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Application of near-infrared reflectance spectroscopy to evaluate the lutein and β-carotene in Chinese kale
Journal of Food Composition and Analysis 22 (2009) 148–153
Contents lists available at ScienceDirect
Journal of Food Composition and Analysis journal homepage: www.elsevier.com/locate/jfca
Original Article
Application of near-infrared reflectance spectroscopy to evaluate the lutein and b-carotene in Chinese kale Xinjuan Chen a,1, Jianguo Wu b,1, Shengjun Zhou a, Yuejian Yang a, Xiaolei Ni b, Jing Yang b, Zhujun Zhu c,*, Chunhai Shi b,** a b c
Institute of Vegetable Science, Zhejiang Academy of Agriculture Science, Hangzhou 310021, China College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China School of Agriculture and Food Science, Zhejiang Forestry University, Lin’an 311300, China
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 January 2008 Received in revised form 3 September 2008 Accepted 13 October 2008
There are wide ranges of lutein and b-carotene in Chinese kale. This work assessed the capability of nearinfrared reflectance spectroscopy (NIRS) to predict the contents of lutein and b-carotene in Chinese kale against HPLC analysis. Compared to other regression methods, the regression of modified partial least squares with math treatments of 2,4,4,1 (where the first number of 2 represents the second derivative of log 1/R, the second of 4 is the gap in data points over which the derivative is calculated, the third and fourth numbers refer to the number of data points used in the first and second smoothing, respectively) showed the best results. The calibration equations of lutein and b-carotene were characterized by the coefficients of determination (RSQ) of 0.983 and 0.982 and standard error of 0.056 and 0.131 mg g 1 DW, respectively. In cross-validation, high 1 minus variance ratio (1 VR) of 0.955 and 0.966 and standard error of 0.092 and 0.179 mg g 1 DW for lutein and b-carotene were obtained, respectively. In external validations, the RSQ were 0.926 and 0.875 with standard error of 0.131 and 0.367 mg g 1 DW, respectively. These results showed that NIRS could be used for the determination of carotenoids in Chinese kale. ß 2008 Elsevier Inc.. All rights reserved.
Keywords: Chinese kale Brassica oleracea alboglabra Near-infrared reflectance spectroscopy (NIRS) Carotenoid extraction method Lutein b-Carotene Food composition Food analysis
1. Introduction Chinese kale (Brassica oleracea alboglabra), also known as Chinese broccoli, Chinese flowering broccoli, kailan (http:// www.worldcrops.org/crops/Chinese-broccoli.cfm (Anon, 2008b); http://www.cherryfarms.co.uk/kailan.asp (Anon, 2008d)), is a source of important phytonutrients such as lutein and b-carotene (http://www.cherryfarms.co.uk/kailan.asp; Kopsell et al., 2004) and commonly consumed as a healthy vegetable in south Asia especially popular in south China. Both lutein and b-carotene (Fig. 1), well-known carotenoids, are widely present in vegetables and fruits: the former is the most
* Corresponding author. Tel.: +86 571 63743001; fax: +86 571 63743001. ** Corresponding author. Tel.: +86 571 86971691; fax: +86 571 86971117. E-mail addresses: [email protected] (Z. Zhu), [email protected] (C. Shi). 1 These authors contributed equally to this work. Abbreviations: Bias, difference of means; HPLC, high-performance liquid chromatography; MPLS, modified partial least squares; NIRS, near-infrared reflectance spectroscopy; PCR, principal component regression; PLS, partial least squares; RPD, ratio of the standard deviation to SEP; RSQ, coefficients of determination; SEC, standard error of calibration; SECV, standard error of cross-validation; SEP, standard error of performance; S.D., standard deviation; 1 VR, coefficient of determination in cross-validation. 0889-1575/$ – see front matter ß 2008 Elsevier Inc.. All rights reserved. doi:10.1016/j.jfca.2008.10.007
important precursors of provitamin A; the latter can increase macular pigment concentration in eyes and may improve visual function (Krinsky, 2001). Recent research has demonstrated that lutein and b-carotene can act as powerful antioxidants and to help guard against aging, cancers and diseases (http://www.luteininfo.com (Anon, 2008c); http://www.nlm.nih.gov/medlineplus/ druginfo/natural/patient-betacarotene.html (Anon, 2008a)). It is very important for nutritionists and researchers to obtain the information on lutein and b-carotene content in vegetables and fruits. The common methods for determining of lutein and bcarotene, based on solvent extraction followed by HPLC analysis, usually involve high cost, labor and time input, and specialized technicians. In addition, the volatile, degradable or hazardous chemicals used in the extraction and HPLC analysis can imply some instability of results and health risk on human health and environment. To date, near-infrared reflectance spectroscopy (NIRS), widely used as a fast, accurate and non-pollution method for qualitative and quantitative analysis in food and pharmaceutical fields for the last 30 years (Cen and He, 2007; Roggo et al., 2007), have been well recommended for the determination of total carotenoids in carrot fruit (Schulz et al., 1998) and wheat seeds (Atienza et al., 2005) and especially some individual carotenoids such as lycopene and lutein
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set at 440 nm and a spherisorb C18 column (250 mm 4.6 mm, 5 mm; Waters, USA). The mobile phase A was acetonitrile:methanol:0.05 mol/l Tris–HCl (72:8:3) and B was methanol:hexane (5:1). The elution procedure is as follows: 100% A for first 4 min, then in a linear gradient from 0% to 100% B in 2.5 min and constant 100% B for 13.5 min, then equilibrated with 100% A for 5 min prior to the injection of next sample. The flow rate was 1.5 ml min 1 at room temperature. HPLC grade of standards of lutein (Sigma–Aldrich, USA) and b-carotene (Sigma–Aldrich, USA) were utilized for the quality and quantity analysis. 2.4. NIRS analysis Fig. 1. Chemical structures of lutein and b-carotene.
