The Impact of Plant-Based Coatings in “ROCHA” Pear Preservation during Cold Storage: A Metabolomic Approach

Fonseca, Alexandre; Dias, Cindy; Amaro, A. L.; Isidoro, N.; Pintado, Manuela; Silvestre, Armando J. D.; Rocha, Sı́lvia M.

doi:10.3390/foods9091299

Cited by 11 publications

(13 citation statements)

References 52 publications

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“…The DVB/CAR/PDMS coating contains both adsorbents that are layered to extend the molecular weight range of analytes extracted with one SPME fiber and the combination with the PDMS, an absorptive-type fiber that also confers a bipolar character (Shirey, 2012; Godage and Gionfriddo, 2019; Carriço et al, 2020). This arrangement explains the high performance of the DVB/CAR/PDMS fiber for a wide range of chemical species and the fact that it is extensively used for profiling of volatile molecules released from various food items, such as table and fortified wine (Perestrelo et al, 2011;Santos et al, 2013;Welke et al, 2014;Whitener et al, 2016;Rocha et al, 2021), beer (Sterckx et al, 2010;Rodriguez-Bencomo et al, 2012;Romero-Medina et al, 2020), sea salt (Donadio et al, 2011;Silva et al, 2015), pear (Wang et al, 2019;Fonseca et al, 2020), distilled beverages (Thibaud et al, 2020), coffee (Ongo et al, 2020;Lopes et al, 2021), elderberry (Salvador et al, 2016), hazelnut (Cordero et al, 2010;Rosso et al, 2020), honey (Čajka et al, 2007;Siegmund et al, 2018), dairy products (Cardinali et al, 2021;Székelyhidi et al, 2021;Liu et al, 2022), virgin olive oil (Purcaro et al, 2014;Ros et al, 2019), and cereals (Buśko et al, 2010). Indeed, DVB/CAR/PDMS fiber exhibited the highest sorption capacity for a wide range of VOCs released from honey, comparing with six other fiber coatings (Čajka et al, 2007).…”

Section: Solid-phase Microextractionmentioning

confidence: 99%

“…Also, to avoid cross-over contaminations, thermal clean-ups between extractions have to be done. All the previously reported advantages are the justification for the fact that SPME has been successfully used as an extraction technique for a wide range of foodstuffs and beverages ( Kataoka et al, 2000 ; Peñalver et al, 2000 ; Vesely et al, 2003 ; Vichi et al, 2003 ; Pawliszyn, 2009 ; Giri et al, 2010 ; Welke et al, 2014 ; Alves et al, 2015 ; Santos et al, 2015 ; Silva et al, 2015 ; Nunes et al, 2016 ; Salvador et al, 2016 ; Xu et al, 2016 ; Martins et al, 2018 , 2020 ; Fonseca et al, 2020 ; Bressanello et al, 2021 ; Lopes et al, 2021 ; Rocha et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Actually, this high-throughput technique has been used to assess several data associated with aroma characteristics of foodstuffs and beverages, and multiple uses covering target and/or non-target analysis have been reported. In particular, GC × GC has been widely used in food applications, for instance for authenticity testing; search for differences between good and/or off odor, varieties or cultivars, and geographical origins; exploring for markers related with technological steps (e.g., ripening, postharvest conditions, fermentation), aroma, or aging; study of the key odorants, among others, as illustrated by the following studies: table, fortified and sparkling wines ( Perestrelo et al, 2010 , 2011 ; Weldegergis et al, 2011 ; Santos et al, 2013 ; Welke et al, 2014 ; Whitener et al, 2016 ; Rocha et al, 2021 ), beer ( Inui et al, 2013 ; Martins et al, 2015 , 2017 , 2018 ), elderberry ( Salvador et al, 2016 ), elderflower ( Salvador et al, 2017 ), grape ( Banerjee et al, 2008 ), aromatic plants ( Shellie et al, 2002 ; Klimánková et al, 2008 ; Petronilho et al, 2011 ), sea salt ( Silva et al, 2010 , 2015 ), coffee ( Bressanello et al, 2021 ; Lopes et al, 2021 ), hazelnut ( Cordero et al, 2010 ; Kiefl et al, 2013 ; Kiefl and Schieberle, 2013 ; Rosso et al, 2020 ), honey ( Čajka et al, 2007 ; Siegmund et al, 2018 ), milk ( Hayward et al, 2010 ), cake ( Rega et al, 2009 ), meat ( Sekhon et al, 2010 ; Planche et al, 2015 ; Li W. et al, 2021 ), pear ( Wang et al, 2019 ; Fonseca et al, 2020 ), and virgin olive oil ( Peres et al, 2013 ; Purcaro et al, 2014 ; Ros et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

2022

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The human senses shape the life in several aspects, namely well-being, socialization, health status, and diet, among others. However, only recently, the understanding of this highly sophisticated sensory neuronal pathway has gained new advances. Also, it is known that each olfactory receptor cell expresses only one type of odorant receptor, and each receptor can detect a limited number of odorant substances. Odorant substances are typically volatile or semi-volatile in nature, exhibit low relative molecular weight, and represent a wide variety of chemical families. These molecules may be released from foods, constituting clouds surrounding them, and are responsible for their aroma properties. A single natural aroma may contain a huge number of volatile components, and some of them are present in trace amounts, which make their study especially difficult. Understanding the components of food aromas has become more important than ever with the transformation of food systems and the increased innovation in the food industry. Two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-ToFMS) seems to be a powerful technique for the analytical coverage of the food aromas. Thus, the main purpose of this review is to critically discuss the potential of the GC × GC–based methodologies, combined with a headspace solvent-free microextraction technique, in tandem with data processing and data analysis, as a useful tool to the analysis of the chemical aroma clouds of foods. Due to the broad and complex nature of the aroma chemistry subject, some concepts and challenges related to the characterization of volatile molecules and the perception of aromas will be presented in advance. All topics covered in this review will be elucidated, as much as possible, with examples reported in recent publications, to make the interpretation of the fascinating world of food aroma chemistry more attractive and perceptive.

