Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality

The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers’ demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding the factors that determine the balance between the yeast-yeast interactions and their responses triggered in complex environments. Our previous studies demonstrated that the strain Hanseniaspora guilliermondii UTAD222 has potential to be used as an adjunct of S. cerevisiae in the wine industry due to its positive impact on the fruity and floral character of wines. To rationalize the use of this yeast consortium, this study aims to understand the influence of production factors such as sugar and nitrogen levels, fermentation temperature, and the level of co-inoculation of H. guilliermondii UTAD222 in shaping fermentation and wine composition. For that purpose, a Central Composite experimental Design was applied to investigate the combined effects of the four factors on fermentation parameters and metabolites produced. The patterns of variation of the response variables were analyzed using machine learning methods, to describe their clustered behavior and model the evolution of each cluster depending on the experimental conditions. The innovative data analysis methodology adopted goes beyond the traditional univariate approach, being able to incorporate the modularity, heterogeneity, and hierarchy inherent to metabolic systems. In this line, this study provides preliminary data and insights, enabling the development of innovative strategies to increase the aromatic and fermentative potential of H. guilliermondii UTAD222 by modulating temperature and the availability of nitrogen and/or sugars in the medium. Furthermore, the strategy followed gathered knowledge to guide the rational development of mixed blends that can be used to obtain a particular wine style, as a function of fermentation conditions.

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Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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Selection of important features and predicting wine quality using machine learning techniques

Procedia Computer Science ◽

10.1016/j.procs.2017.12.041 ◽

2018 ◽

Vol 125 ◽

pp. 305-312 ◽

Cited By ~ 11

Author(s):

Yogesh Gupta

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Wine Quality ◽

Learning Techniques ◽

Selection Of

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A Study and Analysis of Machine Learning Techniques in Predicting Wine Quality

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a5854.0510121 ◽

2021 ◽

Vol 10 (1) ◽

pp. 314-321

Author(s):

Mohit Gupta ◽

Vanmathi C

Keyword(s):

Machine Learning ◽

Human Heart ◽

Machine Learning Techniques ◽

Wine Quality ◽

High Quality ◽

Wine Consumption ◽

Independent Variables ◽

Quantitative Metrics ◽

Learning Techniques

In today’s trend consumers are very much concern about the quality of the product in turn, Industries are all working on various methodologies to ensure the high quality in their products. Most of consumers judge the quality of the product based on the certification obtained for the product. In Earlier days, the quality is measured and validated only through human experts. Nowadays most of the validation tasks are automated through software and this ease the burden of human experts by assisting with them in predicting the quality of the product and that leads to greater a reduction of time spent. Wine consumption has increased rapidly over the last few decades, not only for recreational purposes but also due of its inherent health benefits especially to human heart. This chapter demonstrates the usage of various machine learning techniques in predicting the quality of wine and results are validated through various quantitative metrics. Moreover the contribution of various independent variables facilitating the final outcome is precisely portrayed.

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Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images

Machine Learning ◽

10.4018/978-1-60960-818-7.ch406 ◽

2012 ◽

pp. 817-829

Author(s):

Nikolaos Giannakeas ◽

Dimitrios I. Fotiadis

Keyword(s):

Machine Learning ◽

Image Processing ◽

Expression Profiles ◽

Machine Learning Techniques ◽

Experimental Conditions ◽

Microarray Image ◽

Learning Techniques ◽

Common Substrate ◽

Processing Techniques ◽

Comprehensive Measurement

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

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An Investigation of Wine Quality Testing using Machine Learning Techniques

Journal of Xidian University ◽

10.37896/jxu14.4/408 ◽

2020 ◽

Vol 14 (4) ◽

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Wine Quality ◽

Learning Techniques ◽

Quality Testing

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How to Assign Weights to Values?

Balkan Journal of Philosophy ◽

10.5840/bjp202012213 ◽

2020 ◽

Vol 12 (2) ◽

pp. 111-118

Author(s):

Silviya Serafimova ◽

Keyword(s):

Machine Learning ◽

Human Behavior ◽

Positive Impact ◽

Machine Learning Techniques ◽

Learning Techniques

In this paper, one of my primary objectives is to analyze why adopting particular machine-learning techniques and using a moral AI as an adviser is an insufficient condition for eradicating racist human attitudes. By outlining some difficulties in justifying what artificial “explicit ethical agents” in Moor’s sense should look like, I explore why, even if the development of machine-learning techniques can be accepted in epistemic terms, it does not follow that the techniques in question will have a positive impact in changing immoral human behavior.

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Red Wine Quality Prediction Using Machine Learning Techniques

2020 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci48352.2020.9104095 ◽

2020 ◽

Author(s):

Sunny Kumar ◽

Kanika Agrawal ◽

Nelshan Mandan

Keyword(s):

Machine Learning ◽

Red Wine ◽

Machine Learning Techniques ◽

Quality Prediction ◽

Wine Quality ◽

Learning Techniques

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Gender-Based Analysis of Risk Factors for Dementia Using Senior Cohort

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17197274 ◽

2020 ◽

Vol 17 (19) ◽

pp. 7274

Author(s):

Jaekue Choi ◽

Lee-Nam Kwon ◽

Heuiseok Lim ◽

Hong-Woo Chun

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Factor Analysis ◽

Risk Factor ◽

Machine Learning Techniques ◽

Risk Factor Analysis ◽

Experimental Conditions ◽

Men And Women ◽

Dementia Risk ◽

The Difference

Globally, one of the biggest problems with the increase in the elderly population is dementia. However, dementia still has no fundamental cure. Therefore, it is important to predict and prevent dementia early. For early prediction of dementia, it is crucial to find dementia risk factors that increase a person’s risk of developing dementia. In this paper, the subject of dementia risk factor analysis and discovery studies were limited to gender, because it is assumed that the difference in the prevalence of dementia in men and women will lead to differences in the risk factors for dementia among men and women. This study analyzed the Korean National Health Information System—Senior Cohort using machine-learning techniques. By using the machine-learning technique, it was possible to reveal a very small causal relationship between data that are ignored using existing statistical techniques. By using the senior cohort, it was possible to analyze 6000 data that matched the experimental conditions out of 558,147 sample subjects over 14 years. In order to analyze the difference in dementia risk factors between men and women, three machine-learning-based dementia risk factor analysis models were constructed and compared. As a result of the experiment, it was found that the risk factors for dementia in men and women are different. In addition, not only did the results include most of the known dementia risk factors, previously unknown candidates for dementia risk factors were also identified. We hope that our research will be helpful in finding new dementia risk factors.

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