Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

[1]  Limin Fang,et al.  Application of near infrared spectroscopy for rapid analysis of intermediates of Tanreqing injection. , 2010, Journal of pharmaceutical and biomedical analysis.

[2]  David Riaño,et al.  Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling , 2007, IEEE Geoscience and Remote Sensing Letters.

[3]  Imperatrice Capotorto,et al.  Antioxidant activity induced by main polyphenols present in edible artichoke heads: influence of in vitro gastro-intestinal digestion , 2014 .

[4]  Di Wu,et al.  Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp , 2016, Comput. Electron. Agric..

[5]  Hai-Long Wu,et al.  Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods , 2007 .

[6]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[7]  Rohit Upadhyay,et al.  An Outlook on Chlorogenic Acids—Occurrence, Chemistry, Technology, and Biological Activities , 2013, Critical reviews in food science and nutrition.

[8]  M. Qin,et al.  Chemical Variations of the Essential Oils in Flower Heads of Chrysanthemum indicum L. from China , 2010, Chemistry & biodiversity.

[9]  Hui Jiang,et al.  [Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[10]  Xian Zhang,et al.  Comparative Evaluation of Chrysanthemum Flos from Different Origins by HPLC-DAD-MSn and Relative Response Factors , 2014, Food Analytical Methods.

[11]  Ling Zhou,et al.  Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population. , 2012, Diabetes technology & therapeutics.

[12]  Chao Zhang,et al.  Application of near infrared spectroscopy to the analysis and fast quality assessment of traditional Chinese medicinal products , 2014, Acta pharmaceutica Sinica. B.

[13]  Zaixiang Lou,et al.  Antibacterial activity and mechanism of action of chlorogenic acid. , 2011, Journal of food science.

[14]  Yongjiang Wu,et al.  Mid‐infrared and near‐infrared spectroscopy for rapid detection of Gardeniae Fructus by a liquid‐liquid extraction process , 2017, Journal of pharmaceutical and biomedical analysis.

[15]  Bo Zhang,et al.  Application of Electronic Nose with Multivariate Analysis and Sensor Selection for Botanical Origin Identification and Quality Determination of Honey , 2015, Food and Bioprocess Technology.

[16]  Di Wu,et al.  Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. , 2013, Talanta.

[17]  Asha Gowda Karegowda,et al.  Exudates Detection in Retinal Images using Back Propagation Neural Network , 2011 .

[18]  Duo-jia Wang,et al.  [Application of near-infrared spectroscopy to agriculture and food analysis]. , 2004, Guang pu xue yu guang pu fen xi = Guang pu.

[19]  Shih-Ping Han,et al.  A successive projection method , 1988, Math. Program..

[20]  H. Büning-Pfaue.  Analysis of water in food by near infrared spectroscopy , 2003 .

[21]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[22]  Swanirbhar Majumder,et al.  Back propagation artificial neural network (BPANN) based performance analysis of diesel engine using biodiesel , 2011 .

[23]  Hui Yan,et al.  Rapid identification of three varieties of Chrysanthemum with near infrared spectroscopy , 2014 .

[24]  Wei Xiao,et al.  Influence of Sulfur Fumigation on the Chemical Constituents and Antioxidant Activity of Buds of Lonicera japonica , 2014, Molecules.

[25]  Di Wu,et al.  Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. , 2013, Talanta.

[26]  Tapani Vuorinen,et al.  Development of the partial least squares models for the interpretation of the UV resonance Raman spectra of lignin model compounds , 2005 .

[27]  Dong Wang,et al.  Successive projections algorithm combined with uninformative variable elimination for spectral variable selection , 2008 .

[28]  Yong Lin,et al.  Luteolin, a flavonoid with potential for cancer prevention and therapy. , 2008, Current cancer drug targets.

[29]  Gang Bai,et al.  A rapid integrated bioactivity evaluation system based on near-infrared spectroscopy for quality control of Flos Chrysanthemi. , 2016, Journal of pharmaceutical and biomedical analysis.

[30]  G. Rao,et al.  Chrysanthemum genetic resources and related genera of Chrysanthemum collected in China , 2009, Genetic Resources and Crop Evolution.

[31]  Hui Yan,et al.  A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy , 2014, Pharmacognosy magazine.

[32]  James M. Harnly,et al.  Identification of the phenolic components of chrysanthemum flower (Chrysanthemum morifolium Ramat) , 2010 .