in tomato products (Pedro and Ferreira, 2005), zeaxanthin and lutein in maize seeds (Brenna and Berardo, 2004; Berardo et al., 2004); however, there is very limited correlative research in the non-seed plant tissues. Recently, our research group has succeeded in determining seed traits of crops by NIRS (Wu et al., 2002, 2006; Wu and Shi, 2003, 2004, 2007; Lin et al., 2005). The purpose of this work was to test the potential of NIRS for simultaneously predicting the lutein and b-carotene contents in the non-seed tissues of Chinese kale and to provide a simple, rapid and accurate analysis method via NIRS technique. 2. Materials and methods 2.1. Samples Chinese kale varieties with high variation of agronomy traits were planted under different seasons, soil types and fertilizer conditions in Zhejiang province over a period of 2 years (2004– 2005). Edible tender and succulent plant parts comprising one or several stout and chunky stems accompanied with small and loose terminal floral buds, 6–7 pieces of flat and broad leaves, and 2– 3 lateral branches were separately or mix harvested at the very beginning of flowering, and frozen immediately in liquid nitrogen. After being lyophilized by a freeze dryer (Vir Tis, USA), ground into powder by a WK-200B miller (Dongwan, China) and filtrated with 0.25 mm screen, the total freeze-dried powder of 151 samples was stored in a desiccator at 20 8C prior to HPLC and NIRS analysis. 2.2. Extraction of lutein and b-carotene The extraction of lutein and b-carotene were conducted according to the protocol of Gilmore and Yamamoto (1991) with modifications. About 0.1 g freeze-dried powder was weighed into 10 ml tubes and extracted with 4 ml of acetone for 30 min in an ultrasonic cleaning bath (SK5200H, Kudos, China) operating at 59 kHz frequency. The supernatants were decanted and stored on ice, and the pellets were re-extracted twice with 3 ml of acetone for 10 min under same conditions. Three supernatants were combined and made up to a final volume of 10 ml with acetone. About 1.5 ml of extracts was filtrated by a 0.2-mm organic film and kept at 4 8C prior to HPLC analysis. All procedures were performed under dim light condition and at room temperature. 2.3. HPLC analysis The HPLC analysis was conducted according to the protocol of Gilmore and Yamamoto (1991) with modifications. Ten microlitres of extracts were analyzed in a Shimadzu HPLC system (LC-10AT pump, CTO-10A column oven, SCL-10A vp system controller, Shimadzu, Kyoto, Japan) consisting of a UV-VIS detector (SPD-10A)
A total of 151 samples were scanned on a NIR Systems model 5000 monochromator (NIR Systems Inc., Silver Spring, MD, USA). About 0.5 g freeze-dried powder was loaded in a small ring cup with 20 mm i.d., and reflectance spectra (log 1/R) from 1100 to 2500 nm were recorded at 2 nm intervals. Each sample was subsequently scanned three times and the average spectrum was collected. 2.5. Calibration and validation procedures WINISI (version 1.04) software (Infrasoft International, Port Matila, PA, USA) was used to collect spectra, develop equations and evaluate calibration performance. Principal component regression (PCR), partial least squares (PLS) and modified PLS (MPLS) were used to analyze the regression effect. Different math treatments were applied during equation development in MPLS, e.g. 2,4,4,1, where the first number indicates the order of the derivative (0 represents no derivative, 1 represents the first derivative and 2 represents the second derivative of log 1/ R), the second is the gap in data points over which the derivative is calculated, the third and fourth numbers refer to the number of data points used in the first and second smoothing, respectively. The most significant wave regions for NIRS estimation of lutein and b-carotene were studied by calculating the correlation coefficients between the carotenoids values and the spectral data (second derivative, scatter corrected). 3. Results and discussion 3.1. Lutein and b-carotene composition in the calibration set The composition of lutein and b-carotene in non-seed tissues of 151 Chinese kale samples are listed in Table 1. The calibration set with 122 samples was randomly selected from original set and the rest 29 samples were grouped as external validation set. The ranges of lutein and b-carotene were from 0.044 to 2.223 mg g 1 DW and 0.006 to 4.852 mg g 1 DW, respectively. Wide variations were observed for lutein and b-carotene among samples, which was mainly due to the incorporation of samples from different varieties, organs, and growth conditions. Obviously, due to the limitation of sample number and the relative diversity of carotenoid content, one or two obviously blank distributions of both lutein and b-carotene in the sample set were observed (Fig. 2). Furthermore, there are remarkable bimodal natures of lutein distribution at range of 0.04–0.3 mg g 1 DW and of 0.3–1.7 mg g 1 DW in sample set. For b-carotene, the bimodal nature of distribution occurred at range of 0.01–0.6 mg g 1 DW and 1.0–3.0 mg g 1 DW, respectively. The first peak is primarily related with the distribution of different plant parts of Chinese kale, and the second peak is mainly originated from the different varieties. Both of the blank and bimodal nature indicated that more different sample should be continuously research collected in the future experiments.
