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Review

Artificial intelligence-based approaches for traditional fermented alcoholic beverages’ development: review and prospect

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References

  • Antunes, A. E. C., G. Vinderola, D. Xavier-Santos, and K. Sivieri. 2020. Potential contribution of beneficial microbes to face the COVID-19 pandemic. Food Research International (Ottawa, Ont.) 136:109577. doi: 10.1016/j.foodres.2020.109577.
  • Bar, N., T. Korem, O. Weissbrod, D. Zeevi, D. Rothschild, S. Leviatan, N. Kosower, M. Lotan-Pompan, A. Weinberger, C. I. Le Roy, et al. 2020. A reference map of potential determinants for the human serum metabolome. Nature 588 (7836):135–40., doi: 10.1038/s41586-020-2896-2.
  • Ben-Daya, M., E. Hassini, and Z. Bahroun. 2019. Internet of things and supply chain management: A literature review. International Journal of Production Research 57 (15-16):4719–42. doi: 10.1080/00207543.2017.1402140.
  • Bishop, C. 2006. Pattern recognition and machine learning. New York: Springer.
  • Blikstein, P, and M. Worsley. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3 (2):220–38. doi: 10.18608/jla.2016.32.11.
  • Bokulich, N. A., T. S. Collins, C. Masarweh, G. Allen, H. Heymann, S. E. Ebeler, and D. A. Mills. 2016. Associations among wine grape microbiome, metabolome, and fermentation behavior suggest microbial contribution to regional wine characteristics. mBio 7 (3): e00631–16. doi: 10.1128/mBio.00631-16.
  • Brynjolfsson, E., X. Hui, and M. Liu. 2019. Does machine translation affect international trade? Evidence from a large digital platform. Management Science 65 (12):5449–60. doi: 10.1287/mnsc.2019.3388.
  • Busse, M, and R. Siebert. 2018. The role of consumers in food innovation processes. European Journal of Innovation Management 21 (1):20–43. doi: 10.1108/EJIM-03-2017-0023.
  • Caporaso, J. G., J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello, N. Fierer, A. G. Pena, J. K. Goodrich, J. I. Gordon, et al. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7 (5):335–6. doi: 10.1038/nmeth.f.303.
  • Cavalieri, D., P. E. McGovern, D. L. Hartl, R. Mortimer, and M. Polsinelli. 2003. Evidence for S-cerevisiae fermentation in ancient wine. Journal of Molecular Evolution 57:S226–S232. doi: 10.1007/s00239-003-0031-2.
  • Cejka, P, and J. Olsovska. 2015. Use of sensory analysis of beer in marketing. Kvasny Prumysl 61 (2):38–45.
  • Chang, F., J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems 26 (2):1–26. doi: 10.1145/1365815.1365816.
  • Chen, G.-M., Z.-R. Huang, L. Wu, Q. Wu, W.-L. Guo, W.-H. Zhao, B. Liu, W. Zhang, P.-F. Rao, X.-C. Lv, et al. 2021. Microbial diversity and flavor of Chinese rice wine (Huangjiu): An overview of current research and future prospects. Current Opinion in Food Science 42:37–50. doi: 10.1016/j.cofs.2021.02.017.
  • Chen, B., V. Velchev, J. Palmer, and T. Atkison. 2018. Wineinformatics: A quantitative analysis of wine reviewers. Fermentation 4 (4):82. doi: 10.3390/fermentation4040082.
  • Clarke, J., H. C. Wu, L. Jayasinghe, A. Patel, S. Reid, and H. Bayley. 2009. Continuous base identification for single-molecule nanopore DNA sequencing. Nature Nanotechnology 4 (4):265–70. doi: 10.1038/nnano.2009.12.
  • Cong, R., D. Hai, and X. Yan. 2017. Advances in microbiome study of traditional Chinese fermented foods. Acta Microbiologica Sinica 57 (6):885–98.
  • Costa, M. A. d C., D. L. d S. Vilela, G. M. Fraiz, I. L. Lopes, A. I. M. Coelho, L. C. V. Castro, Jose, and G. Prado Martin. 2021. Effect of kombucha intake on the gut microbiota and obesity-related comorbidities: A systematic review. Critical Reviews in Food Science and Nutrition :1–16. doi: 10.1080/10408398.2021.1995321.
