Submit to Fermentation Review for Fermentation Propose a Special Issue

Journal Menu

Journal Browser

Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Fermentation for Food and Beverages".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 36608

Share This Special Issue

Special Issue Editors

Dr. Claudia Gonzalez Viejo

E-Mail Website
Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: food science and engineering; sensory science; computer vision; sensors; robotics; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sigfredo Fuentes

E-Mail Website
Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of digital technologies based on artificial intelligence (AI) such as computer vision, machine learning, sensor arrays, robotics, biometrics, and remote sensing in the food and beverage industries has been of higher interest in recent years. The latter aids in developing rapid and cost-effective techniques for the processing and assessment of final products. In fermented foods and beverages, the implementation of these novel technologies is becoming more critical, especially for monitoring the production and fermentation process and the quality and acceptability of final products.

This Special Issue focuses on scientific reports and high-quality papers based on the development and application of digital technologies in the field of fermented foods and beverages, including monitoring, the improvement of processing performance, product quality, and acceptability. Submissions may be related to the assessment of parameters such as physicochemical features, the development of volatile compounds such as those pertaining to aromas, carbon dioxide production and release, chemical fingerprinting, and sensory profiles from trained panels or consumer tests. It is highly encouraged that authors incorporate research content that includes the automation and integration of these technologies using artificial intelligence and Digital Twins in the final product and along the production chain.

Dr. Claudia Gonzalez Viejo
Prof. Dr. Sigfredo Fuentes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fermentation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sensors and sensor networks
machine learning
artificial intelligence
computer vision
non-invasive sensing technology
electronic noses
electronic tongues
sensory biometrics
robotics
digital twins

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

18 pages, 2329 KiB

Open AccessArticle

Modelling of Catechin Extraction from Red Grape Solids under Conditions That Simulate Red Wine Fermentation

by Judith Unterkofler, David W. Jeffery, Patrick C. Setford, Jean Macintyre and Richard A. Muhlack

Fermentation 2023, 9(4), 394; https://doi.org/10.3390/fermentation9040394 - 19 Apr 2023

Cited by 1 | Viewed by 2099

Abstract

Digital control systems are well established in many industries and could find application in the wine sector. Of critical importance to red wine quality, the efficient and targeted extraction of polyphenols from red grape solids during alcoholic fermentation could be a focus for automation. Smart technologies such as model predictive control (MPC) or fuzzy logic appear ideal for application in a complex process such as wine polyphenol extraction, but require mathematical models that accurately describe the system. The aim of this study was to derive and validate a model describing the extraction of catechin (a representative polyphenol) from red grape solids under simulated fermentation conditions. The impact of ethanol, fermentable sugar, and temperature on extraction rate was determined, with factor conditions chosen to emulate those present in industry practice. A first-order approach was used to generate an extraction model based on mass conservation that incorporated temperature and sugar dependency. Coefficients of determination (R²) for all test scenarios exceeded 0.94, indicating a good fit to the experimental data. Sensitivity analysis for the extraction rate and internal cross-validation showed the model to be robust, with a small standard error in cross-validation (SECV) of 0.11 and a high residual predictive deviation (RPD) of 17.68. The model that was developed is well suited to digital technologies where low computational overheads are desirable, and industrial application scenarios are presented for future implementation of the work. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

13 pages, 25978 KiB

Open AccessArticle

Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers

by Claudia Gonzalez Viejo, Carmen Hernandez-Brenes, Raul Villarreal-Lara, Irma C. De Anda-Lobo, Perla A. Ramos-Parra, Esther Perez-Carrillo, Jorge A. Clorio-Carrillo, Eden Tongson and Sigfredo Fuentes

Fermentation 2023, 9(3), 269; https://doi.org/10.3390/fermentation9030269 - 9 Mar 2023

Cited by 4 | Viewed by 2211

Abstract

The study of emotional responses from consumers toward beer products is an important digital tool to obtain novel information about the acceptability of beers and their optimal physicochemical composition. This research proposed the use of biometrics to assess emotional responses from Mexican beer consumers while tasting top- and bottom-fermented samples. Furthermore, a novel emotional validation assessment using proven evoking images for neutral, negative, and positive emotions was proposed. The results showed that emotional responses obtained from self-reported emoticons and biometrics are correlated to the specific emotions evoked by the visual, aroma, and taste aspects of beers. Consumers preferred bottom-fermentation beers and disliked the wheat-based and higher-bitterness samples. Chemical compounds and concentrations were in accordance to previously reported research for similar beer styles. However, the levels of hordenine were not high enough to evoke positive emotions in the biometric assessment, which opens additional research opportunities to assess higher concentrations of this alkaloid to increase the happiness perception of low or non-alcoholic beers. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

