Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

References

• Sendker, J.; Sheridan, H. Composition and Quality Control of Herbal Medicines. In Toxicology of Herbal Products; O. Pelkonen, P. Duez, P. M. Vuorela, H. Vuorela, Eds., Springer International Publishing: Cham, 2017; p 29.
   Google Scholar
• Wang, W.-Y.; Xie, Y.; Zhou, H.; Liu, L. Contribution of Traditional Chinese Medicine to the Treatment of COVID-19. Phytomedicine. 2021, 85, 153279. DOI: 10.1016/j.phymed.2020.153279.
   PubMed Web of Science ®Google Scholar
• Chen, K.; Chen, H. Traditional Chinese Medicine for Combating COVID-19. Front. Med. 2020, 14, 529–532. DOI: 10.1007/s11684-020-0802-9.
   PubMedGoogle Scholar
• Tankeu, S.; Vermaak, I.; Chen, W.; Sandasi, M.; Viljoen, A. Differentiation between Two “Fang ji” Herbal Medicines, Stephania Tetrandra and the Nephrotoxic Aristolochia Fangchi, Using Hyperspectral Imaging. Phytochemistry. 2016, 122, 213–222. DOI: 10.1016/j.phytochem.2015.11.008.
   PubMed Web of Science ®Google Scholar
• Lan, Z.; Zhang, Y.; Sun, Y.; Ji, D.; Wang, S.; Lu, T.; Cao, H.; Meng, J. Rapid Quantitative Detection of the Discrepant Compounds in Differently Processed Curcumae Rhizoma Products by FT-NIR Combined with VCPA-GA Technology. J. Pharm. Biomed. Anal. 2021, 195, 113837. DOI: 10.1016/j.jpba.2020.113837.
   PubMed Web of Science ®Google Scholar
• Cheng, T-f.; Jia, Y-r.; Zuo, Z.; Dong, X.; Zhou, P.; Li, P.; Li, F. Quality Assessment of Traditional Chinese Medicine Herb Couple by High-Performance Liquid Chromatography and Mass Spectrometry Combined with Chemometrics. J. Sep. Sci. 2016, 39, 1223–1231. DOI: 10.1002/jssc.201501259.
   PubMedGoogle Scholar
• Wu, X.; Zhang, H.; Fan, S.; Zhang, Y.; Yang, Z.; Fan, S.; Zhuang, P.; Zhang, Y. Quality Markers Based on Biological Activity: A New Strategy for the Quality Control of Traditional Chinese Medicine. Phytomedicine. 2018, 44, 103–108. DOI: 10.1016/j.phymed.2018.01.016.
   PubMed Web of Science ®Google Scholar
• Wu, Y.-W.; Sun, S.-Q.; Zhou, Q.; Leung, H.-W. Fourier Transform Mid-Infrared (MIR) and near-Infrared (NIR) Spectroscopy for Rapid Quality Assessment of Chinese Medicine Preparation Honghua Oil. J. Pharm. Biomed. Anal. 2008, 46, 498–504. DOI: 10.1016/j.jpba.2007.11.021.
   PubMed Web of Science ®Google Scholar
• Gilbert, N. Regulations: Herbal Medicine Rule Book. Nature. 2011, 480, S98–S99. DOI: 10.1038/480S98a.
   PubMedGoogle Scholar
• Wang, J.; van der Heijden, R.; Spruit, S.; Hankermeier, T.; Chan, K.; van der Greef, J.; Xu, G.; Wang, M. Quality and Safety of Chinese Herbal Medicines Guided by a Systems Biology Perspective. J. Ethnopharmacol. 2009, 126, 31–41. DOI: 10.1016/j.jep.2009.07.040.
   PubMed Web of Science ®Google Scholar
• Cao, J.-L.; Wei, J.-C.; Chen, M.-W.; Su, H.-X.; Wan, J.-B.; Wang, Y.-T.; Li, P. Application of Two-Dimensional Chromatography in the Analysis of Chinese Herbal Medicines. J. Chromatogr. A. 2014, 1371, 1–14. DOI: 10.1016/j.chroma.2014.10.078.
   PubMed Web of Science ®Google Scholar
• Liu, X.; Jiang, W.; Su, M.; Sun, Y.; Liu, H.; Nie, L.; Zang, H. Quality Evaluation of Traditional Chinese Medicines Based on Fingerprinting. J. Sep. Sci. 2020, 43, 6–17. DOI: 10.1002/jssc.201900365.
   PubMed Web of Science ®Google Scholar
• Jiang, H.; Gao, Y.; Yang, J-m.; Meng, X-c. Overview of Traditional Chinese Medicine Quality Evaluation Method Based on Overall Research. Zhongguo Zhong Yao za Zhi = Zhongguo Zhongyao Zazhi = Chin. J. Chin. Mater. Medica. 2015, 40, 1027. http://europepmc.org/abstract/MED/26226739.
   PubMedGoogle Scholar
• Liu, C. X.; Chen, S. L.; Xiao, X. H.; Zhang, T. J.; Hou, W. B.; Liao, M. L. A New Concept on Quality Marker of Chinese Materia Medica: Quality Control for Chinese Medicinal Products. Chin. Tradit. Herb. Drugs. 2016, 47, 1443. zcy/article/abstract/20160901
   Google Scholar
• Liu, C. X. Recognizing Healthy Development of Chinese Medicine Industry from Resources-Quality-Quality Markers of Chinese Medicine. Chin. Tradit. Herb. Drugs. 2016, 47, 3149. zcy/article/abstract/20161801
   Google Scholar
• Liu, C-x.; Cheng, Y-y.; Guo, D-a.; Zhang, T-j.; Li, Y-z.; Hou, W-b.; Huang, L-q.; Xu, H-y. A New Concept on Quality Marker for Quality Assessment and Process Control of Chinese Medicines. Chin. Herb. Med. 2017, 9, 3. DOI: 10.1016/S1674-6384(17)60070-4.
   Google Scholar
• Yang, N.; Xiong, A.; Wang, R.; Yang, L.; Wang, Z. Quality Evaluation of Traditional Chinese Medicine Compounds in Xiaoyan Lidan Tablets: Fingerprint and Quantitative Analysis Using UPLC-MS. Molecules. 2016, 21, 83. DOI: 10.3390/molecules21020083.
   PubMedGoogle Scholar
• Yang, D.-Z.; An, Y.-Q.; Jiang, X.-L.; Tang, D.-Q.; Gao, Y.-Y.; Zhao, H.-T.; Wu, X.-W. Development of a Novel Method Combining HPLC Fingerprint and Multi-Ingredients Quantitative Analysis for Quality Evaluation of Traditional Chinese Medicine Preparation. Talanta. 2011, 85, 885–890. DOI: 10.1016/j.talanta.2011.04.059.