  • Dai, X. J., L. Tan, L. A. Ren, Y. Shao, W. Q. Tao, and Y. J. Wang. 2022. COVID-19 Risk Appears to Vary Across Different Alcoholic Beverages. Frontiers in Nutrition 8: 772700. doi: 10.3389/fnut.2021.772700.
  • Dayioglu, M., and U. Turker. 2021. Digital transformation for sustainable future: agriculture 4.0: A review. Journal of Agricultural Sciences-Tarim Bilimleri Dergisi 27 (4):373–99. doi: 10.15832/ankutbd.986431.
  • Dean, J, and S. Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Commun. ACM 51 (1):107–113. doi: 10.1145/1327452.1327492
  • Dresel, M., A. Dunkel, and T. Hofmann. 2015. Sensomics analysis of key bitter compounds in the hard resin of hops (Humulus lupulus L.) and their contribution to the bitter profile of pilsner-type beer. Journal of Agricultural and Food Chemistry 63 (13):3402–18. doi: 10.1021/acs.jafc.5b00239.
  • Du, R., J. Liu, J. Jiang, Y. Wang, X. Ji, N. Yang, Q. Wu, and Y. Xu. 2021. Construction of a synthetic microbial community for the biosynthesis of volatile sulfur compound by multi-module division of labor. Food Chemistry 347:129036. doi: 10.1016/j.foodchem.2021.129036.
  • Galimberti, A., A. Bruno, G. Agostinetto, M. Casiraghi, L. Guzzetti, and M. Labra. 2021. Fermented food products in the era of globalization: Tradition meets biotechnology innovations. Current Opinion in Biotechnology 70:36–41. doi: 10.1016/j.copbio.2020.10.006.
  • Gao, Y., L. Hou, J. Gao, D. Li, Z. Tian, B. Fan, F. Wang, and S. Li. 2021. Metabolomics approaches for the comprehensive evaluation of fermented foods: A review. Foods 10 (10):2294. doi: 10.3390/foods10102294.
  • Gao, Y., H. Liang, and B. Sun. 2021. Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform. Journal of Intelligent & Fuzzy Systems 40 (5):9173–85. doi: 10.3233/JIFS-201579.
  • Gao, F., G. Zeng, B. Wang, J. Xiao, L. Zhang, W. Cheng, H. Wang, H. Li, and X. Shi. 2021. Discrimination of the geographic origins and varieties of wine grapes using high-throughput sequencing assisted by a random forest model. LWT 145:111333. doi: 10.1016/j.lwt.2021.111333.
  • Ghemawat, S., H. Gobioff, and S.-T. Leung. 2003. The Google file system. Symposium on Operating Systems Principles, 10/92003.
  • Gonzalez Viejo, C., S. Fuentes, G. Li, R. Collmann, B. Conde, and D. Torrico. 2016. Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER. Food Research International (Ottawa, Ont.) 89 (Pt 1):504–13. doi: 10.1016/j.foodres.2016.08.045.
  • Hansen, E. B, and S. Bogh. 2021. Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems 58:362–72. doi: 10.1016/j.jmsy.2020.08.009.
  • Hassoun, A., A. Ait-Kaddour, A. M. Abu-Mahfouz, N. Bhojraj Rathod, F. Bader, F. J. Barba, A. Biancolillo, J. Cropotova, C. M. Galanakis, A. R. Jambrak, et al. 2022. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition :1–17. doi: 10.1080/10408398.2022.2034735.
  • Hong, X., J. Chen, L. Liu, H. Wu, H. Tan, G. Xie, Q. Xu, H. Zou, W. Yu, L. Wang, et al. 2016. Metagenomic sequencing reveals the relationship between microbiota composition and quality of Chinese Rice Wine. Scientific Reports 6:26621. doi: 10.1038/srep26621.
  • Idoje, G., T. Dagiuklas, and M. Iqbal. 2021. Survey for smart farming technologies: Challenges and issues. Computers & Electrical Engineering 92:107104. doi: 10.1016/j.compeleceng.2021.107104.
