Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil

Vidigal, Igor G.; Siqueira, Adriano Francisco; Melo, Mariana Pereira de; Giordani, Domingos S.; Silva, Maria Lúcia Caetano Pinto da; Cavalcanti, Eduardo Homem de Siqueira; Ferreira, Ana Lúcia Gabas

doi:10.1016/j.fuel.2020.119024

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…Among these papers, 10 (45.5%) were published in the periodical sensors, whereas the remainder were distributed over a diverse range of journals. A total of 15 articles (68.1%) were classified in the discrimination group (Aliaño-Gonz alez et al, 2018a; Amini and Hosseini-Golgoo, 2012; Bieganowski et al, 2018;Calle et al, 2020;Falatov a et al, 2018Falatov a et al, , 2021Ferreiro-Gonz alez et al, 2016Mahmodi et al, 2019 stochastic analysis (Siqueira et al, 2018(Siqueira et al, , 2019Vidigal et al, 2021). Other methods mentioned were autoregressive moving average with exogenous inputs (ARMAX) (Amini and Hosseini-Golgoo, 2012), wavelet analysis (Osowski and Siwek, 2017) and random forest analyses (Osowski and Siwek, 2017).…”

Section: Overall Analysis Of the Literature Searchmentioning

confidence: 99%

“…Other methods mentioned were autoregressive moving average with exogenous inputs (ARMAX) (Amini and Hosseini-Golgoo, 2012), wavelet analysis (Osowski and Siwek, 2017) and random forest analyses (Osowski and Siwek, 2017). Within all articles classified in the prediction group, four papers predicted the concentration of gases and volatile vapors using simple linear regression (Wu et al, 2020), multiple linear regression (Vidigal et al, 2021), mass balance model/ confidence intervals (Nozza et al, 2016) or ANN (Mumyakmaz and Karabacak, 2015). In addition, Hong (2018) applied mathematical modeling to e-nose profiles to detect and recognize three types of volatile gases and evaluated the transient responses from four sensors and from the wireless sensor matrix, whereas Aliaño-Gonz alez et al (2018b) applied nonlinear regression to e-nose olfactory profiles to analyze samples of gasoline weathered at different times on various types of support (cork, wood, paper and cotton sheet).…”

Section: Overall Analysis Of the Literature Searchmentioning

confidence: 99%

“…While the maximum percentage of samples correctly classified by usual techniques was of 67%, the stochastic method correctly classified 91.6% of samples and, for that, used only the olfactive profiles from three sensors. Moreover, in Vidigal et al (2021), a prediction model for oxidative stability from olfactive profile data was proposed and, through a combination of usual techniques and data from 32 sensors, obtained a prediction quality close to 42%, while, by combination of one of those usual techniques and the stochastic method, the prediction quality doubled (84%), with the use of data from only 11 sensors.…”

Section: Introductionmentioning

confidence: 99%

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A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

Vidigal

Melo

Siqueira

et al. 2022

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Purpose This study aims to describe a bibliometric analysis of recent articles addressing the applications of e- noses with particular emphasis on those dealing with fuel-related products. Documents covering the general area of e-nose research and published between 1975 and 2021 were retrieved from the Web of Science database, and peer-reviewed articles were selected and appraised according to specific descriptors and criteria. Design/methodology/approach Analyses were performed by mapping the knowledge domain using the software tools VOSviewer and RStudio. It was possible to identify the countries, research organizations, authors and disciplines that were most prolific in the area, together with the most cited articles and the most frequent keywords. A total of 3,921 articles published in peer-reviewed journals were initially retrieved but only 47 (1.19%) described fuel-related e-nose applications with original articles published in indexed journals. However, this number was reduced to 38 (0.96%) articles strictly related to the target subject. Findings Rigorous appraisal of these documents yielded 22 articles that could be classified into two groups, those aimed at predicting the values of key parameters and those dealing with the discrimination of samples. Most of these 22 selected articles (68.2%) were published between 2017 and 2021, but little evidence was apparent of international collaboration between researchers and institutions currently working on this topic. The strategy of switching energy systems away from fossil fuels towards low-carbon renewable technologies that has been adopted by many countries will generate substantial research opportunities in the prediction, discrimination and quantification of volatiles in biofuels using e-nose. Research limitations/implications It is important to highlight that the greatest difficulty in using the e-nose is the interpretation of the data generated by the equipment; most studies have so far used the maximum value of the electrical resistance signal of each e-nose sensor as the only data provided by this sensor; however, from 2019 onwards, some works began to consider the entire electrical resistance curve as a data source, extracting more information from it. Originality/value This study opens a new and promising way for the effective use of e-nose as a fuel analysis instrument, as low-cost sensors can be developed for use with the new data analysis methodology, enabling the production of portable, cheaper and more reliable equipment.

