219
Views
1
CrossRef citations to date
0
Altmetric
Review Article

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

&

References

  • World Health Organization. WHO Traditional Medicine Strategy: 2014–2023. WHO Library Cataloguing-in-Publication Data: 2014.
  • Wang, W. Y.; Zhou, H.; Wang, Y. F.; Sang, B. S.; Liu, L. Current Policies and Measures on the Development of Traditional Chinese Medicine in China. Pharmacol. Res. 2021, 163, 105187. DOI: 10.1016/j.phrs.2020.105187.
  • 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.
  • Zhang, J.; Hu, K.; Di, L.; Wang, P.; Liu, Z.; Zhang, J.; Yue, P.; Song, W.; Zhang, J.; Chen, T.; et al. Traditional Herbal Medicine and Nanomedicine: Converging Disciplines to Improve Therapeutic Efficacy and Human Health. Adv. Drug Deliv. Rev. 2021, 178, 113964. DOI: 10.1016/j.addr.2021.113964.
  • Liu, Y.; Yu, Z. B. The Import and Export Trade of Traditional Chinese Medicine has Stepped out of the "Bottom". China Net of Traditional Chinese Medicine. 2020. http://www.cntcm.com.cn/2020-04/30/content_74969.htm.
  • Kajino, A.; Bai, W.; Yoshimura, N.; Takayanagi, M. Identification of Peach and Apricot Kernels for Traditional Chinese Medicines Using near-Infrared Spectroscopy. Vib. Spectrosc. 2021, 113, 103202. DOI: 10.1016/j.vibspec.2020.103202.
  • Perret, C.; Tabin, R.; Marcoz, J. P.; Llor, J.; Cheseaux, J. J. Apparent Life-Threatening Event in Infants: Think about Star Anise Intoxication! Arch Pediatr. 2011, 18, 750–753. DOI: 10.1016/j.arcped.2011.03.024.
  • Tan, C. S.; Leow, S. Y.; Ying, C.; Tan, C. J.; Yoon, T. L.; Chen, J. Y.; Yam, M. F. Comparison of FTIR Spectrum with Chemometric and Machine Learning Classifying Analysis for Differentiating Guan-Mutong a Nephrotoxic and Carcinogenic Traditional Chinese Medicine with Chuan-Mutong. Microchem. J. 2021, 163, 105835. DOI: 10.1016/j.microc.2020.105835.
  • Xue, X.; Xiao, Y.; Gong, L. K.; Guan, S. H.; Liu, Y. Z.; Lu, H. L.; Qi, X. M.; Zhang, Y. H.; Li, Y.; Wu, X. F.; Ren, J. Comparative 28-Day Repeated Oral Toxicity of Longdan Xieganwan, Akebia Trifoliate (Thunb.) Koidz., Akebia Quinata (Thunb.) Decne. and Caulis Aristolochiae Manshuriensis in Mice. J. Ethnopharmacol. 2008, 119, 87–93. DOI: 10.1016/j.jep.2008.05.037.
  • Feng, Y. Research Progress on Identification Methods of Traditional Chinese Medicine. Med J Chin People’s Health 2020, 32, 82–86. DOI: 10.3969/j.issn.1672-0369.2020.18.033.
  • Shao, B.; Li, H.; Shen, J. Z.; Wu, Y. N. Nontargeted Detection Methods for Food Safety and Integrity. Annu. Rev. Food Sci. Technol. 2019, 10, 429–455. DOI: 10.1146/annurev-food-032818-121233.
  • Liu, Z. M.; Yang, M. Q.; Zuo, Y. M.; Wang, Y. Z.; Zhang, J. Y. Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review. Crit. Rev. Anal. Chem. 2022, 52, 1606–1623. DOI: 10.1080/10408347.2021.1905503.
  • Lörchner, C.; Horn, M.; Berger, F.; Fauhl-Hassek, C.; Glomb, M. A.; Esslinger, S. Quality Control of Spectroscopic Data in Non-Targeted Analysis – Development of a Multivariate Control Chart. Food Control 2022, 133, 108601. DOI: 10.1016/j.foodcont.2021.108601.
  • Núñez, N.; Pons, J.; Saurina, J.; Núñez, O. Non-Targeted High-Performance Liquid Chromatography with Ultraviolet and Fluorescence Detection Fingerprinting for the Classification, Authentication, and Fraud Quantitation of Instant Coffee and Chicory by Multivariate Chemometric Methods. LWT 2021, 147, 111646. DOI: 10.1016/j.lwt.2021.111646.
  • Dong, F.; Lin, J. T.; You, J. H.; Ji, J. R.; Xu, X.; Zhang, L. Y.; Jin, Y.; Du, S. H. A Chemometric Modeling-Free near Infrared Barcode Strategy for Smart Authentication and Geographical Origin Discrimination of Chinese Ginseng. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2020, 226, 117555. DOI: 10.1016/j.saa.2019.117555.
  • Amirvaresi, A.; Rashidi, M.; Kamyar, M.; Amirahmadi, M.; Daraei, B.; Parastar, H. Combining Multivariate Image Analysis with High-Performance Thin-Layer Chromatography for Development of a Reliable Tool for Saffron Authentication and Adulteration Detection. J. Chromatogr. A 2020, 1628, 461461. DOI: 10.1016/j.chroma.2020.461461.
  • Peng, C.; Zhu, Y. L.; Yan, F. L.; Su, Y.; Zhu, Y. Q.; Zhang, Z. Y.; Zuo, C. J.; Wu, H.; Zhang, Y. J.; Kan, J. Y.; Peng, D. Y. The Difference of Origin and Extraction Method Significantly Affects the Intrinsic Quality of Licorice: A New Method for Quality Evaluation of Homologous Materials of Medicine and Food. Food Chem. 2021, 340, 127907. DOI: 10.1016/j.foodchem.2020.127907.
  • Esslinger, S.; Riedl, J.; Fauhl-Hassek, C. Potential and Limitations of Non-Targeted Fingerprinting for Authentication of Food in Official Control. Food Res. Int. 2014, 60, 189–204. DOI: 10.1016/j.foodres.2013.10.015.
  • McGrath, T. F.; Haughey, S. A.; Patterson, J.; Fauhl-Hassek, C.; Donarski, J.; Alewijn, M.; van Ruth, S.; Elliott, C. T. What Are the Scientific Challenges in Moving from Targeted to Non-Targeted Methods for Food Fraud Testing and How Can They Be Addressed? – Spectroscopy Case Study. Trends Food Sci. Technol. 2018, 76, 38–55. DOI: 10.1016/j.tifs.2018.04.001.
  • 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.
  • Coskun, O. Separation Techniques: Chromatography. North Clin. Istanb. 2016, 3, 156–160. DOI: 10.14744/nci.2016.32757.
  • Chen, Q. Y. Discussion on the Application of High Performance Liquid Chromatography and Mass Spectrometry Technology in Water Quality Detection. Total Corros. Control 2021, 35, 107–108, +111. DOI: 10.13726/j.cnki.11-2706/tq.2021.10.107.02.
  • Shen, M.; He, Y.; Shi, S. 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.