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Although NIRS procedure was inherently empirical, its underlying analytical basis could be explored by identifying the spectral regions associated with the analyzed trait (Roberts et al., 1997). According to the correlation of structural characteristics and NIR spectra (Cen and He, 2007), it should be quite similar for NIR spectra between lutein and b-carotene in theory. However, some differences were also revealed in the wavelength range of 1452– 2382 nm (Fig. 4). The highest correlated wavelengths (absolute
3.2. NIR spectra analysis NIR spectra for all samples in calibration set were shown in Fig. 3 and the results showed that a considerable contribution was due to diversity among samples in a wide wavelength range (1700–2400 nm), a typical near-infrared range. Indeed, this range had been used to measure maize pigments by NIRS method (Brenna and Berardo, 2004). Table 1 Lutein and b-carotene contents in Chinese kale in the calibration and validation set. Calibration set
Lutein
b-Carotene
Validation set
Sample number
Range
a
Mean
123 122
0.044–2.223 0.006–4.852
0.561 1.057
a
S.D.
Sample number
Rangea
Meana
S.D.
0.466 1.051
28 29
0.104–1.699 0.046–3.674
0.807 1.527
0.509 1.135
S.D., standard deviation. a Expressed in mg g 1 DW.
Fig. 2. Frequency distribution of the lutein (a) and b-carotene (b) contents in the sample set of Chinese kale.
Fig. 3. Typical NIR spectra obtained for the different Chinese kale samples analyzed.
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Fig. 4. Correlation coefficient between contents of lutein and b-carotene and spectra values for the second derivative (2,4,4,1) transformation.
value of higher than 0.9) were 2176, 2336, 1770 and 2066 nm for lutein and 2176, 2336 and 1770 nm for b-carotene, respectively. Most of them belong to the range of spectral regions associated to lutein (1158–2378 nm) found by Brenna and Berardo (2004) in the analysis of maize seeds. Therefore, the major contribution for developing the calibration model for lutein and b-carotene was the whole NIR spectral range. The most significant wavelength for lutein content was 2176 nm, which does not confirm the previous result of Brenna and Berardo (2004), who found 1168 nm as the most relevant wavelength for lutein determination in maize seed. 3.3. NIR calibration model As shown in Table 2, low SEC and high RSQ in calibration set for lutein were obtained both in PLS and MPLS regressions when compared with PCR regression. In cross-validation set, higher S.D. of the reference data to SECV ratio (S.D./SECV) of 4.854 and 4.728 and 1 VR (1 minus variance ratio) of 0.958 and 0.955 were also obtained in these two regressions, respectively. For b-carotene, same phenomena emerged and low SEC (0.126 and 0.131 mg g 1 DW) and high RSQ (0.982), S.D./SECV (5.471 and 5.430) and 1 VR (0.967 and 0.966) were obtained in PLS and MPLS regressions. There were some differences in NIR calibration model for both lutein and b-carotene between MPLS and PLS regression; however, the use of principal component regression gave less accurate results than those obtained from MPLS and PLS regression. The equations in PLS and MPLS regression for lutein and bcarotene showed high RSQ values, and low standard errors in calibration and cross-validation. It implied that a reliable estimation of lutein and b-carotene could be obtained by NIRS with both
PLS and MPLS regressions. Moreover, considering the algorithm of MPLS, this method was selected to employ the subsequent tests. The good relationships of NIRS predicted versus HPLC data for lutein and b-carotene were shown in Fig. 5 in calibration set under MPLS. A group of samples covering a wider range for NIRS calibration could probably result in much more accurate calibration equations (Velasco and Becker, 1998). In the present study, the calibration set included samples collected from different organs and varieties which were cultivated in greenhouse or field at different locations and cultured with different fertilizers during different years. The good results obtained in the calibration set for both lutein and bcarotene could be explained by this large variability among analyzed samples. 3.4. Math treatment in MPLS The results in Table 3 show that the second derivative of the spectra (2,4,4,1) in lutein could give the better results than those obtained from another second derivative (2,8,8,1). The lowest SEC (0.131 mg g 1 DW) and highest RSQ (0.982) of lutein were obtained by transforming the raw optical data into the second derivative (2,4,4,1) prior to calibration. Furthermore, the highest S.D./SECV of 4.728 and 1 VR of 0.955 were also obtained in 2,4,4,1. Like the calibration for lutein, the second derivative method (2,4,4,1) could also be selected to develop the calibration equation for b-carotene. For the NIRS analysis of lutein and b-carotene, high RSQ of 0.97 and 0.82 for lutein and b-carotene in maize seeds had been reported by Brenna and Berardo (2004) in MPLS with 2,8,6.
Table 2 Calibration and cross-validation statistics for lutein and b-carotene in Chinese kale at different regression methods. Components
Regression methods
Sample number
Calibration a
Cross-validation a
S.D.
SEC
RSQ
SECV
S.D./SECV
1
Lutein
PCR PLS MPLS
119 116 116
0–1.848 0–1.814 0–1.823
0.533 0.518 0.519
0.438 0.432 0.435
0.114 0.062 0.056
0.933 0.98 0.983
0.119 0.089 0.092
3.681 4.854 4.728
0.926 0.958 0.955
b-Carotene
PCR PLS MPLS
118 114 116
0–3.941 0–3.793 0–3.881
0.991 0.936 0.967
0.983 0.952 0.972
0.233 0.126 0.131
0.944 0.982 0.982
0.239 0.174 0.179
4.113 5.471 5.430
0.941 0.967 0.966
Range
Mean
a
VR
PCR, principal component regression; PLS, partial least squares; MPLS, modified PLS; S.D., standard deviation; SEC, standard error of calibration; RSQ, coefficient of determination in the calibration; SECV, standard error of cross-validation; S.D./SECV, ratio of S.D. to SECV; 1 VR, coefficient of determination in the cross-validation. a Expressed in mg g 1 DW.
