JournalofnearInfraredSpectroScopy

191

ISSn: 0967-0335 © IM publications llp 2011 doi: 10.1255/jnirs.927 all rights reserved

artemisinin and its derivatives are drugs used to treat malaria. artemisinin is a sesquiterpene lactone with an endo peroxide bridge that is difficult to synthesise in a cost-effective manner, so direct extraction from the leaves of Artemisia annua (A. annua) remains an essential step in drug manufacture.1,2 agronomical research is active for this species, with the principal aim being to improve artemisinin yield, thereby reducing the quantity of leaf material required per extraction.3–5 Mediplant has been working for more than 20 years on this member of the asteraceae and has acquired a thorough knowledge of cultural techniques, drying and stocking procedures. Mediplant has also developed A. annua hybrids that are particularly rich in artemisinin.3,6 Such a breeding research programme is dependent upon an easy, rapid, low-cost and reliable analytical technique to quantify the artemisinin content of large numbers of samples.

the reference methods currently used to quantify artem-isinin content are high-performance liquid chromatography (Hplc) and thin-layer chromatography (tlc) techniques;7 however, such techniques are time consuming, expensive and need a fully adapted laboratory and specialised scientific personnel.

for years, near infrared (nIr) spectroscopy has been widely developed and is now considered to be a helpful tool in various fields of the agricultural supply chain. numerous studies have presented the potential of such a technique to determine the quality of both fruit8–15 and vegetable samples.16–24 during the last decade, nIr spectroscopy has been further developed to quantify constituents in medicinal and aromatic plants.25–29 to date, only one study has reported the feasibility of determining artemisinin from A. annua l. by using nIr spectroscopy.30 In

Determination of artemisinin and moisture content of Artemisia annua L. dry powder using a hand-held near infrared spectroscopy device

Cédric Camps,a,* Marine Toussirot,a,b Mélanie Quennozb and Xavier Simonnetb

aagroscope changins–Wädenswil (acW), research centre conthey, route des vergers 18, cH-1964, conthey, Switzerland. e-mail: cedric.camps@acw.admin.chbMediplant, route des vergers 18, cH-1964, conthey, Switzerland

A rapid, low-cost method, based on near-infrared spectroscopy (NIR), was developed to determine artemisinin and moisture content in dry powder of Artemisia annua (A. annua) leaves. A calibration set of 60 samples and validation set of 40 samples of A. annua hybrids exhibiting artemisinin content of between 0.7% and 1.6% was used. Results of partial least squares modelling indicated that NIR was accurate in predicting artemisinin content. Root mean square error values of cross-validation (RMSECV) and prediction (RMSEV) of 0.1% were calculated, in both cases. A model of moisture content was particularly accurate with RMSECV and RMSEP values of 0.8% (R = 0.99) and 1.4%, respectively.

Keywords: hand-held NIR, Phazir, PLS, artemisinin content, moisture content

Introduction

C. Camps et al., J. Near Infrared Spectrosc. 19, 191–198 (2011)received: 31 January 2011 n revised: 6 april 2011 n accepted: 21 april 2011 n publication: 23 June 2011

192 Determination of Artemisinin and Moisture Content of Artemisia annua L.

that study, the authors determined artemisinin with an accu-racy of 0.544‰ and an R2 value of 0.988.

the aim of the present research, therefore, was to develop a low-cost and rapid method, based on nIr spectroscopy, to quantify the artemisinin content in the dry powder from leaves of A. annua. the calibration model was developed using 60 samples of A. annua hybrids, permitting the analysis of a representative range of artemisinin contents. In addition, because the moisture content of samples could influence the quality of the models, a calibration was also performed to determine moisture content by nIr spectroscopy.