   PubMed Web of Science ®Google Scholar
• Zhou, X.; Li, Y.; Zhang, M.; Hao, J.; Gu, Q.; Liu, H.; Chen, W.; Shi, Y.; Dong, B.; Zhang, Y. Spectrum-Effect Relationship between UPLC Fingerprints and Antilung Cancer Effect of Si Jun Zi Tang. Evid. Based Complementary Altern. Med. 2019, 2019, 7282681. DOI: 10.1155/2019/7282681.
   Google Scholar
• Zhang, X.-F.; Chen, J.; Yang, J.-L.; Shi, Y.-P. UPLC-MS/MS Analysis for Antioxidant Components of Lycii Fructus Based on Spectrum-Effect Relationship. Talanta. 2018, 180, 389–395. DOI: 10.1016/j.talanta.2017.12.078.
   PubMed Web of Science ®Google Scholar
• Zhang, A.-H.; Sun, H.; Yan, G.-L.; Han, Y.; Zhao, Q.-Q.; Wang, X.-J. Chinmedomics: A Powerful Approach Integrating Metabolomics with Serum Pharmacochemistry to Evaluate the Efficacy of Traditional Chinese Medicine. Eng. 2019, 5, 60. DOI: 10.1016/j.eng.2018.11.008.
   Google Scholar
• Liu, S.; Liang, Y.-Z.; Liu, H-t. Chemometrics Applied to Quality Control and Metabolomics for Traditional Chinese Medicines. J. Chromatogr. B. 2016, 1015, 82. DOI: 10.1016/j.jchromb.2016.02.011.
   PubMedGoogle Scholar
• Xu, J.; Han, Q.-B.; Li, S.-L.; Chen, X.-J.; Wang, X.-N.; Zhao, Z.-Z.; Chen, H.-B. Chemistry, Bioactivity and Quality Control of Dendrobium, a Commonly Used Tonic Herb in Traditional Chinese Medicine. Phytochem. Rev. 2013, 12, 341. DOI: 10.1007/s11101-013-9310-8.
   Web of Science ®Google Scholar
• Jiang, Y.; David, B.; Tu, P.; Barbin, Y. Recent Analytical Approaches in Quality Control of Traditional Chinese Medicines—A Review. Anal. Chim. Acta. 2010, 657, 9–18. DOI: 10.1016/j.aca.2009.10.024.
   PubMed Web of Science ®Google Scholar
• Dadashpour, M.; Rasooli, I.; Sefidkon, F.; Rezaei, M. B.; Darvish Alipour Astaneh, S. Lipid Peroxidation Inhibition, Superoxide Anion and Nitric Oxide Radical Scavenging Properties of Thymus Daenensis and Anethum Graveolens Essential Oils. JMPIR. 2011, 10, 109. http://jmp.ir/article-1-238-fa.html.
   Google Scholar
• Hashemi, B.; Akram, F.-A.; Amirazad, H.; Dadashpour, M.; Sheervalilou, M.; Nasrabadi, D.; Ahmadi, M.; Sheervalilou, R.; Ameri Shah Reza, M.; Ghazi, F.; Roshangar, L. Emerging Importance of Nanotechnology-Based Approaches to Control the COVID-19 Pandemic; Focus on Nanomedicine Iterance in Diagnosis and Treatment of COVID-19 Patients. J. Drug Deliv. Sci. Technol. 2022, 67, 102967. DOI: 10.1016/j.jddst.2021.102967.
   PubMed Web of Science ®Google Scholar
• Mogheri, F.; Jokar, E.; Afshin, R.; Akbari, A. A.; Dadashpour, M.; Firouzi-Amandi, A.; Serati-Nouri, H.; Zarghami, N. Co-Delivery of Metformin and Silibinin in Dual-Drug Loaded Nanoparticles Synergistically Improves Chemotherapy in Human Non-Small Cell Lung Cancer A549 Cells. J. Drug Delivery Sci. Technol. 2021, 66, 102752. DOI: 10.1016/j.jddst.2021.102752.
   Web of Science ®Google Scholar
• Serati-Nouri, H.; Rasoulpoor, S.; Pourpirali, R.; Sadeghi-Soureh, S.; Esmaeilizadeh, N.; Dadashpour, M.; Roshangar, L.; Zarghami, N. In Vitro Expansion of Human Adipose-Derived Stem Cells with Delayed Senescence through Dual Stage Release of Curcumin from Mesoporous Silica Nanoparticles/Electrospun Nanofibers. Life Sci. 2021, 285, 119947. DOI: 10.1016/j.lfs.2021.119947.
   PubMed Web of Science ®Google Scholar
• Huang, B.-M.; Zha, Q.-L.; Chen, T.-B.; Xiao, S.-Y.; Xie, Y.; Luo, P.; Wang, Y.-P.; Liu, L.; Zhou, H. Discovery of Markers for Discriminating the Age of Cultivated Ginseng by Using UHPLC-QTOF/MS Coupled with OPLS-DA. Phytomedicine. 2018, 45, 8–17. DOI: 10.1016/j.phymed.2018.03.011.
   PubMed Web of Science ®Google Scholar
• Zhang, X.; Zhao, Y.; Guo, L.; Qiu, Z.; Huang, L.; Qu, X. Differences in Chemical Constituents of Artemisia Annua L from Different Geographical Regions in China. PLoS One. 2017, 12, e0183047. 101371/journal.pone.0183047 DOI: 10.1371/journal.pone.0183047.
   PubMed Web of Science ®Google Scholar
• Ba, Y.; Xiao, R.; Chen, Q.-J.; Xie, L.-Y.; Xu, R.-R.; Yu, P.; Chen, X.-Q.; Wu, X. Comprehensive Quality Evaluation of Polygoni Orientalis Fructus and Its Processed Product: chemical Fingerprinting and Simultaneous Determination of Seven Major Components Coupled with Chemometric Analyses. Phytochem Anal. 2021, 32, 141–152. DOI: 10.1002/pca.2890.
   PubMed Web of Science ®Google Scholar
• Borràs, E.; Ferré, J.; Boqué, R.; Mestres, M.; Aceña, L.; Busto, O. Data Fusion Methodologies for Food and Beverage Authentication and Quality Assessment – A Review. Anal. Chim. Acta. 2015, 891, 1–14. DOI: 10.1016/j.aca.2015.04.042.
