Qianyi Luo^{a},
Yonghuan Yun^{a},
Wei Fan^{b},
Jianhua Huang^{c},
Lixian Zhang^{a},
Baichuan Deng^{a} and
Hongmei Lu*^{a}
^{a}College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China. E-mail: Hongmeilu@csu.edu.cn; Fax: +86 731 8887 9616; Tel: +86 731 8883 6376
^{b}Joint Lab for Biological Quality and Safety, College of Bioscience and Biotechnology, Hunan Agriculture University, Changsha 410128, PR China
^{c}Department of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, PR China

Received
29th September 2014
, Accepted 1st December 2014

First published on 2nd December 2014

A method for rapid quantitative analysis of epimedin A, B, C and icariin in Epimedium was developed based on Fourier transform near infrared (FT-NIR) spectroscopy, and by adopting high performance liquid chromatography-diode array detection (HPLC-DAD) as the reference method. Multivariate calibrations models were built by partial least squares regression (PLSR) based on the full absorbance spectra (10000–4000 cm^{−1}) or only the most informative key variables selected by the competitive adaptive reweighted sampling (CARS) method. In comparison, the accuracy of the CARS-PLSR method was apparently higher than full spectrum-PLSR for four kinds of investigated flavonoids. For CARS-PLSR, the coefficients of determination (R^{2}) for prediction were 0.8969, 0.8810, 0.9273 and 0.9325 and the root mean square errors of prediction (RMSEP) were 0.1789, 0.2572, 1.2872 and 0.3615 for epimedin A, B, C and icariin, respectively. The good performance indicates that the combination of NIR spectroscopy with CARS-PLSR is an effective method for determination of epimedin A, B, C and icariin in Epimedium with fast, economic and nondestructive advantages compared to traditional chemical methods.

Flavonoids are generally considered as the major active constituents of Epimedium, and over 141 flavonoids, including flavone and its derivatives, have been found from 17 Epimedium species.^{6} Among them, epimedin A, B, C and icariin, which make up more than 52% of total flavonoids in epimedium, are perceived as major bioactive components.^{7} Four flavonoids exhibit a promising therapeutic efficacy of antitumor, cardiac-cerebral vascular disease, anti-oxidant, antidepressants and antiobesity.^{4,8,9} Quantitative and qualitative studies about Epimedium have been extensively investigated. Many analytical techniques, such as HPLC,^{7} capillary zone electrophoresis (CZE)^{10} and micellar electrokinetic chromatography (MEKC),^{11} have been applied for the investigation of epimedin A, B, C and icariin simultaneously. However, these methods are time-consuming, laborious and need additional reagents in sample processing. To address these issues, it is necessary to develop new methods for determining the four flavonoids in Epimedium.

As a rapid, economical and nondestructive analytical technique, FT-NIR spectroscopy has been widely applied in quality evaluation and quality control of food, agriculture, and pharmaceutical products, such as honey,^{12} tea^{13} and honeysuckle.^{14} It is applied based on molecular overtone and combination vibrations of the fundamental –OH, –CH, and –NH bonds, which are the main recordable phenomena in the radiation region (12500–4000 cm^{−1}) of near-infrared spectrum.^{15} However, NIR spectra are often highly correlated due to the strongly overlapped and broad absorption bands,^{16} so the data are often calibrated with the classical partial least squares regression (PLSR).^{17,18} A calibration process on the basis of full-range spectra is time consuming and adverse to fulfilling the high-speed features of NIR spectroscopy. Instead, the selected informative wavelengths instead of full-spectrum can result in a better quantitative calibration model.^{19,20} Li et al. demonstrated that CARS performed a competitive selection of some key wavelengths which were interpretable to the chemical property of interest, by comparing CARS^{21} with a moving window (MW)^{22} and a Monte Carlo uninformative variable elimination (MC-UVE)^{23} selection method.

This study combined CARS with PLSR algorithm to determine epimedin A, B, C and icaniin in Epimedium. CARS was applied to select key wavelengths from the full-range of NIRS. The objectives of this work include two aspects: (I) to establish the relationships between the NIR spectroscopy spectra and the content of the four flavonoids, (II) to discuss the benefits of selecting the most informative spectral variable (with CARS-PLSR compared with full spectrum-PLSR) for calibration accuracy and model parsimony.