Materials and methodsDetermination of artemisinin contenta set of 60 samples of A. annua dry powder were analysed in the present study. the samples correspond to hybrids of A. annua selected from the breeding programme at Mediplant. A. annua l. plants were grown on the experimental site in conthey, Switzerland. plants were harvested in autumn 2009 and dried at 35°c with pulsed air. leaves were separated from the stems and then ground with a centrifuge grinder (≤0.5 mm) (fritsch pulverisette 14; fritsch, Idar-oberstein, Germany). determination of the artemisinin content (ac) was performed as described by Quennoz et al.7 one hundred milligrams of powder were mixed with 5 ml of toluene in a polytron type pt 20.00 (Kinematica, lucerne, Switzerland) for 30 s, and then centrifuged at 1390 × g for 5 min. one microlitre of plant extract was applied immediately to a tlc plate at 15 mm of the inferior edge of precoated silica gel tlc plates 60f254 (Merck, darmstadt, Germany), 10 × 20 cm. the manual application was done with a microcapillary of 1 µl, using a nanomat (camag, Muttenz, Switzerland). fifteen samples were applied on one plate. Samples were applied as indi-cated in table 1. to avoid edge effects, a standard solution of 100 ng of artemisinin was applied at positions 1 and 15. one known sample was applied at the second position; this sample was analysed previously by Hplc; this enabled us to check the quality of the plate. the second unknown sample (at the fourth position) was applied again at the eleventh position in order to determine the within-run precision. a stock solution of artemisinin (provided by botanical extracts epZ ltd, athi river, Kenya; batch no. 127/1/2901, nMr purity: >99.0%) was prepared in toluene (1 mg ml−1). five standard solutions of artemisinin (100 ng ml−1, 150 ng ml−1, 200 ng ml−1, 250 ng ml−1 and 300 ng ml−1) were then prepared and spotted on the silica plate.

chromatography was performed on the silica plate with n-hexane (purity, 95%) and diethyl ether (purity, 99%) (6:5, v/v) as the eluent in a twin trough developing chamber (camag); the migration distance is 60 mm (about 6 min migration time). pre-equilibration with a saturation pad (camag) took 30 min. the migration was ascendant and linear. for thermal in situ derivatisation of artemisinin spots, the developed plate was dried for 3 min, immersed for 1 s in a solution of acetic acid

(100% purity)/sulfuric acid (96% purity)/anisaldehyde (98% purity) (100:2:1, v/v/v) and then heated on a tlc plate heater III (camag) at 105°c for about 5 min. densitometric evalua-tion was performed by a tlc Scanner 2 (camag) by meas-uring absorbance at 530 nm (tungsten lamp, scanning speed 4 mm s−1). the calibration range was 100–300 ng ml−1 of artemisinin. Statistical data were obtained with catS software (camag).

In order to avoid bias due to differences in water content of dried leaves, dry matter content was determined and ac was finally expressed in per cent (grams of artemisinin per 100 g of dW).

these 60 samples were used as reference value of ac in A. annua powder to build a calibration model based on nIr–infrared measurements. forty unknown samples of A. annua powder were used as the external validation set.

Determination of moisture contentIn order to create a sample set with high variability in moisture content (Mc), A. annua dry powder was placed in a cooled room (2°c, 95% rH). during 91 h of cold storage, 17 powder samples were collected. the Mc (%) was measured for each sample [equation (1)]: = ×100 (1)

dWMc

fW

where dW is the dry weight (in grams) and fW is the fresh weight (in grams).

figure 1 shows the Mc of the A. annua powder as a function of duration in the cooled room. Mc values for the 17 samples tested ranged from 7.21% to 31.65%. these samples were used as reference values of Mc in A. annua powder in order to build

Position Sample type Artemisinin content (ng)

1 Standard 100 2 Known sample ? 3 Sample 1 Being determined 4 Sample 2 Being determined 5 Sample 3 Being determined 6 Standard 100 7 Standard 150 8 Standard 200 9 Standard 25010 Standard 30011 Sample 1 Being determined12 Sample 2 Being determined13 Sample 4 Being determined14 Sample 6 Being determined15 Standard 100

Table 1. Sequence of the deposits on the silica plate, positions 1 to 15, sample type and artemisinin content.

C. Camps et al., J. Near Infrared Spectrosc. 19, 191–198 (2011) 193

a calibration model based on nIr–infrared measurements. ten unknown samples of A. annua powder placed in a cooled room were used as an external validation set.

Near infrared measurementsnear infrared deviceSpectra were acquired in reflectance mode by using a MeMS-based spectrometer, the handheld nIr pHaZIr 1018 (anatec, eke, Belgium). Samples of A. annua dry powder were placed in adapted vials closed with a plastic cap (vials for pHaZIr pcX-acc-4 solids adapter, 15 mm internal diameter). Spectral acquisition was carried out in direct contact analysis mode by placing the vials in the specific pHaZIr adapter situated at the end of the nIr pistol. absorbance spectra, corresponding to an average of 10 scans, were recorded at a resolution of 8 nm from 1000 nm to 1800 nm. a white reference scan was carried out before analysing the set of samples by using a piece of Spectralon.

