Current State and Perspective in the Models Applicable to Oenology

Şipoş, Anca

doi:10.5772/intechopen.71711

Cited by 4 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…150 mL of hydrolysates were poured into a 250 mL flask equipped with cotton plugs and were sterilised by autoclaving at 120 °C for 20 min. The fermentation was realised at 30 °C under a 200 rpm agitation (Şipoş, 2018).…”

Section: Methodsmentioning

confidence: 99%

High yield of glucose from cassava starch hydrolysis by poly(4‐vinylpyridine) hydrochloride

Ramanandraibe¹,

Rakotondramanga²,

Alson³

et al. 2020

Int J of Food Sci Tech

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

High yield of glucose from cassava starch hydrolysis by poly(4‐vinylpyridine) hydrochloride

Ramanandraibe¹,

Rakotondramanga²,

Alson³

et al. 2020

Int J of Food Sci Tech

View full text Add to dashboard Cite

show abstract

“…Alcoholic fermentation involves many variables (both process' variables and yeasts), such as the variety of must, microbiota, and winemaking technology. Process' variables are often controlled empirically [6], while yeast growth is checked either directly through plate count and molecular methods or indirectly through chemical parameters such as CO 2 emission, alcohol production, sugar consumption [7]. These approaches could be time consuming or expensive, while a predictive model could be useful to know a priori the process.…”

Section: Alcoholic Fermentationmentioning

confidence: 99%

Wine Microbiology and Predictive Microbiology: A Short Overview on Application, and Perspectives

et al. 2022

View full text Add to dashboard Cite

Predictive microbiology (PM) is an essential element in food microbiology; its aims are the determination of the responses of a given microorganism combining mathematical models with experimental data under certain environmental conditions, and the simulation a priori of the growth/inactivation of a population based on the known traits of a food matrix. Today, a great variety of models exist to describe the behaviour of several pathogenic and spoilage microorganisms in foods. In winemaking, many mathematical models have been used for monitoring yeast growth in alcoholic fermentation as well as to predict the risk of contamination of grapes and grape products by mycotoxin producing fungi over the last years, but the potentialities of PM in wine microbiology are underestimated. Thus, the goals of this review are to show some applications and perspectives in the following fields: (1) kinetics of alcoholic and malolactic fermentation; (2) models and approaches for yeasts and bacteria growth/inactivation; (3) toxin production and removal.

show abstract

“…A good solution to obtain indirect information from a process consists of building a neuronal model, which-based on the experimental data available-can offer information concerning the values of state variables, which can then be used in an automation control system [2].…”

Section: Modeling the Alcoholic Fermentation Process Of The White Win...mentioning

confidence: 99%

“…are favourable, a biotechnological process can induce the growth of a microorganism population, called a biomass, in a vessel through the consumption of some nutrients (carbon, nitrogen, oxygen, vitamins, etc.) that represent the substrate [2]. In the vessel (bioreactor), many biochemical and biological reactions take place simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process

et al. 2020

View full text Add to dashboard Cite

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.

show abstract

Current State and Perspective in the Models Applicable to Oenology

Cited by 4 publications

References 21 publications

High yield of glucose from cassava starch hydrolysis by poly(4‐vinylpyridine) hydrochloride

High yield of glucose from cassava starch hydrolysis by poly(4‐vinylpyridine) hydrochloride

Wine Microbiology and Predictive Microbiology: A Short Overview on Application, and Perspectives

Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process

Contact Info

Product

Resources

About