   PubMed Web of Science ®Google Scholar
• Hall, D. L.; Llinas, J. An Introduction to Multisensor Data Fusion. Proc. IEEE. 1997, 85, 6. DOI: 10.1109/5.554205.
   Web of Science ®Google Scholar
• Castanedo, F. A Review of Data Fusion Techniques. Sci. World J. 2013, 2013, 1–19. DOI: 10.1155/2013/704504.
   Web of Science ®Google Scholar
• Zhang, C.; Zheng, X.; Ni, H.; Li, P.; Li, H.-J. Discovery of Quality Control Markers from Traditional Chinese Medicines by Fingerprint-Efficacy Modeling: Current Status and Future Perspectives. J. Pharm. Biomed. Anal. 2018, 159, 296–304. DOI: 10.1016/j.jpba.2018.07.006.
   PubMed Web of Science ®Google Scholar
• Wen-Feng, M.; Jun, X.; Yan-Qi, H.; Hong-Bing, Z.; Su-Xiao, G.; Tie-Jun, Z.; Chang-Xiao, L. Practice and Progress on Bionic Technology in Identification on Five Tastes of Chinese Materia Medica. Chin. Tradit. Herb. Drugs. 2018, 49, 993. DOI: 10.7501/j.issn.0253-2670.2018.05.001.
   Google Scholar
• Kiani, S.; Minaei, S.; Ghasemi-Varnamkhasti, M. Fusion of Artificial Senses as a Robust Approach to Food Quality Assessment. J. Food Eng. 2016, 171, 230. DOI: 10.1016/j.jfoodeng.2015.10.007.
   Web of Science ®Google Scholar
• Di Rosa, A. R.; Leone, F.; Cheli, F.; Chiofalo, V. Fusion of Electronic Nose, Electronic Tongue and Computer Vision for Animal Source Food Authentication and Quality Assessment – A Review. J. Food Eng. 2017, 210, 62. DOI: 10.1016/j.jfoodeng.2017.04.024.
   Web of Science ®Google Scholar
• Gardner, J.; Bartlett, P. Electronic Noses. Principles and Applications; Oxford, UK: Oxford University Press, 1999.
   Google Scholar
• James, D.; Scott, S. M.; Ali, Z.; O’Hare, W. T. Chemical Sensors for Electronic Nose Systems. Microchim. Acta. 2005, 149, 1. DOI: 10.1007/s00604-004-0291-6.
   Web of Science ®Google Scholar
• Wilson, A. D.; Baietto, M. Applications and Advances in Electronic-Nose Technologies. Sensors (Basel). 2009, 9, 5099–5148. https://www.mdpi.com/1424-8220/9/7/5099. DOI: 10.3390/s90705099.
   PubMed Web of Science ®Google Scholar
• Ross, C. F. Considerations of the Use of the Electronic Tongue in Sensory Science. Curr. Opin. Food Sci. 2021, 40, 87. DOI: 10.1016/j.cofs.2021.01.011.
   Google Scholar
• Calvini, R.; Pigani, L. Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes. Sensors. 2022, 22, 577. DOI: 10.3390/s22020577.
   PubMed Web of Science ®Google Scholar
• Wang, W.; Liu, Y. 3 – Electronic Tongue for Food Sensory Evaluation. In Evaluation Technologies for Food Quality; J. Zhong and X. Wang, Eds.; Duxford: Elsevier, 2019, p 23. DOI: 10.1016/B978-0-12-814217-2.00003-2
   Google Scholar
• Cheng, X.; Ji, H.; Cheng, X.; Wang, D.; Li, T.; Ren, K.; Qu, S.; Pan, Y.; Liu, X. Characterization, Classification, and Authentication of Polygonatum Sibiricum Samples by Volatile Profiles and Flavor Properties. Molecules. 2022, 27, 25. https://www.mdpi.com/1420-3049/27/1/25. DOI: 10.3390/molecules27010025.
   Google Scholar
• Li, Q.; Yu, X.; Xu, L.; Gao, J.-M. Novel Method for the Producing Area Identification of Zhongning Goji Berries by Electronic Nose. Food Chem. 2017, 221, 1113–1119. DOI: 10.1016/j.foodchem.2016.11.049.
   PubMed Web of Science ®Google Scholar
• Fei, C.; Ren, C.; Wang, Y.; Li, L.; Li, W.; Yin, F.; Lu, T.; Yin, W. Identification of the Raw and Processed Crataegi Fructus Based on the Electronic Nose Coupled with Chemometric Methods. Sci. Rep. 2021, 11, 1. DOI: 10.1038/s41598-020-79717-w.
   PubMedGoogle Scholar
• Pan, M. H.; Su, Y. F.; Liu, X. J.; Xiang, S. Q.; Ding, Y. X.; Li, Q. Identification of Forsythia Suspensa (Thunb.) Vahl in Different Harvest Periods Using Intelligent Sensory Technologies, HPLC Characteristic Fingerprint Coupled with Chemometrics. Phytochem. Anal. 2022, 33, 490–501. DOI: 10.1002/pca.3104.
   PubMedGoogle Scholar
• Lan, Z.; Zhang, Y.; Sun, Y.; Ji, D.; Wang, S.; Lu, T.; Cao, H.; Meng, J. A Mid-Level Data Fusion Approach for Evaluating the Internal and External Changes Determined by FT-NIR, Electronic Nose and Colorimeter in Curcumae Rhizoma Processing. J. Pharm. Biomed. Anal. 2020, 188, 113387. DOI: 10.1016/j.jpba.2020.113387.
   PubMedGoogle Scholar
• Shen, M.-R.; He, Y.; Shi, S.-M. Development of Chromatographic Technologies for the Quality Control of Traditional Chinese Medicine in the Chinese Pharmacopoeia. J. Pharm. Anal. 2021, 11, 155–162. DOI: 10.1016/j.jpha.2020.11.008.
   PubMed Web of Science ®Google Scholar
• Dupuy, N.; Galtier, O.; Ollivier, D.; Vanloot, P.; Artaud, J. Comparison between NIR, MIR, Concatenated NIR and MIR Analysis and Hierarchical PLS Model. Application to Virgin Olive Oil Analysis. Anal. Chim. Acta. 2010, 666, 23–31. DOI: 10.1016/j.aca.2010.03.034.
   PubMed Web of Science ®Google Scholar
• Gong, D.; Chen, J.; Sun, Y.; Liu, X.; Sun, G. Multiple Wavelengths Maximization Fusion Fingerprint Profiling for Quality Evaluation of Compound Liquorice Tablets and Related Antioxidant Activity Analysis. Microchem. J. 2021, 160, 105671. DOI: 10.1016/j.microc.2020.105671.
