Nonlinear classification of commercial Mexican tequilas

Ballabio, Davide; Gómez-Carracedo, M.P.; Pérez-Caballero, Guadalupe

doi:10.1002/cem.2939

Cited by 11 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is widely used in several completely different fields; detection of regions of interests, image editing, texture analysis, visual aesthetic and quality assessment (Datta et al, 2006; Marchesotti et al, 2011; Romero et al, 2012; Fernandez-Lozano et al, 2015; Mata et al, 2018) and more recently in Carballal et al (2019b) or for microbiome analysis (Liu et al, 2017; Roguet et al, 2018), authentication of tequilas (Pérez-Caballero et al, 2017; Andrade et al, 2017), pathogenic point mutations (Rogers et al, 2018) or forensic identification (Gómez et al, 2018). Finally, with regard to the extraction of characteristics from images, some recently published works have been revised (Xu, Wang & Wang, 2018; Ali et al, 2016b; Wang et al, 2018; Sun et al, 2018; Zafar et al, 2018b).…”

Section: Methodsmentioning

confidence: 99%

Visual complexity modelling based on image features fusion of multiple kernels

Fernández-Lozano

Carballal

Machado

et al. 2019

PeerJ

View full text Add to dashboard Cite

Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans’ perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

show abstract

Section: Methodsmentioning

confidence: 99%

Visual complexity modelling based on image features fusion of multiple kernels

Fernández-Lozano

Carballal

Machado

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…Ceballos-Magaña et al [99] analyzed the mineral content in tequilas from different regions, and developed models using the linear support vector machine (SVM) algorithm and testing different percentages of data division from 40 to 70% for training, thus obtaining accuracies within the 96-100% range to classify samples per region for authenticity purposes. Another application of ML in tequila was published by Andrade et al [100] who analyzed samples with an ultraviolet-visible (UV-VIS) spectrometer and used the absorbance values within the 250-550 nm range as inputs to classify samples into three different types: (i) White, (ii) rested and (iii) aged. The authors compared different algorithms from discriminant analysis, SVM, and counter-propagation ANN, getting the best results from quadratic discriminant analysis combined with principal components analysis (PCA) with an accuracy of 89%.…”

Section: Machine Learning In Alcoholic Beveragesmentioning

confidence: 99%

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

et al. 2019

View full text Add to dashboard Cite

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

show abstract

“…Machine learning is widely used in several completely different fields; detection of regions of interests, image editing, texture analysis, visual aesthetic and quality assessment (Datta et al, 2006;Marchesotti et al, 2011;Romero et al, 2012;Fernandez-Lozano et al, 2015;Mata et al, 2018) and more recently in Carballal et al (2019b) or for microbiome analysis (Liu et al, 2017;Roguet et al, 2018), authentication of tequilas (Pérez-Caballero et al, 2017;Andrade et al, 2017), pathogenic point mutations (Rogers et al, 2018) or forensic identification (Gómez et al, 2018). Finally, with regard to the extraction of characteristics from images, some recently published works have been revised (Xu et al, 2018;Ali et al, 2016b;Wang et al, 2018;Sun et al, 2018;Zafar et al, 2018b).…”

Section: /20mentioning

confidence: 99%

Peer Review #3 of "Visual complexity modelling based on image features fusion of multiple kernels (v0.3)"

2019

View full text Add to dashboard Cite

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field Forsythe et al. (2011); Machado et al. (2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

show abstract

Nonlinear classification of commercial Mexican tequilas

Cited by 11 publications

References 31 publications

Visual complexity modelling based on image features fusion of multiple kernels

Visual complexity modelling based on image features fusion of multiple kernels

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Peer Review #3 of "Visual complexity modelling based on image features fusion of multiple kernels (v0.3)"

Contact Info

Product

Resources

About