{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T19:36:49Z","timestamp":1790624209560,"version":"4.1.0"},"reference-count":80,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T00:00:00Z","timestamp":1750464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices\u2014the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel\u2019s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination (R2) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing.<\/jats:p>","DOI":"10.3390\/s25133877","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T04:50:05Z","timestamp":1750654205000},"page":"3877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Proposed SmartBarrel System for Monitoring and Assessment of Wine Fermentation Processes Using IoT Nose and Tongue Devices"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1360-3367","authenticated-orcid":false,"given":"Sotirios","family":"Kontogiannis","sequence":"first","affiliation":[{"name":"Laboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3305-6389","authenticated-orcid":false,"given":"Meropi","family":"Tsoumani","sequence":"additional","affiliation":[{"name":"Laboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9359-3604","authenticated-orcid":false,"given":"George","family":"Kokkonis","sequence":"additional","affiliation":[{"name":"Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1695-8999","authenticated-orcid":false,"given":"Christos","family":"Pikridas","sequence":"additional","affiliation":[{"name":"School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yorgos","family":"Kotseridis","sequence":"additional","affiliation":[{"name":"Enology Laboratory, Agricultural University of Athens, Iera Odos 86, Votanikos, 11855 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bastard, A., and Chaillet, A. (2023). Digitalization from vine to wine: Successes and remaining challenges\u2014A review. BIO Web Conf., 68.","DOI":"10.1051\/bioconf\/20236801034"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e14321","DOI":"10.1111\/jfpe.14321","article-title":"Internet of Things (IoT) in the food fermentation process: A bibliometric review","volume":"46","author":"Adeleke","year":"2023","journal-title":"J. Food Process Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Adamashvili, N., Zhizhilashvili, N., and Tricase, C. (2024). The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain. Computers, 13.","DOI":"10.3390\/computers13030072"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.talanta.2004.04.005","article-title":"Monitoring large scale wine fermentations with infrared spectroscopy","volume":"64","author":"Urtubia","year":"2004","journal-title":"Talanta"},{"key":"ref_5","unstructured":"(2024, March 11). Agrovin Company Tank Control Ulisses TDR2, Product Datasheet, 2024. Integrated Real-Time Temperature, Electrochemical Potential, Liquid Level Tank Monitoring and Pressurised Air Control System forWineries. Available online: https:\/\/agrovin.com\/agrv\/pdf\/FTinnovaciones\/en\/Ficha_Tecnica_ULISESTDR2_en.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"114922","DOI":"10.1016\/j.measurement.2024.114922","article-title":"Devices for monitoring oenological processes: A review","volume":"235","author":"Gersnoviez","year":"2024","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.3390\/s130302848","article-title":"A Novel Low-Cost Sensor Prototype for Monitoring Temperature during Wine Fermentation in Tanks","volume":"13","author":"Sainz","year":"2013","journal-title":"Sensors"},{"key":"ref_8","unstructured":"Naveed, M. (2024). The Adoption of 4.0 Agriculture for Wine Production in Order to Improve Efficiency, Sustainability and Competitiveness, Universita degli Studi di Foggia. Technical Report."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karaiskos, V., Zinas, N., Gkamas, T., Karolos, I., Pikridas, C., Vrettos, N., Tsioukas, V., and Kontogiannis, S. (2022, January 23\u201325). Proposed Industry 4.0 Maintenance Framework for Critical and Demanding Infrastructures and Processes. Proceedings of the 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932947"},{"key":"ref_10","first-page":"1","article-title":"AI: A New and Impactful Player in the Quality Evaluation of Wine","volume":"7","author":"Cao","year":"2025","journal-title":"Harv. Data Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5058","DOI":"10.1007\/s11947-025-03774-0","article-title":"Advances in Wine Processing: Current Insights, Prospects, and Technological Interventions","volume":"18","author":"Dewan","year":"2025","journal-title":"Food