   Google Scholar
• Chen, J.; Gong, D.; Liu, X.; Sun, G.; Sun, W. Quality and Antioxidant Activity Evaluation of Dandelion by HPLC with Five-Wavelength Fusion Fingerprint. New J. Chem. 2021, 45, 9856. DOI: 10.1039/D1NJ01422F.
   Google Scholar
• Li, Y.; Shen, Y.; Yao, C-l.; Guo, D-a. Quality Assessment of Herbal Medicines Based on Chemical Fingerprints Combined with Chemometrics Approach: A Review. J. Pharm. Biomed. Anal. 2020, 185, 113215. DOI: 10.1016/j.jpba.2020.113215.
   PubMed Web of Science ®Google Scholar
• Zhou, W.; Liu, Y.; Wang, J.; Guo, Z.; Shen, A.; Liu, Y.; Liang, X. Application of Two-Dimensional Liquid Chromatography in the Separation of Traditional Chinese Medicine. J. Sep. Sci. 2020, 43, 87–104. DOI: 10.1002/jssc.201900765.
   PubMedGoogle Scholar
• Li, Z.; Chen, K.; Guo, M-z.; Tang, D-q. Two-Dimensional Liquid Chromatography and Its Application in Traditional Chinese Medicine Analysis and Metabonomic Investigation. J. Sep. Sci. 2016, 39, 21–37. DOI: 10.1002/jssc.201500634.
   PubMed Web of Science ®Google Scholar
• Moros, J.; Garrigues, S.; Guardia, M. d. l. Vibrational Spectroscopy Provides a Green Tool for Multi-Component Analysis. TrAC, Trends Anal. Chem. 2010, 29, 578. DOI: 10.1016/j.trac.2009.12.012.
   Web of Science ®Google Scholar
• Barbosa, C. D.; Baqueta, M. R.; Rodrigues Santos, W. C.; Gomes, D.; Alvarenga, V. O.; Teixeira, P.; Albano, H.; Rosa, C. A.; Valderrama, P.; Lacerda, I. C. A. Data Fusion of UPLC Data, NIR Spectra and Physicochemical Parameters with Chemometrics as an Alternative to Evaluating Kombucha Fermentation. LWT. 2020, 133, 109875. DOI: 10.1016/j.lwt.2020.109875.
   Google Scholar
• D.-W.Sun. Hyperspectral Imaging for Food Quality Analysis and Control; Academic Press: Cambridge, MA, USA, 2010.
   Google Scholar
• Lu, Y.; Huang, Y.; Lu, R. Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review. Appl. Sci. 2017, 7, 189. https://www.mdpi.com/2076-3417/7/2/189. DOI: 10.3390/app7020189.
   Google Scholar
• Kiani, S.; van Ruth, S. M.; Minaei, S.; Ghasemi-Varnamkhasti, M. Hyperspectral Imaging, a Non-Destructive Technique in Medicinal and Aromatic Plant Products Industry: Current Status and Potential Future Applications. Comput. Electron. Agric. 2018, 152, 9. DOI: 10.1016/j.compag.2018.06.025.
   Web of Science ®Google Scholar
• Kabir, M. H.; Guindo, M. L.; Chen, R.; Liu, F.; Luo, X.; Kong, W. Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria Thunbergii. Molecules. 2022, 27, 6042. https://www.mdpi.com/1420-3049/27/18/6042. DOI: 10.3390/molecules27186042.
   PubMedGoogle Scholar
• Li, S.; Jiao, C.; Xu, Z.; Wu, Y.; Forsberg, E.; Peng, X.; He, S. Determination of Geographic Origins and Types of Lindera Aggregata Samples Using a Portable Short-Wave Infrared Hyperspectral Imager. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 279, 121370. DOI: 10.1016/j.saa.2022.121370.
   PubMedGoogle Scholar
• Cui, J.; Li, K.; Hao, J.; Dong, F.; Wang, S.; Rodas-González, A.; Zhang, Z.; Li, H.; Wu, K. Identification of near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network. Foods. 2022, 11, 1936. https://www.mdpi.com/2304-8158/11/13/1936.
   PubMedGoogle Scholar
• Wei, Y.; Hu, W.; Wu, F.; He, Y. Polysaccharide Determination and Habitat Classification for Fresh Dendrobiums with Hyperspectral Imagery and Modified RBFNN. RSC Adv. 2021, 12, 1141–1148. DOI: 10.1039/d1ra08577h.
   PubMedGoogle Scholar
• Jiao, C.; Xu, Z.; Bian, Q.; Forsberg, E.; Tan, Q.; Peng, X.; He, S. Machine Learning Classification of Origins and Varieties of Tetrastigma Hemsleyanum Using a Dual-Mode Microscopic Hyperspectral Imager. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 261, 120054. DOI: 10.1016/j.saa.2021.120054.
   PubMedGoogle Scholar
• Liu, Y.; Wang, Q.; Gao, X.; Xie, A. Total Phenolic Content Prediction in Flos Lonicerae Using Hyperspectral Imaging Combined with Wavelengths Selection Methods. J. Food Process. Eng. 2019, 42, e13224. DOI: 10.1111/jfpe.13224.
   Web of Science ®Google Scholar
• Arancibia, J. A.; Damiani, P. C.; Escandar, G. M.; Ibañez, G. A.; Olivieri, A. C. A Review on Second- and Third-Order Multivariate Calibration Applied to Chromatographic Data. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2012, 910, 22–30. DOI: 10.1016/j.jchromb.2012.02.004.
   PubMedGoogle Scholar
• Amigo, J. M. Chapter 1.1 – Hyperspectral and Multispectral Imaging: Setting the Scene. In Data Handling in Science and Technology; J. M. Amigo, Ed.; Elsevier: Amsterdam, The Netherlands, 2019, p 3.
   Google Scholar
• Azcarate, S. M.; Ríos-Reina, R.; Amigo, J. M.; Goicoechea, H. C. Data Handling in Data Fusion: Methodologies and Applications. TrAC, Trends Anal. Chem. 2021, 143, 116355. DOI: 10.1016/j.trac.2021.116355.
   Web of Science ®Google Scholar
• Cocchi, M. Chapter 1 – Introduction: Ways and Means to Deal with Data from Multiple Sources. In Data Handling in Science and Technology; M. Cocchi, Ed.; Elsevier, 2019, p 1. DOI:10.1016/B978-0-444-63984-4.00001-6
   Google Scholar
• Zou, Y.; Wan, H.; Zhang, X.; Ha, D.; Wang, P. Electronic Nose and Electronic Tongue. In Bioinspired Smell and Taste Sensors; P. Wang, Q. Liu, C. Wu, K. J. Hsia, Eds.; Springer Netherlands: Dordrecht, 2015, p 19.
