Special Issue "Smart Production and Assessment of Fermented Foods and Beverages Using Digital Technologies"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 8318

Special Issue Editors

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
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 2000 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

Published Papers (8 papers)

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Research

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Article
Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers
Fermentation 2023, 9(3), 269; https://doi.org/10.3390/fermentation9030269 - 09 Mar 2023
Viewed by 431
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 [...] Read more.
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
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Communication
Characterization of Sparkling Wine Based on Polyphenolic Profiling by Liquid Chromatography Coupled to Mass Spectrometry
Fermentation 2023, 9(3), 223; https://doi.org/10.3390/fermentation9030223 - 25 Feb 2023
Viewed by 380
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, [...] Read more.
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
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Article
Impacts of Reduced (Vacuum) Pressure on Yeast Fermentation as Assessed Using Standard Methods and Automated Image Analysis
Fermentation 2023, 9(2), 155; https://doi.org/10.3390/fermentation9020155 - 05 Feb 2023
Viewed by 529
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 [...] Read more.
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
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Article
Spatially Offset Raman Spectroscopic (SORS) Analysis of Wine Alcoholic Fermentation: A Preliminary Study
Fermentation 2023, 9(2), 115; https://doi.org/10.3390/fermentation9020115 - 25 Jan 2023
Viewed by 531
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 [...] Read more.
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
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Article
The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies
Fermentation 2023, 9(1), 68; https://doi.org/10.3390/fermentation9010068 - 13 Jan 2023
Viewed by 759
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 [...] Read more.
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
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Article
Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle
Fermentation 2023, 9(1), 10; https://doi.org/10.3390/fermentation9010010 - 23 Dec 2022
Viewed by 1187
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, [...] Read more.
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
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Article
Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling
Fermentation 2022, 8(10), 516; https://doi.org/10.3390/fermentation8100516 - 07 Oct 2022
Cited by 4 | Viewed by 1146
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, [...] Read more.
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
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Review

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Review
Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications
Fermentation 2022, 8(7), 302; https://doi.org/10.3390/fermentation8070302 - 26 Jun 2022
Cited by 5 | Viewed by 2340
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) [...] Read more.
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
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