Bioprocess Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ammoniaci, M., Kartsiotis, S.P., Perria, R., and Storchi, P. (2021). State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture. Agriculture, 11.","DOI":"10.3390\/agriculture11030201"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kontogiannis, S., Koundouras, S., and Pikridas, C. (2024). Proposed Fuzzy-Stranded-Neural Network Model That Utilizes IoT Plant-Level Sensory Monitoring and Distributed Services for the Early Detection of Downy Mildew in Viticulture. Computers, 13.","DOI":"10.3390\/computers13030063"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1007\/s11694-023-02027-y","article-title":"Detection and analysis of electrochemical signals in wine fermentation process","volume":"17","author":"Li","year":"2023","journal-title":"J. Food Meas. Charact."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bender, M., Kirdan, E., Pahl, M.O., and Carle, G. (2021, January 9\u201312). Open-Source MQTT Evaluation. Proceedings of the IEEE 18th Annual Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49032.2021.9369499"},{"key":"ref_16","unstructured":"Gon\u00e7alves, F., Oliveira, N., Duarte, D.P., and Nogueira, R.N. (, January July). WinePlus 1110: The Remote and Real-Time Wine Fermentation Process Monitoring System. Proceedings of the 42th World Congress of Vine and Wine 2017, Geneva, Switzerland. Available online: https:\/\/www.researchgate.net\/publication\/318403336_Winegrid_R_the_remote_and_real-time_wine_production_process_monitoring_system."},{"key":"ref_17","first-page":"372","article-title":"A Mobile Control System of Fermentation Temperature on Winemaking","volume":"6","author":"Chiou","year":"2022","journal-title":"Int. J. Food Sci. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3509607","DOI":"10.1109\/TIM.2022.3162289","article-title":"Automated Density Measurement With Real-Time Predictive Modeling of Wine Fermentations","volume":"71","author":"Nelson","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Masetti, G., Marazzi, F., Di Cecilia, L., and Rovati, L. (2018, January 16\u201318). IOT-Based Measurement System for Wine Industry. Proceedings of the 2018 Workshop on Metrology for Industry 4.0 and IoT, Brescia, Italy.","DOI":"10.1109\/METROI4.2018.8428308"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ntalianis, K., and Croitoru, A. (2018). Sensor Module for Monitoring Wine Fermentation Process. Applied Physics, System Science and Computers, Springer.","DOI":"10.1007\/978-3-319-53934-8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vasilescu, A., Fanjul-Bolado, P., Titoiu, A.M., Porumb, R., and Epure, P. (2019). Progress in Electrochemical (Bio)Sensors for Monitoring Wine Production. Chemosensors, 7.","DOI":"10.3390\/chemosensors7040066"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nanou, E., Mavridou, E., Milienos, F., Papadopoulos, G., Temp\u00e8re, S., and Kotseridis, Y. (2020). Odor Characterization of White Wines Produced from Indigenous Greek Grape Varieties Using the Frequency of Attribute Citation Method with Trained Assessors. Foods, 9.","DOI":"10.3390\/foods9101396"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kontogiannatos, D., Troianou, V., Dimopoulou, M., Hatzopoulos, P., and Kotseridis, Y. (2021). Oenological Potential of Autochthonous Saccharomyces cerevisiae Yeast Strains from the Greek Varieties of Agiorgitiko and Moschofilero. Beverages, 7.","DOI":"10.3390\/beverages7020027"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Niyigaba, T., K\u00fc\u00e7\u00fckg\u00f6z, K., Ko\u0142o\u017cyn-Krajewska, D., Kr\u00f3likowski, T., and Trz\u0105skowska, M. (2025). Advances in Fermentation Technology: A Focus on Health and Safety. Appl. Sci., 15.","DOI":"10.3390\/app15063001"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Izquierdo-Bueno, I., Moraga, J., Cantoral, J.M., Carb\u00fa, M., Garrido, C., and Gonz\u00e1lez-Rodr\u00edguez, V.E. (2024). Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management. Appl. Sci., 14.","DOI":"10.20944\/preprints202409.0580.v1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alfieri, G., Modesti, M., Riggi, R., and Bellincontro, A. (2024). Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry. Sensors, 24.","DOI":"10.3390\/s24072293"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1002\/jsfa.13178","article-title":"Differentiation through E-nose and GC-FID data modeling of ros\u00e9 sparkling wines elaborated via traditional and Charmat methods","volume":"105","author":"Modesti","year":"2025","journal-title":"J. Sci. Food Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Summerson, V., Gonzalez Viejo, C., Pang, A., Torrico, D.D., and Fuentes, S. (2021). Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling. Molecules, 26.","DOI":"10.3390\/molecules26165108"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shahid, A., Choi, J.H., Rana, A., and Kim, H.S. (2018). Least Squares Neural Network-Based Wireless E-Nose System Using an SnO2 Sensor Array. Sensors, 18.","DOI":"10.3390\/s18051446"},{"key":"ref_30","unstructured":"Rodr\u00edguez M\u00e9ndez, M.L. (2016). Chapter 28\u2013Alcoholic Fermentation Using Electronic Nose and Electronic Tongue. Electronic Noses and Tongues in Food Science, Academic Press."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tomtsis, D., Kontogiannis, S., Kokkonis, G., and Zinas, N. (2016, January 25\u201327). IoT Architecture for Monitoring Wine Fermentation Process of Debina Variety Semi-Sparkling Wine. Proceedings of the SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM\u201916, Kastoria, Greece.","DOI":"10.1145\/2984393.2984398"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1002\/jsfa.13336","article-title":"Optimizing the winemaking process: NIR spectroscopy and e-nose analysis for the online monitoring of fermentation","volume":"105","author":"Littarru","year":"2025","journal-title":"J. Sci. Food Agric."},{"key":"ref_33","unstructured":"Modesti, M., Alfieri, G., Pardini, L., Cerreta, R., Mencarelli, F., and Bellincontro, A. (2025, May 04). Application of a Low-Cost Device VIS-NIRs-Based for Polyphenol Monitoring During the Vinification Process. IVES Conference Series. Available online: https:\/\/ives-openscience.eu\/14037\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.17660\/ActaHortic.2023.1370.6","article-title":"Development of a micro-Vis-NIR and SAW Nanobiosensor to Measure Polyphenols in Must\/Wine On-Time and Online","volume":"1370","author":"Taglieri","year":"2023","journal-title":"Acta Hortic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.aca.2003.08.066","article-title":"Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy","volume":"513","author":"Cozzolino","year":"2004","journal-title":"Anal. Chim. Acta"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dieguez, A., Moreno, S., Moncada-Madrazo, S., Caravaca, O., Dieguez, J., Canals, J., Benito-Altamirano, I., Prades, J.D., and Vila, A. (2024). Chip-Sized Microscopy for Continuous Monitoring: Application in White Wine Fermentation and Yeast Cell Counting via Deep Learning. Eng. Proc., 78.","DOI":"10.3390\/engproc2024078001"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hall, T., and Rasheed, K. (2025). A Survey of Machine Learning Methods for Time Series Prediction. Appl. Sci., 15.","DOI":"10.3390\/app15115957"},{"key":"ref_38","unstructured":"Foumani, N.M., Miller, L., Tan, C.W., Webb, G.I., Forestier, G., and Salehi, M. (2023). Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0956-7135(99)00015-8","article-title":"A new fuzzy control system for white wine fermentation","volume":"10","author":"Martinez","year":"1999","journal-title":"Food Control"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.procs.2025.01.021","article-title":"An Integrated Approach Based on Fuzzy Logic and Machine Learning Techniques for Reliable Wine Quality Prediction","volume":"252","author":"Kaur","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107158","DOI":"10.1016\/j.compchemeng.2020.107158","article-title":"Identification of chemical markers to detect abnormal wine fermentation using support vector machines","volume":"145","author":"Urtubia","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ye, M., Liu, Y., and Li, Q. (2021). Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. Sensors, 21.","DOI":"10.3390\/s21227620"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16258","DOI":"10.3390\/s140916258","article-title":"Monitoring and Evaluation of Alcoholic Fermentation Processes Using a Chemocapacitor Sensor Array","volume":"14","author":"Oikonomou","year":"2014","journal-title":"Sensors"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s00449-011-0557-4","article-title":"Prediction of problematic wine fermentations using artificial neural networks","volume":"34","author":"Urtubia","year":"2011","journal-title":"Bioprocess Biosyst. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Silva, R., Gomes, V., Mendes-Faia, A., and Melo-Pinto, P. (2018). Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries. Remote Sens., 10.","DOI":"10.3390\/rs10020312"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sipos, A., Florea, A., Arsin, M., and Fiore, U. (2021). Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process. Processes, 1.","DOI":"10.3390\/pr9010074"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Florea, A., Sipos, A., and Stoisor, M.C. (2022). Applying AI Tools for Modeling, Predicting and Managing the White Wine Fermentation Process. Fermentation, 8.","DOI":"10.3390\/fermentation8040137"},{"key":"ref_48","unstructured":"ThingsBoard (2020, October 20). ThingsBoard Open-Source IoT Platform. Available online: https:\/\/thingsboard.io\/."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Seesaard, T., and Wongchoosuk, C. (2022). Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications. Fermentation, 8.","DOI":"10.3390\/fermentation8070302"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Oikonomou, P., Goustouridis, D., Raptis, I., Manoli, K., and Sanopoulou, M. (2009, January 25\u201328). Must fermentation progress monitoring by polymer coated capacitive vapour sensor arrays. Proceedings of the SENSORS, 2009 IEEE, Christchurch, New Zealand.","DOI":"10.1109\/ICSENS.2009.5398443"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6692","DOI":"10.1109\/JSEN.2020.2975284","article-title":"A Low-Cost IoT Device to Monitor in Real-Time Wine Alcoholic Fermentation Evolution Through CO2 Emissions","volume":"20","author":"Martin","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lozano, J., Santos, J., and Horrillo, M. (2016). Wine Applications With Electronic Noses. Electronic Noses and Tongues in Food Science, Academic Press.","DOI":"10.1016\/B978-0-12-800243-8.00014-7"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Vanaraj, R., I.P, B., Mayakrishnan, G., Kim, I.S., and Kim, S.C. (2025). A Systematic Review of the Applications of Electronic Nose and Electronic Tongue in Food Quality Assessment and Safety. Chemosensors, 13.","DOI":"10.3390\/chemosensors13050161"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.aca.2013.09.048","article-title":"On-line Monitoring of Food Fermentation Processes Using Electronic Noses and Electronic Tongues: A Review","volume":"804","author":"Peris","year":"2013","journal-title":"Anal. Chim. Acta"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3687","DOI":"10.1007\/s11694-025-03217-6","article-title":"Integration of low-cost multispectral sensors and electronic nose for enhanced fermentation monitoring in tempeh production","volume":"19","author":"Syahputra","year":"2025","journal-title":"J. Food Meas. Charact."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/BF03175601","article-title":"Identification of yeast population dynamics of spontaneous fermentation in Beijing wine region, China","volume":"59","author":"Sun","year":"2009","journal-title":"Ann. Microbiol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10068-024-01744-4","article-title":"Application of machine learning for quantitative analysis of industrial fermentation using image processing","volume":"34","author":"Jeong","year":"2025","journal-title":"Food Sci. Biotechnol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., Sha, S., Yin, H., Zhang, H., Wang, Y., Zhao, B., and Song, F. (2019). Analysis of the tendency for the electronic conductivity to change during alcoholic fermentation. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-41225-x"},{"key":"ref_59","first-page":"4330","article-title":"Integration of a Low-Cost Electronic Nose and a Voltammetric Electronic Tongue for Red Wines Identification","volume":"8","author":"Han","year":"2020","journal-title":"J. Food Sci. Nutr."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.foodchem.2014.02.080","article-title":"Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools","volume":"158","year":"2014","journal-title":"Food Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"100046","DOI":"10.1016\/j.fochx.2019.100046","article-title":"Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques","volume":"3","year":"2019","journal-title":"Food Chem. X"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3390\/info15040178","article-title":"A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones","volume":"15","author":"Kontogiannis","year":"2024","journal-title":"Information"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"298","DOI":"10.5344\/ajev.2006.57.3.298","article-title":"Structure and Properties of Wine Pigments and Tannins","volume":"57","author":"Cheynier","year":"2006","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_64","first-page":"1","article-title":"Tannins and anthocyanins: From their origin to wine analysis\u2014A review","volume":"39","author":"Nel","year":"2018","journal-title":"South Afr. J. Enol. Vitic."