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Fig. 5. Validation scatter plot for lutein (a) and b-carotene (b) at MPLS regression in the calibration set of Chinese kale.
Table 3 The effects of spectra data math treatments on NIRS calibration for lutein and b-carotene in Chinese kale at MPLS regression. Math treatmenta
Sample number
Calibration b
Cross-validation
Range
Mean
b
S.D.
SEC
RSQ
SECV
1
S.D./SECV VR
Lutein
0,0,1,1 1,4,4,1 1,8,8,1 2,4,4,1 2,8,8,1
116 117 115 116 116
0–1.861 0–1.848 0–1.849 0–1.823 0–1.845
0.528 0.528 0.523 0.519 0.525
0.444 0.440 0.442 0.435 0.440
0.096 0.088 0.087 0.056 0.091
0.953 0.960 0.961 0.983 0.957
0.102 0.101 0.096 0.092 0.104
0.947 0.947 0.953 0.955 0.944
4.347 4.341 4.611 4.728 4.244
b-Carotene
0,0,1,1 1,4,4,1 1,8,8,1 2,4,4,1 2,8,8,1
118 114 113 116 116
0–3.941 0–3.868 0–3.828 0–3.881 0–3.881
0.991 0.954 0.939 0.967 0.966
0.983 0.971 0.963 0.972 0.972
0.202 0.148 0.144 0.131 0.158
0.958 0.977 0.978 0.982 0.974
0.216 0.174 0.175 0.179 0.197
0.952 0.968 0.967 0.966 0.959
4.557 5.579 5.510 5.422 4.924
S.D., standard deviation; SEC, standard error of calibration; RSQ, coefficient of determination in the calibration; SECV, standard error of cross-validation; 1 determination in the cross-validation; S.D./SECV, ratio of S.D. to SECV. a Math treatment (derivative, gap, first smooth, second smooth). b Expressed in mg g 1 DW.
However, significant improved results of higher values of RSQ for lutein (0.983) and b-carotene (0.982) in MPLS with 2,4,4,1 were obtained in present experiment. 3.5. External validation statistics in MPLS The external validation statistics for lutein and b-carotene are shown in Table 4. Significant correlation of the plotting reference values versus predicted values could be found with high RSQ of 0.926 and 0.875 for lutein and b-carotene, respectively, confirmed also by the ratio of the standard deviation to SEP (RPD) of 3.641 and 2.787, the bias values of 0.009 and 0.014 and a slope of 0.985 and 0.964 for lutein and b-carotene, respectively.
VR, coefficient of
Table 4 External validation statistics for lutein and b-carotene in Chinese kale at MPLS regression with math treatment (2,4,4,1).
Lutein b-Carotene
Sample number
Meana
S.D.
SEP
RSQ
27 27
0.784 1.38
0.477 1.023
0.131 0.367
0.926 0.875
Bias
0.009 0.014
Slope
RPD
0.985 0.964
3.641 2.787
S.D., standard deviation; SEP, standard error of the prediction; RSQ, coefficient of determination in the external validation; bias, difference of means (laboratory minus predicted by NIRS); RPD, ratio of the standard deviation to SEP. a Expressed in mg g 1 DW.
X. J. Chen et al. / Journal of Food Composition and Analysis 22 (2009) 148–153
4. Conclusions Against the results of HPLC, the regression of MPLS with math treatment (2,4,4,1) showed an excellent estimation ability on the quantitative analysis of lutein and b-carotene in Chinese kale. For the first time, NIRS analysis has demonstrated that it can be used to estimate the composition of lutein and b-carotene in freeze-dried samples. The NIRS equations of lutein and b-carotene developed in the present work represent only a first step, and they should be tested, expanded, and improved in future by adding other special samples of Chinese kale and other vegetables. Acknowledgements The project is supported by the Science and Technology Office of Zhejiang Province (No. 2007C22016) and 151 Foundation for the Talents of Zhejiang Province and we thank Dr. K.B. Kumar, Professor Z.H. Xu and Mr. Tottekkaad for improving English for this manuscript. References Anon, 2008a. b-Carotene. http://www.nlm.nih.gov/medlineplus/druginfo/natural/ patient-betacarotene.html (retrieved 23.08.2007). Anon, 2008b. Chinese broccoli Brassica oleracea. http://www.worldcrops.org/crops/ Chinese-broccoli.cfm (retrieved 23.08. 2007). Anon, 2008c. How lutein supports human health. http://www.luteininfo.com (retrieved 23.08.2007). Anon, 2008d. Products—Kai Lan (Brassica alboglabra). http://www.cherryfarms.co.uk/kailan.asp (retrieved 23.08.2007). Atienza, S.G., Avila, C.M., Ramirez, M.C., Martin, A., 2005. Application of near infrared reflectance spectroscopy to the determination of carotenoid content in tritordeum for breeding purposes. Aust. J. Agric. Res. 56, 85–89. Berardo, N., Brenna, O.V., Amato, A., Valoti, P., Pisacane, V., Motto, M., 2004. Carotenoids concentration among maize genotypes measured by near infrared reflectance spectroscopy (NIRS). Innovat. Food Sci. Emerg. Tech. 5, 393–398.
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Contents lists available at ScienceDirect
Journal of Food Composition and Analysis journal homepage: www.elsevier.com/locate/jfca
Original Article
Application of near-infrared reflectance spectroscopy to evaluate the lutein and b-carotene in Chinese kale Xinjuan Chen a,1, Jianguo Wu b,1, Shengjun Zhou a, Yuejian Yang a, Xiaolei Ni b, Jing Yang b, Zhujun Zhu c,*, Chunhai Shi b,** a b c
Institute of Vegetable Science, Zhejiang Academy of Agriculture Science, Hangzhou 310021, China College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China School of Agriculture and Food Science, Zhejiang Forestry University, Lin’an 311300, China
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 January 2008 Received in revised form 3 September 2008 Accepted 13 October 2008
There are wide ranges of lutein and b-carotene in Chinese kale. This work assessed the capability of nearinfrared reflectance spectroscopy (NIRS) to predict the contents of lutein and b-carotene in Chinese kale against HPLC analysis. Compared to other regression methods, the regression of modified partial least squares with math treatments of 2,4,4,1 (where the first number of 2 represents the second derivative of log 1/R, the second of 4 is the gap in data points over which the derivative is calculated, the third and fourth numbers refer to the number of data points used in the first and second smoothing, respectively) showed the best results. The calibration equations of lutein and b-carotene were characterized by the coefficients of determination (RSQ) of 0.983 and 0.982 and standard error of 0.056 and 0.131 mg g 1 DW, respectively. In cross-validation, high 1 minus variance ratio (1 VR) of 0.955 and 0.966 and standard error of 0.092 and 0.179 mg g 1 DW for lutein and b-carotene were obtained, respectively. In external validations, the RSQ were 0.926 and 0.875 with standard error of 0.131 and 0.367 mg g 1 DW, respectively. These results showed that NIRS could be used for the determination of carotenoids in Chinese kale. ß 2008 Elsevier Inc.. All rights reserved.
Keywords: Chinese kale Brassica oleracea alboglabra Near-infrared reflectance spectroscopy (NIRS) Carotenoid extraction method Lutein b-Carotene Food composition Food analysis
1. Introduction Chinese kale (Brassica oleracea alboglabra), also known as Chinese broccoli, Chinese flowering broccoli, kailan (http:// www.worldcrops.org/crops/Chinese-broccoli.cfm (Anon, 2008b); http://www.cherryfarms.co.uk/kailan.asp (Anon, 2008d)), is a source of important phytonutrients such as lutein and b-carotene (http://www.cherryfarms.co.uk/kailan.asp; Kopsell et al., 2004) and commonly consumed as a healthy vegetable in south Asia especially popular in south China. Both lutein and b-carotene (Fig. 1), well-known carotenoids, are widely present in vegetables and fruits: the former is the most
* Corresponding author. Tel.: +86 571 63743001; fax: +86 571 63743001. ** Corresponding author. Tel.: +86 571 86971691; fax: +86 571 86971117. E-mail addresses: [email protected] (Z. Zhu), [email protected] (C. Shi). 1 These authors contributed equally to this work. Abbreviations: Bias, difference of means; HPLC, high-performance liquid chromatography; MPLS, modified partial least squares; NIRS, near-infrared reflectance spectroscopy; PCR, principal component regression; PLS, partial least squares; RPD, ratio of the standard deviation to SEP; RSQ, coefficients of determination; SEC, standard error of calibration; SECV, standard error of cross-validation; SEP, standard error of performance; S.D., standard deviation; 1 VR, coefficient of determination in cross-validation. 0889-1575/$ – see front matter ß 2008 Elsevier Inc.. All rights reserved. doi:10.1016/j.jfca.2008.10.007
important precursors of provitamin A; the latter can increase macular pigment concentration in eyes and may improve visual function (Krinsky, 2001). Recent research has demonstrated that lutein and b-carotene can act as powerful antioxidants and to help guard against aging, cancers and diseases (http://www.luteininfo.com (Anon, 2008c); http://www.nlm.nih.gov/medlineplus/ druginfo/natural/patient-betacarotene.html (Anon, 2008a)). It is very important for nutritionists and researchers to obtain the information on lutein and b-carotene content in vegetables and fruits. The common methods for determining of lutein and bcarotene, based on solvent extraction followed by HPLC analysis, usually involve high cost, labor and time input, and specialized technicians. In addition, the volatile, degradable or hazardous chemicals used in the extraction and HPLC analysis can imply some instability of results and health risk on human health and environment. To date, near-infrared reflectance spectroscopy (NIRS), widely used as a fast, accurate and non-pollution method for qualitative and quantitative analysis in food and pharmaceutical fields for the last 30 years (Cen and He, 2007; Roggo et al., 2007), have been well recommended for the determination of total carotenoids in carrot fruit (Schulz et al., 1998) and wheat seeds (Atienza et al., 2005) and especially some individual carotenoids such as lycopene and lutein
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set at 440 nm and a spherisorb C18 column (250 mm 4.6 mm, 5 mm; Waters, USA). The mobile phase A was acetonitrile:methanol:0.05 mol/l Tris–HCl (72:8:3) and B was methanol:hexane (5:1). The elution procedure is as follows: 100% A for first 4 min, then in a linear gradient from 0% to 100% B in 2.5 min and constant 100% B for 13.5 min, then equilibrated with 100% A for 5 min prior to the injection of next sample. The flow rate was 1.5 ml min 1 at room temperature. HPLC grade of standards of lutein (Sigma–Aldrich, USA) and b-carotene (Sigma–Aldrich, USA) were utilized for the quality and quantity analysis. 2.4. NIRS analysis Fig. 1. Chemical structures of lutein and b-carotene.