   Google Scholar
• Savitzky, A.; Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 1627. DOI: 10.1021/ac60214a047.
   Web of Science ®Google Scholar
• Barnes, R. J.; Dhanoa, M. S.; Lister, S. J. Standard Normal Variate Transformation and De-Trending of near-Infrared Diffuse Reflectance Spectra. Appl. Spectrosc. 1989, 43, 772. DOI: 10.1366/0003702894202201.
   Web of Science ®Google Scholar
• Kohler, A.; Zimonja, M.; Segtnan, V.; Martens, H. 2.09 – Standard Normal Variate, Multiplicative Signal Correction and Extended Multiplicative Signal Correction Preprocessing in Biospectroscopy. In Comprehensive Chemometrics; S. D. Brown, R. Tauler, B. Walczak, Eds.; Elsevier: Oxford, 2009, p 139.
   Google Scholar
• Silvestri, M.; Elia, A.; Bertelli, D.; Salvatore, E.; Durante, C.; Li Vigni, M.; Marchetti, A.; Cocchi, M. A Mid Level Data Fusion Strategy for the Varietal Classification of Lambrusco PDO Wines. Chemom. Intell. Lab. Syst. 2014, 137, 181. DOI: 10.1016/j.chemolab.2014.06.012.
   Google Scholar
• Kuncheva, L. I. Combining Pattern Classifiers: Methods and Algorithms; John Wiley & Sons, Hoboken, NJ, USA, 2014.
   Google Scholar
• Xiao, F.; Qin, B. A Weighted Combination Method for Conflicting Evidence in Multi-Sensor Data Fusion. Sensors. 2018, 18, 1487. https://www.mdpi.com/1424-8220/18/5/1487. DOI: 10.3390/s18051487.
   PubMedGoogle Scholar
• Roussel, S.; Bellon-Maurel, V.; Roger, J.-M.; Grenier, P. Fusion of Aroma, FT-IR and UV Sensor Data Based on the Bayesian Inference. Application to the Discrimination of White Grape Varieties. Chemom. Intell. Lab. Syst. 2003, 65, 209. DOI: 10.1016/S0169-7439(02)00111-9.
   Google Scholar
• Zhao, G.; Chen, A.; Lu, G.; Liu, W. Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence. Tsinghua Sci. Technol. 2020, 25, 12. DOI: 10.26599/TST.2018.9010138.
   Google Scholar
• Xu, Y.; Zhang, J.; Wang, Y. Recent Trends of Multi-Source and Non-Destructive Information for Quality Authentication of Herbs and Spices. Food Chem. 2023, 398, 133939. DOI: 10.1016/j.foodchem.2022.133939.
   PubMed Web of Science ®Google Scholar
• Márquez, C.; López, M. I.; Ruisánchez, I.; Callao, M. P. FT-Raman and NIR Spectroscopy Data Fusion Strategy for Multivariate Qualitative Analysis of Food Fraud. Talanta. 2016, 161, 80–86. DOI: 10.1016/j.talanta.2016.08.003.
   PubMed Web of Science ®Google Scholar
• Yao, S.; Li, J.-Q.; Duan, Z.-L.; Li, T.; Wang, Y.-Z. Fusion of Ultraviolet and Infrared Spectra Using Support Vector Machine and Random Forest Models for the Discrimination of Wild and Cultivated Mushrooms. Anal. Lett. 2020, 53, 1019. DOI: 10.1080/00032719.2019.1692857.
   Web of Science ®Google Scholar
• Nunes, K. M.; Andrade, M. V. O.; Santos Filho, A. M. P.; Lasmar, M. C.; Sena, M. M. Detection and Characterisation of Frauds in Bovine Meat in Natura by Non-Meat Ingredient Additions Using Data Fusion of Chemical Parameters and ATR-FTIR Spectroscopy. Food Chem. 2016, 205, 14–22. DOI: 10.1016/j.foodchem.2016.02.158.
   PubMed Web of Science ®Google Scholar
• Borràs, E.; Ferré, J.; Boqué, R.; Mestres, M.; Aceña, L.; Calvo, A.; Busto, O. Prediction of Olive Oil Sensory Descriptors Using Instrumental Data Fusion and Partial Least Squares (PLS) Regression. Talanta. 2016, 155, 116–123. DOI: 10.1016/j.talanta.2016.04.040.
   PubMed Web of Science ®Google Scholar
• Belmonte-Sánchez, J. R.; Romero-González, R.; Martínez Vidal, J. L.; Arrebola, F. J.; Garrido Frenich, A. 1H NMR and Multi-Technique Data Fusion as Metabolomic Tool for the Classification of Golden Rums by Multivariate Statistical Analysis. Food Chem. 2020, 317, 126363. DOI: 10.1016/j.foodchem.2020.126363.
   PubMed Web of Science ®Google Scholar
• Huang, F.; Song, H.; Guo, L.; Guang, P.; Yang, X.; Li, L.; Zhao, H.; Yang, M. Detection of Adulteration in Chinese Honey Using NIR and ATR-FTIR Spectral Data Fusion. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 235, 118297. DOI: 10.1016/j.saa.2020.118297.
   PubMed Web of Science ®Google Scholar
• Alaerts, G.; Dejaegher, B.; Smeyers-Verbeke, J.; Vander Heyden, Y. Recent Developments in Chromatographic Fingerprints from Herbal Products: Set-Up and Data Analysis. Comb. Chem. High Throughput Screen. 2010, 13, 900–922. DOI: 10.2174/138620710793360284.
   PubMed Web of Science ®Google Scholar
• Mishra, P.; Roger, J.-M.; Jouan-Rimbaud-Bouveresse, D.; Biancolillo, A.; Marini, F.; Nordon, A.; Rutledge, D. N. Recent Trends in Multi-Block Data Analysis in Chemometrics for Multi-Source Data Integration. TrAC, Trends Anal. Chem. 2021, 137, 116206. DOI: 10.1016/j.trac.2021.116206.
   Web of Science ®Google Scholar
• Kaiser, H. F. The Application of Electronic Computers to Factor Analysis. Edu. Psychol. Meas. 1960, 20, 141. DOI: 10.1177/001316446002000116.
   Web of Science ®Google Scholar
• Kucharska-Ambrożej, K.; Karpinska, J. The Application of Spectroscopic Techniques in Combination with Chemometrics for Detection Adulteration of Some Herbs and Spices. Microchem. J. 2020, 153, 104278. DOI: 10.1016/j.microc.2019.104278.