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.tifs.2013.02.004","article-title":"Effects on colour and phenolic composition of sugar concentration processes in dried-on- or dried-off-vine grapes and their aged or not natural sweet wines","volume":"31","year":"2013","journal-title":"Trends Food Sci. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Goode, J. (2018). Flawless: Understanding Faults in Wine, University of California Press.","DOI":"10.1525\/9780520971318"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Cosme, F., Nunes, F.M., and Filipe-Ribeiro, L. (2021). Chemistry and Biochemistry of Winemaking, Wine Stabilization and Aging, BoD\u2014Books on Demand.","DOI":"10.5772\/intechopen.87471"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e70026","DOI":"10.1002\/cem.70026","article-title":"Being Aware of Data Leakage and Cross-Validation Scaling in Chemometric Model Validation","volume":"39","author":"Kiraly","year":"2025","journal-title":"J. Chemom."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Heberger, K. (2024). Frequent Errors in Modeling by Machine Learning: A Prototype Case of Predicting the Timely Evolution of COVID-19 Pandemic. Algorithms, 17.","DOI":"10.3390\/a17010043"},{"key":"ref_70","unstructured":"Warner, J., Sexauer, J., Broeck, W.V.d., Kinoshita, B.P., Balinski, J., Clauss, C., and Unnikrishnan, A. (2024, December 20). JDWarner\/Scikit-Fuzzy: Scikit-Fuzzy 0.5.0. Available online: https:\/\/github.com\/scikit-fuzzy\/scikit-fuzzy\/releases."},{"key":"ref_71","unstructured":"AJ Alves and Robotic Research Group (RRG), State University of Piau\u00ed (UESPI\u2013Teresina) (2024, October 22). eFLL: Embedded Fuzzy Logic Library. Software Library. Available online: https:\/\/github.com\/alvesoaj\/eFLL."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Darias-Martin, J., Socas-Hernandez, A., Diaz-Romero, C., and Diaz-Diaz, E. (2003). Comparative study of methods for determination of titrable acidity in wine. J. Food Compos. Anal., 16.","DOI":"10.1016\/S0889-1575(03)00032-2"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"40","DOI":"10.5344\/ajev.1980.31.1.40","article-title":"The Prediction of Fermentation Behavior by a Kinetic Model","volume":"31","author":"Boulton","year":"1980","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nelson, J., and Boulton, R. (2024). Models for Wine Fermentation and Their Suitability for Commercial Applications. Fermentation, 10.","DOI":"10.3390\/fermentation10060269"},{"key":"ref_75","unstructured":"GoogleTF (2020, October 15). Tensorflow 2.0:A Machine Learning System for Deep Neural Networks. Available online: https:\/\/tensorflow.org."},{"key":"ref_76","unstructured":"GoogleKeras (2020, March 22). Keras: The Python Deep Learning API. Available online: https:\/\/keras.io\/api\/."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Aranda, A., Orozco, H., Picazo, C., and Matallana, E. (2019). Yeast Life Span and its Impact on Food Fermentations. Fermentation, 5.","DOI":"10.3390\/fermentation5020037"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Prusova, B., Humaj, J., Kulhankova, M., Kumsta, M., Sochor, J., and Baron, M. (2023). Capture of Fermentation Gas from Fermentation of Grape Must. Foods, 12.","DOI":"10.3390\/foods12030574"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.aca.2016.03.046","article-title":"Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis","volume":"921","author":"Tencate","year":"2016","journal-title":"Anal. Chim. Acta"},{"key":"ref_80","unstructured":"European Union (2025, April 24). Regulation (EC) No 178\/2002 of the European Parliament and of the Council of 28 January 2002 Laying Down the General Principles and Requirements of Food Law, Chapter I, Articles 17,18,19. 2002. Official Journal of the European Communities L 31 1.2.2002; pp.1\u201324. Available online: https:\/\/eur-lex.europa.eu\/eli\/reg\/2002\/178\/oj\/eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/13\/3877\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:56:26Z","timestamp":1760032586000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/13\/3877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,21]]},"references-count":80,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["s25133877"],"URL":"https:\/\/doi.org\/10.3390\/s25133877","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,21]]}}}