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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
07/06/2021 |
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Data da última atualização: |
24/11/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BORBA, K. L.; AYKAS, D. P.; COLNAGO, L. A.; FERREIRA, M. D.; RODRIGUEZ-SAONA, L. E. |
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Afiliação: |
LUIZ ALBERTO COLNAGO, CNPDIA; MARCOS DAVID FERREIRA, CNPDIA. |
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Título: |
Portable near infrared spectroscopy as a tool for fresh tomato quality control analysis in the field. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Applied Sciences, v. 11, a. 3209, 2021. |
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Páginas: |
1-14 |
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ISSN: |
2076-3417 |
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DOI: |
https://doi.org/ 10.3390/app11073209 |
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Idioma: |
Inglês |
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Conteúdo: |
Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes nondestructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way. |
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Palavras-Chave: |
Field production; Fresh tomato quality; Handheld spectrometer; Partial least squares (PLS). |
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Categoria do assunto: |
-- |
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Marc: |
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001 2132205
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008 2021 bl uuuu u00u1 u #d
022 $a2076-3417
024 7 $ahttps://doi.org/ 10.3390/app11073209$2DOI
100 1 $aBORBA, K. L.
245 $aPortable near infrared spectroscopy as a tool for fresh tomato quality control analysis in the field.$h[electronic resource]
260 $c2021
300 $a1-14
520 $aPortable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes nondestructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.
653 $aField production
653 $aFresh tomato quality
653 $aHandheld spectrometer
653 $aPartial least squares (PLS)
700 1 $aAYKAS, D. P.
700 1 $aCOLNAGO, L. A.
700 1 $aFERREIRA, M. D.
700 1 $aRODRIGUEZ-SAONA, L. E.
773 $tApplied Sciences$gv. 11, a. 3209, 2021.
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Registro original: |
Embrapa Instrumentação (CNPDIA) |
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