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Analysis of institutional authors

Moya-Almeida VAuthorDiezma-Iglesias BAuthorCorrea-Hernando EAuthorVaquero-Miguel CAuthorAlvarado-Arias NAuthor
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Setpoint temperature estimation to achieve target solvent concentrations in S. cerevisiae fermentations using inverse neural networks and fuzzy logic

Publicated to:Engineering Applications Of Artificial Intelligence. 127 107248- - 2024-01-01 127(), DOI: 10.1016/j.engappai.2023.107248

Authors: Moya-Almeida, V; Diezma-Iglesias, B; Correa-Hernando, E; Vaquero-Miguel, C; Alvarado-Arias, N

Affiliations

Univ Hemisferios UHE, Fac Ingn, Paseo Univ 300, Quito 170147, Ecuador - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Agron Alimentaria & Biosis, Dept Quim & Tecnol Alimentos, EnotecUPM, Ave Puerta Hierro 2-4, Madrid 28040, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Agron Alimentaria & Biosis, Lab Propiedades Fis & Tecn Avanzadas Agroalimentac, Ave Puerta Hierro 2-4, Madrid 28040, Spain - Author
Univ UTE, Fac Arquitectura & Urbanismo, Calle Rumipamba S-N & Bourgeois, Quito, Ecuador - Author
Universidad Politécnica de Madrid - Author
Universidad Politécnica de Madrid , Universidad de los Hemisferios (UHE) - Author
Universidad UTE - Author
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Abstract

Over the years, many technical advances have been made to improve the final quality of beers by controlling the concentrations of compounds obtained at the end of alcoholic fermentation. However, these efforts have mainly focused on increasing ethanol and reducing other compounds considered defects. This study addresses the challenge of obtaining specific concentrations of four solvent compounds (isobutanol, ethyl acetate, amyl alcohols, and n-propanol) produced by the yeast S. cerevisiae Safale S04, determined by an expert. A model based on four inverse neural networks (INNs) has been developed to predict the target temperature required to achieve the desired concentrations. These INNs have been trained using virtual data generated by four artificial neural networks (ANNs), as described in detail in previous work. For implementation, a fuzzy control system based on the Mamdani inference method was utilized. To experimentally validate the results, four complete fermentations were conducted. The INNs were found to be accurate tools for predicting the target temperatures based on predetermined compound concentrations, with R2 values ranging from 0.982 to 0.986. When comparing the experimental concentration data, the most accurate prediction was achieved for n-propanol, with an average error of 0.18 mg L−1, while ethyl acetate had an error of 0.25 mg L−1, isobutanol had an error of 0.48 mg L−1, and amyl alcohols, being the least precise prediction, had an error of 0.83 mg L−1.

Keywords
beer fermentationbeer qualityfuzzy controllerinverse neural networkss. cerevisiaeBeer fermentationBeer qualityControl-systemExpert systemFuzzy controllerInverse neural networksS. cerevisiae

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Engineering Applications Of Artificial Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 24/197, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-05-16:

  • WoS: 3
  • Scopus: 3
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-16:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 45.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 45 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/85818/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 32
  • Downloads: 8
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Ecuador.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MOYA ALMEIDA, VINICIO ANTONIO) and Last Author (ALVARADO ARIAS, NATALIA).