A machine learning application in wine quality prediction

Bhardwaj, Piyush; Tiwari, Parul; Olejar, Kenneth J.; Parr, Wendy V.; Kulasiri, Don

doi:10.1016/j.mlwa.2022.100261

Cited by 39 publications

(28 citation statements)

References 35 publications

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“…Precision refers to the value obtained when True Positive is divided by the sum of True Positive and False Positive values of a confusion matrix [4].…”

Section: Methodsmentioning

confidence: 99%

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Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis

Thapa¹

2022

Int. J. Res. Publ. Rev.

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The major objective of this paper is to predict wine quality based on chemical attributes using binary logistic regression model. The evaluation of wine quality in this study is analyzed based on statistical methods, correlation analysis, and regression analysis. This study highlights several features are not relevant (only six essential variables were enough) to predict wine quality. The result showed that this method is robust enough to predict wine quality with an overall accuracy of more than 75%. Similar techniques or machine learning methods can help in predicting more accurately.

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“…Precision refers to the value obtained when True Positive is divided by the sum of True Positive and False Positive values of a confusion matrix [4].…”

Section: Methodsmentioning

confidence: 99%

“…The true negative rate, also known as specificity, is the number of accurate negative class predictions divided by sum of True Negative and False Positive of a confusion matrix [4]. Specificity = TNR / (TNR + FPR)…”

Section: Methodsmentioning

confidence: 99%

Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis

Thapa¹

2022

Int. J. Res. Publ. Rev.

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“…Extra trees classifier, Gradient boosting classifier, Extreme gradient boost (XGB), and Random forest (RF) classifier (as shown in Table 3) are among the most well-known feature selection methods used to understand important chemical data [37]. In this paper, feature selection methods were used to understand which chemical parameters could characterize New Zealand Pinot noir wines' regions of origin, vintages (old/new vintages), and price points by using in-house developed codes based on Python.…”

Section: Select Important Key Chemical Parameters To Influence Region...mentioning

confidence: 99%

“…Extra trees classifier builds a set of unpruned decision trees using the standard top-down technique [37] Gradient boosting builds new models from an ensemble of weak models, aiming to minimize the loss function [37] See Table 2 3…”

Section: Select Important Key Chemical Parameters To Influence Region...mentioning

confidence: 99%

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Deed

Kilmartin

et al. 2023

Foods

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Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (ML) was designed to predict Pinot noir wines’ provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (ML) could predict Pinot noir wines’ vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

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“…Product distribution and marketing in the automobile E-commerce business, on the other hand, face several challenges [8], [9]. These include analyzing whether the product has a low defect rate throughout its warranty period and mapping failure features to the device's existing feature set [10].…”

Section: Introductionmentioning

confidence: 99%

Editorial cloud collaborative service improves authorized industrial server database performance

Eswara

Muthukumar²

2022

IJEECS

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The E-commerce platform for the automotive industry has various obstacles regarding product distribution and sales marketing. Problems such as identifying the product's low defect rate and mapping failure features to the product's existing quality set, both of which occur in the actual world, are included in this category. With the help of an editorial cloud collaborative service, this study will focus primarily on identifying the core causes of product failure. The machine learning confidential (MLC) protocol is used to authenticate the editor's identity when accessing the industrial server's database. A cloud-based collaborative editing service can also be used to extract the root cause of a specific problem from customer complaints. There may be some product flaws that can be remedied with extra features gathered from the end-user to understand real-world practicality better and ensure product accuracy reaches 100% target.

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A machine learning application in wine quality prediction

Cited by 39 publications

References 35 publications

Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis

Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Editorial cloud collaborative service improves authorized industrial server database performance

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