One gram pulverized sample was weighed accurately and mixed with 25 mL 80% ethanol solution in a 100 mL conical flask. The mixture was sonicated for 30 min at the extraction temperature of 40 °C. The supernatant was filtered and the filtrate was collected as the crude extract. The crude extract was transferred to a 250 mL volumetric flask, and adjusted to 250 mL (V_{solution}) with 80% ethanol solution. The solution was filtered through 0.45 μm membranes before HPLC analysis.

In PLSR model, there are some of spectral variables that contain irrelevant information or noise for modeling Y. Eliminating these variables from the pertinent information is conducive to improving the model. In this work, CARS algorithm^{21} was used to select the key wavelengths that had large absolute regression coefficients in PLSR model. In CARS, the first step was to sample in the model space combined with Monte Carlo strategy. Then, enforced wavelength reduction and adaptive reweighted sampling (ARS) were employed to remain informative variables. In enforced wavelength reduction procedure of one variable subset, the variables were indexed by absolute values of regression coefficients. It was demonstrated that a large absolute regression coefficient indicates an important variable in a model.^{26,27} A number of variables with small absolute regression coefficients were removed. CARS uses exponentially decreasing function (EDF) to remove the variables which are less important. In this step, the runs of EDF are set to N, which means that finding an optimal variable subset would undergo N runs to iteratively filter the variables with small absolute regression coefficients. In the ith run of EDF, the number of remaining variables is calculated as follows:

r_{i} = pe^{−ki}
| (1) |

(2) |

Following EDF-based enforced wavelength reduction, ARS was employed in CARS to further eliminate wavelengths by mimicking the ‘survival of the fittest’ principle on which Darwin's evolution theory is based. Finally, 10-fold cross-validation was applied to choose the optimal subset of variables with the lowest root mean square error of cross validation (RMSECV). The performance of the calibration model was evaluated in terms of the RMSE of calibration (RMSEC) and determination coefficient (R^{2}) for calibration set (R_{c}^{2}) in the calibration process. The RMSE of prediction (RMSEP) and determination coefficient (R^{2}) for prediction set (R_{p}^{2}) were used to evaluate the performance of the prediction set in the prediction process. Based on the guideline of Williams.^{28} R^{2} indicates the percentage of the variance in the Y variable that is accounted for by the X variable. R^{2} value greater than 0.90 denotes a good prediction, and 0.82–0.90 is considered to be indicative of a good prediction, whereas 0.66–0.81 indicates an approximate quantitative prediction.

Fig. 1 Representative HPLC-DAD chromatogram at optimized conditions (the peaks marked with 1–4 were epimedin A, B, C and icariin, respectively). |

Compounds | Retention time (min) | Calibration curves | R | Linear range (μg mL^{−1}) |
Limit of detection (μg mL^{−1}) |
Repeatability (RSD%, n = 6) | Recovery (%, n = 3) |
---|---|---|---|---|---|---|---|

Epimedin A | 16.15 | y = 16.392x − 3.2226 | 0.9999 | 0.25–80 | 0.013 | 0.11 | 105 |

Epimedin B | 16.84 | y = 19.838x − 3.0745 | 0.9999 | 0.28–120 | 0.007 | 0.11 | 108 |

Epimedin C | 17.37 | y = 20.607x − 3.5987 | 0.9999 | 0.50–250 | 0.005 | 0.16 | 90 |

Icariin | 18.39 | y = 23.581x − 5.7396 | 0.9999 | 0.25–250 | 0.004 | 0.13 | 92 |

MSC | SNV | Smooth + MSC | Smooth + SNV | MSC + S/G 1d | SNV + S/G 1d | ||
---|---|---|---|---|---|---|---|