12 pages, 1905 KiB

Open AccessCommunication

Characterization of Sparkling Wine Based on Polyphenolic Profiling by Liquid Chromatography Coupled to Mass Spectrometry

by Eleonora Oliva, Aina Mir-Cerdà, Manuel Sergi, Sònia Sentellas and Javier Saurina

Fermentation 2023, 9(3), 223; https://doi.org/10.3390/fermentation9030223 - 25 Feb 2023

Cited by 6 | Viewed by 1879

Abstract

Polyphenols are phytochemicals naturally present in wines that arouse much interest in the scientific community due to their healthy properties. In addition, their role as descriptors of various wine qualities, such as the geographical origin or the grape variety, cannot be underestimated. Here, Pinot Noir and Xarel·lo monovarietal samples belonging to the sparkling wine production process have been studied, corresponding to base wines from a first alcoholic fermentation (plus malolactic in some cases), base wines resulting from tartaric stabilization, and sparkling wines from a second alcoholic fermentation aged for 3 and 7 months. One of the objectives of this paper is to obtain valuable chemical and oenological information by processing a huge amount of data with suitable chemometric methods. High-performance liquid chromatography coupled with ultraviolet spectroscopy and tandem mass spectrometry (HPLC-UV-MS/MS) has been used for the determination of polyphenols in wines and related samples. The method relies on reversed-phase mode and further detection by multiple reaction monitoring. Concentrations of relevant phenolic compounds have been determined, and the resulting compositional data have been used for characterization purposes. Exploratory studies by principal component analysis have shown that samples can be discriminated according to varietal and quality issues. Further classification models have been established to assign unknown samples to their corresponding classes. For this purpose, a sequential classification tree has been designed involving both variety and quality classes, and an excellent classification rate has been achieved. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

14 pages, 2632 KiB

Open AccessArticle

Impacts of Reduced (Vacuum) Pressure on Yeast Fermentation as Assessed Using Standard Methods and Automated Image Analysis

by Mario Guadalupe-Daqui, Mandi Chen, Paul J. Sarnoski, Renée M. Goodrich-Schneider and Andrew J. MacIntosh

Fermentation 2023, 9(2), 155; https://doi.org/10.3390/fermentation9020155 - 5 Feb 2023

Cited by 2 | Viewed by 3589

Abstract

In this study the combinatory effect of several extrinsic factors on reduced (vacuum) pressure fermentations was explored. Specifically, the pressure, temperature, and FAN levels of high gravity Saccharomyces cerevisiae fermentations were manipulated, while yeast morphology was assessed using automated multivariate image analysis. Fermentation attributes including yeast growth, viability, and ethanol production were monitored using standard methods. Across all FAN and temperature levels, reduced pressure (vacuum pressure) fermentations resulted in a greater than or equal number of cells in suspension, higher average viability, and greater ethanol production in comparison to atmospheric pressure fermentations; however, the magnitude of the effect varied with extrinsic factors. The image analysis revealed that while yeast size was extremely variable across all fermentations, the ratio of vacuole to cell area consistently decreased over each fermentation and could be used to predict the point where the yeast experienced a sharp decline in viability ending the fermentation. This study showed that a combination of traditional measurements and novel automated analyses can be used by brewers to anticipate performance and endpoints of their fermentations, and that reduced pressure can have significant effects upon the rate and final ethanol concentration of variable industrial fermentations. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

12 pages, 2047 KiB

Open AccessArticle

Spatially Offset Raman Spectroscopic (SORS) Analysis of Wine Alcoholic Fermentation: A Preliminary Study

by Daniel Schorn-García, Jokin Ezenarro, Laura Aceña, Olga Busto, Ricard Boqué, Barbara Giussani and Montserrat Mestres

Fermentation 2023, 9(2), 115; https://doi.org/10.3390/fermentation9020115 - 25 Jan 2023