Within this framework, nIr measurements were performed five times by rotating the vial a few degrees between each measurement. a total of 300 and 85 spectra were collected to calibrate the models for the prediction of ac and Mc, respec-tively. In the same way, 200 and 50 spectra were collected and constituted the validation sets of the ac and Mc models, respectively.

Spectral pre-treatmentstwo pre-treatments were applied to the spectra to compensate for the effects of uncontrolled baseline and intensity variations. In the first pre-treatment, spectra were corrected using the standard normal variate (SnV) method.31 the corrected spec-tral data are given by equation (2):

( )= =

ij iij corrected with 1 2 p (2)

x - xx , i ,

Sd

where xij is the absorbance of the spectrum i (for p data

points) measured at wavelength j, x–i is the average value of the absorbance of the uncorrected spectrum and SD is the standard deviation of the spectrum, defined as equation (3):

( )=-

=-

å2

1 (3)1

p

ij ijx x

SDp

In the second pre-treatment, a second derivative was applied on spectra in order to minimise the overlapping effect of absorp-tion bands and variations due to radiation scattering.32 other pre-treatments were explored (data not shown) but those reported in the manuscript produced the most accurate models.

partial least squares regressionpartial least squares regressions (plS) were carried out to perform linear models of prediction between spectral data and the references values (ac or Mc). In order to obtain effi-cient and robust models, a minimum number of latent vari-ables was used in the models.33 the models were built using a leave-one-out procedure.34

the accuracy of the predictions has been discussed according to the R value of calibration and cross-validation (Rc

2, RcV2), the root mean square error of calibration, cross-

validation and external validation [RMSEC, RMSECV, RMSEP, equation (4)]: ( )ˆ

/ /

=

=-

=å

2

1 (4)i n

i iiy y

RMSEC CV Pn

where y i is the predicted value and yi the actual values of ac or Mc in the plS model and n is the number of samples in the plS model.

the robustness of the plS models has been discussed according to the ratio of performance to deviation of cross-validation (RPD) [equation (5)]: = (5)SDRPD

RMSECV

Results and discussionDetermination of artemisinin content by near infrared spectroscopya set of 60 samples of A. annua dry powder was used to perform the calibration step. the range of ac values was from 0.7% to 1.6%. In order to test the normality of distribu-tion of the ac values, a Shapiro–Wilk35 test at a threshold of 5% was performed on ac raw data. the calculated prob-ability was P = 0.577 showing the normal distribution of the ac values (figure 2). a normal distribution of the reference method is suitable for building a correct linear regression model. the normal distribution of reference values makes it possible to avoid establishing a regression model with a “false” high correlation coefficient value based on two sets of widely spaced points.

13

Figure 1.

Figure 2.

Figure 1. Determination of MC of A. annua powder as a function of duration in cooled room (RH = 90%). R2 is the coefficient of determination of the polynomial regression characterising the MC in A. annua powder as a function of the duration in the cooled room.

194 Determination of Artemisinin and Moisture Content of Artemisia annua L.

the spectral collection for calibration comprised 300 spectra (figure 3), corresponding to five spectral acquisition per dry powder sample (n = 60). prior to chemometric analysis, the spectral set was pre-treated using a second derivative (step 7). table 2 presents the plS values obtained according to the ac prediction. the R values were 0.96 and 0.94 for the calibra-tion and the cross-validation steps. the RMSEC and RMSECV were 0.1% and 0.1%, respectively. figure 4 shows the scatter plot of actual values of ac according to the predicted values of ac. the data points lie in an approximately straight line with minimal dispersion, confirming good performance of the plS model.

