Abstract
Chlorogenic acid (CGA), as a major active component, is an important index for evaluating the quality of FlosLonicerae. Hyperspectral imaging (HSI) technology was applied for nondestructive estimating CGA content in FlosLonicerae. In order to obtain the best performance of calibration models, nine different pretreatment methods were investigated and compared based on partial least squares regression (PLSR) models. The optimal method was determined as a standard normal variable (SNV) method with RP2 of 0.9766 and RMSEP of 2.711 for further analysis. To simplify calibration models, different variables selection methods, including the uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), UVE–CARS, UVE–SPA, CARS–SPA, and UVE–CARS–SPA, were used to extracted characteristic wavelengths from the full spectrum. And then PLSR and least squares support vector machine (LS-SVM) were established based on full spectrum and the selected characteristic wavelengths, respectively. The results showed that the nonlinear UVE–CARS–LS–SVM model (RP2 = 0.9785 and RMSEP = 2.496) was the optimal model for predicting CGA content in FlosLonicerae. Therefore, this study revealed that the combination of HSI with SNV preprocessing method, UVE–CARS variable selection method and LS-SVM modeling had great potential to nondestructively and rapidly determine CGA content in Flos Lonicerae during storage.
Similar content being viewed by others
References
H. Jiang, L. Yang, X.D. Xing, M.L. Yan, B.Y. Yang, L. Yang, M.Y. Cui, Q.H. Wang, H.X. Kuang, Development of a newly and friendly method to evaluate of phenolic compounds from Flos Lonicerae Japonicae by ultra-high performance supercritical fluid chromatography (UHPSFC) combined with chemometrics. Anal. Methods 10, 4292–4300 (2018)
Y. Yang, L. Wang, Y. Wu, X. Liu, Y. Bi, W. Xiao, Y. Chen, On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm. Spectrochim. Acta A 182, 73–80 (2017)
X. Shang, H. Pan, M. Li, X. Miao, H. Ding, Lonicera japonica Thunb.: ethnopharmacology, phytochemistry and pharmacology of an important traditional Chinese medicine. J. Ethnopharmacol. 138(1), 1–21 (2011)
D.X. Kong, Y.Q. Li, M. Bai, H.J. He, G.X. Liang, H. Wu, Correlation between the dynamic accumulation of the main effective components and their associated regulatory enzyme activities at different growth stages in Lonicera japonica Thunb. Ind. Crop. Prod. 96, 16–22 (2017)
Y. Liu, S. Miao, J. Wu, J. Liu, H. Yu, X. Duan, Drying characteristics and modeling of vacuum far-infrared radiation drying of Flos Lonicerae. J. Food Process. Preserv. 39(4), 338–348 (2015)
X. Qi, X. Yu, D. Xu, H. Fang, K. Dong, W. Li, C. Liang, Identification and analysis of CYP450 genes from transcriptome of Lonicera japonica and expression analysis of chlorogenic acid biosynthesis related CYP450s. PeerJ 5, e3781 (2017)
M.H. Duan, T. Fang, J.F. Ma, Q.L. Shi, Y. Peng, F.H. Ge, X.L. Wang, Homogenate-assisted high-pressure disruption extraction for determination of phenolic acids in Lonicerae japonicae Flos. J. Chromatogr. B 1097–1098, 119–127 (2018)
A. Hunyadi, A. Martins, T.J. Hsieh, A. Seres, I. Zupko, Chlorogenic acid and rutin play a major role in the in vivo anti-diabetic activity of Morus alba leaf extract on type II diabetic rats. PLoS ONE 7(11), e50619 (2012)
X.H. Yao, J.Y. Xu, J.Y. Hao, Y. Wan, T. Chen, D.Y. Zhang, L. Li, Microwave assisted extraction for the determination of chlorogenic acid in Flos Lonicerae by direct analysis in real time mass spectrometry (DART-MS). J. Chromatogr. B 1092, 82–87 (2018)