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Section: Overall Analysis Of the Literature Searchmentioning

confidence: 99%

Section: Overall Analysis Of the Literature Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

Vidigal

Melo

Siqueira

et al. 2022

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“…Chemical sensor arrays, with multiple chemical sensors integrated on a chip, are attracting increasing interest in applications in the fields of healthcare, , environmental and food monitoring, − and industrial production. , The biomimetic concept of chemical sensor arrays is inspired by the biological olfactory system. Numerous olfactory receptors, which enable efficient combinatorial coding, are essential for distinguishing complex odors consisting of hundreds or thousands of different chemicals. , In a sensor array, chemical sensors mimic olfactory receptors, indicating the significance of large-scale integration of various sensors on a chip.…”

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confidence: 99%

Impact of Lateral SnO₂ Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array

et al. 2022

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We propose a rational strategy to fabricate thermally robust, highly integrated molecular and gas sensors utilizing a lateral SnO 2 nanofilm channel geometry on a 1024 crossbar sensor array. The proposed lateral channel geometry substantially suppresses the detrimental effects of parasitic interconnect wire resistances compared with those of a conventional vertical sandwich-type crossbar array because of its excellent resistance controllability. A conductive oxide top-contact electrode on the lateral SnO 2 nanofilm channel enhances the thermal stability at temperatures of up to 500 °C in ambient air. Integrating this lateral SnO 2 nanofilm geometry with analog circuits enables the operation of a 1024 crossbar sensor array without selector devices to avoid sneak currents. The developed 1024 crossbar sensor array system detects the local spatial distribution of the molecular gas concentration. The spatial data of molecular concentrations include molecule-specific data to distinguish various volatile molecules based on their vapor pressures. Thus, this integrated crossbar sensor array system using lateral nanofilm geometry offers a platform for robust, reliable, highly integrated molecular and gas sensors.

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“…Rapid population growth, fast urbanization, and a booming fast-food sector are having an increasing impact on the environment, among other things, through the pollution caused by food waste. Waste cooking oil (WCO), a left-over of deep-frying, contains triglycerides, polar compounds, and nonvolatile compounds . It is estimated that almost 16.5 million tons of WCO are produced yearly .…”

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confidence: 99%

Reprogramming Escherichia coli Metabolism for Bioplastics Synthesis from Waste Cooking Oil

Cheng

Zhao

et al. 2021

ACS Synth. Biol.

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The recycle and reutilization of food wastes is a promising alternative for supporting and facilitating circular economy. However, engineering industrially relevant model organisms to use food wastes as their sole carbon source has remained an outstanding challenge so far. Here, we reprogrammed Escherichia coli metabolism using modular pathway engineering followed by laboratory adaptive evolution to establish a strain that can efficiently utilize waste cooking oil (WCO) as the sole carbon source to produce monomers of bioplastics, namely, medium-chain α,ω-dicarboxylic acids (MCDCAs). First, the biosynthetic pathway of MCDCAs was designed and rewired by modifying the βoxidation pathway and introducing an ω-oxidation pathway. Then, metabolic engineering and laboratory adaptive evolution were applied for improving the pathway efficiency of fatty acids utilization. Finally, the engineered strain E. coli AA0306 was able to produce 15.26 g/L MCDCAs with WCO as the sole carbon source. This study provides an economically attractive strategy for biomanufacturing bioplastics from food wastes, which has a great potentiality to be developed as a wide range of enabling biotechnologies for achieving green revolution.

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Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil

Abstract: Graphical abstract

Cited by 21 publications

References 37 publications

A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

Impact of Lateral SnO₂ Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array

Reprogramming Escherichia coli Metabolism for Bioplastics Synthesis from Waste Cooking Oil

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Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil

Abstract: Graphical abstract

Cited by 21 publications

References 37 publications

A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

A systematic bibliometric analysis of studies dealing with fuel-related e-nose applications

Impact of Lateral SnO2 Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array

Reprogramming Escherichia coli Metabolism for Bioplastics Synthesis from Waste Cooking Oil

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Product

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Impact of Lateral SnO₂ Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array