Discrimination and Prediction of Lonicerae japonicae Flos and Lonicerae Flos and Their Related Prescriptions by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Combined with Multivariate Statistical Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Sample Preparation
2.2. Spectral Acquisition and Data Preprocessing
2.3. Chemometrics Methods
2.3.1. Random Forest (RF)
2.3.2. Support Vector Machine (SVM)
2.3.3. Partial Least Squares Discrimination Analysis (PLS-DA)
2.4. Data Analysis
3. Results and Discussion
3.1. Band Assignment Comparison between LJF and LF
3.2. Classification of LJF and LF
3.3. Classification of LJF- and LF-Related Prescriptions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LJF | Lonicerae japonicae Flos |
LF | Lonicerae Flos |
ATR-FTIR | attenuated total reflectance Fourier transform spectroscopy |
PLS-DA | partial least squares-linear discriminant analysis |
RF | random forest |
SVM | support vector machine |
FT-IR | Fourier-transform infrared |
LC-MS | liquid chromatography-mass spectrometer |
NMR | nuclear magnetic resonance spectroscopy |
FT-NIRs | Fourier-transform near infrared spectroscopy |
EWMA | exponentially weighted moving average |
min-max | minimum-maximum |
ATR | attenuated total reflection |
airPLS | adaptive iteratively reweighted penalized least squares |
MSC | multiplicative scatter correction |
SNV | standard normal variate |
OOB | out-of-bag |
KS | Kennard–Stone |
SLT | statistical learning theory |
SRM | structural risk minimization |
PCA | principal component analysis |
TN | true negative |
TP | true positive |
FN | false negative |
FP | false positive |
TCM | traditional Chinese medicine |
References
- Chinese Pharmacopoeia Commission. Pharmacopoeia of the People’s Republic of China; China Medical Science Press: Beijing, China, 2020; Volume 1, pp. 31–32, 229–230. [Google Scholar]
- Lin, H.W.; Lee, Y.J.; Yang, D.J.; Hsieh, M.C.; Chen, C.C.; Hsu, W.L.; Chang, Y.Y.; Liu, C.W. Anti-inflammatory effects of Flos Lonicerae Japonicae Water Extract are regulated by the STAT/NF-κB pathway and HO-1 expression in Virus-infected RAW264.7 cells. Int. J. Med. Sci. 2021, 18, 2285. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Li, W.; Fu, C.; Li, Y.; Li, W.; Fu, C.; Song, Y.; Fu, Q. Lonicerae japonicae flos and Lonicerae flos: A systematic review of ethnopharmacology, phytochemistry and pharmacology. Phytochem. Rev. 2020, 19, 1–61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tang, Y.; Zeng, T.; Zafar, S.; Yuan, H.; Li, B.; Peng, C.; Wang, S.; Jian, Y.; Qin, Y.; Choudhary, M.I.; et al. Lonicerae Flos: A review of chemical constituents and biological activities. Digit. Chin. Med. 2018, 1, 173–188. [Google Scholar] [CrossRef]
- Liu, S.; Zhou, L.; Huang, J.; Zeng, H.; Qiao, Z.; Li, Y.; Zhang, G. Comparative Analysis of the Complete Chloroplast Genome Sequences of Four Origin Plants of Lonicerae Flos (Lonicera; Caprifoliaceae). Phyton-Int. J. Exp. Bot. 2022, 91, 1503–1516. [Google Scholar] [CrossRef]
- Yu, J.; Wu, X.; Liu, C.; Newmaster, S.; Ragupathy, S.; Kress, W.J. Progress in the use of DNA barcodes in the identification and classification of medicinal plants. Ecotoxicol. Environ. Saf. 2021, 208, 111691. [Google Scholar] [CrossRef]
- Zhang, F.; Shi, P.; Liu, H.; Zhang, Y.; Yu, X.; Li, J.; Pu, G. A simple, rapid, and practical method for distinguishing Lonicerae Japonicae Flos from Lonicerae Flos. Molecules 2019, 24, 3455. [Google Scholar] [CrossRef] [Green Version]
- Baravkar, A.A.; Kale, R.N.; Sawant, S.D. FT-IR spectroscopy: Principle, technique and mathematics. Int. J. Pharma Bio Sci. 2011, 2, 513–519. [Google Scholar]
- Feng, J.; Liu, Y.; Shi, X.; Wang, Q. Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. J. Food Meas. Charact. 2018, 12, 2184–2192. [Google Scholar] [CrossRef]