  • Xu, M. Z.; Huang, B. B.; Gao, F.; Zhai, C. C.; Yang, Y. Y.; Li, L. L.; Wang, W. Y.; Shi, L. W. Assesment of Adulterated Traditional Chinese Medicines in China: 2003–2017. Front. Pharmacol. 2019, 10, 1446. DOI: 10.3389/fphar.2019.01446.
  • Pasquini, B.; Orlandini, S.; Goodarzi, M.; Caprini, C.; Gotti, R.; Furlanetto, S. Chiral Cyclodextrin-Modified Micellar Electrokinetic Chromatography and Chemometric Techniques for Green Tea Samples Origin Discrimination. Talanta 2016, 150, 7–13. DOI: 10.1016/j.talanta.2015.12.003.
  • Bondia-Pons, I.; Savolainen, O.; Törrönen, R.; Martinez, J. A.; Poutanen, K.; Hanhineva, K. Metabolic Profiling of Goji Berry Extracts for Discrimination of Geographical Origin by Non-Targeted Liquid Chromatography Coupled to Quadrupole Time-of-Flight Mass Spectrometry. Food Res. Int. 2014, 63, 132–138. DOI: 10.1016/j.foodres.2014.01.067.
  • Bombarda, I.; Dupuy, N.; Da, J. P.; Gaydou, E. M. Comparative Chemometric Analyses of Geographic Origins and Compositions of Lavandin Var. Grosso Essential Oils by Mid Infrared Spectroscopy and Gas Chromatography. Anal. Chim. Acta 2008, 613, 31–39. DOI: 10.1016/j.aca.2008.02.038.
  • Jing, J.; Parekh, H. S.; Wei, M.; Ren, W. C.; Chen, S. B. Advances in Analytical Technologies to Evaluate the Quality of Traditional Chinese Medicines. Trends Analyt. Chem. 2013, 44, 39–45. DOI: 10.1016/j.trac.2012.11.006.
  • Edwards, M.; Boswell, H.; Gorecki, T. Comprehensive Multidimensional Chromatography. CCHG 2015, 2, 80–109. DOI: 10.2174/2213240602666150722232236.
  • Pollo, B. J.; Teixeira, C. A.; Belinato, J. R.; Furlan, M. F.; Cunha, I. C. d. M.; Vaz, C. R.; Volpato, G. V.; Augusto, F. Chemometrics, Comprehensive Two-Dimensional Gas Chromatography and “Omics” Sciences: Basic Tools and Recent Applications. TrAC - Trend Anal. Chem. 2021, 134, 116111. DOI: 10.1016/j.trac.2020.116111.
  • Amaral, M. S. S.; Nolvachai, Y.; Marriott, P. J. Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update. Anal. Chem. 2020, 92, 85–104. DOI: 10.1021/acs.analchem.9b05412.
  • Cacciola, F.; Rigano, F.; Dugo, P.; Mondello, L. Comprehensive Two-Dimensional Liquid Chromatography as a Powerful Tool for the Analysis of Food and Food Products. Trends Analyt. Chem. 2020, 127, 115894. DOI: 10.1016/j.trac.2020.115894.
  • Chen, Y. L.; Li, L. N.; Xu, R.; Li, F.; Gu, L. H.; Liu, H. W.; Wang, Z. T.; Yang, L. Characterization of Natural Herbal Medicines by Thin-Layer Chromatography Combined with Laser Ablation-Assisted Direct Analysis in Real-Time Mass Spectrometry. J. Chromatogr. A 2021, 1654, 462461. DOI: 10.1016/j.chroma.2021.462461.
  • Zhang, L.; Meng, J.; Gou, C. L.; Liu, Z.; Yuan, Y. W. Research Progress of Components Detection and Traceability Technology of Wolfberry. J. Instrum. Anal. 2018, 37, 862–870. DOI: 10.3969/j.issn.1004-4957.2018.07.020.
  • Zhen, X. T.; Zhu, S. C.; Shi, M. Z.; Yu, Y. L.; Yan, T. C.; Yue, Z. X.; Gu, Y. X.; Zheng, H.; Cao, J. Analysis of Flavonoids in Citrus Fruits by Capillary Zone Electrophoresis Coupled with Quadrupole Time-of-Flight Mass Spectrometry Using Chemometrics. J. Food Compos. Anal. 2022, 106, 104275. DOI: 10.1016/j.jfca.2021.104275.
  • Wu, Y. L.; Li, D.; Kang, L.; Han, L. J.; Pan, C. P. Research Progress of Mass Spectrometry in Pesticide Residue Analysis. J. Mass Spectrom. Soc. 2021, 42, 691–708. DOI: 10.7538/zpxb.2021.0092.
  • Verpoorte, R.; Choi, Y. H.; Kim, H. K. NMR-Based Metabolomics at Work in Phytochemistry. Phytochem. Rev. 2007, 6, 3–14. DOI: 10.1007/s11101-006-9031-3.
  • Cao, S. R.; Du, H.; Tang, B. B.; Xi, C. X.; Chen, Z. Q. Non-Target Metabolomics Based on High-Resolution Mass Spectrometry Combined with Chemometric Analysis for Discriminating Geographical Origins of Rhizoma Coptidis. Microchem. J. 2021, 160, 105685. DOI: 10.1016/j.microc.2020.105685.
  • Wang, Y.; He, T.; Wang, J. J.; Wang, L.; Ren, X. Y.; He, S. H.; Liu, X. Y.; Dong, Y.; Ma, J. M.; Song, R. L.; et al. High Performance Liquid Chromatography Fingerprint and Headspace Gas Chromatography-Mass Spectrometry Combined with Chemometrics for the Species Authentication of Curcumae Rhizoma. J. Pharm. Biomed. Anal. 2021, 202, 114144. DOI: 10.1016/j.jpba.2021.114144.
  • Qin, H. W.; Wang, Y. Z.; Yang, W. Z.; Yang, S. B.; Zhang, J. Y. Comparison of Metabolites and Variety Authentication of Amomum Tsao-ko and Amomum Paratsao-ko Using GC-MS and NIR Spectroscopy. Sci. Rep. 2021, 11, 15200. DOI: 10.1038/s41598-021-94741-0.
  • Li, C. R.; Li, M. N.; Yang, H.; Li, P.; Gao, W. Rapid Characterization of Chemical Markers for Discrimination of Moutan Cortex and Its Processed Products by Direct Injection-Based Mass Spectrometry Profiling and Metabolomic Method. Phytomedicine 2018, 45, 76–83. DOI: 10.1016/j.phymed.2018.04.003.
  • Craig, A. P.; Franca, A. S.; Irudayaraj, J. Surface-Enhanced Raman Spectroscopy Applied to Food Safety. Annu. Rev. Food Sci. Technol. 2013, 4, 369–380. DOI: 10.1146/annurev-food-022811-101227.
  • Deidda, R.; Sacre, P. Y.; Clavaud, M.; Coïc, L.; Avohou, H.; Hubert, P.; Ziemons, E. Vibrational Spectroscopy in Analysis of Pharmaceuticals: Critical Review of Innovative Portable and Handheld NIR and Raman Spectrophotometers. TrAC - Trend Anal. Chem. 2019, 114, 251–259. DOI: 10.1016/j.trac.2019.02.035.