Committee for the Pharmacopoeia of PR China, Pharmacopoeia of the PR China (China Medical Science and Technology Press, Beijing, 2015)
Q. Zhang, J. Li, C. Wang, W. Sun, Z. Zhang, W. Cheng, A gradient HPLC method for the quality control of chlorogenic acid, linarin and luteolin in Flos Chrysanthemi Indici suppository. J. Pharmaceut. Biomed. 43(2), 753–757 (2007)
S. Han, Capillary electrophoresis with chemiluminescence detection of rutin and chlorogenic acid based on its enhancing effect for the luminol-ferricyanide system. Anal. Sci. 21(11), 1371 (2005)
Y. Zhang, Y. Xiu, C. Ren, C. Chen, High-throughput system metabolomics method reveals new mechanistic insights of chlorogenic acid by using liquid chromatography coupled to high resolution mass spectrometry. RSC Adv. 8(13), 7205–7212 (2018)
A. Murauer, R. Bakry, H. Schottenberger, C. Huck, M. Ganzera, An innovative monolithic zwitterionic stationary phase for the separation of phenolic acids in coffee bean extracts by capillary electrochromatography. Anal. Chim. Acta 963, 136 (2017)
D. Wu, D.W. Sun, Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—Part II: Applications. Innov. Food Sci. Emerg. Technol. 19(1), 15–28 (2013)
J. Li, L. Chen, Comparative analysis of models for robust and accurate evaluation of soluble solids content in ‘Pinggu’ peaches by hyperspectral imaging. Comput. Electron. Agric. 142, 524–535 (2017)
X. Li, Y. Wei, J. Xu, X. Feng, F. Wu, R. Zhou, J. Jin, K. Xu, X. Yu, Y. He, SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology. Postharvest Biol. Technol. 143, 112–118 (2018)
Y. Seo, B. Park, A. Hinton, S.C. Yoon, K.C. Lawrence, Identification of Staphylococcus species with hyperspectral microscope imaging and classification algorithms. J. Food Meas. Charact. 10(2), 253–263 (2016)
W.H. Su, D.W. Sun, Fourier transform infrared and Raman and Hyperspectral imaging techniques for quality determinations of powdery foods: a review. Compr. Rev. Food Sci. Food Saf. 17(1), 104–122 (2017)
Y. Liu, Y. Sun, A. Xie, H. Yu, Y. Yin, X. Li, X. Duan, Potential of hyperspectral imaging for rapid prediction of anthocyanin content of purple-fleshed sweet potato slices during drying process. Food Anal. Methods 10(12), 3836–3846 (2017)
J. Jiang, H. Cen, C. Zhang, X. Lyu, H. Weng, H. Xu, Y. He, Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis. Postharvest Biol. Technol. 146, 147–154 (2018)
B. Lu, J. Sun, N. Yang, X. Wu, X. Zhou, J. Shen, Quantitative detection of moisture content in rice seeds based on hyperspectral technique. J. Food Process Eng. 41(8), e12916 (2018)
D. Zhang, L. Xu, D. Liang, C. Xu, X. Jin, S. Weng, Fast prediction of sugar content in Dangshan Pear (Pyrus spp.) using hyperspectral imagery data. Food Anal. Methods 11(8), 2336–2345 (2018)
S. Khoshnoudi-Nia, M. Moosavi-Nasab, S.M. Nassiri, Z. Azimifar, Determination of total viable count in rainbow-trout fish fillets based on hyperspectral imaging system and different variable selection and extraction of reference data methods. Food Anal. Methods 11(12), 3481–3494 (2018)
H. Yu, H. Liu, N. Wang, Y. Yang, A. Shi, L. Liu, H. Hu, R.I. Mzimbiri, Q. Wang, Rapid and visual measurement of fat content in peanuts by using the hyperspectral imaging technique with chemometrics. Anal. Methods 8(41), 7482–7492 (2016)
K. Mollazade, Non-destructive identifying level of browning development in button mushroom (Agaricus bisporus) using hyperspectral imaging associated with chemometrics. Food Anal. Methods 10(8), 2743–2754 (2017)