In order to evaluate the robustness of the plS model, the RPD was calculated. the RPD value obtained for the cross-validation step was 2.32.

the RPD value was calculated to verify the applicability of the calibration.36 RPD is the ratio of the standard error of predic-tion (SEP) to the standard deviation (SD) of the reference data. In the present study, we replaced the SEP with the RMSECV. this ratio was defined using the following ranges: 0.0–2.3 as not recommended, 2.4–3.0 as rough screening quality, 3.1–4.9 as screening quality, 5.0–6.4 as quality control, 6.5–8.0 as process control, and >8.1 suitable for any application.

the plS model of ac presents a RPD value suitable for rough screening. this apparently low RPD value is mainly due to the low standard deviation of ac reference values, between 0.7% and 1.6%. In such a situation, Williams and norris36 discussed that a low RPD resulting from the small SD of samples may still be valid when the sample population size is reasonable (60 or more), and we should therefore look at the other parameters, R and RMSECV values. accordingly, these values provide evidence that the model is correct and robust. In a last step, the plS model was submitted to an external

validation performed on 40 unknown samples. the results confirmed the precision of the model with a RMSEV of 0.1%. finally, the model of ac prediction, while apparently correct, could be improved by including a set of samples aiming at enlarging the range of reference values.

Quantification of bio-molecules in plants by nIr spectros-copy is becoming increasingly popular. Studies have reported

13

Figure 1.

Figure 2.

Figure 2. Distribution of AC values (grey bars) and the normal curve (m = 1.1795, s = 0.2015).

14

Figure 3.

Figure 4.

Figure 3. Typical raw NIR spectra of A. annua dry powder.

Parameter Artemisinin content

Moisture content

l (nm) 948–1781 1210–1797

LV 6 3range of values (%)

0.7–1.6 7.2–31.6

calibrationn 60 17Rc 0.96 0.99RMSEC (%) 0.1 0.6cross-validationn 60 17Rcv 0.94 0.99RMSECV (%) 0.1 0.8RPD 2.32 7.47Validationn 40 10RMSEP (%) 0.1 1.4

Rc/Rcv: correlation coefficient of calibration/cross-validation models. RMSEC/RMSECV/RMSEP: root mean square error of calibration/cross-validation/prediction. LV: number of latent variables. l (nm): spectral range used for the calibration of plS models. n: number of samples used in the plS model.

Table 2. Partial least squares values of calibration, cross-validation and validation of artemisinin content and moisture content.

C. Camps et al., J. Near Infrared Spectrosc. 19, 191–198 (2011) 195

significant results showing the ability of nIr spectroscopy to quantify compounds such as alkaloids, tocopherols, phenolic compounds, glucosinolates, essential oils, ginsenosides, or glycoside compounds.29 In our specific area of research, the development of rapid and low-cost techniques for ac quan-tification remains important because the production sites for artemisinin are located in developing countries. for this reason, a nIr-based quantification technique would be more suitable and easier to use than chemical analysis requiring expensive tools and fully adapted laboratories.

Determination of moisture content in dry powders by NIR spectroscopya set of 85 spectra pre-treated by SnV were used to perform a calibration model of Mc. table 2 presents the plS values obtained from the prediction of Mc. the R values were 0.99 for both calibration and cross-validation steps. the RMSEC and RMSECV were 0.6% and 0.8%, respectively. figure 5 plots the actual values of Mc according to the predicted values of Mc. as above, in the case of ac prediction, the data points lie along a quasi-straight line. the RPD value for the cross-validation step was 7.47. In an external validation step, a RMSEV value of 1.4% was obtained.

the determination of Mc in samples being analysed by nIr spectroscopy is important for two reasons. first, it is well known that the realisation of a robust calibration model based on samples with high differences in Mc37 is especially difficult. In this way, the determination of Mc of samples to be analysed could be considered a prerequisite step to ensure that the Mc of a new sample to be analysed is consistent with the Mc of samples used in the calibration step.

Second, the water content may have an impact on product quality. It is known that the community involved in collecting and buying dried leaves of A. annua for use by artemisinin producers in developing countries are increasingly interested

in the determination of the water content in the dried leaf product. the drying step is performed by hand and water content in dried leaves is very variable. thus, given that buyers pay for the product based on weight, they would benefit from methodology which allowed them to adjust the price based on the water content of dried leaves.