  • Bec, K. B.; Grabska, J.; Huck, C. W. Biomolecular and Bioanalytical Applications of Infrared Spectroscopy - A Review. Anal. Chim. Acta 2020, 1133, 150–177. DOI: 10.1016/j.aca.2020.04.015.
  • Ma, H.; Shao, Y. W.; Chen, J. S.; Pan, D. Y.; Si, L. T.; Liu, X. S.; Wang, J.; Chen, Y.; Wu, Y. J. Maintaining the Predictive Abilities of near-Infrared Spectroscopy Models for the Determination of Multi-Parameters in White Paeony Root. Infrared Phys. Technol. 2020, 109, 103419. DOI: 10.1016/j.infrared.2020.103419.
  • Chen, H.; Tan, C.; Li, H. J. Untargeted Identification of Adulterated Sanqi Powder by near-Infrared Spectroscopy and One-Class Model. J. Food Compos. Anal. 2020, 88, 103450. DOI: 10.1016/j.jfca.2020.103450.
  • Zhang, S. J.; Zhang, Y. G.; Li, D. H.; Wu, H. W.; Niu, J. T.; Si, X. L.; Li, Y. F. Near-Infrared Sepctral Analysis of Licorice and Its Different Gun Products. Chem. World 2021, 62, 618–622. DOI: 10.19500/j.cnki.0367-6358.20200610.
  • Cen, H. Y.; He, Y. Theory and Application of near Infrared Reflectance Spectroscopy in Determination of Food Quality. Trends Food Sci. Technol. 2007, 18, 72–83. DOI: 10.1016/j.tifs.2006.09.003.
  • Li, T.; Su, C. Authenticity Identification and Classification of Rhodiola Species in Traditional Tibetan Medicine Based on Fourier Transform near-Infrared Spectroscopy and Chemometrics Analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 204, 131–140. DOI: 10.1016/j.saa.2018.06.004.
  • Boughattas, F.; Le Fur, B.; Karoui, R. Mid Infrared Spectroscopy Coupled with Chemometric Tools for Qualitative Analysis of Canned Tuna with Sunflower Medium. J. Food Compos. Anal. 2020, 91, 103519. DOI: 10.1016/j.jfca.2020.103519.
  • Ozaki, Y. Infrared Spectroscopy, Mid-Infrared, Near-Infrared, and Far-Infrared/Terahertz Spectroscopy. Anal. Sci. 2021, 37, 1193–1212. DOI: 10.2116/analsci.20R008.
  • Ham, W. S.; Kim, J.; Park, D. J.; Ryu, H. C.; Jang, Y. P. Discrimination of Cynanchum Wilfordii and Cynanchum Auriculatum by Terahertz Spectroscopic Analysis. Phytochem. Anal. 2018, 29, 472–475. DOI: 10.1002/pca.2751.
  • Beard, M. C.; Turner, G. M.; Schmuttenmaer, C. A. Terahertz Spectroscopy. J. Phys. Chem. B 2002, 106, 7146–7159. DOI: 10.1021/jp020579i.
  • Gandhi, K.; Sharma, R.; Seth, R.; Mann, B. Detection of Coconut Oil in Ghee Using ATR-FTIR and Chemometrics. Appl. Food Res. 2022, 2, 100035. DOI: 10.1016/j.afres.2021.100035.
  • Hssaini, L.; Razouk, R.; Charafi, J.; Houmanat, K.; Hanine, H. Fig Seeds: Combined Approach of Lipochemical Assessment Using Gas Chromatography and FTIR-ATR Spectroscopy Using Chemometrics. Vib. Spectrosc. 2021, 114, 103251. DOI: 10.1016/j.vibspec.2021.103251.
  • Noda, I. Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and Other Types of Spectroscopy. Appl. Spectrosc. 1993, 47, 1329–1336. DOI: 10.1366/0003702934067694.
  • Noda, I. 2DCOS and I. Three Decades of Two-Dimensional Correlation Spectroscopy. J. Mol. Struct. 2016, 1124, 3–7. DOI: 10.1016/j.molstruc.2016.01.035.
  • Zhang, L.; Li, C.; Peng, D.; Yi, X.; He, S.; Liu, F.; Zheng, X.; Huang, W. E.; Zhao, L.; Huang, X. Raman Spectroscopy and Machine Learning for the Classification of Breast Cancers. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 264, 120300. DOI: 10.1016/j.saa.2021.120300.
  • Gredilla, A.; Fdez-Ortiz de Vallejuelo, S.; Elejoste, N.; de Diego, A.; Madariaga, J. M. Non-Destructive Spectroscopy Combined with Chemometrics as a Tool for Green Chemical Analysis of Environmental Samples: A Review. TrAC - Trend Anal. Chem. 2016, 76, 30–39. DOI: 10.1016/j.trac.2015.11.011.
  • Wang, H. P.; Xin, Y. J.; Ma, H. Z.; Fang, P. P.; Li, C. H.; Wan, X.; He, Z. P.; Jia, J. J.; Ling, Z. C. Rapid Detection of Chinese-Specific Peony Seed Oil by Using Confocal Raman Spectroscopy and Chemometrics. Food Chem. 2021, 362, 130041. DOI: 10.1016/j.foodchem.2021.130041.
  • Jones, R. R.; Hooper, D. C.; Zhang, L.; Wolverson, D.; Valev, V. K. Raman Techniques: Fundamentals and Frontiers. Nanoscale Res. Lett. 2019, 14, 231. DOI: 10.1186/s11671-019-3039-2.
  • Wang, J. J.; Chen, Q. S.; Belwal, T. R.; Lin, X. Y.; Luo, Z. S. Insights into Chemometric Algorithms for Quality Attributes and Hazards Detection in Foodstuffs Using Raman/Surface Enhanced Raman Spectroscopy. Compr. Rev. Food Sci. Food Saf. 2021, 20, 2476–2507. DOI: 10.1111/1541-4337.12741.
  • Nelson, P.; Adimabua, P.; Wang, A.; Zou, S.; Shah, N. C. Surface-Enhanced Raman Spectroscopy for Rapid Screening of Cinnamon Essential Oils. Appl. Spectrosc. 2020, 74, 1341–1349. DOI: 10.1177/0003702820931154.
  • Ma, J.; Sun, D. W.; Pu, H. B.; Cheng, J. H.; Wei, Q. Y. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu. Rev. Food Sci. Technol. 2019, 10, 197–220. DOI: 10.1146/annurev-food-032818-121155.
  • Wang, H. G.; He, H. J.; Liu, L.; Ma, H. J.; Liu, X.; Mo, H. Z.; Liu, R. B.; Pan, R. S.; Kang, Z. L.; Hu, M. M.; et al. Recent Progress in Hyperspectral Imaging for Nondestructive Evaluation of Fish Quality. Food Sci. 2019, 40, 329–338. DOI: 10.7506/spkx1002-6630-20180129-392.
  • Choi, J. Y.; Kim, H. C.; Moon, K. D. Geographical Origin Discriminant Analysis of Chia Seeds (Salvia Hispanica L.) Using Hyperspectral Imaging. J. Food Compos. Anal. 2021, 101, 103916. DOI: 10.1016/j.jfca.2021.103916.