in tomato products (Pedro and Ferreira, 2005), zeaxanthin and lutein in maize seeds (Brenna and Berardo, 2004; Berardo et al., 2004); however, there is very limited correlative research in the non-seed plant tissues. Recently, our research group has succeeded in determining seed traits of crops by NIRS (Wu et al., 2002, 2006; Wu and Shi, 2003, 2004, 2007; Lin et al., 2005). The purpose of this work was to test the potential of NIRS for simultaneously predicting the lutein and b-carotene contents in the non-seed tissues of Chinese kale and to provide a simple, rapid and accurate analysis method via NIRS technique. 2. Materials and methods 2.1. Samples Chinese kale varieties with high variation of agronomy traits were planted under different seasons, soil types and fertilizer conditions in Zhejiang province over a period of 2 years (2004– 2005). Edible tender and succulent plant parts comprising one or several stout and chunky stems accompanied with small and loose terminal floral buds, 6–7 pieces of flat and broad leaves, and 2– 3 lateral branches were separately or mix harvested at the very beginning of flowering, and frozen immediately in liquid nitrogen. After being lyophilized by a freeze dryer (Vir Tis, USA), ground into powder by a WK-200B miller (Dongwan, China) and filtrated with 0.25 mm screen, the total freeze-dried powder of 151 samples was stored in a desiccator at 20 8C prior to HPLC and NIRS analysis. 2.2. Extraction of lutein and b-carotene The extraction of lutein and b-carotene were conducted according to the protocol of Gilmore and Yamamoto (1991) with modifications. About 0.1 g freeze-dried powder was weighed into 10 ml tubes and extracted with 4 ml of acetone for 30 min in an ultrasonic cleaning bath (SK5200H, Kudos, China) operating at 59 kHz frequency. The supernatants were decanted and stored on ice, and the pellets were re-extracted twice with 3 ml of acetone for 10 min under same conditions. Three supernatants were combined and made up to a final volume of 10 ml with acetone. About 1.5 ml of extracts was filtrated by a 0.2-mm organic film and kept at 4 8C prior to HPLC analysis. All procedures were performed under dim light condition and at room temperature. 2.3. HPLC analysis The HPLC analysis was conducted according to the protocol of Gilmore and Yamamoto (1991) with modifications. Ten microlitres of extracts were analyzed in a Shimadzu HPLC system (LC-10AT pump, CTO-10A column oven, SCL-10A vp system controller, Shimadzu, Kyoto, Japan) consisting of a UV-VIS detector (SPD-10A)
A total of 151 samples were scanned on a NIR Systems model 5000 monochromator (NIR Systems Inc., Silver Spring, MD, USA). About 0.5 g freeze-dried powder was loaded in a small ring cup with 20 mm i.d., and reflectance spectra (log 1/R) from 1100 to 2500 nm were recorded at 2 nm intervals. Each sample was subsequently scanned three times and the average spectrum was collected. 2.5. Calibration and validation procedures WINISI (version 1.04) software (Infrasoft International, Port Matila, PA, USA) was used to collect spectra, develop equations and evaluate calibration performance. Principal component regression (PCR), partial least squares (PLS) and modified PLS (MPLS) were used to analyze the regression effect. Different math treatments were applied during equation development in MPLS, e.g. 2,4,4,1, where the first number indicates the order of the derivative (0 represents no derivative, 1 represents the first derivative and 2 represents the second derivative of log 1/ R), the second is the gap in data points over which the derivative is calculated, the third and fourth numbers refer to the number of data points used in the first and second smoothing, respectively. The most significant wave regions for NIRS estimation of lutein and b-carotene were studied by calculating the correlation coefficients between the carotenoids values and the spectral data (second derivative, scatter corrected). 3. Results and discussion 3.1. Lutein and b-carotene composition in the calibration set The composition of lutein and b-carotene in non-seed tissues of 151 Chinese kale samples are listed in Table 1. The calibration set with 122 samples was randomly selected from original set and the rest 29 samples were grouped as external validation set. The ranges of lutein and b-carotene were from 0.044 to 2.223 mg g 1 DW and 0.006 to 4.852 mg g 1 DW, respectively. Wide variations were observed for lutein and b-carotene among samples, which was mainly due to the incorporation of samples from different varieties, organs, and growth conditions. Obviously, due to the limitation of sample number and the relative diversity of carotenoid content, one or two obviously blank distributions of both lutein and b-carotene in the sample set were observed (Fig. 2). Furthermore, there are remarkable bimodal natures of lutein distribution at range of 0.04–0.3 mg g 1 DW and of 0.3–1.7 mg g 1 DW in sample set. For b-carotene, the bimodal nature of distribution occurred at range of 0.01–0.6 mg g 1 DW and 1.0–3.0 mg g 1 DW, respectively. The first peak is primarily related with the distribution of different plant parts of Chinese kale, and the second peak is mainly originated from the different varieties. Both of the blank and bimodal nature indicated that more different sample should be continuously research collected in the future experiments.
