-variables removed are in fact not orthogonal to the first component. However, the O-PLS model was only used for interpretation and the PLS model will be used as the final calibration model. These results clearly demonstrate the feasibility of FT-NIR for rapidly determining the concentration of P57 in H. gordonii raw plant materials. Considering the high cost of other analytical methods, the small range of error of prediction using the FT-NIR method is negligible. 3.2. FT-MIR Chemometric analysis of the FT-MIR data did not reveal promising results. Before pre-processing the R2 was 0.1775 with an RMSEP of 0.09%. The R2 improved only marginally after several pre-processing methods were applied. The best correlation between LC–MS values and predicted FT-MIR values was established using multiplicative scattering correction (MSC) pre-processing leading to an R2 value of 0.4997 and RMSEP of 0.07%. Even with the removal of several outliers the predictive ability of the model did not improve appreciably. FT-MIR spectroscopy does not hold promise as a suitable method to accurately determine the amount of P57 in H. gordonii raw plant material compared to FT-NIR and is therefore not further discussed in this article. According to H. gordonii growers, the (unwritten in-house) industry specification for good quality raw material is considered to be a P57 concentration of 0.300%. Since plant samples are variable, the acceptable relative standard deviation (RSD) is considered to be < 20%. The acceptable range would thus be 0.240–0.360% (±0.06%) although a higher P57 content would not be considered problematic. The accuracy of the PLS model for FT-NIR is 0.9629 with an RMSEP of 0.03% which implies a range of 0.270–0.330%, thus better than the acceptable RSD. In the context of the claimed industry standard this error of prediction level would be acceptable. The levels of P57 in the 146 samples determined by LC–MS ranged from 0.000–0.430% with only 11% of the samples falling within the acceptable range and only 4% of the samples had P57 levels higher than 0.300%. This suggests that this specification set by the industry may be unrealistic. The wide variation of P57 levels within the samples is built into the data set and as a result the calibration model can be used for a wide range of samples. As has been mentioned in the introduction, only P57 is currently used to determine the quality of H. gordonii raw material and it is evident from this study that FT-NIR can be used to rapidly and accurately quantify P57 in powdered H. gordonii raw material. Thus, this is a simple and much more cost-effective method of quantification of P57 in raw H. gordonii plant material as opposed to other resource- and time-consuming analytical methods such as LC–MS. Acknowledgements The authors thank Tshwane University of Technology and the National Research Foundation for financial support. The authors

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