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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
14/01/2019 |
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Data da última atualização: |
14/01/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
ÁVILA, S.; HORNUNG, P. S.; TEIXEIRA, G. L.; BEUX, M. R.; LAZZAROTTO, M.; RIBANI, R. H. |
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Afiliação: |
Suelen Ávila, UFPR; Polyanna Silveira Hornung, UFPR; Gerson Lopes Teixeira, UFPR; Márcia Regina Beux, UFPR; MARCELO LAZZAROTTO, CNPF; Rosemary Hoffmann Ribani, UFPR. |
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Título: |
A chemometric approach for moisture control in stingless bee honey using near infrared spectroscopy. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Journal of Near Infrared Spectroscopy, v. 26, n. 6, p. 379-388, Dec. 2018. |
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DOI: |
10.1177/0967033518805254 |
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Idioma: |
Inglês |
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Conteúdo: |
Honey is a product that is often adulterated by the addition of water. Stingless bee honey naturally has a higher moisture content than that produced by the traditional Apis mellifera. In most countries, there is a lack of quality standards and methods to characterise and assure the authenticity of stingless bee honey, which demands for the development of fast methods to assess its main properties, avoiding potential fraud. Thus, this work aimed to develop a non-destructive moisture determination method for stingless bee honey based on diffuse reflectance near infrared spectroscopy combined with chemometrics. Thirty-two honey samples from four stingless bee species (Melipona quadrifasciata, Melipona marginata, Melipona bicolor and Scaptotrigona bipuncata) were used to develop calibration models using partial least squares regression analyses. Results revealed intense absorption bands in C-H, O-H and C-O vibrations in the spectra of stingless bee honey. The calibration model was used to predict the moisture content in honey from an external group. The prediction of the honey's moisture showed good correlation (r2 = 0.93) with the refraction index method and an average error of 2.14%. The statistics variables for the calibration (R2 = 0.947, SEP = 1.005 and RPD = 4.3) revealed that this model can be used to predict the moisture from stingless bee honey and that near infrared spectroscopy is a reliable tool to be applied in quality control with rapid, simple and accurate results. MenosHoney is a product that is often adulterated by the addition of water. Stingless bee honey naturally has a higher moisture content than that produced by the traditional Apis mellifera. In most countries, there is a lack of quality standards and methods to characterise and assure the authenticity of stingless bee honey, which demands for the development of fast methods to assess its main properties, avoiding potential fraud. Thus, this work aimed to develop a non-destructive moisture determination method for stingless bee honey based on diffuse reflectance near infrared spectroscopy combined with chemometrics. Thirty-two honey samples from four stingless bee species (Melipona quadrifasciata, Melipona marginata, Melipona bicolor and Scaptotrigona bipuncata) were used to develop calibration models using partial least squares regression analyses. Results revealed intense absorption bands in C-H, O-H and C-O vibrations in the spectra of stingless bee honey. The calibration model was used to predict the moisture content in honey from an external group. The prediction of the honey's moisture showed good correlation (r2 = 0.93) with the refraction index method and an average error of 2.14%. The statistics variables for the calibration (R2 = 0.947, SEP = 1.005 and RPD = 4.3) revealed that this model can be used to predict the moisture from stingless bee honey and that near infrared spectroscopy is a reliable tool to be applied in quality control with rapid, simple and accurate result... Mostrar Tudo |
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Palavras-Chave: |
Controle de umidade; Meliponini; Moisture; Multivariate calibration; Partial least square regression. |
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Thesagro: |
Mel. |
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Thesaurus NAL: |
Quality control. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02346naa a2200277 a 4500
001 2103811
005 2019-01-14
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1177/0967033518805254$2DOI
100 1 $aÁVILA, S.
245 $aA chemometric approach for moisture control in stingless bee honey using near infrared spectroscopy.$h[electronic resource]
260 $c2018
520 $aHoney is a product that is often adulterated by the addition of water. Stingless bee honey naturally has a higher moisture content than that produced by the traditional Apis mellifera. In most countries, there is a lack of quality standards and methods to characterise and assure the authenticity of stingless bee honey, which demands for the development of fast methods to assess its main properties, avoiding potential fraud. Thus, this work aimed to develop a non-destructive moisture determination method for stingless bee honey based on diffuse reflectance near infrared spectroscopy combined with chemometrics. Thirty-two honey samples from four stingless bee species (Melipona quadrifasciata, Melipona marginata, Melipona bicolor and Scaptotrigona bipuncata) were used to develop calibration models using partial least squares regression analyses. Results revealed intense absorption bands in C-H, O-H and C-O vibrations in the spectra of stingless bee honey. The calibration model was used to predict the moisture content in honey from an external group. The prediction of the honey's moisture showed good correlation (r2 = 0.93) with the refraction index method and an average error of 2.14%. The statistics variables for the calibration (R2 = 0.947, SEP = 1.005 and RPD = 4.3) revealed that this model can be used to predict the moisture from stingless bee honey and that near infrared spectroscopy is a reliable tool to be applied in quality control with rapid, simple and accurate results.
650 $aQuality control
650 $aMel
653 $aControle de umidade
653 $aMeliponini
653 $aMoisture
653 $aMultivariate calibration
653 $aPartial least square regression
700 1 $aHORNUNG, P. S.
700 1 $aTEIXEIRA, G. L.
700 1 $aBEUX, M. R.
700 1 $aLAZZAROTTO, M.
700 1 $aRIBANI, R. H.
773 $tJournal of Near Infrared Spectroscopy$gv. 26, n. 6, p. 379-388, Dec. 2018.
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