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Although NIRS procedure was inherently empirical, its underlying analytical basis could be explored by identifying the spectral regions associated with the analyzed trait (Roberts et al., 1997). According to the correlation of structural characteristics and NIR spectra (Cen and He, 2007), it should be quite similar for NIR spectra between lutein and b-carotene in theory. However, some differences were also revealed in the wavelength range of 1452– 2382 nm (Fig. 4). The highest correlated wavelengths (absolute
3.2. NIR spectra analysis NIR spectra for all samples in calibration set were shown in Fig. 3 and the results showed that a considerable contribution was due to diversity among samples in a wide wavelength range (1700–2400 nm), a typical near-infrared range. Indeed, this range had been used to measure maize pigments by NIRS method (Brenna and Berardo, 2004). Table 1 Lutein and b-carotene contents in Chinese kale in the calibration and validation set. Calibration set
Lutein
b-Carotene
Validation set
Sample number
Range
a
Mean
123 122
0.044–2.223 0.006–4.852
0.561 1.057
a
S.D.
Sample number
Rangea
Meana
S.D.
0.466 1.051
28 29
0.104–1.699 0.046–3.674
0.807 1.527
0.509 1.135
S.D., standard deviation. a Expressed in mg g 1 DW.
Fig. 2. Frequency distribution of the lutein (a) and b-carotene (b) contents in the sample set of Chinese kale.
Fig. 3. Typical NIR spectra obtained for the different Chinese kale samples analyzed.
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Fig. 4. Correlation coefficient between contents of lutein and b-carotene and spectra values for the second derivative (2,4,4,1) transformation.
value of higher than 0.9) were 2176, 2336, 1770 and 2066 nm for lutein and 2176, 2336 and 1770 nm for b-carotene, respectively. Most of them belong to the range of spectral regions associated to lutein (1158–2378 nm) found by Brenna and Berardo (2004) in the analysis of maize seeds. Therefore, the major contribution for developing the calibration model for lutein and b-carotene was the whole NIR spectral range. The most significant wavelength for lutein content was 2176 nm, which does not confirm the previous result of Brenna and Berardo (2004), who found 1168 nm as the most relevant wavelength for lutein determination in maize seed. 3.3. NIR calibration model As shown in Table 2, low SEC and high RSQ in calibration set for lutein were obtained both in PLS and MPLS regressions when compared with PCR regression. In cross-validation set, higher S.D. of the reference data to SECV ratio (S.D./SECV) of 4.854 and 4.728 and 1 VR (1 minus variance ratio) of 0.958 and 0.955 were also obtained in these two regressions, respectively. For b-carotene, same phenomena emerged and low SEC (0.126 and 0.131 mg g 1 DW) and high RSQ (0.982), S.D./SECV (5.471 and 5.430) and 1 VR (0.967 and 0.966) were obtained in PLS and MPLS regressions. There were some differences in NIR calibration model for both lutein and b-carotene between MPLS and PLS regression; however, the use of principal component regression gave less accurate results than those obtained from MPLS and PLS regression. The equations in PLS and MPLS regression for lutein and bcarotene showed high RSQ values, and low standard errors in calibration and cross-validation. It implied that a reliable estimation of lutein and b-carotene could be obtained by NIRS with both
PLS and MPLS regressions. Moreover, considering the algorithm of MPLS, this method was selected to employ the subsequent tests. The good relationships of NIRS predicted versus HPLC data for lutein and b-carotene were shown in Fig. 5 in calibration set under MPLS. A group of samples covering a wider range for NIRS calibration could probably result in much more accurate calibration equations (Velasco and Becker, 1998). In the present study, the calibration set included samples collected from different organs and varieties which were cultivated in greenhouse or field at different locations and cultured with different fertilizers during different years. The good results obtained in the calibration set for both lutein and bcarotene could be explained by this large variability among analyzed samples. 3.4. Math treatment in MPLS The results in Table 3 show that the second derivative of the spectra (2,4,4,1) in lutein could give the better results than those obtained from another second derivative (2,8,8,1). The lowest SEC (0.131 mg g 1 DW) and highest RSQ (0.982) of lutein were obtained by transforming the raw optical data into the second derivative (2,4,4,1) prior to calibration. Furthermore, the highest S.D./SECV of 4.728 and 1 VR of 0.955 were also obtained in 2,4,4,1. Like the calibration for lutein, the second derivative method (2,4,4,1) could also be selected to develop the calibration equation for b-carotene. For the NIRS analysis of lutein and b-carotene, high RSQ of 0.97 and 0.82 for lutein and b-carotene in maize seeds had been reported by Brenna and Berardo (2004) in MPLS with 2,8,6.
Table 2 Calibration and cross-validation statistics for lutein and b-carotene in Chinese kale at different regression methods. Components
Regression methods
Sample number
Calibration a
Cross-validation a
S.D.
SEC
RSQ
SECV
S.D./SECV
1
Lutein
PCR PLS MPLS
119 116 116
0–1.848 0–1.814 0–1.823
0.533 0.518 0.519
0.438 0.432 0.435
0.114 0.062 0.056
0.933 0.98 0.983
0.119 0.089 0.092
3.681 4.854 4.728
0.926 0.958 0.955
b-Carotene
PCR PLS MPLS
118 114 116
0–3.941 0–3.793 0–3.881
0.991 0.936 0.967
0.983 0.952 0.972
0.233 0.126 0.131
0.944 0.982 0.982
0.239 0.174 0.179
4.113 5.471 5.430
0.941 0.967 0.966
Range
Mean
a
VR
PCR, principal component regression; PLS, partial least squares; MPLS, modified PLS; S.D., standard deviation; SEC, standard error of calibration; RSQ, coefficient of determination in the calibration; SECV, standard error of cross-validation; S.D./SECV, ratio of S.D. to SECV; 1 VR, coefficient of determination in the cross-validation. a Expressed in mg g 1 DW.