Epimedin A | RMSEC | 0.1031 | 0.1307 | 0.0979 | 0.1216 | 0.0486 | 0.0596 |

R_{c}^{2} |
0.9818 | 0.9715 | 0.9836 | 0.9753 | 0.9961 | 0.9940 | |

RMSEP | 0.2214 | 0.2190 | 0.2759 | 0.2568 | 0.2700 | 0.2609 | |

R_{p}^{2} |
0.8790 | 0.8566 | 0.8120 | 0.8029 | 0.8065 | 0.8278 | |

Epimedin B | RMSEC | 0.1044 | 0.1034 | 0.1785 | 0.1872 | 0.1836 | 0.1882 |

R_{c}^{2} |
0.9909 | 0.9912 | 0.9734 | 0.9710 | 0.9731 | 0.9715 | |

RMSEP | 0.3999 | 0.4302 | 0.3901 | 0.3855 | 0.4387 | 0.4628 | |

R_{p}^{2} |
0.7166 | 0.6435 | 0.7303 | 0.7139 | 0.5106 | 0.4852 | |

Epimedin C | RMSEC | 0.4417 | 0.5509 | 0.5706 | 0.5084 | 0.3370 | 0.7789 |

R_{c}^{2} |
0.9904 | 0.9850 | 0.9840 | 0.9873 | 0.9948 | 0.9723 | |

RMSEP | 2.1433 | 2.3207 | 2.3316 | 2.5840 | 2.5263 | 2.2491 | |

R_{p}^{2} |
0.7360 | 0.6905 | 0.6875 | 0.6162 | 0.4221 | 0.5406 | |

Icariin | RMSEC | 0.3725 | 0.1987 | 0.2369 | 0.3426 | 0.3255 | 0.3200 |

R_{c}^{2} |
0.9567 | 0.9878 | 0.9825 | 0.9638 | 0.9671 | 0.9684 | |

RMSEP | 0.7065 | 0.4810 | 0.5680 | 0.5518 | 0.8590 | 0.8405 | |

R_{p}^{2} |
0.7541 | 0.8789 | 0.8410 | 0.8406 | 0.6466 | 0.6408 |

Flavonoids | nLVs^{a} |
nVAR^{b} |
Calibration set (N = 60) | External validation set (N = 15) | ||
---|---|---|---|---|---|---|

R_{c}^{2} |
RMSEC | R_{p}^{2} |
RMSEP | |||

a nLVs denotes the number of latent variables.b nVAR stands for the number selected variables. | ||||||

Full spectrum-PLS regression | ||||||

Epimedin A (mg g^{−1}) |
16 | 1557 | 0.9715 | 0.1307 | 0.8566 | 0.2190 |

Epimedin B (mg g^{−1}) |
18 | 1557 | 0.9710 | 0.1872 | 0.7139 | 0.3855 |

Epimedin C (mg g^{−1}) |
18 | 1557 | 0.9904 | 0.4417 | 0.7360 | 2.1433 |

Icariin (mg g^{−1}) |
18 | 1557 | 0.9878 | 0.1987 | 0.8789 | 0.4810 |

CARS-PLS regression | ||||||

Epimedin A (mg g^{−1}) |
12 | 47 | 0.9567 | 0.1441 | 0.8969 | 0.1789 |

Epimedin B (mg g^{−1}) |
16 | 50 | 0.9559 | 0.1001 | 0.8810 | 0.2572 |

Epimedin C (mg g^{−1}) |
14 | 47 | 0.9517 | 0.5295 | 0.9273 | 1.2872 |

Icariin (mg g^{−1}) |
11 | 44 | 0.9426 | 0.2655 | 0.9325 | 0.3615 |

CARS-PLSR models for 4 flavonoids were developed under the conditions of the most appropriate wavelengths selected by CARS and the same optimized spectra pre-treatments corresponding to PLSR models. The results of the CARS models are shown in Table 3 and Fig. 4. Through comparison of the full-spectrum PLSR models with CARS-PLSR ones, it is clearly showed that the irrelevant variables can be removed effectively and the predictive precision can be improved markedly by CARS method. RMSEP decreased from 0.2190, 0.3855, 2.1433 and 0.4810 to 0.1789, 0.2572, 1.2872 and 0.3615; R_{p}^{2} increased from 0.8566, 0.7139, 0.7360 and 0.8789 to 0.8969, 0.8810, 0.9273 and 0.9325 for epimedin A, B, C and icariin, respectively. After using the key variables, the performance for epimedin C obtain the most obvious improvement from an approximate quantitative prediction to an excellent prediction, followed by epimedin B. At the same time, one can see that the performance for epimedin C and icariin were improved to excellent predictions and superior to epimedin A and B. The reason may be that the contents of epimedin A and B are too low (epimedin A 0.0355–3.1552 mg g^{−1}, epimedin B 0.044–5.3131 mg g^{−1}), and it is difficult to measure more accurately. In general, the results proved that CARS method could obtain more accurate and parsimonious model for determination of epimedin A, B, C and icariin in Epimedium using NIR spectroscopic methods.

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