X. Chu, W. Wang, S.C. Yoon, X. Ni, G.W. Heitschmidt, Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging. Biosyst. Eng. 157, 13–23 (2017)
P.T. Guo, Z. Shi, M.F. Li, W. Luo, Z.Z. Cha, A robust method to estimate foliar phosphorus of rubber trees with hyperspectral reflectance. Ind. Crop. Prod. 126, 1–12 (2018)
L. Huang, J. Zhao, Q. Chen, Y. Zhang, Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging. Food Res. Int. 54(1), 821–828 (2013)
J. Sun, B. Ma, J. Dong, R. Zhu, R. Zhang, W. Jiang, Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms. J. Food Process Eng. 40(3), e12496 (2017)
J. Feng, Y. Liu, X. Shi, Q. Wang, Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. J. Food Meas. Charact. 12(3), 2184–2192 (2018)
W. Di, S. Hui, H. Yong, X. Yu, Y. Bao, Potential of hyperspectral imaging and multivariate analysis for rapid and non-invasive detection of gelatin adulteration in prawn. J. Food Eng. 119(3), 680–686 (2013)
L. Huang, H. Liu, B. Zhang, D. Wu, Application of electronic nose with multivariate analysis and sensor selection for botanical origin identification and quality determination of honey. Food Bioprocess Technol. 8(2), 359–370 (2015)
H. Zhu, B. Chu, C. Zhang, F. Liu, L. Jiang, Y. He, Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Sci. Rep. 7(1), 4125 (2017)
C. Shi, J. Qian, W. Zhu, H. Liu, S. Han, X. Yang, Nondestructive determination of freshness indicators for tilapia fillets stored at various temperatures by hyperspectral imaging coupled with RBF neural networks. Food Chem. 275, 497–503 (2019)
Y. Sun, Y. Liu, H. Yu, A. Xie, X. Li, Y. Yin, Non-destructive prediction of moisture content and freezable water content of purple-fleshed sweet potato slices during drying process using hyperspectral imaging. Food Anal. Methods 10(5), 1535–1546 (2017)
L. Nie, Z. Dai, S. Ma, Enhanced accuracy of near-infrared spectroscopy for traditional Chinese medicine with competitive adaptive reweighted sampling. Anal. Lett. 49(14), 2259–2267 (2016)
Y.C. Yang, D.W. Sun, N.N. Wang, Rapid detection of browning levels of lychee pericarp as affected by moisture contents using hyperspectral imaging. Comput. Electron. Agric. 113, 203–212 (2015)
Y. Pan, D.W. Sun, J.H. Cheng, Z. Han, Non-destructive detection and screening of non-uniformity in microwave sterilization using hyperspectral imaging analysis. Food Anal. Methods 11(6), 1568–1580 (2018)
X. Yu, L. Tang, X. Wu, H. Lu, Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm. Food Anal. Methods 11(3), 768–780 (2018)
Q. Dai, J.H. Cheng, D.W. Sun, Z.W. Zhu, H.B. Pu, Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chem. 197, 257–265 (2016)
Y. Liu, Q. Wang, Q. Xu, J. Feng, H. Yu, Y. Yin, Non-destructive detection of Flos Lonicerae treated by sulfur fumigation based on hyperspectral imaging. J. Food Meas. Charact. 12(4), 2809–2818 (2018)
Acknowledgements
The authors express their sincere appreciation to the College Scientific and Technological Innovation Talents Program of Henan province (Project 19HASTIT013), the Natural Science Foundation of Henan Province (Project 162300410100) and the Science and Technology Project of Henan Province (Project 172102310617) for supporting this study financially.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, Q., Liu, Y., Gao, X. et al. Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae. Food Measure 13, 2603–2612 (2019). https://doi.org/10.1007/s11694-019-00180-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-019-00180-x