In all cases, the performance of the models reported in the literature is consistent with the results of this study. using a hand-held nIr spectrometer, a study reported that it was possible to determine the Mc of paper with an accuracy of about 1%.38 In the agronomical field, numerous applications were attempted to determine the Mc of various products. recently, Mc of dates was determined with a high accuracy (R = 0.99).39 In the same study, the authors calculated a RPD value of 7.1, which is close to our calculation (RPD = 7.47). prediction of dry sausage Mc has also been carried out with a precision 0.7% and a R of about 0.9.40 In the food industry, Mc of dry potato samples was determined with a precision of 1%.41

the present model of Mc prediction relies on a wavelength range between 1210 nm and 1797 nm (figure 6). In this range, a strong nIr absorption band for water was found at around 1400–1440 nm. this spectral region corresponds to the first overtone of the o–H chemical bond, which is characteristic of water.29,42–44

Conclusionthe aim of the present study was to initiate the development of a new nIr-based method capable of quantifying the artem-isinin content of A. annua leaves. the development of this method is particularly crucial in order to analyse the large number of samples corresponding to hundreds of hybrids from the breeding programme directed by Mediplant.

14

Figure 3.

Figure 4.

Figure 4. Actual values versus predicted values with the PLS model of artemisinin.

15

Figure 5.

Figure 6.

Figure 5. Actual values versus predicted values with the PLS model of moisture content of A. annua dry powders.

196 Determination of Artemisinin and Moisture Content of Artemisia annua L.

the results obtained showed the feasibility of artemisinin quantification by using nIr spectroscopy. the accuracy of the prediction is about 0.1% in a fully external validation. this threshold of precision has been judged as correct for applica-tion of the method to a breeding programme.

regardless of the RPD value, the robustness of the model could be further improved by enriching the calibration set with new samples of dry powders from other production sites where hybrids with very low and very high artemisinin content exist.

References1. e.a. Brisibe, e.a. uyoh, f. Brisibe, p.M. Magalhaes

and J.f.S. ferreira, “Building a golden triangle for the production and use of artemisinin derivatives against falciparum malaria in africa”, Afr. J. Biotechnol. 7, 4884 (2008).

2. H.J. Woerdenbag, c.B. lugt and n. pras, “Artemisia annua l.: a source of novel antimalarial drugs”, Pharm. Weekbl. Sci. 12, 169 (1990). doi: 10.1007/Bf01980041

3. n. delabays, X. Simonnet and M. Gaudin, “the genet-ics of artemisinin content in Artemisia annua l. and the breeding of high yielding cultivars”, Curr. Med. Chem. 8, 1795 (2001).

4. J.c. laughlin, G.n. Heazlewood and B. Beattie , “cultivation of Artemisia annua l”, in Artemisia (Industrial profiles), ed by c.W. Wright. taylor and francis, london, uK, p. 159 (2002)

5. X. Simonnet, M. Gaudin, H. Hausammann and c. Vergères, “le fanage au champ d’Artemisia annua l.: élever la teneur en artémisinine et abaisser les coûts

de production”, Rev. Suisse Vitic. Arboric. Hortic. 33, 263 (2001).

6. X. Simonnet, M. Quennoz and c. carlen, “new Artemisia annua hybrids with high artemisinin content”, Acta Hortic. 769, 371 (2008).

7. M. Quennoz, c. Bastian, X. Simonnet and a.f. Grogg, “Quantification of the total amount of artemisinin in leaf samples by thin layer chromatography”, Chimia 64, 755 (2010). doi: 10.2533/chimia.2010.755

8. c. camps and d. christen, “non-destructive assessment of apricot fruit quality by portable visible–near infrared spectroscopy”, LWT—Food Sci. Technol. 42, 1125 (2009).

9. c. camps, p. Guillermin, J.c. Mauget and d. Bertrand, “discrimination of storage duration of apples stored in a cooled room and shelf-life by visible–near infrared spectroscopy”, J. Near Infrared Spectrosc. 15, 169 (2007). doi: 10.1255/jnirs.726

10. V. Bellon Maurel, M. crochon and J.M. roger, “GloVe: an instrumented glove to non destructively and rap-idly assess fruit quality”, in Proceedings of the 6th International Symposium on Fruit Nut and Vegetable Production Engineering, 6th International Symposium, potsdam, Germany, pp. 597–602 (2002), http://cemdoc.cemagref.fr/cemoa/puB00010475.