   Web of Science ®Google Scholar
• Du, Q.; Cai, Y.; Chen, Z.; Wei, D.; Cao, Y.; Chen, Y.; Yu, S.; Zhao, Q.; Wu, J.; Liu, M. Determination of Trace Elements in Corydalis Conspersa and Corydalis Linarioides by ICP-AES. J. Chem. 2020, 2020, 6567015. DOI: 10.1155/2020/6567015.
   Google Scholar
• Goodarzi, M.; Russell, P. J.; Vander Heyden, Y. Similarity Analyses of Chromatographic Herbal Fingerprints: A Review. Anal. Chim. Acta. 2013, 804, 16–28. DOI: 10.1016/j.aca.2013.09.017.
   PubMed Web of Science ®Google Scholar
• Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme Learning Machine: Theory and Applications. Neurocomputing. 2006, 70, 489. DOI: 10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Wu, X.-M.; Zhang, Q.-Z.; Wang, Y.-Z. Traceability of Wild Paris Polyphylla Smith Var. yunnanensis Based on Data Fusion Strategy of FT-MIR and UV–Vis Combined with SVM and Random Forest. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 205, 479–488. DOI: 10.1016/j.saa.2018.07.067.
   PubMed Web of Science ®Google Scholar
• Sun, W.; Zhang, X.; Zhang, Z.; Zhu, R. Data Fusion of near-Infrared and Mid-Infrared Spectra for Identification of Rhubarb. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2017, 171, 72–79. DOI: 10.1016/j.saa.2016.07.039.
   PubMed Web of Science ®Google Scholar
• Pan Y. J.; Zhang, T.; Shen, Y.-L.; Zhao, Z.-T.; Zuo, Y.-Z.; Wang, W.-Y. L. Liquid Chromatography Tandem Mass Spectrometry Combined with Fourier Transform Mid-Infrared Spectroscopy and Chemometrics for Comparative Analysis of Raw and Processed Gentiana Rigescens. J. Liq. Chromatog. Relat. Technol. 2015, 38, 1407. DOI: 10.1080/10826076.2015.1053912.
   Web of Science ®Google Scholar
• Wu, Y.-P.; Meng, X.-S.; Bao, Y.-R.; Wang, S.; Kang, T.-G. Simultaneous Quantitative Determination of Nine Active Chemical Compositions in Traditional Chinese Medicine Glycyrrhiza by RP-HPLC with Full-Time Five-Wavelength Fusion Method. Am. J. Chin. Med. 2013, 41, 211–219. DOI: 10.1142/S0192415X13500158.
   PubMed Web of Science ®Google Scholar
• Chang, J. B.; Lane, M. E.; Yang, M.; Heinrich, M. Disentangling the Complexity of a Hexa-Herbal Chinese Medicine Used for Inflammatory Skin Conditions—Predicting the Active Components by Combining LC-MS-Based Metabolite Profiles and in Vitro Pharmacology. Front Pharmacol. 2018, 9, 1091. 103389/fphar.2018.01091 DOI: 10.3389/fphar.2018.01091.
   PubMedGoogle Scholar
• Zhang, C.; Su, J. Application of near Infrared Spectroscopy to the Analysis and Fast Quality Assessment of Traditional Chinese Medicinal Products. Acta Pharm. Sin. B. 2014, 4, 182–192. DOI: 10.1016/j.apsb.2014.04.001.
   PubMed Web of Science ®Google Scholar
• Liu, C.; Zuo, Z.; Xu, F.; Wang, Y. Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review. Crit. Rev. Anal. Chem. 2022, 1–26. DOI: 10.1080/10408347.2021.2023460.
   Google Scholar
• Oliveri, P.; Malegori, C.; Mustorgi, E.; Casale, M. Qualitative Pattern Recognition in Chemistry: Theoretical Background and Practical Guidelines. Microchem. J. 2021, 162, 105725. DOI: 10.1016/j.microc.2020.105725.
   Web of Science ®Google Scholar
• Morais, C.; Lima, K. Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry. J. Braz. Chem. Soc. 2017, 29, 472–481. DOI: 10.21577/0103-5053.20170159.
   Google Scholar
• Hafezi, M. H.; Liu, L.; Millward, H. Learning Daily Activity Sequences of Population Groups Using Random Forest Theory. Transp. Res. Rec. 2018, 2672, 194. DOI: 10.1177/0361198118773197.
   Web of Science ®Google Scholar
• Dai, T. t.; Dong, Y. s. Introduction of SVM Related Theory and Its Application Research. Presented at the 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2020. DOI: 10.1109/AEMCSE50948.2020.00056.
   Google Scholar
• Phyu, T. N. Survey of Classification Techniques in Data Mining. Lect. Notes Eng. Comput. Sci. 2009, 2174, 727–731.
   Google Scholar
• Alizadeh, A.; Mosalanezhad, F.; Afroozeh, A.; Akbari, E.; Buntat, Z. Support Vector Regression and Neural Networks Analytical Models for Gas Sensor Based on Molybdenum Disulfide. Microsyst. Technol. 2019, 25, 115. 101007/s00542-018-3942-y DOI: 10.1007/s00542-018-3942-y.
   Google Scholar
• Oliveira, M. M.; Cruz-Tirado, J. P.; Barbin, D. F. Nontargeted Analytical Methods as a Powerful Tool for the Authentication of Spices and Herbs: A Review. Compr. Rev. Food Sci. Food Saf. 2019, 18, 670–689. DOI: 10.1111/1541-4337.12436.
   PubMed Web of Science ®Google Scholar
• Wangen, L. E.; Kowalski, B. R. A Multiblock Partial Least Squares Algorithm for Investigating Complex Chemical Systems. J. Chemom. 1989, 3, 3. DOI: 10.1002/cem.1180030104.
   Google Scholar
• Löfstedt, T.; Trygg, J. OnPLS—A Novel Multiblock Method for the Modelling of Predictive and Orthogonal Variation. J. Chemom. 2011, 25, 441. DOI: 10.1002/cem.1388.
   Web of Science ®Google Scholar
• Næs, T.; Tomic, O.; Afseth, N. K.; Segtnan, V.; Måge, I. Multi-Block Regression Based on Combinations of Orthogonalisation, PLS-Regression and Canonical Correlation Analysis. Chemom. Intell. Lab. Syst. 2013, 124, 32. DOI: 10.1016/j.chemolab.2013.03.006.