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Fig. 5. Validation scatter plot for lutein (a) and b-carotene (b) at MPLS regression in the calibration set of Chinese kale.
Table 3 The effects of spectra data math treatments on NIRS calibration for lutein and b-carotene in Chinese kale at MPLS regression. Math treatmenta
Sample number
Calibration b
Cross-validation
Range
Mean
b
S.D.
SEC
RSQ
SECV
1
S.D./SECV VR
Lutein
0,0,1,1 1,4,4,1 1,8,8,1 2,4,4,1 2,8,8,1
116 117 115 116 116
0–1.861 0–1.848 0–1.849 0–1.823 0–1.845
0.528 0.528 0.523 0.519 0.525
0.444 0.440 0.442 0.435 0.440
0.096 0.088 0.087 0.056 0.091
0.953 0.960 0.961 0.983 0.957
0.102 0.101 0.096 0.092 0.104
0.947 0.947 0.953 0.955 0.944
4.347 4.341 4.611 4.728 4.244
b-Carotene
0,0,1,1 1,4,4,1 1,8,8,1 2,4,4,1 2,8,8,1
118 114 113 116 116
0–3.941 0–3.868 0–3.828 0–3.881 0–3.881
0.991 0.954 0.939 0.967 0.966
0.983 0.971 0.963 0.972 0.972
0.202 0.148 0.144 0.131 0.158
0.958 0.977 0.978 0.982 0.974
0.216 0.174 0.175 0.179 0.197
0.952 0.968 0.967 0.966 0.959
4.557 5.579 5.510 5.422 4.924
S.D., standard deviation; SEC, standard error of calibration; RSQ, coefficient of determination in the calibration; SECV, standard error of cross-validation; 1 determination in the cross-validation; S.D./SECV, ratio of S.D. to SECV. a Math treatment (derivative, gap, first smooth, second smooth). b Expressed in mg g 1 DW.
However, significant improved results of higher values of RSQ for lutein (0.983) and b-carotene (0.982) in MPLS with 2,4,4,1 were obtained in present experiment. 3.5. External validation statistics in MPLS The external validation statistics for lutein and b-carotene are shown in Table 4. Significant correlation of the plotting reference values versus predicted values could be found with high RSQ of 0.926 and 0.875 for lutein and b-carotene, respectively, confirmed also by the ratio of the standard deviation to SEP (RPD) of 3.641 and 2.787, the bias values of 0.009 and 0.014 and a slope of 0.985 and 0.964 for lutein and b-carotene, respectively.
VR, coefficient of
Table 4 External validation statistics for lutein and b-carotene in Chinese kale at MPLS regression with math treatment (2,4,4,1).
Lutein b-Carotene
Sample number
Meana
S.D.
SEP
RSQ
27 27
0.784 1.38
0.477 1.023
0.131 0.367
0.926 0.875
Bias
0.009 0.014
Slope
RPD
0.985 0.964
3.641 2.787
S.D., standard deviation; SEP, standard error of the prediction; RSQ, coefficient of determination in the external validation; bias, difference of means (laboratory minus predicted by NIRS); RPD, ratio of the standard deviation to SEP. a Expressed in mg g 1 DW.
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4. Conclusions Against the results of HPLC, the regression of MPLS with math treatment (2,4,4,1) showed an excellent estimation ability on the quantitative analysis of lutein and b-carotene in Chinese kale. For the first time, NIRS analysis has demonstrated that it can be used to estimate the composition of lutein and b-carotene in freeze-dried samples. The NIRS equations of lutein and b-carotene developed in the present work represent only a first step, and they should be tested, expanded, and improved in future by adding other special samples of Chinese kale and other vegetables. Acknowledgements The project is supported by the Science and Technology Office of Zhejiang Province (No. 2007C22016) and 151 Foundation for the Talents of Zhejiang Province and we thank Dr. K.B. Kumar, Professor Z.H. Xu and Mr. Tottekkaad for improving English for this manuscript. References Anon, 2008a. b-Carotene. http://www.nlm.nih.gov/medlineplus/druginfo/natural/ patient-betacarotene.html (retrieved 23.08.2007). Anon, 2008b. Chinese broccoli Brassica oleracea. http://www.worldcrops.org/crops/ Chinese-broccoli.cfm (retrieved 23.08. 2007). Anon, 2008c. How lutein supports human health. http://www.luteininfo.com (retrieved 23.08.2007). Anon, 2008d. Products—Kai Lan (Brassica alboglabra). http://www.cherryfarms.co.uk/kailan.asp (retrieved 23.08.2007). Atienza, S.G., Avila, C.M., Ramirez, M.C., Martin, A., 2005. Application of near infrared reflectance spectroscopy to the determination of carotenoid content in tritordeum for breeding purposes. Aust. J. Agric. Res. 56, 85–89. Berardo, N., Brenna, O.V., Amato, A., Valoti, P., Pisacane, V., Motto, M., 2004. Carotenoids concentration among maize genotypes measured by near infrared reflectance spectroscopy (NIRS). Innovat. Food Sci. Emerg. Tech. 5, 393–398.
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