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Section: Solid-phase Microextractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

2022

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“…However, access to properly and interpretable data from microbe's metabolite pro les can be a real challenge due to the complexity of these samples. Therefore the use of high throughput and high sensitive analytical tools like comprehensive two dimensional gas chromatography (GC×GC) has been used in order to explore and extract useful data from microbial metabolome (Alves et al 2015;Mousavi et al 2016;Cardoso et al 2017;Rees et al 2017;de Souza et al 2018b;Matos et al 2019;Fonseca et al 2020;Martins et al 2020;Fang et al 2021). GC×GC provides higher sensitivity and resolution when compared to conventional one-dimensional gas chromatography (1D-GC), which ful ls the requirements for the analysis of complex biological samples.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Aspergillus niger metabolite biomarkers for in situ and early evaluation of table grapes contamination

Belinato

Costa

Almeida

et al. 2021

Preprint

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Aspergillus niger volatilome was recently explored using advanced gas chromatography tools tandem with multivariate analysis, which allowed to propose a molecular biomarker pattern for this fungus. A. niger is a ubiquitous fungus responsible for food contamination, being reported as one of the main agents of the black mold disease, a serious post-harvest pathology of table grapes. In this work the metabolite pattern already proposed was tested in three different conditions, i.e., by using : (i) 1 day of growth time for A. niger cultures; (ii) 1 day of growth time through A. niger cultures obtained from previously contaminated grapes and iii) in situ SPME approach directly on previously contaminated table grapes with A. niger. Supervised multivariate analysis was performed which revealed that after 1 day of inoculation it was possible to detect the A. niger biomarkers, which allows to infer its presence. Furthermore, the follow-up of this set of metabolites showed that they can be employed to confirm the presence of the pathogen in two varieties of table grapes. The results obtained confirm the potential applicability of the pattern of A. niger biomarkers to early detect the fungi (after 1 day of contamination) and also may be further explored for access food susceptibility to fungi contamination, based on a direct analysis of food item and taking advantage of the high sensitivity of the GC×GC-ToFMS.

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“…However, access to properly interpretable data from a specific microbe's metabolite profile can be a real challenge due to the complexity of these samples. Therefore, the use of high throughput and highly sensitive tools like comprehensive two dimensional gas chromatography (GC×GC) has been used in order to explore and extract useful data from microbial metabolome [22,27,[31][32][33][34][35][36][37]. GC×GC provides higher sensitivity and resolution when compared to conventional onedimensional gas chromatography ( 1 D-GC), which fulfils the requirements for the analysis of complex biological samples.…”

Section: Introductionmentioning

confidence: 99%

Mapping Aspergillus niger Metabolite Biomarkers for In Situ and Early Evaluation of Table Grapes Contamination

Belinato

Costa

Almeida

et al. 2021

Foods

Self Cite

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The Aspergillus niger exometabolome was recently investigated using advanced gas chromatography in tandem with multivariate analysis, which allowed a metabolite biomarker pattern to be proposed. Microbial metabolomics patterns have gained enormous relevance, mainly due to the amount of information made available, which may be useful in countless processes. One of the great challenges in microbial metabolomics is related to applications in more complex systems of metabolomics information obtained from studies carried out in culture media, as complications may occur due to the dynamic nature of biological systems. Thus, the main objective of this research was to evaluate the applicability of the A. niger metabololite biomarkers pattern for in situ and early evaluation of table grapes contamination, used as study model. A. niger is a ubiquitous fungus responsible for food contamination, being reported as one of the main agents of the black mold disease, a serious post-harvest pathology of table grapes. This work included analysis from 1 day of growth time of pure A. niger cultures, A. niger cultures obtained from previously contaminated grapes, and finally, an in situ solid-phase microextraction (SPME) approach directly on previously contaminated table grapes. Supervised multivariate analysis was performed which revealed that after 1 day of inoculation it was possible to detect A. niger biomarkers, which can be extremely useful in making this type of method possible for the rapid detection of food contamination. The results obtained confirm the potential applicability of the pattern of A. niger biomarkers for early detection of the fungi (after 1 day of contamination), and may be further explored for access food susceptibility to fungi contamination, based on direct analysis of the food item.

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The Impact of Plant-Based Coatings in “ROCHA” Pear Preservation during Cold Storage: A Metabolomic Approach

Cited by 11 publications

References 52 publications

Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

Mapping the Aspergillus niger metabolite biomarkers for in situ and early evaluation of table grapes contamination

Mapping Aspergillus niger Metabolite Biomarkers for In Situ and Early Evaluation of Table Grapes Contamination

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