  • Jagtap, S., F. Bader, G. Garcia-Garcia, H. Trollman, T. Fadiji, and K. Salonitis. 2020. Food logistics 4.0: Opportunities and challenges. Logistics 5 (1):2. doi: 10.3390/logistics5010002.
  • Jian, C., W. Chao, Z. Qi, and Z. Juan. 2021. Research status and application prospect of frontier technology of traditional fermented food in China. Journal of Food Science and Technology, China 39 (2):1–7. doi: 10.12301/j.issn.2095-6002.2021.02.001.
  • Jin, G., Y. Zhu, and Y. Xu. 2017. Mystery behind Chinese liquor fermentation. Trends in Food Science & Technology 63:18–28. doi: 10.1016/j.tifs.2017.02.016.
  • Kantor, P. B. 2001. Foundations of Statistical Natural Language Processing. Information Retrieval 4 (1):80–1. doi: 10.1023/A:1011424425034.
  • Kapp, J. M, and W. Sumner. 2019. Kombucha: A systematic review of the empirical evidence of human health benefit. Annals of Epidemiology 30:66–70. doi: 10.1016/j.annepidem.2018.11.001.
  • Katz, S. E. 2003. Wild Fermentation: The Flavor, Nutrition, and Craft of Live-Culture Foods
  • Le Roy, C. I., P. M. Wells, J. Si, J. Raes, J. T. Bell, and T. D. Spector. 2020. Red wine consumption associated with increased gut microbiota α-diversity in 3 independent cohorts. Gastroenterology 158 (1):270–2.e2. doi: 10.1053/j.gastro.2019.08.024.
  • Lee, C. H, and H.-J. Yoon. 2017. Medical big data: Promise and challenges. Kidney Research and Clinical Practice 36 (1):3–11. doi: 10.23876/j.krcp.2017.36.1.3.
  • Lesschaeve, I. 2007. Sensory evaluation of wine and commercial realities: Review of current practices and perspectives. American Journal of Enology and Viticulture 58 (2):252–8.
  • Lim, H., F. Cankara, C.-J. Tsai, O. Keskin, R. Nussinov, and A. Gursoy. 2022. Artificial intelligence approaches to human-microbiome protein-protein interactions. Current Opinion in Structural Biology 73:102328– doi: 10.1016/j.sbi.2022.102328.
  • Linardatos, P., V. Papastefanopoulos, and S. Kotsiantis. 2020. Explainable AI: A review of machine learning interpretability methods. Entropy 23 (1):18. doi: 10.3390/e23010018.
  • Lopatkin, A. J, and J. J. Collins. 2020. Predictive biology: Modelling, understanding and harnessing microbial complexity. Nature Reviews. Microbiology 18 (9):507–20. doi: 10.1038/s41579-020-0372-5.
  • Lu, Y. 2019. Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics 6 (1):1–29. doi: 10.1080/23270012.2019.1570365.
  • Mahmud, M., M. S. Kaiser, T. M. McGinnity, and A. Hussain. 2021. Deep learning in mining biological data. Cognitive Computation 13 (1):1–33. doi: 10.1007/s12559-020-09773-x.
  • Mallick, H., S. Ma, E. A. Franzosa, T. Vatanen, X. C. Morgan, and C. Huttenhower. 2017. Experimental design and quantitative analysis of microbial community multiomics. Genome Biology 18 (1):228. doi: 10.1186/s13059-017-1359-z.
  • Mannaa, M., G. Han, Y. S. Seo, and I. Park. 2021. Evolution of food fermentation processes and the use of multi-omics in deciphering the roles of the microbiota. Foods 10 (11):2861. doi: 10.3390/foods10112861.
  • Mardis, E. R. 2008. Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics 9:387–402. doi: 10.1146/annurev.genom.9.081307.164359.
  • Marsh, A. J., C. Hill, R. P. Ross, and P. D. Cotter. 2014. Fermented beverages with health-promoting potential: Past and future perspectives. Trends in Food Science & Technology 38 (2):113–24. doi: 10.1016/j.tifs.2014.05.002.
  • Mastroberardino, P., G. Calabrese, F. Cortese, and M. Petracca. 2019. Sustainability in the wine sector. British Food Journal 122 (8):2497–511. doi: 10.1108/BFJ-07-2019-0475.
  • McElhinney, J. M. W. R., M. K. Catacutan, A. Mawart, A. Hasan, and J. Dias. 2022. Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges. Frontiers in Microbiology 13:851450. doi: 10.3389/fmicb.2022.851450.
  • McGovern, P. E., J. H. Zhang, J. G. Tang, Z. Q. Zhang, G. R. Hall, R. A. Moreau, A. Nunez, E. D. Butrym, M. P. Richards, C. S. Wang, et al. 2004. Fermented beverages of pre- and proto-historic China. Proceedings of the National Academy of Sciences of the United States of America 101 (51):17593–8. doi: 10.1073/pnas.0407921102.
  • Nguyen, H. T., B. N. Do, K. M. Pham, G. B. Kim, H. T. B. Dam, T. T. Nguyen, T. T. P. Nguyen, Y. H. Nguyen, K. Sorensen, A. Pleasant, et al. 2020. Fear of COVID-19 scale-associations of its scores with health literacy and health-related behaviors among medical students. International Journal of Environmental Research and Public Health 17 (11):4164. doi: 10.3390/ijerph17114164.
  • Nicolotti, L., V. Mall, and P. Schieberle. 2019. Characterization of key aroma compounds in a commercial rum and an australian red wine by means of a new sensomics-based expert system (SEBES)-An approach to use artificial intelligence in determining food odor codes. Journal of Agricultural and Food Chemistry 67 (14):4011–22. doi: 10.1021/acs.jafc.9b00708.
  • Peng, Qi, H. Zheng, K. Meng, Y. Zhu, W. Zhu, H. Zhu, C. Shen, J. Fu, N. L. Elsheery, G. Xie, J, et al. 2022. The way of Qu-making significantly affected the volatile flavor compounds in Huangjiu (Chinese rice wine) during different brewing stages. Food Science & Nutrition 10 (7):2255–70. doi: 10.1002/fsn3.2835.
  • Prakash, O., Y. Shouche, K. Jangid, and J. E. Kostka. 2013. Microbial cultivation and the role of microbial resource centers in the omics era. Applied Microbiology and Biotechnology 97 (1):51–62. doi: 10.1007/s00253-012-4533-y.
  • Rizo, J., D. Guillen, A. Farres, G. Diaz-Ruiz, S. Sanchez, C. Wacher, and R. Rodriguez-Sanoja. 2020. Omics in traditional vegetable fermented foods and beverages. Critical Reviews in Food Science and Nutrition 60 (5):791–809. doi: 10.1080/10408398.2018.1551189.
  • Salehan, M, and D. J. Kim. 2016. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems 81:30–40. doi: 10.1016/j.dss.2015.10.006.
  • Samek, W., T. Wiegand, and K. R. Müller. 2017. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. ITU Journal 1:1–10.
  • Samek, W., T. Wiegand, and K.-R. Muller. 2017. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. ITU Journal: ICT Discoveries - Special Issue 1 - the Impact of Artificial Intelligence (AI) on Communication Networks and Services 1:1–10. doi: 10.48550/arXiv.1708.08296.
  • Sanna, V, and L. Pretti. 2015. Effect of wine barrel ageing or sapa addition on total polyphenol content and antioxidant activities of some Italian craft beers. International Journal of Food Science & Technology 50 (3):700–7. doi: 10.1111/ijfs.12666.
  • Senecal, S., P. J. Kalczynski, and J. Nantel. 2005. Consumers’ decision-making process and their online shopping behavior: A clickstream analysis. Journal of Business Research 58 (11):1599–608. doi: 10.1016/j.jbusres.2004.06.003.
  • Sun, X., Q. Qian, Y. Xiong, Q. Xie, X. Yue, J. Liu, S. Wei, and Q. Yang. 2022. Characterization of the key aroma compounds in aged Chinese Xiaoqu Baijiu by means of the sensomics approach. Food Chemistry 384:132452. doi: 10.1016/j.foodchem.2022.132452.
  • Tamang, J. P., S. Dong-Hwa, J. Su-Jin, and C. Soo-Wan. 2016. Functional properties of microorganisms in fermented foods. Frontiers in Microbiology 7:578. doi: 10.3389/fmicb.2016.00578.
  • Tan, W. K., Z. Husin, M. L. Yasruddin, and M. A. H. Ismail. 2022. Recent technology for food and beverage quality assessment: A review. Journal of Food Science and Technology 1–14. doi: 10.1007/s13197-022-05439-8.
  • Tonkin, E., J. Brimblecombe, and T. P. Wycherley. 2017. Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review. Advances in Nutrition (Bethesda, Md.) 8 (2):308–22. doi: 10.3945/an.116.013748.
  • Vrzal, T, and J. Olsovska. 2019. Sensomics: Basic principles and practice. Kvasny PRUMYSL 65 (5):166–73. doi: 10.18832/kp2019.65.166.
  • Wang, Y., C. Zhang, F. Liu, Z. Jin, and X. Xia. 2022. Ecological succession and functional characteristics of lactic acid bacteria in traditional fermented foods. Critical Reviews in Food Science and Nutrition :1–15. doi: 10.1080/10408398.2021.2025035.
  • Wedajo Lemi, B. 2020. Microbiology of Ethiopian Traditionally Fermented Beverages and Condiments. International Journal of Microbiology 2020:1478536– doi: 10.1155/2020/1478536.
  • Wei, J., Y. Nie, H. Du, and Y. Xu. 2021. How trophic interactions drive the spontaneous construction of microbial community in traditional fermented foods: A review. Microbiology China 48 (11):4412–24.
  • Xie, G., J. Cai, B. Qian, L. Wang, Z. Lu, F. Lyu, and Y. Ding. 2020. Changes of flavor components during mechanized brewing process of dry Huangjiu fermented in tank. Food and Fermentation Industries 46 (11):157–64.
  • Xie, G., H. Zheng, Z. Qiu, Z. Lin, Q. Peng, G. Dula Bealu, N. I. Elsheery, Y. L. C. Shen, J. Fu, H. Yang, et al. 2021. Study on relationship between bacterial diversity and quality of Huangjiu (Chinese Rice Wine) fermentation. Food Science & Nutrition 9 (7):3885–92. doi: 10.1002/fsn3.2369.
  • Yang, Y., W. Hu, Y. Xia, Z. Mu, L. Tao, X. Song, H. Zhang, B. Ni, and L. Ai. 2020. Flavor formation in chinese rice wine (Huangjiu): Impacts of the Flavor-active microorganisms, raw materials, and fermentation technology. Frontiers in Microbiology 11: 1–14. doi: 10.3389/fmicb.2020.580247.
  • Yu, H., D. Zheng, T. Xie, J. Xie, H. Tian, L. Ai, and C. Chen. 2022. Comprehensive two-dimensional gas chromatography mass spectrometry-based untargeted metabolomics to clarify the dynamic variations in the volatile composition of Huangjiu of different ages. Journal of Food Science 87 (4):1563–74. doi: 10.1111/1750-3841.16047.
  • Zeng, M., H. Cao, M. Chen, and Y. Li. 2019. User behaviour modeling, recommendations, and purchase prediction during shopping festivals. Electronic Markets 29 (2):263–74. doi: 10.1007/s12525-018-0311-8.
  • Zhang, J., H. Huang, G. Song, K. Huang, Y. Luo, Q. Liu, X. He, and N. Cheng. 2022. Intelligent biosensing strategies for rapid detection in food safety: A review. Biosensors and Bioelectronics 202:114003. doi: 10.1016/j.bios.2022.114003.
  • Zhang, X., Z. Lu, L. Chai, J. Shi, and Z. Xu. 2019. Research strategies for microbial ecology of traditional chinese fermented foods. Scientia Sinica Vitae 49 (5):575–84. doi: 10.1360/N052018-00255.
  • Zhang, J, and D. Tao. 2021. Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet of Things Journal 8 (10):7789–817. doi: 10.1109/JIOT.2020.3039359.
  • Zhou, Y., Y. Shi, Y. Lu, L. Zhu, and X. Chen. 2021. Research progress on microbial interaction in traditional fermentation. Journal of Chinese Institute of Food Science and Technology 21 (11):349–58. doi: 10.16429/j.1009-7848.2021.11.038.

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