   Google Scholar
• Ni, Y.; Mei, M.; Kokot, S. One- and Two-Dimensional Gas Chromatography–Mass Spectrometry and High Performance Liquid Chromatography–Diode-Array Detector Fingerprints of Complex Substances: A Comparison of Classification Performance of Similar, Complex Rhizoma Curcumae Samples with the Aid of Chemometrics. Anal. Chim. Acta. 2012, 712, 37–44. DOI: 10.1016/j.aca.2011.11.010.
   PubMed Web of Science ®Google Scholar
• LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature. 2015, 521, 436–444. DOI: 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Bengio, Y.; Courville, A.; Vincent, P. Representation Learning: A Review and New Perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1798–1828. DOI: 10.1109/TPAMI.2013.50.
   PubMed Web of Science ®Google Scholar
• Cai, Y.; Guan, K.; Peng, J.; Wang, S.; Seifert, C.; Wardlow, B.; Li, Z. A High-Performance and in-Season Classification System of Field-Level Crop Types Using Time-Series Landsat Data and a Machine Learning Approach. Remote Sens. Environ. 2018, 210, 35. DOI: 10.1016/j.rse.2018.02.045.
   Web of Science ®Google Scholar
• Han, Y.; Liu, Z.; Khoshelham, K.; Bai, S. H. Quality Estimation of Nuts Using Deep Learning Classification of Hyperspectral Imagery. Comput. Electron. Agric. 2021, 180, 105868. DOI: 10.1016/j.compag.2020.105868.
   Google Scholar
• Wu, Z.; Shen, C.; van den Hengel, A. Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognit. 2019, 90, 119. DOI: 10.1016/j.patcog.2019.01.006.
   Web of Science ®Google Scholar
• Liu, Y.; Pu, H.; Sun, D.-W. Efficient Extraction of Deep Image Features Using Convolutional Neural Network (CNN) for Applications in Detecting and Analysing Complex Food Matrices. Trends Food Sci. Technol. 2021, 113, 193. DOI: 10.1016/j.tifs.2021.04.042.
   Web of Science ®Google Scholar
• Dong, J.-E.; Zuo, Z.-T.; Zhang, J.; Wang, Y.-Z. Geographical Discrimination of Boletus Edulis Using Two Dimensional Correlation Spectral or Integrative Two Dimensional Correlation Spectral Image with ResNet. Food Control. 2021, 129, 108132. DOI: 10.1016/j.foodcont.2021.108132.
   Web of Science ®Google Scholar
• Chen, X.; Liu, H.; Li, J.; Wang, Y. Rapid Identification of Total Phenolic Content Levels in Boletes by Two-Dimensional Correlation Spectroscopy Combined with Deep Learning. Vib. Spectrosc. 2022, 121, 103404. DOI: 10.1016/j.vibspec.2022.103404.
   Google Scholar
• Jing, W.; Zhao, X.; Li, M.; Hu, X.; Cheng, X.; Ma, S.; Wei, F. Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis. Molecules. 2022, 27, 3892. https://www.mdpi.com/1420-3049/27/12/3892. DOI: 10.3390/molecules27123892.
   PubMedGoogle Scholar
• Wang, L.; Wang, Q.; Wang, Y.; Wang, Y. Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia Cocos with Different Data Fusion Approaches. J. Anal. Methods Chem. 2021, 2021, 5818999. DOI: 10.1155/2021/5818999.
   PubMed Web of Science ®Google Scholar
• Fei, P. Yi.; Fei, P. Yi.; Zhi-Tian, Z.; Zhi-Tian, Z.; Qing-Zhi, Z.; Yuan-Zhong, W.; Yuanzhong, W. Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris Polyphylla Var. yunnanensis. Molecules. 2019, 24, 2559. DOI: 10.3390/molecules24142559.
   PubMedGoogle Scholar
• Yun L. Z.; Jin, Y. Z.; Jin, Y.; Yuanzhong W. FT-MIR and NIR Spectral Data Fusion: A Synergetic Strategy for the Geographical Traceability of Panax Notoginseng. Anal. BioanalChem. 2018, 410, 91. DOI: 10.1007/s00216-017-0692-0.
   Google Scholar
• Zhang, J.; Wang, Y. Z.; Yang, M. Q.; Yang, W. Z.; Yang, S. B.; Zhang, J. Y. Identification and Evaluation of Polygonatum Kingianum with Different Growth Ages Based on Data Fusion Strategy. Microchem. J. 2021, 160, 105662. DOI: 10.1016/j.microc.2020.105662.
   Web of Science ®Google Scholar
• Zhao, S.; Song, W.; Hou, Z.; Wang, Z. Classification of Ginseng according to Plant Species, Geographical Origin, and Age Using Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging. J. Anal. At. Spectrom. 2021, 36, 1704. DOI: 10.1039/D1JA00136A.
   Google Scholar
• Wang, Y.; Zuo, Z.-T.; Shen, T.; Huang, H.-Y.; Wang, Y.-Z. Authentication of Dendrobium Species Using near-Infrared and Ultraviolet–Visible Spectroscopy with Chemometrics and Data Fusion. Anal. Lett. 2018, 51, 2792. DOI: 10.1080/00032719.2018.1451874.
   Web of Science ®Google Scholar
• Li, Y.; Zhang, J.; Li, T.; Liu, H.; Li, J.; Wang, Y. Geographical Traceability of Wild Boletus Edulis Based on Data Fusion of FT-MIR and ICP-AES Coupled with Data Mining Methods (SVM). Spectrochim. Acta A Mol. Biomol. Spectrosc. 2017, 177, 20–27. DOI: 10.1016/j.saa.2017.01.029.
   PubMed Web of Science ®Google Scholar
• Wu, X.-M.; Zuo, Z.-T.; Zhang, Q.-Z.; Wang, Y.-Z. Classification of Paris Species according to Botanical and Geographical Origins Based on Spectroscopic, Chromatographic, Conventional Chemometric Analysis and Data Fusion Strategy. Microchem. J. 2018, 143, 367. DOI: 10.1016/j.microc.2018.08.035.
   Google Scholar
• Liu, L.; Zuo, Z-t.; Wang, Y-z.; Xu, F-r. A Fast Multi-Source Information Fusion Strategy Based on FTIR Spectroscopy for Geographical Authentication of Wild Gentiana Rigescens. Microchem. J. 2020, 159, 105360. DOI: 10.1016/j.microc.2020.105360.
   Web of Science ®Google Scholar
• Wang, Y.; Zuo, Z.-T.; Huang, H.-Y.; Wang, Y.-Z. Original Plant Traceability of Dendrobium Species Using Multi-Spectroscopy Fusion and Mathematical Models. R. Soc. Open Sci. 2019, 6, 190399. DOI: 10.1098/rsos.190399.
