A Low-Cost IoT Device to Monitor in Real-Time Wine Alcoholic Fermentation Evolution Through CO<sub>2</sub>Emissions

Cañete-Carmona, Eduardo; Gallego-Martínez, Juan-José; Martin, Charles L.; Brox, Maria; Luna-Rodríguez, Juan-Jesús

doi:10.1109/jsen.2020.2975284

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…Several in-line and on-line methods to monitor alcoholic fermentation have been investigated, including in-situ transflectance near-infrared spectroscopy [7,8], and Raman spectroscopy probes [9]; automated flow-through mid-infrared spectroscopy [10], Fourier transform infrared spectroscopy [11], and piezoelectric MEMS resonators [12]; non-invasive Raman spectroscopy through transparent vessel walls [13]; and CO 2 emission monitoring [14]. Ultrasonic (US) sensors are an attractive monitoring technique owing to their low Fermentation 2021, 7, 34 2 of 13 cost and have previously been used to study fermentation, including as in-line methods on circulation lines [15], in-situ in tanks [16], and using non-invasive, through-transmission of the fermenting media [17,18].…”

Section: Introductionmentioning

confidence: 99%

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

et al. 2021

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Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2 = 0.952, mean absolute error (MAE) = 0.265, mean squared error (MSE) = 0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2 = 0.948, MAE = 0.283, MSE = 0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

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Section: Introductionmentioning

confidence: 99%

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

et al. 2021

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“…The air data filtered and processed by the fusion station more accurately reflects the air environment in space [21,22]. The processing of the data uploaded by each relay fusion station by the total inspection center station can be regarded as a system transformation from a set of input modes to a set of output modes.…”

Section: Global Fusion Algorithmmentioning

confidence: 99%

Building an indoor air quality monitoring system based on the architecture of the Internet of Things

Sung

Hsiao

2021

J Wireless Com Network

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With rapidly changing technology, people have more and more requirements for thermal comforts regarding indoor temperature, humidity, and wind speed, and pay more attention to air quality. Indoor air quality has serious effects on the elderly, children, and those with respiratory allergies. Based on the architecture of the Internet of Things smart home, this study constructed an indoor air quality monitoring system to explore how people can live in an environment with good air quality. Among the numerous air quality indices (AQIs), the carbon dioxide index and AQI of the American Society of Heating, Refrigerating and Air-Conditioning Engineers are selected as the indices suitable for this study. The common points of the two indices are combined, and then, based on the data of the Environmental Protection Administration, indoor and outdoor environmental parameters are analyzed, and controllable environment variables are simulated to analyze their effects on air quality. This study designed effective load control using fuzzy control and developed a fuzzy rule base for simulation of the environment variables. Decision logic was used to replace the threshold control of indoor air quality in the past, and a comfortable air quality monitoring system was designed by combining the Arduino Uno development board and ESP8266 Wi-Fi wireless transmission modules.

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“…In addition to the possible evolution of the reactors, their instrumentation with new or more efficient sensors is also a potential area of evolution. The major challenge is no longer to monitor the main reaction, for which many sensors have already been proposed, even recently [63], but to obtain complementary information, mainly on yeasts and their physiological state and on the main molecules of interest (polyphenols, aromas, and so on).…”

Section: Sensorsmentioning

confidence: 99%

Study of Oenological Fermentation: Which Strategy and Which Tools?

et al. 2021

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Wine fermentation is a specific and complex research subject and its control is essential to ensure full process completion while improving wine quality. It displays several specificities, in particular, (i) musts with a very high sugar content, low pH, and some limiting nutrients, as well as a great variability in must composition according to the year, grape variety, and so on; (ii) atypical fermentation conditions with non-isothermal temperature profiles, a quasi-anaerobiosis and legal constraints with a limited and predefined list of authorized operations. New challenges have emerged, related to the increasing diversity of commercially available yeast strains; the fluctuating composition of musts, particularly owing to climate change; and sustainability, which has become a key issue. This paper synthesizes approaches implemented to address all these issues. It details the example of our laboratory that, for many years, has been developing an integrated approach to study yeast diversity, understand their metabolism, and develop new fermentation control strategies. This approach requires the development of specific fermentation devices to study yeast metabolism in a controlled environment that mimics practical conditions and to develop original fermentation control strategies. All these tools are described here, together with their role in the overall scientific strategy and complementary approaches in the literature.

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A Low-Cost IoT Device to Monitor in Real-Time Wine Alcoholic Fermentation Evolution Through CO₂Emissions

Cited by 18 publications

References 12 publications

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Building an indoor air quality monitoring system based on the architecture of the Internet of Things

Study of Oenological Fermentation: Which Strategy and Which Tools?

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A Low-Cost IoT Device to Monitor in Real-Time Wine Alcoholic Fermentation Evolution Through CO2Emissions

Cited by 18 publications

References 12 publications

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Building an indoor air quality monitoring system based on the architecture of the Internet of Things

Study of Oenological Fermentation: Which Strategy and Which Tools?

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Product

Resources

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A Low-Cost IoT Device to Monitor in Real-Time Wine Alcoholic Fermentation Evolution Through CO₂Emissions