11. S. Bureau, d. ruiz, M. reich, B. Gouble, d. Bertrand, J.M. audergon and c. renard, “rapid and non-destructive analysis of apricot fruit quality using ft–near-infrared spectroscopy”, Food Chem. 113, 1323 (2009). doi: 10.1016/j.foodchem.2008.08.066

12. p. carlini, r. Massantini and f. Mencarelli, “Vis–nIr measurement of soluble solids in cherry and apricot by plS regression and wavelength selection”, J. Agric. Food Chem. 48, 5236 (2000). doi: 10.1021/jf000408f

15

Figure 5.

Figure 6.

Figure 6. Loadings vectors of PLS models for the prediction of moisture content.

C. Camps et al., J. Near Infrared Spectrosc. 19, 191–198 (2011) 197

13. S.r. delwiche, W. Mekwatanakarn and c.y. Wang, “Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy”, Horttechnology 18, 410 (2008).

14. V.a. McGlone, r.B. Jordan and p.J. Martinsen, “Vis/nIr estimation at harvest of pre- and post-storage quality indices for ‘royal Gala’ apple”, Postharvest Biol. Technol. 25, 135 (2002). doi: 10.1016/S0925-5214(01)00180-6

15. a. peirs, J. lammertyn, K. ooms and B.M. nicolai, “prediction of the optimal picking date of different apple cultivars by means of VIS/nIr-spectroscopy”, Postharvest Biol. Technol. 21, 189 (2001). doi: 10.1016/S0925-5214(00)00145-9

16. J.y. chen, y. Miao, H. Zhang and r. Matsunaga, “non-destructive determination of carbohydrate content in potatoes using near infrared spectroscopy”, J. Near Infrared Spectrosc. 12, 311 (2004). doi: 10.1255/jnirs.439

17. r. Massantini, p. carlini and G. anelli, “nIr spectros-copy to evaluate the fruits and vegetables quality during the postharvest”, Industrie Alimentari 36, 321 (1997).

18. a.K. thybo, I.e. Bechmann, M. Martens and S.B. engelsen, “prediction of sensory texture of cooked potatoes using uniaxial compression, near infrared spectroscopy and low field H-1 nMr spectroscopy”, LWT–Food Sci. Technol. 33, 103 (2000).

19. M. Baranska, W. Schütze and H. Schulz, “determination of lycopene and beta-carotene content in tomato fruits and related products: comparison of ft–raman, atr-Ir, and nIr spectroscopy”, Anal. Chem. 78, 8456 (2006). doi: 10.1021/ac061220j

20. a. clement, M. dorais and M. Vernon, “nondestructive measurement of fresh tomato lycopene content and other physicochemical characteristics using visible–nIr spectroscopy”, J. Agric. Food Chem. 56, 9813 (2008). doi: 10.1021/jf801299r

21. a. clement, M. dorais and M. Vernon, “Multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by vis-nIr spectroscopy”, J. Agric. Food Chem. 56, 1538 (2008). doi: 10.1021/jf072182n

22. t. de nardo, c. Shiroma-Kian, y. Halim, d. francis and l.e. rodriguez-Saona, “rapid and simultaneous determination of lycopene and beta-carotene contents in tomato juice by infrared spectroscopy”, J. Agric. Food Chem. 57, 1105 (2009). doi: 10.1021/jf802920z

23. a.M.K. pedro and M.M.c. ferreira, “nondestructive determination of solids and carotenoids in tomato prod-ucts by near-infrared spectroscopy and multivariate calibration”, Anal. Chem. 77, 2505 (2005). doi: 10.1021/ac048651r

24. a.f.c. pereira, M.J.c. pontes, f.f. Gambarra, S.r.B. Santos, r.K.H. Galvao and M.c.u. araujo, “nIr spectrometric determination of quality parameters in vegetable oils using iplS and variable selection”, Food Res. Int. 41, 341 (2008). doi: 10.1016/j.foodres.2007.12.013

25. M. Baranska, H. Schulz, p. rosch, M.a. Strehle and J. popp, “Identification of secondary metabolites in medicinal and spice plants by nIr-ft–raman microspectroscopic mapping”, Analyst 129, 926 (2004). doi: 10.1039/b408933m

26. c.W. Huck, “non-invasive infrared spectroscopic tech-niques for the characterization of medicinal plants and their constituents”, Planta Med. 75, 889 (2009). doi: 10.1055/s-0029-1234278

27. l. Xiang, G.Q. fan, J.H. li, H. Kang, y.l. yan, J.H. Zheng and d. Guo, “the application of an artificial neural net-work in the identification of medicinal rhubarbs by near-infrared spectroscopy”, Phytochem. Anal. 13, 272 (2002). doi: 10.1002/pca.654

28. X.r. Zhu, y. Shan, G.y. li, Q. fan, X.y. Shi, y.J. Qiao and Z.y. Zhang, “determination of hesperidin content in guogongjiu medicinal wine based on nIr spectrometry and least squares support vector machines”, Spectrosc. Spectral. Anal. 29, 2471 (2009).