  • Yin, W. J.; Ru, C. L.; Zheng, J.; Zhang, L.; Yan, J. Z.; Zhang, H. J. Fusion of Spectrum and Image Features to Identify Glycyrrhizae Radix et Rhizoma from Different Origins Based on Hyperspectral Imaging Technology. Zhongguo Zhong Yao Za Zhi 2021, 46, 923–930. DOI: 10.19540/j.cnki.cjcmm.20201120.103.
  • Nietner, T.; Haughey, S. A.; Ogle, N.; Fauhl-Hassek, C.; Elliott, C. T. Determination of Geographical Origin of Distillers Dried Grains and Solubles Using Isotope Ratio Mass Spectrometry. Food Res. Int. 2014, 60, 146–153. DOI: 10.1016/j.foodres.2013.11.002.
  • Abbas, O.; Zadravec, M.; Baeten, V.; Mikus, T.; Lesic, T.; Vulic, A.; Prpic, J.; Jemersic, L.; Pleadin, J. Analytical Methods Used for the Authentication of Food of Animal Origin. Food Chem. 2018, 246, 6–17. DOI: 10.1016/j.foodchem.2017.11.007.
  • Du, H.; Tang, B. B.; Cao, S. R.; Xi, C. X.; Li, X. L.; Zhang, L.; Wang, G. M.; Lai, G. Y.; Chen, Z. Q. Combination of Stable Isotopes and Multi-Elements Analysis with Chemometric for Determining the Geographical Origins of Rhizoma Coptidis. Microchem. J. 2020, 152, 104427. DOI: 10.1016/j.microc.2019.104427.
  • Fu, H. Y.; Wei, L. N.; Chen, H. Y.; Yang, X. L.; Kang, L. P.; Hao, Q. X.; Zhou, L.; Zhan, Z. L.; Liu, Z.; Yang, J.; Guo, L. P. Combining Stable C, N, O, H, Sr Isotope and Multi-Element with Chemometrics for Identifying the Geographical Origins and Farming Patterns of Huangjing Herb. J. Food Compos. Anal. 2021, 102, 103972. DOI: 10.1016/j.jfca.2021.103972.
  • Hatzakis, E. Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review. Compr. Rev. Food Sci. Food Saf. 2019, 18, 189–220. DOI: 10.1111/1541-4337.12408.
  • Zhao, F.; Li, W. Z.; Pan, J. Y.; Qu, H. B. Process Characterization for Ethanol Precipitation of Salviae Miltiorrhizae Radix et Rhizoma (Danshen) Using 1H NMR Spectroscopy and Chemometrics. Process Biochem. 2021, 101, 218–229. DOI: 10.1016/j.procbio.2020.11.026.
  • Sun, T.; Zhang, Y.; Wang, X.; Zhang, Y. Y.; Liu, Z.; Liu, W.; Chen, P.; Zhang, Z. H.; Yu, Y. J. Chemometric Strategy for Aligning Chemical Shifts in 1H NMR to Improve Geographical Origin Discrimination: A Case Study for Chinese Goji Honey. Microchem. J. 2022, 174, 107062. DOI: 10.1016/j.microc.2021.107062.
  • Dowlatabadi, R.; Farshidfar, F.; Zare, Z.; Pirali, M.; Rabiei, M.; Khoshayand, M. R.; Vogel, H. J. Detection of Adulteration in Iranian Saffron Samples by 1H NMR Spectroscopy and Multivariate Data Analysis Techniques. Metabolomics 2017, 13, 19. DOI: 10.1007/s11306-016-1155-x.
  • Di Anibal, C. V.; Callao, M. P.; Ruisanchez, I. 1H NMR and UV-Visible Data Fusion for Determining Sudan Dyes in Culinary Spices. Talanta 2011, 84, 829–833. DOI: 10.1016/j.talanta.2011.02.014.
  • Gu, M. X.; Xie, R. H.; Jin, G. W.; Xu, C. Y.; Wang, S.; Liu, J. L.; Wei, H. Y. Quantitative Evaluation for Fluid Components on 2D NMR Spectrum Using Blind Source Separation. J. Magn. Reson. 2021, 332, 107079. DOI: 10.1016/j.jmr.2021.107079.
  • Hedenström, M.; Wiklund, S.; Sundberg, B.; Edlund, U. Visualization and Interpretation of OPLS Models Based on 2D NMR Data. Chemometr. Intell. Lab. 2008, 92, 110–117. DOI: 10.1016/j.chemolab.2008.01.003.
  • Xing, X.; Hsieh, Y. S. Y.; Yap, K.; Ang, M. E.; Lahnstein, J.; Tucker, M. R.; Burton, R. A.; Bulone, V. Isolation and Structural Elucidation by 2D NMR of Planteose, a Major Oligosaccharide in the Mucilage of Chia (Salvia Hispanica L.) Seeds. Carbohydr. Polym. 2017, 175, 231–240. DOI: 10.1016/j.carbpol.2017.07.059.
  • Escudero, R.; Segura, J.; Velasco, R.; Valhondo, M.; Romero de Avila, M. D.; Garcia-Garcia, A. B.; Cambero, M. I. Electron Spin Resonance (ESR) Spectroscopy Study of Cheese Treated with Accelerated Electrons. Food Chem. 2019, 276, 315–321. DOI: 10.1016/j.foodchem.2018.09.101.
  • Wang, L. Y.; Xiao, R.; Mo, J. H. Quantitative Detection Method of Semiquinone Free Radicals on Particulate Matters Using Electron Spin Resonance Spectroscopy. Sustain. Cities Soc. 2019, 49, 101614. DOI: 10.1016/j.scs.2019.101614.
  • Barba, F. J.; Roohinejad, S.; Ishikawa, K.; Leong, S. Y.; El-Din A Bekhit, A.; Saraiva, J. A.; Lebovka, N. Electron Spin Resonance as a Tool to Monitor the Influence of Novel Processing Technologies on Food Properties. Trends Food Sci. Technol. 2020, 100, 77–87. DOI: 10.1016/j.tifs.2020.03.032.
  • Sahu, I. D.; Lorigan, G. A. Electron Paramagnetic Resonance as a Tool for Studying Membrane Proteins. Biomolecules 2020, 10, 763. DOI: 10.3390/biom10050763.
  • Bortolin, E.; Cardamone, C.; Chiaravalle, A. E.; Deiana, G.; Di Schiavi, M. T.; D'Oca, M. C.; Marchesani, G.; Quattrini, M. C.; Sangiorgi, E.; Tomaiuolo, M.; Boniglia, C. Irradiation Detection of Herbal Ingredients Used in Plant Food Supplements by Electron Spin Resonance on Samples Pre-Treated with Alcoholic Extraction. Radiat. Phys. Chem. 2020, 176, 108946. DOI: 10.1016/j.radphyschem.2020.108946.
  • de Abreu, C. R.; de Souza, E. S.; Martins, L. L.; Cordeiro, T. C.; Carrasquilla, A. A. G.; Guimarães, A. O. Application of the Electron Spin Resonance Technique in the Characterization of Brazilian Oils: Correlation with Their Biodegradation Level and Polar Composition. Energy Fuels 2020, 34, 13837–13848. DOI: 10.1021/acs.energyfuels.0c02624.