- Zhang, Y.C.; Deng, J.; Lin, X.L.; Li, Y.M.; Sheng, H.X.; Xia, B.H.; Lin, L.M. Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica. Int. J. Anal. Chem. 2022, 2022, 8850914. [Google Scholar] [CrossRef]
- Sun, S.; Zhou, Q.; Chen, J. Infrared Spectroscopy for Complex Mixtures; Chemical Industry: Beijing, China, 2011; pp. 24–66. [Google Scholar]
- Filik, J.; Frogley, M.D.; Pijanka, J.K.; Wehbe, K.; Cinque, G. Electric field standing wave artefacts in FTIR micro-spectroscopy of biological materials. Analyst 2012, 137, 853–861. [Google Scholar] [CrossRef]
- Zhao, X.; Zhu, H.; Chen, J.; Ao, Q. FTIR, XRD and SEM Analysis of Ginger Powders with Different Size. J. Food Process. Preserv. 2015, 39, 2017–2026. [Google Scholar] [CrossRef]
- Guiliano, M.; Asia, L.; Onoratini, G.; Mille, G. Applications of diamond crystal ATR FTIR spectroscopy to the characterization of ambers. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2007, 67, 1407–1411. [Google Scholar] [CrossRef]
- Escamilla, M.N.; Sanz, F.R.; Li, H.; Schönbichler, S.; Yang, B.; Bonn, G.; Huck, C. Rapid determination of baicalin and total baicalein content in Scutellariae radix by ATR-IR and NIR spectroscopy. Talanta 2013, 114, 304–310. [Google Scholar] [CrossRef]
- Nikzad-Langerodi, R.; Ortmann, S.; Pferschy-Wenzig, E.M.; Bochkov, V.; Zhao, Y.M.; Miao, J.H.; Atanasov, A.G. Assessment of anti-inflammatory properties of extracts from Honeysuckle (Lonicera sp. L., Caprifoliaceae) by ATR-FTIR spectroscopy. Talanta 2017, 175, 264–272. [Google Scholar] [CrossRef]
- Schönbichler, S.; Bittner, L.; Pallua, J.; Popp, M.; Abel, G.; Bonn, G.; Huck, C. Simultaneous quantification of verbenalin and verbascoside in Verbena officinalis by ATR-IR and NIR spectroscopy. J. Pharm. Biomed. Anal. 2013, 84, 97–102. [Google Scholar] [CrossRef]
- Li, W.; Cheng, Z.; Wang, Y.; Qu, H. Quality control of Lonicerae Japonicae Flos using near infrared spectroscopy and chemometrics. J. Pharm. Biomed. Anal. 2013, 72, 33–39. [Google Scholar] [CrossRef]
- Baek, S.J.; Park, A.; Ahn, Y.-J.; Choo, J. Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst 2015, 140, 250–257. [Google Scholar] [CrossRef] [Green Version]
- Strobl, C.; Boulesteix, A.-L.; Zeileis, A.; Hothorn, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 2007, 8, 25. [Google Scholar] [CrossRef] [Green Version]
- Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273–297. [Google Scholar] [CrossRef]
- Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 2003, 43, 1882–1889. [Google Scholar] [CrossRef]
- Chauchard, F.; Cogdill, R.; Roussel, S.; Roger, J.M.; Bellon-Maurel, V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes. Chemom. Intell. Lab. Syst. 2004, 71, 141–150. [Google Scholar] [CrossRef] [Green Version]
- Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 27–29 July 1992; ACM: New York, NY, USA, 1992; pp. 144–152. [Google Scholar]
- Huang, J.; Liu, J.; Wang, K.; Yang, Z.; Liu, X. Classification and identification of molecules through factor analysis method based on terahertz spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 198, 198–203. [Google Scholar] [CrossRef]
- Maguire, A.; Vega-Carrascal, I.; Bryant, J.; White, L.; Howe, O.; Lyng, F.M.; Meade, A.D. Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy. Analyst 2015, 140, 2473–2481. [Google Scholar] [CrossRef]
- Liang, R.P.; Huang, S.Y.; Shi, S.P.; Sun, X.Y.; Suo, S.B.; Qiu, J.D. A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization. Comput. Biol. Med. 2012, 42, 180–187. [Google Scholar] [CrossRef]
- Meyer, D.; Wien, F.T. Support Vector Machines. The Interface to Libsvm in Package e1071. 2015. Available online: https://mran.revolutionanalytics.com/snapshot/2016-03-14/web/packages/e1071/vignettes/svmdoc.pdf (accessed on 14 June 2022).
