Thales Leandro Coutinho de Oliveira, Gabriela de Barros Silva Haddad, Alcinéia de Lemos Souza Ramos, Eduardo Mendes Ramos, Roberta Hilsdorf Piccoli and Marcelo Cristianini
The purpose of this paper is to evaluate the optimization of high hydrostatic pressure (HHP) processing for the microbial inactivation on low-sodium sliced vacuum-packaged turkey…
Abstract
Purpose
The purpose of this paper is to evaluate the optimization of high hydrostatic pressure (HHP) processing for the microbial inactivation on low-sodium sliced vacuum-packaged turkey breast supplemented with a natural antimicrobial compound (carvacrol).
Design/methodology/approach
A response surface methodology was used to model and describe the effects of different pressures (200–650 MPa) and holding times (30–300 s) during HHP processing of low-salt ready-to-eat turkey breast supplemented with 200 mg/kg of carvacrol on survival of the target pathogen (Listeria sp.) and spoilage microflora and on the quality attributes, including pH, syneresis, CIE color and lipid oxidation.
Findings
The HHP parameters influenced (p<0.05) the lethality rates and syneresis but did not affect the pH values and lipid oxidation of the products evaluated. According to the required performance criteria for Listeria post-lethality treatment, a treatment at 600 MPa/180 s (at 25°C) appears to be suitable for the studied low-sodium product. The HHP bacterial inactivation effects can notably be potentiated via the presence of carvacrol, and is useful at sensory acceptable sub-inhibitory levels.
Originality/value
This study shows that combined HHP plus additives may produce similar safety and shelf-life extension effects with mild HHP treatments, creating a global increase in the quality of HHP-processed food in addition to reducing costs on equipment maintenance and increasing industry productivity.
Details
Keywords
Ana Rocío Cárdenas Maita, Lucas Corrêa Martins, Carlos Ramón López Paz, Sarajane Marques Peres and Marcelo Fantinato
Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information…
Abstract
Purpose
Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining.
Design/methodology/approach
The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining.
Findings
The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis.
Originality/value
Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.
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Keywords
Carlos Castillo, Marcelo Mendoza and Barbara Poblete
Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper…
Abstract
Purpose
Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved.
Design/methodology/approach
The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to Twitter topics in Spanish, automatically detected during a natural disaster.
Findings
There are measurable differences in the way microblog messages propagate. The paper shows that these differences are related to the newsworthiness and credibility of the information conveyed, and describes features that are effective for classifying information automatically as credible or not credible.
Originality/value
The paper first tests the approach under normal conditions, and then the paper extends the findings to a disaster management situation, where many news and rumors arise. Additionally, by analyzing the transfer of our classifiers across languages, the paper is able to look more deeply into which topic-features are more relevant for credibility assessment. To the best of our knowledge, this is the first paper that studies the power of prediction of social media for information credibility, considering model transfer into time-sensitive and language-sensitive contexts.