  • Jiang, S. H.; Xie, Y. F.; Li, M.; Guo, Y. H.; Cheng, Y. L.; Qian, H.; Yao, W. R. Evaluation on the Oxidative Stability of Edible Oil by Electron Spin Resonance Spectroscopy. Food Chem. 2020, 309, 125714. DOI: 10.1016/j.foodchem.2019.125714.
  • Kalinowska, K.; Bystrzanowska, M.; Tobiszewski, M. Chemometrics Approaches to Green Analytical Chemistry Procedure Development. Curr. Opin. Green Sustain. Chem. 2021, 30, 100498. DOI: 10.1016/j.cogsc.2021.100498.
  • Mishra, P.; Biancolillo, A.; Roger, J. M.; Marini, F.; Rutledge, D. N. New Data Preprocessing Trends Based on Ensemble of Multiple Preprocessing Techniques. TrAC - Trend Anal. Chem. 2020, 132, 116045. DOI: 10.1016/j.trac.2020.116045.
  • Pasquini, C. Near Infrared Spectroscopy: A Mature Analytical Technique with New Perspectives - A Review. Anal. Chim. Acta 2018, 1026, 8–36. DOI: 10.1016/j.aca.2018.04.004.
  • Skov, T.; van den Berg, F.; Tomasi, G.; Bro, R. Automated Alignment of Chromatographic Data. J. Chemom. 2006, 20, 484–497. DOI: 10.1002/cem.1031.
  • Eilers, P. H. C. Parametric Time Warping. Anal. Chem. 2004, 76, 404–411. DOI: 10.1021/ac034800e.
  • Gad, H. A.; El-Ahmady, S. H.; Abou-Shoer, M. I.; Al-Azizi, M. M. Application of Chemometrics in Authentication of Herbal Medicines: A Review. Phytochem. Anal. 2013, 24, 1–24. DOI: 10.1002/pca.2378.
  • van Nederkassel, A. M.; Daszykowski, M.; Massart, D. L.; Vander Heyden, Y. Prediction of Total Green Tea Antioxidant Capacity from Chromatograms by Multivariate Modeling. J. Chromatogr. A 2005, 1096, 177–186. DOI: 10.1016/j.chroma.2005.03.102.
  • Diwu, P. Y.; Bian, X. H.; Wang, Z. F.; Liu, W. Study on the Selection of Spectral Preprocessing Method. Spectrosc. Spect. Anal. 2019, 39, 2800–2806. DOI: 10.3964/j.issn.1000-0593(2019)09-2800-07.
  • Vidal, M.; Amigo, J. M. Pre-Processing of Hyperspectral Images. Essential Steps before Image Analysis. Chemometr. Intell. Lab. 2012, 117, 138–148. DOI: 10.1016/j.chemolab.2012.05.009.
  • Brown, C. D.; Vega-Montoto, L.; Wentzell, P. D. Derivative Preprocessing and Optimal Corrections for Baseline Drift in Multivariate Calibration. Appl. Spectrosc. 2000, 54, 1055–1068. DOI: 10.1366/0003702001950571.
  • Rinnan, A.; Berg, F. v d.; Engelsen, S. B. Review of the Most Common Pre-Processing Techniques for Near-Infrared Spectra. TrAC - Trend Anal. Chem. 2009, 28, 1201–1222. DOI: 10.1016/j.trac.2009.07.007.
  • Lieber, C. A.; Mahadevan-Jansen, A. Automated Method for Subtraction of Fluorescence from Siological Raman Spectra. Appl. Spectrosc. 2003, 57, 1363–1367. DOI: 10.1366/000370203322554518.
  • Subasi, A. Data Preprocessing. In Practical Machine Learning for Data Analysis Using Python; Academic Press: London, 2020, pp 27–89. DOI: 10.1016/b978-0-12-821379-7.00002-3.
  • Mei, L.; Zhang, F. L.; Gao, Q. Overview of Outlier Detection Technology. Appl. Res. Comput. 2020, 37, 3521–3527. DOI: 10.19734/j.issn.1001-3695.2019.09.0513.
  • Daszykowski, M.; Kaczmarek, K.; Vander Heyden, Y.; Walczak, B. Robust Statistics in Data Analysis—A Review. Chemometr. Intell. Lab. 2007, 85, 203–219. DOI: 10.1016/j.chemolab.2006.06.016.
  • Samariya, D.; Thakkar, A. A Comprehensive Survey of Anomaly Detection Algorithms. Ann. Data. Sci. 2021. DOI: 10.1007/s40745-021-00362-9.
  • Huang, Y. P.; Wu, Z. W.; Su, R. H.; Ruan, G. H.; Du, F. Y.; Li, G. K. Current Application of Chemometrics in Traditional Chinese Herbal Medicine Research. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2016, 1026, 27–35. DOI: 10.1016/j.jchromb.2015.12.050.
  • Wolpert, D. H. The Lack of a Priori Distinctions between Learning Algorithms. Neural. Comput. 1996, 8, 1341–1390. DOI: 10.1162/neco.1996.8.7.1341.
  • Moshkbar-Bakhshayesh, K. Investigating the Performance of the Supervised Learning Algorithms for Estimating NPPs Parameters in Combination with the Different Feature Selection Techniques. Ann. Nucl. Energy 2021, 158, 108299. DOI: 10.1016/j.anucene.2021.108299.
  • de Araujo Gomes, A.; Azcarate, S. M.; Diniz, P.; de Sousa Fernandes, D. D.; Veras, G. Variable Selection in the Chemometric Treatment of Food Data: A Tutorial Review. Food Chem. 2022, 370, 131072. DOI: 10.1016/j.foodchem.2021.131072.
  • Zhou, L.; Zhang, C.; Qiu, Z. J.; He, Y. Information Fusion of Emerging Non-Destructive Analytical Techniques for Food Quality Quthentication: A Survey. TrAC - Trend Anal. Chem. 2020, 127, 115901. DOI: 10.1016/j.trac.2020.115901.
  • Zhou, J. R.; Hong, X.; Jin, P. Q. Information Fusion for Multi-Source Material Data: Progress and Challenges. Appl. Sci. 2019, 9, 3473. DOI: 10.3390/app9173473.
  • 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.
  • Wu, X. M.; Zuo, Z. T.; Zhang, Q. Z.; Wang, Y. Z. FT-MIR and UV–Vis Data Fusion Strategy for Origins Discrimination of Wild Paris Polyphylla Smith Var. yunnanensis. Vib. Spectrosc. 2018, 96, 125–136. DOI: 10.1016/j.vibspec.2018.04.001.
  • 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.
  • 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.
  • Robert, C.; Jessep, W.; Sutton, J. J.; Hicks, T. M.; Loeffen, M.; Farouk, M.; Ward, J. F.; Bain, W. E.; Craigie, C. R.; Fraser-Miller, S. J.; Gordon, K. C. Evaluating Low- Mid- and High-Level Fusion Strategies for Combining Raman and Infrared Spectroscopy for Quality Assessment of Red Meat. Food Chem. 2021, 361, 130154. DOI: 10.1016/j.foodchem.2021.130154.
