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| 47. |  | SHIROMA, L. S.; JONSSON, C. M.; QUEIROZ, S. C. do N. de; BOTTOLI, C. B. G. Desenvolvimento e validação de método para determinação de florfenicol e seu metabólito em tilápia por LC-MS/MS. In: ENCONTRO NACIONAL DE QUÍMICA ANALÍTICA, 19.; CONGRESSO IBERO-AMERICANO DE QUÍMICA ANALÍTICA, 7., 2018, Caldas Novas, GO. Inovação para a química analítica sustentável: livro de resumos... Caldas Novas: SBQ: UFG: UnB, 2018. p. 524.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 60. | | MIELE, A.; RIZZON, L. A.; QUEIROZ, S. C. do N. de; GIANELLO, C. Physicochemical composition, minerals, and pesticide residues in organic grape juices. Food Science and Technology, Campinas, v. 35, n. 1, p. 120-126,jan/mar. 2015.| Biblioteca(s): Embrapa Uva e Vinho. |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
20/07/2020 |
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Data da última atualização: |
12/04/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
TERRA, L. R.; QUEIROZ, S. C. do N. de; TERAO, D.; FERREIRA, M. M. C. |
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Afiliação: |
LARISSA ROCHA TERRA, IQ-Unicamp; SONIA CLAUDIA DO N DE QUEIROZ, CNPMA; DANIEL TERAO, CNPMA; MARCIA MIGUEL CASTRO FERREIRA, IQ-UNICAMP. |
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Título: |
Detection and discrimination of Carica papaya fungi through the analysis of volatile metabolites by gas chromatography and analysis of variance-principal component analysis. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Chemometrics, v. 34, n. 12, article e3244, 2020. Special Issue. |
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DOI: |
https://doi.org/10.1002/cem.3244 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Conventional detection and identification of the fungi causing postharvest diseases in fruits are time-consuming, laborious, and can only be performed after the manifestation of symptoms. In this work, an alternative method based on headspace analysis, and which allows the early detection of fungi species frequently found papaya fruit, is presented. Volatile compounds of four in vitro fungi cultures (Alternaria alternata, Colletotrichum gloeosporioides, Fusarium solani, and Lasiodiplodia theobromae) were extracted using solid-phase microextraction (SPME) and analyzed by gas chromatography coupled with mass spectrometry. The resulting chromatographic fingerprints were explored by conventional principal component analysis (PCA) and analysis of variance (ANOVA)?PCA. Decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA improved the distinction of the control and fungi samples. The main chromatographic peaks referring to the metabolites produced by each species were successfully identified using the proposed analysis. A primary alcohol with five carbons and phenylethyl alcohol were observed in all fungi species and so could be used as an indicative of postharvest disease. Although unique metabolites were detected for all fungi species, only those from C. gloeosporioides and another from F. solani could be surely identified, such as thymolmethyl and 3,6-dimethylhept-6-en-4-yn-3-ol, respectively. The in vitro results obtained are promising, and it is expected that the biomarkers detected in this work will be useful in future development of methods for the early detection and classification of papaya fungi species. MenosAbstract: Conventional detection and identification of the fungi causing postharvest diseases in fruits are time-consuming, laborious, and can only be performed after the manifestation of symptoms. In this work, an alternative method based on headspace analysis, and which allows the early detection of fungi species frequently found papaya fruit, is presented. Volatile compounds of four in vitro fungi cultures (Alternaria alternata, Colletotrichum gloeosporioides, Fusarium solani, and Lasiodiplodia theobromae) were extracted using solid-phase microextraction (SPME) and analyzed by gas chromatography coupled with mass spectrometry. The resulting chromatographic fingerprints were explored by conventional principal component analysis (PCA) and analysis of variance (ANOVA)?PCA. Decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA improved the distinction of the control and fungi samples. The main chromatographic peaks referring to the metabolites produced by each species were successfully identified using the proposed analysis. A primary alcohol with five carbons and phenylethyl alcohol were observed in all fungi species and so could be used as an indicative of postharvest disease. Although unique metabolites were detected for all fungi species, only those from C. gloeosporioides and another from F. solani could be surely identified, such as thymolmethyl and 3,6-dimethylhept-6-en-4-yn-3-ol, respective... Mostrar Tudo |
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Palavras-Chave: |
Fruit; GC-MS; Microbial metabolomics; PCA; SPME. |
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Thesagro: |
Análise Química; Carica Papaya; Cromatografia Gasosa; Fungo; Mamão. |
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Thesaurus NAL: |
Chemometrics; Disease detection; Fungal diseases of plants; Gas chromatography-mass spectrometry; Metabolomics. |
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Categoria do assunto: |
W Química e Física |
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Marc: |
LEADER 02829naa a2200349 a 4500
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008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/cem.3244$2DOI
100 1 $aTERRA, L. R.
245 $aDetection and discrimination of Carica papaya fungi through the analysis of volatile metabolites by gas chromatography and analysis of variance-principal component analysis.$h[electronic resource]
260 $c2020
520 $aAbstract: Conventional detection and identification of the fungi causing postharvest diseases in fruits are time-consuming, laborious, and can only be performed after the manifestation of symptoms. In this work, an alternative method based on headspace analysis, and which allows the early detection of fungi species frequently found papaya fruit, is presented. Volatile compounds of four in vitro fungi cultures (Alternaria alternata, Colletotrichum gloeosporioides, Fusarium solani, and Lasiodiplodia theobromae) were extracted using solid-phase microextraction (SPME) and analyzed by gas chromatography coupled with mass spectrometry. The resulting chromatographic fingerprints were explored by conventional principal component analysis (PCA) and analysis of variance (ANOVA)?PCA. Decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA improved the distinction of the control and fungi samples. The main chromatographic peaks referring to the metabolites produced by each species were successfully identified using the proposed analysis. A primary alcohol with five carbons and phenylethyl alcohol were observed in all fungi species and so could be used as an indicative of postharvest disease. Although unique metabolites were detected for all fungi species, only those from C. gloeosporioides and another from F. solani could be surely identified, such as thymolmethyl and 3,6-dimethylhept-6-en-4-yn-3-ol, respectively. The in vitro results obtained are promising, and it is expected that the biomarkers detected in this work will be useful in future development of methods for the early detection and classification of papaya fungi species.
650 $aChemometrics
650 $aDisease detection
650 $aFungal diseases of plants
650 $aGas chromatography-mass spectrometry
650 $aMetabolomics
650 $aAnálise Química
650 $aCarica Papaya
650 $aCromatografia Gasosa
650 $aFungo
650 $aMamão
653 $aFruit
653 $aGC-MS
653 $aMicrobial metabolomics
653 $aPCA
653 $aSPME
700 1 $aQUEIROZ, S. C. do N. de
700 1 $aTERAO, D.
700 1 $aFERREIRA, M. M. C.
773 $tJournal of Chemometrics$gv. 34, n. 12, article e3244, 2020. Special Issue.
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