   PubMedGoogle Scholar
• Liu, Z.; Yang, S.; Wang, Y.; Zhang, J. Multi-Platform Integration Based on NIR and UV–Vis Spectroscopies for the Geographical Traceability of the Fruits of Amomum Tsao-ko. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 258, 119872. DOI: 10.1016/j.saa.2021.119872.
   PubMed Web of Science ®Google Scholar
• Jiménez-Carvelo, A. M.; Cruz, C. M.; Olivieri, A. C.; González-Casado, A.; Cuadros-Rodríguez, L. Classification of Olive Oils according to Their Cultivars Based on Second-Order Data Using LC-DAD. Talanta. 2019, 195, 69–76. DOI: 10.1016/j.talanta.2018.11.033.
   PubMed Web of Science ®Google Scholar
• Song, X.-C.; Canellas, E.; Asensio, E.; Nerín, C. Predicting the Antioxidant Capacity and Total Phenolic Content of Bearberry Leaves by Data Fusion of UV–Vis Spectroscopy and UHPLC/Q-TOF-MS. Talanta. 2020, 213, 120831. DOI: 10.1016/j.talanta.2020.120831.
   PubMedGoogle Scholar
• Wang, Y.; Yang, Y.; Sun, H.; Dai, J.; Zhao, M.; Teng, C.; Ke, Z.; Yang, M.; Zhong, L.; Zhu, W. Application of a Data Fusion Strategy Combined with Multivariate Statistical Analysis for Quantification of Puerarin in Radix Puerariae. Vib. Spectrosc. 2020, 108, 103057. DOI: 10.1016/j.vibspec.2020.103057.
   Google Scholar
• Qi, L.; Ma, Y.; Zhong, F.; Shen, C. Comprehensive Quality Assessment for Rhizoma Coptidis Based on Quantitative and Qualitative Metabolic Profiles Using High Performance Liquid Chromatography, Fourier Transform near-Infrared and Fourier Transform Mid-Infrared Combined with Multivariate Statistical Analysis. J. Pharm. Biomed. Anal. 2018, 161, 436–443. DOI: 10.1016/j.jpba.2018.09.012.
   PubMed Web of Science ®Google Scholar
• Tao, L.; Via, B.; Wu, Y.; Xiao, W.; Liu, X. NIR and MIR Spectral Data Fusion for Rapid Detection of Lonicera Japonica and Artemisia Annua by Liquid Extraction Process. Vib. Spectrosc. 2019, 102, 31. DOI: 10.1016/j.vibspec.2019.03.005.
   Google Scholar
• Jiang, C.; Liu, Y.; Qu, H. Data Fusion Strategy Based on near Infrared Spectra and Ultraviolet Spectra for Simultaneous Determination of Ginsenosides and Saccharides in Chinese Herbal Injection. Anal. Methods. 2013, 5, 4467. DOI: 10.1039/c3ay26540d.
   Google Scholar
• Dong, F.; Hao, J.; Luo, R.; Zhang, Z.; Wang, S.; Wu, K.; Liu, M. Identification of the Proximate Geographical Origin of Wolfberries by Two-Dimensional Correlation Spectroscopy Combined with Deep Learning. Comput. Electron. Agric. 2022, 198, 107027. DOI: 10.1016/j.compag.2022.107027.
   Google Scholar
• Yue, J.; Li, Z.; Zuo, Z.; Zhao, Y.; Zhang, J.; Wang, Y. Study on the Identification and Evaluation of Growth Years for Paris Polyphylla Var. yunnanensis Using Deep Learning Combined with 2DCOS. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 261, 120033. DOI: 10.1016/j.saa.2021.120033.
   PubMed Web of Science ®Google Scholar
• Ding, Y.-G.; Zhang, Q.-Z.; Wang, Y.-Z. A Fast and Effective Way for Authentication of Dendrobium Species: 2DCOS Combined with ResNet Based on Feature Bands Extracted by Spectrum Standard Deviation. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 261, 120070. DOI: 10.1016/j.saa.2021.120070.
   PubMed Web of Science ®Google Scholar
• Zhou, B.; Jia, M.; Zhang, F.; Qi, J.; Yu, B. A New Data Processing Strategy Combined with a Convolutional Neural Network for Rapid and Accurate Prediction of Geographical Classifications of Natural Products. Chemom. Intell. Lab. Syst. 2022, 227, 104594. DOI: 10.1016/j.chemolab.2022.104594.
   Google Scholar
• Xiao, Q.; Bai, X.; Gao, P.; He, Y. Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave near-Infrared and near Infrared Hyperspectral Imaging. Sensors. 2020, 20, 4940. https://www.mdpi.com/1424-8220/20/17/4940. DOI: 10.3390/s20174940.
   PubMedGoogle Scholar
• Wu, N.; Zhang, C.; Bai, X.; Du, X.; He, Y. Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network. Molecules. 2018, 23, 2831. https://www.mdpi.com/1420-3049/23/11/2831. DOI: 10.3390/molecules23112831.
   PubMed Web of Science ®Google Scholar
• Noda, I. Two-Dimensional Infrared Spectroscopy. J. Amer. Chem. SOC. 1989, 111, 8116. DOI: 10.1021/ja00203a008.
   Web of Science ®Google Scholar
• Noda, I. Two-Trace Two-Dimensional (2T2D) Correlation Spectroscopy – A Method for Extracting Useful Information from a Pair of Spectra. J. Mol. Struct. 2018, 1160, 471. DOI: 10.1016/j.molstruc.2018.01.091.
   Google Scholar
• Shi, Y.; Yuan, H.; Xiong, C.; Zhang, Q.; Jia, S.; Liu, J.; Men, H. Improving Performance: A Collaborative Strategy for the Multi-Data Fusion of Electronic Nose and Hyperspectral to Track the Quality Difference of Rice. Sens. Actuators, B. 2021, 333, 129546. DOI: 10.1016/j.snb.2021.129546.
   Google Scholar
• Jiang, S.; Ni, C.; Chen, G.; Liu, Y. A Novel Data Fusion Strategy Based on Multiple Intelligent Sensory Technologies and Its Application in the Quality Evaluation of Jinhua Dry-Cured Hams. Sens. Actuators, B. 2021, 344, 130324. DOI: 10.1016/j.snb.2021.130324.
   Google Scholar
• Fathizadeh, Z.; Aboonajmi, M.; Hassan-Beygi, S. R. Classification of Apples Based on the Shelf Life Using ANN and Data Fusion. Food Anal. Methods. 2021, 14, 706. DOI: 10.1007/s12161-020-01913-1.
   Google Scholar