  • Meng, T.; Jing, X. Y.; Yan, Z.; Pedrycz, W. A Survey on Machine Learning for Data Fusion. Inform. Fusion 2020, 57, 115–129. DOI: 10.1016/j.inffus.2019.12.001.
  • Bu, Y. P.; Dai, X. J.; Zhang, Y.; Su, L.; Wang, Q. H. Application Progress on Spectral Data Fusion Technology in Edible Fungi Quality Evaluation. J. Fungal Res. 2021. DOI: 10.13341/j.jfr.2021.1428.
  • Yang, Q. L.; Deng, X. J.; Sun, X. D.; Niu, B.; Gu, S. Q.; Chen, Q. Application and Research Progress of Spectral Data Fusion Technology in Food Testing. Sci. Technol. Food Ind. 2020, 41, 324–329. DOI: 10.13386/j.issn1002-0306.2020.18.051.
  • Watson, L. M. Using Unsupervised Machine Learning to Identify Changes in Eruptive Behavior at Mount Etna, Italy. J. Volcanol. Geoth. Res. 2020, 405, 107042. DOI: 10.1016/j.jvolgeores.2020.107042.
  • Huang, J. H. An Introduction to Statistical Learning: With Applications in R by Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Witten. JABES 2014, 19, 556–557. DOI: 10.1007/s13253-014-0179-9.
  • Vachko, G. Similarity Analysis and Classification of Large Information Sets and Images by on-Line Unsupervised Learning. Proceedings of the Fuzzy System Symposium of the Japan Intelligent Information Fuzzy Society, 2007; Vol. 23, pp 18–23.
  • Chen, X. Q.; Jin, Z. H.; Wang, Q. C.; Yang, W. M.; Liao, Q. M.; Meng, H. Y. Unsupervised Visual Feature Learning Based on Similarity Guidance. Neurocomputing 2022, 490, 358–369. DOI: 10.1016/j.neucom.2021.11.102.
  • Buchaiah, S.; Shakya, P. Bearing Fault Diagnosis and Prognosis Using Data Fusion Based Feature Extraction and Feature Selection. Measurement 2022, 188, 110506. DOI: 10.1016/j.measurement.2021.110506.
  • Chander, S.; Vijaya, P. Unsupervised Learning Methods for Data Clustering. In Artificial Intelligence in Data Mining; 2021, pp 41–64. DOI: 10.1016/b978-0-12-820601-0.00002-1.
  • Gan, Y. S.; Xiao, Y.; Wang, S. H.; Guo, H. Y.; Liu, M.; Wang, Z. H.; Wang, Y. S. Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC. Molecules 2019, 24, 1687. DOI: 10.3390/molecules24091687.
  • Gelß, P.; Klus, S.; Schuster, I.; Schütte, C. Feature Space Approximation for Kernel-Based Supervised Learning. Knowl.-Based Syst. 2021, 221, 106935. DOI: 10.1016/j.knosys.2021.106935.
  • Jiang, T.; Gradus, J. L.; Rosellini, A. J. Supervised Machine Learning: A Brief Primer. Behav. Ther. 2020, 51, 675–687. DOI: 10.1016/j.beth.2020.05.002.
  • Rabbani, M.; Wang, Y.; Khoshkangini, R.; Jelodar, H.; Zhao, R.; Bagheri Baba Ahmadi, S.; Ayobi, S. A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy 2021, 23, 529. DOI: 10.3390/e23050529.
  • Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in Metabonomics. J. Proteome Res. 2007, 6, 469–479. DOI: 10.1021/pr060594q.
  • Pandiselvam, R.; Mahanti, N. K.; Manikantan, M. R.; Kothakota, A.; Chakraborty, S. K.; Ramesh, S. V.; Beegum, P. P. S. Rapid Detection of Adulteration in Desiccated Coconut Powder: Vis-NIR Spectroscopy and Chemometric Approach. Food Control 2022, 133, 108588. DOI: 10.1016/j.foodcont.2021.108588.
  • Li, Y.; Shen, Y.; Yao, C. L.; Guo, D. 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.
  • Naccarato, A.; Furia, E.; Sindona, G.; Tagarelli, A. Multivariate Class Modeling Techniques Applied to Multielement Analysis for the Verification of the Geographical Origin of Chili Pepper. Food Chem. 2016, 206, 217–222. DOI: 10.1016/j.foodchem.2016.03.072.
  • Wang, L.; Wang, X. H.; Liu, X. Y.; Wang, Y.; Ren, X. Y.; Dong, Y.; Song, R. L.; Ma, J. M.; Fan, Q. Q.; Wei, J.; et al. Fast Discrimination and Quantification Analysis of Curcumae Radix from Four Botanical Origins Using NIR Spectroscopy Coupled with Chemometrics Tools. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 254, 119626. DOI: 10.1016/j.saa.2021.119626.
  • Kong, W. J.; An, H. J.; Zhang, J.; Sun, L.; Nan, Y.; Song, A. L.; Zhou, L. D. Development of a High-Performance Liquid Chromatography with Tandem Mass Spectrometry Method for Identifying Common Adulterant Content in Saffron (Crocus Sativus L.). J. Pharm. Pharmacol. 2019, 71, 1864–1870. DOI: 10.1111/jphp.13152.
  • Ruggiero, L.; Amalfitano, C.; Di Vaio, C.; Adamo, P. Use of near-Infrared Spectroscopy Combined with Chemometrics for Authentication and Traceability of Intact Lemon Fruits. Food Chem. 2022, 375, 131822. DOI: 10.1016/j.foodchem.2021.131822.
  • Zhao, Z. Z.; Guo, P.; Brand, E. The Formation of Daodi Medicinal Materials. J. Ethnopharmacol. 2012, 140, 476–481. DOI: 10.1016/j.jep.2012.01.048.
  • Zhang, X. B.; Zhao, Y. P.; Guo, L. P.; Qiu, Z. D.; Huang, L. Q.; Qu, X. B. Differences in Chemical Constituents of Artemisia Annua L from Different Geographical Regions in China. PLoS One 2017, 12, e0183047. DOI: 10.1371/journal.pone.0183047.
  • Wang, Q. Q.; Huang, H. Y.; Wang, Y. Z. Geographical Authentication of Macrohyporia Cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy. Molecules 2019, 24, 1320. DOI: 10.3390/molecules24071320.
  • Liu, Z. M.; Yang, S. B.; Wang, Y. Z.; Zhang, J. Y. Discrimination of the Fruits of Amomum Tsao-ko according to Geographical Origin by 2DCOS Image with RGB and Resnet Image Analysis Techniques. Microchem. J. 2021, 169, 106545. DOI: 10.1016/j.microc.2021.106545.
  • Chang, Y.; Wu, H.; Wang, T.; Chen, Y.; Yang, J.; Fu, H. Y.; Yang, X.; Li, X. F.; Zhang, G.; Yu, R. Q. Geographical Origin Traceability of Traditional Chinese Medicine Atractylodes Macrocephala Koidz. by Using Multi-Way Fluorescence Fingerprint and Chemometric Methods. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 269, 120737. DOI: 10.1016/j.saa.2021.120737.