29. d. cozzolino, “near infrared spectroscopy in natural products analysis”, Planta Med. 75, 746 (2009). doi: 10.1055/s-0028-1112220

30. l. Hao, y.-p. Huang, B.-r. Xiang and l.-B. Qu, “determination of arteannuin in Artemisia annua l. by near infrared spectroscopy”, Chin. J. Pharm. 38, 584 (2007).

31. r.J. Barnes, M.S. dhanoa and S.J. lister, “Standard normal variate transformation and de-treding of near-infrared diffuse reflectance spectra”, Appl. Spectrosc. 43, 772 (1989). doi: 10.1366/0003702894202201

32. e. Moons and G. Sinnaeve, “non-destructive vis and nIr spectroscopy measurement for the determination of apple internal quality”, Acta Hortic. 517, 441 (2000).

33. a. peirs, J. tirry, B. Verlinden, p. darius and B. nicolaî, “effect of biological variability on the robustness of nIr models for soluble solids content of apples”, Postharv. Biol. Technol. 28, 269 (2003). doi: 10.1016/S0925-5214(02)00196-5

34. a.M. Mouazen, J. de Baerdemaeker and H. ramon, “effect of wavelength range on the measurement accu-racy of some selected soil constituents using visual–near infrared spectroscopy”, J. Near Infrared Spectrosc. 14, 189 (2006). doi: 10.1255/jnirs.614

35. S.S. Shapiro and M.B. Wilk, “an analysis of variance test for normality (complete samples)”, Biometrika 52, 591 (1965).

36. p. Williams and K. norris (eds), Near-Infrared Technology in the Agricultural and Food Industries, 2nd edition. american association of cereal chemists, St paul, Minnesota, uSa (2001).

37. y. li, y.M. Wei, B. Zhang and y.l. yan, “Study on the robust nIr calibration models for moisture”, Spectrosc. Spectral. Anal. 25, 1963 (2005).

38. W.f. Mcclure, d. Moody, d.l. Stanfield and o. Kinoshita, “Hand-held nIr spectrometry. part II: an economical no-moving parts spectrometer for measuring

198 Determination of Artemisinin and Moisture Content of Artemisia annua L.

chlorophyll and moisture”, Appl. Spectrosc. 56, 720 (2002). doi: 10.1366/000370202760077432

39. S.a. Mireei, S.S. Mohtasebi, r. Massudi, S. rafiee, a.S. arabanian and a. Berardinelli, “non-destructive measurement of moisture and soluble solids content of Mazafati date fruit by nIr spectroscopy”, Aust. J. Crop Sci. 4, 175 (2010).

40. t. Mishiro, M. Sugiyama, y. Minegishi, H. abe, S. Kawano and M. Iwamoto, “nondestructive determination of moisture-content in dry sausage using near-infrared (nIr) transmittance method”, J. Jpn. Soc. Food Sci. Technol—Nippon Shokuhin Kagaku Kogaku Kaishi 42, 436 (1995).

41. r.S. yeh, r.c. anantheswaran, J. Shenk and V.M. puri, “determination of moisture profile in foods during micro-

wave-heating using vis-nIr spectroscopy”, LWT–Food Sci. Technol. 27, 358 (1994).

42. M. Blanco and I. Villarroya, “nIr spectroscopy: a rapid-response analytical tool”, Trends Anal. Chem. 21, 240 (2002).

43. d. cozzolino, W. cynkar, l. Janik, r. dambergs and M. Gishen, “analysis of grape and wine by near infrared spectroscopy—a review”, J. Near Infrared Spectrosc. 14, 279 (2006). doi: 10.1255/jnirs.679

44. W.f. Mcclure, “204 years of near infrared technology: 1800–2003”, J. Near Infrared Spectrosc. 11, 487 (2003). doi: 10.1255/jnirs.399