Cited by 3 | Viewed by 2281

Abstract

Spatially offset Raman spectroscopy (SORS) is a non-invasive analytical technique that allows the analysis of samples through a container. This makes it an effective tool for studying food and beverage products, as it can measure the sample without being affected by the packaging or the container. In this study, a portable SORS equipment was used for the first time to analyse the alcoholic fermentation process of white wine. Different sample measurement arrangements were tested in order to determine the most effective method for monitoring the fermentation process and predicting key oenological parameters. The best results were obtained when the sample was directly measured through the glass container in which the fermentation was occurring. This allowed for the accurate monitoring of the process and the prediction of density and pH with a root mean square error of cross-validation (RMSECV) of 0.0029 g·L⁻¹ and 0.04, respectively, and R² values of 0.993 and 0.961 for density and pH, respectively. Additionally, the sources of variability depending on the measurement arrangements were studied using ANOVA-Simultaneous Component Analysis (ASCA). Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

19 pages, 2840 KiB

Open AccessArticle

The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies

by Hanjing Wu, Claudia Gonzalez Viejo, Sigfredo Fuentes, Frank R. Dunshea and Hafiz A. R. Suleria

Fermentation 2023, 9(1), 68; https://doi.org/10.3390/fermentation9010068 - 13 Jan 2023

Cited by 10 | Viewed by 4600

Abstract

Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

13 pages, 3296 KiB

Open AccessArticle

Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle

by Natalie Harris, Claudia Gonzalez Viejo, Christopher Barnes and Sigfredo Fuentes

Fermentation 2023, 9(1), 10; https://doi.org/10.3390/fermentation9010010 - 23 Dec 2022

Cited by 7 | Viewed by 3204

Abstract

Due to increased fraud rates through counterfeiting and adulteration of wines, it is important to develop novel non-invasive techniques to assess wine quality and provenance. Assessment of quality traits and provenance of wines is predominantly undertaken with complex chemical analysis and sensory evaluation, which tend to be costly and time-consuming. Therefore, this study aimed to develop a rapid and non-invasive method to assess wine vintages and quality traits using digital technologies. Samples from thirteen vintages from Dookie, Victoria, Australia (2000–2021) of Shiraz were analysed using near-infrared spectroscopy (NIR) through unopened bottles to assess the wine chemical fingerprinting. Three highly accurate machine learning (ML) models were developed using the NIR absorbance values as inputs to predict (i) wine vintage (Model 1; 97.2%), (ii) intensity of sensory descriptors (Model 2; R = 0.95), and (iii) peak area of volatile aromatic compounds (Model 3; R = 0.88). The proposed method will allow the assessment of provenance and quality traits of wines without the need to open the wine bottle, which may also be used to detect wine fraud and provenance. Furthermore, low-cost NIR devices are available in the market with required spectral range and sensitivity, which can be affordable for winemakers and retailers and can be used with the machine learning models proposed here. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

16 pages, 3705 KiB

Open AccessArticle

Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling

by Claudia Gonzalez Viejo, Natalie M. Harris and Sigfredo Fuentes

Fermentation 2022, 8(10), 516; https://doi.org/10.3390/fermentation8100516 - 7 Oct 2022

Cited by 15 | Viewed by 8414

Abstract

Sourdough bread (SB) has increased popularity due to health benefits and higher interest in artisan breadmaking due to social isolation during the COVID-19 pandemic. However, quality traits and consumer assessment are still limited to complex laboratory analysis and sensory trials. In this research, new and emerging digital technologies were tested to assess quality traits of SB made from six different flour sources. The results showed that machine learning (ML) models developed to classify the type of wheat used for flours (targets) from near-infrared (NIR) spectroscopy data (Model 1) and a low-cost electronic nose (Model 2) as inputs rendered highly accurate and precise models (96.3% and 99.4%, respectively). Furthermore, ML regression models based on the same inputs for NIR (Model 3) and e-nose (Model 4) were developed to automatically assess 16 volatile aromatic compounds (targets) using GC-MS as ground-truth. To reiterate, models with high accuracy and performance were obtained with correlation (R), determination coefficients (R²), and slope (b) of R = 0.97; R² = 0.94 and b = 0.99 for Model 3 and R = 0.99; R² = 0.99 and b = 0.99 for Model 4. The development of low-cost instrumentation and sensors could make possible the accessibility of hardware and software to the industry and artisan breadmakers to assess quality traits and consistency of SB. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Figure 1

Review

Jump to: Research

24 pages, 3576 KiB

Open AccessReview

Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications

by Thara Seesaard and Chatchawal Wongchoosuk

Fermentation 2022, 8(7), 302; https://doi.org/10.3390/fermentation8070302 - 26 Jun 2022

Cited by 31 | Viewed by 6487

Abstract

Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications. Full article

(This article belongs to the Special Issue Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies)

► Show Figures

Journal Menu

Journal Browser

Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI