Wine quality analysis by the structural causal model (SCM)
Bayes network modelling for structural causal analysis between wine physicochemical data and quantitative human quality blind assessments is applied. The large dataset of white and red "Vinho Verde'' wine samples from Portugal, which was available from an open data repository for mach...
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| Format: | Article |
| Language: | English |
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Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek
2023-01-01
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| Series: | Croatian Journal of Food Science and Technology |
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| Online Access: | https://hrcak.srce.hr/file/449892 |
| _version_ | 1797384821056471040 |
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| author | Želimir Kurtanjek |
| author_facet | Želimir Kurtanjek |
| author_sort | Želimir Kurtanjek |
| collection | DOAJ |
| description | Bayes network modelling for structural causal analysis between wine physicochemical data and quantitative human quality blind assessments is applied. The large dataset of white and red "Vinho Verde'' wine samples from Portugal, which was available from an open data repository for machine learning at the University of California at Irving, was analysed. The dataset contains 4898 white and 1599 red samples evaluated by blind tastes by a minimum of 3 sensory assessors and 12 physicochemical properties. The casual effects of wine analytic data on human quality evaluations are evaluated numerically by Bayes neural networks for adjusted sets of the covariates as marginal distributions and presented graphically as partial dependence plots. Structural causal analysis revealed important differences between the most important variables for quality predictions and the individual causal effects. Bayes neural network models of the partial dependencies show more pronounced nonlinear effects for red wines compared to white wine quality. The artificial intelligence models with boosted random decision tree forests for untrained wine samples yield a 5% relative standard error of predictions compared to 12% for the linear models and ordinary least squares estimation. For red wine, the most important direct causal quality effects are caused by alcohol, volatile acidity, and sulphates. Alcohol improves quality with a maximum plateau at 14%, while volatile acidity has a strong proportional negative effect. The effect of sulphates is highly nonlinear with maximum positive effect at a concentration of 1 g/L of \(K_{2}SO_{4}\). For the white wine samples causal effects are linear with positive effects of alcohol and negative effects of volatile and fixed acidity. The developed structural causal model enables evaluation of targeted wine production interventions, named as “doing x, do(x) models”, as restructured adjusted Bayes networks. It leads to potential applications of artificial intelligence in wine production technology and process quality control. |
| first_indexed | 2024-03-08T21:45:12Z |
| format | Article |
| id | doaj.art-951c66df5978447ab1bfe9be176cf0f2 |
| institution | Directory Open Access Journal |
| issn | 1847-3466 1848-9923 |
| language | English |
| last_indexed | 2024-03-08T21:45:12Z |
| publishDate | 2023-01-01 |
| publisher | Josip Juraj Strossmayer University of Osijek, Faculty of Food Technology Osijek |
| record_format | Article |
| series | Croatian Journal of Food Science and Technology |
| spelling | doaj.art-951c66df5978447ab1bfe9be176cf0f22023-12-20T09:31:52ZengJosip Juraj Strossmayer University of Osijek, Faculty of Food Technology OsijekCroatian Journal of Food Science and Technology1847-34661848-99232023-01-01152173184191Wine quality analysis by the structural causal model (SCM)Želimir Kurtanjek0University of Zagreb, Faculty of Food Technology and Biotechnology, Zagreb, CroatiaBayes network modelling for structural causal analysis between wine physicochemical data and quantitative human quality blind assessments is applied. The large dataset of white and red "Vinho Verde'' wine samples from Portugal, which was available from an open data repository for machine learning at the University of California at Irving, was analysed. The dataset contains 4898 white and 1599 red samples evaluated by blind tastes by a minimum of 3 sensory assessors and 12 physicochemical properties. The casual effects of wine analytic data on human quality evaluations are evaluated numerically by Bayes neural networks for adjusted sets of the covariates as marginal distributions and presented graphically as partial dependence plots. Structural causal analysis revealed important differences between the most important variables for quality predictions and the individual causal effects. Bayes neural network models of the partial dependencies show more pronounced nonlinear effects for red wines compared to white wine quality. The artificial intelligence models with boosted random decision tree forests for untrained wine samples yield a 5% relative standard error of predictions compared to 12% for the linear models and ordinary least squares estimation. For red wine, the most important direct causal quality effects are caused by alcohol, volatile acidity, and sulphates. Alcohol improves quality with a maximum plateau at 14%, while volatile acidity has a strong proportional negative effect. The effect of sulphates is highly nonlinear with maximum positive effect at a concentration of 1 g/L of \(K_{2}SO_{4}\). For the white wine samples causal effects are linear with positive effects of alcohol and negative effects of volatile and fixed acidity. The developed structural causal model enables evaluation of targeted wine production interventions, named as “doing x, do(x) models”, as restructured adjusted Bayes networks. It leads to potential applications of artificial intelligence in wine production technology and process quality control.https://hrcak.srce.hr/file/449892wine qualityartificial intelligencestructural causal model |
| spellingShingle | Želimir Kurtanjek Wine quality analysis by the structural causal model (SCM) Croatian Journal of Food Science and Technology wine quality artificial intelligence structural causal model |
| title | Wine quality analysis by the structural causal model (SCM) |
| title_full | Wine quality analysis by the structural causal model (SCM) |
| title_fullStr | Wine quality analysis by the structural causal model (SCM) |
| title_full_unstemmed | Wine quality analysis by the structural causal model (SCM) |
| title_short | Wine quality analysis by the structural causal model (SCM) |
| title_sort | wine quality analysis by the structural causal model scm |
| topic | wine quality artificial intelligence structural causal model |
| url | https://hrcak.srce.hr/file/449892 |
| work_keys_str_mv | AT zelimirkurtanjek winequalityanalysisbythestructuralcausalmodelscm |