- Platt, J.C.; Cristianini, N.; Shawe-Taylor, J. Large margin DAGs for multiclass classification. Advances in Neural Information Processing Systems. In Proceedings of the 12th International Conference on Neural Information Processing Systems, Online, 29 November 1999; pp. 547–553. [Google Scholar]
- Joachims, T. Making large-scale SVM learning practical. In Advances in Kernel Methods: Support Vector Learning; Scholkoph, B., Burges, C., Smofa, A., Eds.; MIT Press: Cambridge, MA, USA, 1999. [Google Scholar]
- Ståhle, L.; Wold, S. Partial least squares analysis with cross-validation for the two-class problem: A Monte Carlo study. J. Chemom. 1987, 1, 185–196. [Google Scholar] [CrossRef]
- Sandt, C.; Féraud, O.; Oudrhiri, N.; Bonnet, M.L.; Meunier, M.C.; Valogne, Y.; Bertrand, A.; Raphaël, M.; Griscelli, F.; Turhan, A.G. Identification of spectral modifications occurring during reprogramming of somatic cells. PLoS ONE 2012, 7, e30743. [Google Scholar]
- Bruun, S.W.; Kohler, A.; Adt, I.; Sockalingum, G.D.; Manfait, M.; Martens, H. Correcting attenuated total reflection–Fourier transform infrared spectra for water vapor and carbon dioxide. Appl. Spectrosc. 2006, 60, 1029–1039. [Google Scholar] [CrossRef]
- Schulz, H.; Baranska, M. Identification and quantification of valuable plant substances by IR and Raman spectroscopy. Vib. Spectrosc. 2007, 43, 13–25. [Google Scholar] [CrossRef]
- Wang, L.; Yang, Q.; Zhao, H. Sub-regional identification of peanuts from Shandong Province of China based on Fourier transform infrared (FT-IR) spectroscopy. Food Control 2021, 124, 107879. [Google Scholar] [CrossRef]
- Liu, J.; Mukherjee, J.; Hawkes, J.J.; Wilkinson, S.J. Optimization of lipid production for algal biodiesel in nitrogen stressed cells of Dunaliella salina using FTIR analysis. J. Chem. Technol. Biotechnol. 2013, 88, 1807–1814. [Google Scholar] [CrossRef]
- Heredia-Guerrero, J.A.; Benítez, J.J.; Domínguez, E.; Bayer, I.S.; Cingolani, R.; Athanassiou, A.; Heredia, A. Infrared and Raman spectroscopic features of plant cuticles: A review. Front. Plant Sci. 2014, 5, 305. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yeats, T.H.; Rose, J.K. The formation and function of plant cuticles. Plant Physiol. 2013, 163, 5–20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mazurek, S.; Mucciolo, A.; Humbel, B.M.; Nawrath, C. Transmission Fourier transform infrared microspectroscopy allows simultaneous assessment of cutin and cell-wall polysaccharides of Arabidopsis petals. Plant J. 2013, 74, 880–891. [Google Scholar] [CrossRef] [PubMed]
- Kong, J.; Yu, S. Fourier Transform Infrared Spectroscopic Analysis of Protein Secondary Structures. Acta Biochim. Biophys. Sin. 2007, 39, 549–559. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Kong, D.; Wu, H. Comprehensive chemical analysis of the flower buds of five Lonicera species by ATR-FTIR, HPLC-DAD, and chemometric methods. Rev. Bras. Farmacogn. 2018, 28, 533–541. [Google Scholar] [CrossRef]
- Ordoudi, S.A.; de los Mozos Pascual, M.; Tsimidou, M.Z. On the quality control of traded saffron by means of transmission Fourier-transform mid-infrared (FT-MIR) spectroscopy and chemometrics. Food Chem. 2014, 150, 414–421. [Google Scholar] [CrossRef]
- Staniszewska-Slezak, E.; Fedorowicz, A.; Kramkowski, K.; Leszczynska, A.; Chlopicki, S.; Baranska, M.; Malek, K. Plasma biomarkers of pulmonary hypertension identified by Fourier transform infrared spectroscopy and principal component analysis. Analyst 2015, 140, 2273–2279. [Google Scholar] [CrossRef]
- Lammers, K.; Arbuckle-Keil, G.; Dighton, J. FT-IR study of the changes in carbohydrate chemistry of three New Jersey pine barrens leaf litters during simulated control burning. Soil Biol. Biochem. 2009, 41, 340–347. [Google Scholar] [CrossRef]
- Chun-Yun, C.; Lian-Wen, Q.; Hui-Jun, L.; Ping, L.; Ling, Y.; Hong-Liang, M.; Dan, T. Simultaneous determination of iridoids, phenolic acids, flavonoids, and saponins in Flos Lonicerae and Flos Lonicerae Japonicae by HPLC-DAD-ELSD coupled with principal component analysis. J. Sep. Sci. 2007, 30, 3181–3192. [Google Scholar] [CrossRef]
- Ren, M.T.; Chen, J.; Song, Y.; Sheng, L.S.; Li, P.; Qi, L.W. Identification and quantification of 32 bioactive compounds in Lonicera species by high performance liquid chromatography coupled with time-of-flight mass spectrometry. J. Pharm. Biomed. Anal. 2008, 48, 1351–1360. [Google Scholar] [CrossRef]
- Chen, J.; Guo, B.; Yan, R.; Sun, S.; Zhou, Q. Rapid and automatic chemical identification of the medicinal flower buds of Lonicera plants by the benchtop and hand-held Fourier transform infrared spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2017, 182, 81–86. [Google Scholar] [CrossRef]
- Rui, Y.; Chen, J.B.; Sun, S.Q.; Guo, B.L. Rapid identification of Lonicerae japonicae Flos and Lonicerae Flos by Fourier transform infrared (FT-IR) spectroscopy and two-dimensional correlation analysis. J. Mol. Struct. 2016, 1124, 110–116. [Google Scholar]
LJF (cm−1) | LF (cm−1) | Vibration | Suggested Biomolecular Assignment |
---|---|---|---|
4000–3500 | 4000–3500 | O-H, v | water |
3350 | 3357 | O-H, v | saccharides |
2920 | 2920 | CH2, CH3νas | lipids (cutin and waxes), proteins, carbohydrates |
2851 | 2850 | CH2, νs | lipids (cutin and waxes), proteins, carbohydrates |
2442–2208 | 2442–2208 | C-O-C, v | CO2 |
1730 | 1735 | C=O, v | lipids (cutin and waxes) |
1633 | 1629 | C-O, v C-N, v | amide I band |
1545 | Amide II bands | proteins | |
1528 | Amide II bands | phenolic acids, flavonoids | |
14,401,374 | 14,401,376 | O-H, v O-H, v | organic acid, flavonoids organic acid, flavonoids |
1400 | C-H, δ | saccharides | |
1321 1259 | 1314 1260 | C-O, v C-O, v | lipids, flavonoid lipids, flavonoid |
1147 | 1152 | C-O, v CO-O-C, νas | cholesterol ester, oligosaccharides, triacylglycerols |
1047 | 1049 | C-O, v | starch |
930 | C-O-C, skeletal | saccharides | |
817 | 813 | C-H, δoop | |
781 | COO−, skeletal | saponins |
RF Model | |||||
---|---|---|---|---|---|
Pretreatment Methods | SENS | SPEC | ACC | MCC | AUC |
No methods | 0.9167 | 0.8250 | 0.8750 | 0.7481 | 0.9190 |
Vector (first) | 0.9706 | 1 | 0.9844 | 0.9692 | 0.9710 |
Vector (second) | 0.9583 | 1 | 0.9773 | 0.9554 | 0.9630 |
Min-max | 0.9375 | 0.9750 | 0.9545 | 0.9097 | 0.9368 |
Area | 0.9500 | 0.9375 | 0.9432 | 0.8859 | 0.9491 |
EWMA | 0.9167 | 0.9000 | 0.9091 | 0.8167 | 0.9090 |
MSC | 0.9500 | 0.9583 | 0.9545 | 0.9083 | 0.9310 |
RC | 0.9500 | 0.9583 | 0.9545 | 0.9083 | 0.9430 |
S-G | 0.9250 | 0.9167 | 0.9205 | 0.8401 | 0.9168 |
SNV | 0.9500 | 0.9375 | 0.9432 | 0.8859 | 0.9291 |
airPLS | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9690 |
PLS-DA Model | |||||
Pretreatment methods | SENS | SPEC | ACC | MCC | AUC |
No methods | 0.9412 | 1 | 0.9687 | 0.9393 | 0.9229 |
Vector (first) | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9710 |
Vector (second) | 0.9500 | 0.9583 | 0.9545 | 0.9083 | 0.9630 |
Min-max | 0.9333 | 0.9667 | 0.9687 | 0.9389 | 0.9218 |
Area | 0.9118 | 0.9706 | 0.9531 | 0.9104 | 0.9091 |
EWMA | 0.9412 | 1 | 0.9687 | 0.9393 | 0.9290 |
MSC | 0.9706 | 0.9667 | 0.9687 | 0.9373 | 0.9610 |
RC | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9430 |
S-G | 0.9412 | 1 | 0.9687 | 0.9393 | 0.9268 |
SNV | 0.9667 | 0.9706 | 0.9687 | 0.9373 | 0.9491 |
airPLS | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9690 |
SVM Model | |||||
Pretreatment methods | SENS | SPEC | ACC | MCC | AUC |
No methods | 0.9500 | 0.9792 | 0.9659 | 0.9314 | 0.9390 |
Vector (first) | 0.9792 | 0.9981 | 0.9716 | 0.9724 | 0.9710 |
Vector (second) | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9630 |
Min-max | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9668 |
Area | 0.7250 | 0.5208 | 0.6136 | 0.2490 | 0.1291 |
EWMA | 0.9500 | 0.9792 | 0.9659 | 0.9314 | 0.9290 |
MSC | 0.9250 | 0.9792 | 0.9545 | 0.9089 | 0.9010 |
RC | 0.9500 | 0.9583 | 0.9545 | 0.9083 | 0.9230 |
S-G | 0.9500 | 0.9792 | 0.9659 | 0.9314 | 0.9168 |
SNV | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9491 |
airPLS | 0.9500 | 0.9792 | 0.9659 | 0.9314 | 0.9390 |
Calibration Set | SENS | SPEC | ACC | MCC | AUC |
---|---|---|---|---|---|
RF | 0.9706 | 1 | 0.9844 | 0.9692 | 0.9775 |
PLS-DA | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9546 |
SVM | 0.9792 | 0.9981 | 0.9716 | 0.9724 | 0.9668 |
Validation set | SENS | SPEC | ACC | MCC | AUC |
RF | 0.9706 | 0.9981 | 0.9744 | 0.9592 | 0.9425 |
PLS-DA | 0.9250 | 0.9792 | 0.9545 | 0.9089 | 0.9006 |
SVM | 0.9500 | 0.9792 | 0.9659 | 0.9314 | 0.9218 |
ntree | SENS | SPEC | ACC | MCC | AUC |
---|---|---|---|---|---|
100 | 0.9750 | 0.9792 | 0.9773 | 0.9554 | 0.9390 |
200 | 0.9286 | 1 | 0.9583 | 0.9188 | 0.9010 |
300 | 0.9706 | 1 | 0.9844 | 0.9692 | 0.9775 |
500 | 0.9583 | 0.9750 | 0.9659 | 0.9316 | 0.9168 |
800 | 0.9583 | 0.9750 | 0.9659 | 0.9316 | 0.9168 |
1000 | 0.9286 | 1 | 0.9583 | 0.9188 | 0.9018 |
mtry | SENS | SPEC | ACC | MCC | AUC |
82 | 0.9583 | 1 | 0.9773 | 0.9554 | 0.9390 |
84 | 0.9583 | 0.9750 | 0.9659 | 0.9316 | 0.9168 |
86 | 0.9792 | 1 | 0.9886 | 0.9774 | 0.9875 |
88 | 0.9706 | 1 | 0.9844 | 0.9692 | 0.9775 |
90 | 0.9583 | 0.9750 | 0.9659 | 0.9316 | 0.9168 |
92 | 0.9583 | 1 | 0.9773 | 0.9554 | 0.9390 |
94 | 0.9792 | 0.9750 | 0.9773 | 0.9542 | 0.9390 |
96 | 0.9583 | 0.9750 | 0.9659 | 0.9316 | 0.9168 |
Normalization Method | SENS | SPEC | ACC | MCC | AUC |
---|---|---|---|---|---|
Vector (First) | |||||
4000–600 cm−1 except for water vapor, carbon dioxide region | 0.9750 | 0.9792 | 0.9773 | 0.9542 | 0.9425 |
2000–600 cm−1 | 0.9792 | 0.9750 | 0.9773 | 0.9542 | 0.9390 |
4000–2000 cm−1 | 0.9583 | 1 | 0.9773 | 0.9554 | 0.9390 |
4000–600 cm−1 | 0.9706 | 1 | 0.9844 | 0.9692 | 0.9775 |
VIP Cutoff | SENS | SPEC | ACC | MCC | AUC |
---|---|---|---|---|---|
0.05 | 0.9412 | 1 | 0.9688 | 0.9393 | 0.9168 |
0.01 | 0.9750 | 1 | 0.9886 | 0.9773 | 0.9775 |
0.015 | 0.9512 | 0.9867 | 0.9731 | 0.9465 | 0.9425 |
0.020 | 0.9000 | 0.9706 | 0.9375 | 0.8758 | 0.8625 |
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Tang, Y.-Q.; Li, L.; Lin, T.-F.; Lin, L.-M.; Li, Y.-M.; Xia, B.-H. Discrimination and Prediction of Lonicerae japonicae Flos and Lonicerae Flos and Their Related Prescriptions by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Combined with Multivariate Statistical Analysis. Molecules 2022, 27, 4640. https://doi.org/10.3390/molecules27144640
Tang Y-Q, Li L, Lin T-F, Lin L-M, Li Y-M, Xia B-H. Discrimination and Prediction of Lonicerae japonicae Flos and Lonicerae Flos and Their Related Prescriptions by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Combined with Multivariate Statistical Analysis. Molecules. 2022; 27(14):4640. https://doi.org/10.3390/molecules27144640
Chicago/Turabian StyleTang, Yang-Qiannan, Li Li, Tian-Feng Lin, Li-Mei Lin, Ya-Mei Li, and Bo-Hou Xia. 2022. "Discrimination and Prediction of Lonicerae japonicae Flos and Lonicerae Flos and Their Related Prescriptions by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Combined with Multivariate Statistical Analysis" Molecules 27, no. 14: 4640. https://doi.org/10.3390/molecules27144640