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Registro Completo |
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
18/01/2016 |
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Data da última atualização: |
18/05/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
RAMBO, M. K. D.; FERREIRA, M. M. C.; AMORIM, E. P. |
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Afiliação: |
M. K. D. RAMBO, UFT; M. M. C. FERREIRA, UNICAMP; EDSON PERITO AMORIM, CNPMF. |
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Título: |
Multi-product calibration models using NIR spectroscopy. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Chemometrics and Intelligent Laboratory Systems, n. 151, p.108?114, 201 6. |
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ISSN: |
0169-7439 |
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DOI: |
10.1016/j.chemolab.2015.12.013 |
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Idioma: |
Inglês |
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Conteúdo: |
The physical-chemical composition of multiple biomasses can be predicted from one single calibration model instead of compositional prediction conducted by individual models. In this work, multi-product models, involving banana, coffee and coconut samples were built by partial least square regression (PLS) for ten different chemical constituents (total lignin, klason lignin, acid insoluble lignin, acid soluble lignin, extractives, moisture, ash, glucose, xylose and total sugars). The developed PLS models show satisfactory results, with relative error (RE%) less than 20.00, except for ash and xylose models; ratio performance deviation (RPD) values above than 4.4 and range error ratio (RER) values above 4.00. This means that all models are qualified for screening calibration. Principal component analysis (PCA) was useful to demonstrate the possibility and the rationale for combining three biomass residues into one calibration model. The results have shown the potential of NIR in combination with chemometrics to quantify the chemical composition of feedstocks. |
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Palavras-Chave: |
Coffee. |
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Thesagro: |
Banana; Café; Coco; Composição quimíca. |
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Thesaurus Nal: |
Chemical composition; Coconuts. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 01784naa a2200253 a 4500
001 2034148
005 2023-05-18
008 2016 bl uuuu u00u1 u #d
022 $a0169-7439
024 7 $a10.1016/j.chemolab.2015.12.013$2DOI
100 1 $aRAMBO, M. K. D.
245 $aMulti-product calibration models using NIR spectroscopy.$h[electronic resource]
260 $c2016
520 $aThe physical-chemical composition of multiple biomasses can be predicted from one single calibration model instead of compositional prediction conducted by individual models. In this work, multi-product models, involving banana, coffee and coconut samples were built by partial least square regression (PLS) for ten different chemical constituents (total lignin, klason lignin, acid insoluble lignin, acid soluble lignin, extractives, moisture, ash, glucose, xylose and total sugars). The developed PLS models show satisfactory results, with relative error (RE%) less than 20.00, except for ash and xylose models; ratio performance deviation (RPD) values above than 4.4 and range error ratio (RER) values above 4.00. This means that all models are qualified for screening calibration. Principal component analysis (PCA) was useful to demonstrate the possibility and the rationale for combining three biomass residues into one calibration model. The results have shown the potential of NIR in combination with chemometrics to quantify the chemical composition of feedstocks.
650 $aChemical composition
650 $aCoconuts
650 $aBanana
650 $aCafé
650 $aCoco
650 $aComposição quimíca
653 $aCoffee
700 1 $aFERREIRA, M. M. C.
700 1 $aAMORIM, E. P.
773 $tChemometrics and Intelligent Laboratory Systems$gn. 151, p.108?114, 201 6.
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