  • da Silva Bruni, A. R.; de Oliveira, V.; Fernandez, A. S. T.; Sakai, O. A.; Marco, P. H.; Valderrama, P. Attenuated Total Reflectance Fourier Transform (ATR-FTIR) Spectroscopy and Chemometrics for Organic Cinnamon Evaluation. Food Chem. 2021, 365, 130466. DOI: 10.1016/j.foodchem.2021.130466.
  • Wu, X. M.; Zhang, Q.; Wang, Y. Traceability the Provenience of Cultivated Paris Polyphylla Smith Var. yunnanensis Using ATR-FTIR Spectroscopy Combined with Chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2019, 212, 132–145. DOI: 10.1016/j.saa.2019.01.008.
  • Liu, L.; Li, W. Y.; Zuo, Z. T.; Wang, Y. Z. Multisource Information Fusion Strategies of Mass Spectrometry and Fourier Transform Infrared Spectroscopy Data for Authenticating the Age and Parts of Vietnamese Ginseng. J. Chemometr. 2021, 35, e3376. DOI: 10.1002/cem.3376.
  • Bertoldi, D.; Cossignani, L.; Blasi, F.; Perini, M.; Barbero, A.; Pianezze, S.; Montesano, D. Characterisation and Geographical Traceability of Italian Goji Berries. Food Chem. 2019, 275, 585–593. DOI: 10.1016/j.foodchem.2018.09.098.
  • Yang, Z.; Wang, Z. Q.; Yuan, W. H.; Li, C. H.; Jing, X. Y.; Han, H. Classification of Wolfberry from Different Geographical Origins by Using Electronic Tongue and Deep Learning Algorithm. IFAC-PapersOnLine 2019, 52, 397–402. DOI: 10.1016/j.ifacol.2019.12.592.
  • Hu, L.; Yin, C.; Ma, S.; Liu, Z. Comparison and Application of Fluorescence EEMs and DRIFTS Combined with Chemometrics for Tracing the Geographical Origin of Radix Astragali. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 205, 207–213. DOI: 10.1016/j.saa.2018.07.033.
  • Ding, Y. G.; Zhang, Q. Z.; Wang, Y. Z. A Fast and Effective Way for Authentication of Dendrobium Species: 2DCOS Combined with ResNet Cased on Feature Bands Extracted by Spectrum Standard Deviation. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 261, 120070. DOI: 10.1016/j.saa.2021.120070.
  • Qi, L. M.; Ma, Y. T.; Zhong, F. R.; 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.
  • Liang, J.; Li, M. G.; Du, Y.; Yan, C. H.; Zhang, Y.; Zhang, T. L.; Zheng, X. H.; Li, H. Data Fusion of Laser Induced Breakdown Spectroscopy (LIBS) and Infrared Spectroscopy (IR) Coupled with Random Forest (RF) for the Classification and Discrimination of Compound Salvia Miltiorrhiza. Chemometr. Intell. Lab. 2020, 207, 104179. DOI: 10.1016/j.chemolab.2020.104179.
  • 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.
  • Wang, C. Y.; Tang, L.; Jiang, T.; Zhou, Q.; Li, J.; Wang, Y. Z.; Kong, C. H. Geographical Traceability of Eucommia Ulmoides Leaves Using Attenuated Total Reflection Fourier Transform Infrared and Ultraviolet-Visible Spectroscopy Combined with Chemometrics and Data Fusion. Ind. Crop. Prod. 2021, 160, 113090. 113090. DOI: 10.1016/j.indcrop.2020.113090.
  • Hegazi, N. M.; Khattab, A. R.; Frolov, A.; Wessjohann, L. A.; Farag, M. A. Authentication of Saffron Spice Accessions from Its Common Substitutes via a Multiplex Approach of UV/VIS Fingerprints and UPLC/MS Using Molecular Networking and Chemometrics. Food Chem. 2022, 367, 130739. DOI: 10.1016/j.foodchem.2021.130739.
  • Mahgoub, Y. A.; Shawky, E.; Darwish, F. A.; El Sebakhy, N. A.; El-Hawiet, A. M. Near-Infrared Spectroscopy Combined with Chemometrics for Quality Control of German Chamomile (Matricaria Recutita L.) and Detection of Its Adulteration by Related Toxic Plants. Microchem. J. 2020, 158, 105153. DOI: 10.1016/j.microc.2020.105153.
  • Amirvaresi, A.; Nikounezhad, N.; Amirahmadi, M.; Daraei, B.; Parastar, H. Comparison of Near-Infrared (NIR) and Mid-Infrared (MIR) Spectroscopy Based on Chemometrics for Saffron Authentication and Adulteration Detection. Food Chem. 2021, 344, 128647. DOI: 10.1016/j.foodchem.2020.128647.
  • Hu, L. Q.; Ma, S.; Yin, C. L. Discrimination of Geographical Origin and Detection of Adulteration of Kudzu Root by Fluorescence Spectroscopy Coupled with Multi-Way Pattern Recognition. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2018, 193, 87–94. DOI: 10.1016/j.saa.2017.12.011.
  • Li, M. X.; Li, Y. Z.; Chen, Y.; Wang, T.; Yang, J.; Fu, H. Y.; Yang, X. L.; Li, X. F.; Zhang, G.; Chen, Z. P.; Yu, R. Q. Excitation-Emission Matrix Fluorescence Spectroscopy Combined with Chemometrics Methods for Rapid Identification and Quantification of Adulteration in Atractylodes Macrocephala Koidz. Microchem. J. 2021, 171, 106884. DOI: 10.1016/j.microc.2021.106884.
  • Yang, J.; Yin, C. L.; Miao, X.; Meng, X. R.; Liu, Z. M.; Hu, L. Q. Rapid Discrimination of Adulteration in Radix Astragali Combining Diffuse Reflectance Mid-Infrared Fourier Transform Spectroscopy with Chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2021, 248, 119251. DOI: 10.1016/j.saa.2020.119251.
  • Yang, H. L.; Bao, L. Y.; Liu, Y. F.; Luo, S. T.; Zhao, F. Y.; Chen, G. Y.; Liu, F. Identification and Quantitative Analysis of Salt-Adulterated Honeysuckle Using Infrared Spectroscopy Coupled with Multi-Chemometrics. Microchem. J. 2021, 171, 106829. DOI: 10.1016/j.microc.2021.106829.
  • Li, R. K.; Hou, K. X.; Zhang, L. N.; Lou, C. G.; Liu, X. L. Identification of Three Chinese Herbai Medicines Based on Terahertz Time Domain Spectroscopy. J. Hebei Univ., Nat. Sci. Ed. 2020, 40, 379–384. DOI: 10.3969/j.issn.1000-1565.2020.04.007.
  • Yang, W. Z.; Qiao, X.; Li, K.; Fan, J. R.; Bo, T.; Guo, D. A.; Ye, M. Identification and Differentiation of Panax Ginseng, Panax Quinquefolium, and Panax Notoginseng by Monitoring Multiple Diagnostic Chemical Markers. Acta Pharm. Sin B 2016, 6, 568–575. DOI: 10.1016/j.apsb.2016.05.005.
  • Wang, K. R.; Wang, C. H.; Li, Z.; Lin, J.; Xu, X. P.; Gu, R. Identification of Main Rubus Species by Infrared Spectroscopy Combined with Chemometrics. Chin. Tradit. Pat. Med. 2022, 44, 142–147. DOI: 10.3969/j.issn.1001-1528.2022.01.027.
  • Avula, B.; Bae, J. Y.; Chittiboyina, A. G.; Wang, Y. H.; Wang, M.; Srivedavyasasri, R.; Ali, Z.; Li, J.; Wu, C.; Khan, I. A. Comparative Analysis of Five Salvia Species Using LC-DAD-QToF. J. Pharm. Biomed. Anal. 2022, 209, 114520. DOI: 10.1016/j.jpba.2021.114520.
  • Wu, M. Q.; Tang, H. F.; Chen, S. Research Progress on Quality Control of Active Components in Different Medicinal Parts of Nelumbo Nucifera Gaertn. Guangzhou Chem. Ind. 2022, 50, 32–34, +43.
  • Chen, Y. Y.; Li, Q.; Qiu, D. Y. The Dynamic Accumulation Rules of Chemical Components in Different Medicinal Parts of Angelica Sinensis by GC-MS. Molecules 2022, 27, 4617. DOI: 10.3390/molecules27144617.
  • Li, S. P.; Chen, Y.; Duan, Y.; Zhao, Y. H.; Zhang, D.; Zang, L. Y.; Ya, H. Y. Widely Targeted Metabolomics Analysis of Different Parts of Salsola Collina Pall. Molecules 2021, 26, 1126. DOI: 10.3390/molecules26041126.
  • Cao, J. L.; Lei, T.; Wu, S. J.; Li, H. Y.; Deng, Y.; Lin, R. Z.; Ning, N.; Geng, C. X.; Wang, S. P.; Wu, X.; et al. Development of a Comprehensive Method Combining UHPLC-CAD Fingerprint, Multi-Components Quantitative Analysis for Quality Evaluation of Zishen Yutai Pills: A Step towards Quality Control of Chinese Patent Medicine. J. Pharm. Biomed. Anal. 2020, 191, 113570. DOI: 10.1016/j.jpba.2020.113570.
  • Xu, L. L.; Jiao, Y.; Cui, W. L.; Wang, B.; Guo, D. X.; Xue, F.; Mu, X. R.; Li, H. F.; Lin, Y. Q.; Lin, H. B. Quality Evaluation of Traditional Chinese Medicine Prescription in Naolingsu Capsule Based on Combinative Method of Fingerprint, Quantitative Determination, and Chemometrics. J. Anal. Methods Chem. 2022, 2022, 1429074. DOI: 10.1155/2022/1429074.
  • Xiao, J. J.; Duan, J. S.; Xu, X.; Li, S. N.; Wang, F.; Fang, Q. K.; Liao, M.; Cao, H. Q. Behavior of Pesticides and Their Metabolites in Traditional Chinese Medicine Paeoniae Radix Alba during Processing and Associated Health Risk. J. Pharm. Biomed. Anal. 2018, 161, 20–27. DOI: 10.1016/j.jpba.2018.08.029.
  • Chen, Y. N.; Dong, H. J.; Li, J. K.; Guo, L. P.; Wang, X. Evaluation of a Nondestructive NMR and MRI Method for Monitoring the Drying Process of Gastrodia Elata Blume. Molecules 2019, 24, 236. DOI: 10.3390/molecules24020236.
  • Suo, T. C.; Wang, H. X.; Shi, X. J.; Feng, L. L.; Cai, J. Y.; Duan, Y.; Bao, H. M.; Wu, X. L.; Zhang, Y.; Yu, H. S.; Li, Z. Combining Near Infrared Spectroscopy with Predictive Model and Expertise to Monitor Herb Extraction Processes. J. Pharm. Biomed. Anal. 2018, 148, 214–223. DOI: 10.1016/j.jpba.2017.10.004.
  • Zhao, Y. D.; Chen, S. F.; Wang, Y. D.; Lv, C. N.; Wang, J.; Lu, J. C. Effect of Drying Processes on Prenylflavonoid Content and Antioxidant Activity of Epimedium Koreanum Nakai. J. Food Drug Anal. 2018, 26, 796–806. DOI: 10.1016/j.jfda.2017.05.011.
  • Cao, M. Y.; Liu, Y. Y.; Jiang, W. M.; Meng, X. X.; Zhang, W.; Chen, W. D.; Peng, D. Y.; Xing, S. H. UPLC/MS-Based Untargeted Metabolomics Reveals the Changes of Metabolites Profile of Salvia Miltiorrhiza Bunge during Sweating Processing. Sci. Rep. 2020, 10, 19524. DOI: 10.1038/s41598-020-76650-w.
  • Li, W. L.; Xing, L. H.; Cai, Y.; Qu, H. B. Classification and Quantification Analysis of Radix Scutellariae from Different Origins with Near Infrared Diffuse Reflection Spectroscopy. Vib. Spectrosc. 2011, 55, 58–64. DOI: 10.1016/j.vibspec.2010.07.004.
  • Hao, J. W.; Chen, Y.; Chen, N. D. Assessment of ATR-NIR and ATR-MIR Spectroscopy as an Analytical Tool for the Quantification of the Total Polyphenols in Dendrobium Huoshanense. Phytochem. Anal. 2020, 31, 366–374. DOI: 10.1002/pca.2903.
  • Wang, L.; Ren, X. Y.; Wang, Y.; Liu, X. Y.; Dong, Y.; Ma, J. M.; Song, R. L.; Yu, A. X.; Wei, J.; Fan, Q. q.; et al. HPLC Fingerprint and UV–Vis Spectroscopy Coupled with Chemometrics for Curcumae Radix Species Discrimination and Three Bioactive Compounds Prediction. Microchem. J. 2021, 166, 106254. DOI: 10.1016/j.microc.2021.106254.
  • Obeidat, R.; Ispas, A.; Aleodor, B.; Bendic, V. Blockchain Technology—Applicability in the Traceability of a Product throughout the Supply Chain. Macromol. Symp. 2021, 396, 2000270. DOI: 10.1002/masy.202000270.
  • Yang, X. T.; Li, M. Q.; Yu, H. J.; Wang, M. T.; Xu, D. M.; Sun, C. H. A Trusted Blockchain-Based Traceability System for Fruit and Vegetable Agricultural Products. IEEE Access 2021, 9, 36282–36293. DOI: 10.1109/ACCESS.2021.3062845.
  • Mao, T. Y.; Fan, Y.; Yang, J.; Wei, H. B. A Research on Tea Traceability Consensus Mechanism Based on Blockchain Technology. New Developments of IT, IoT and ICT Applied to Agriculture; Springer Singapore: Berlin, 2021, 129–137. DOI: 10.1007/978-981-15-5073-7_13.
  • Shew, A. M.; Snell, H. A.; Nayga, R. M.; Lacity, M. C. Consumer Valuation of Blockchain Traceability for Beef in the United States. Appl. Econ. Perspect. Pol. 2022, 44, 299–323. DOI: 10.1002/aepp.13157.
  • Dong, J. E.; Zhang, S.; Li, T.; Wang, Y. Z. 2DCOS Combined with CNN and Blockchain to Trace the Species of Boletes. Microchem. J. 2022, 177, 107260. DOI: 10.1016/j.microc.2022.107260.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.