2024-01-152024-01-152022-04-25POMPEU, Darly Rodrigues. Farinha de mandioca (manihot esculenta) e tucupi: uma abordagem analítica utilizando espectroscopia no infravermelho próximo (NIRS) e ferramentas quimiométricas. Orientador: Rosinelson da silva pena .2022. 116 f. Tese (Doutorado em Ciência e Tecnologia de Alimentos) - Universidade Federal do Pará, Instituto de Tecnologia, Belém, 2022. Disponível em: https://repositorio.ufpa.br/jspui/handle/2011/16172 . Acesso em:.https://repositorio.ufpa.br/jspui/handle/2011/16172The near infrared spectroscopy (NIRS) coupled to chemometrics has been used as an alternative tool for quick and reliable solutions. Cassava flour (CF) can be classified as fermented and non-fermented types. Tucupi is a yellow broth, acidic, mostly aromatic and widely used in Regional dishes in Para state. This thesis proposed to apply for the first time the NIRS associated with chemometrics to predict quality parameters from CF and tucupi, as well as to discriminate fermented and non-fermented CF. One hundred six samples of CF was investigated and nine physicochemical parameters of CF were evaluated. Calibration equations with independent validation were developed to predict all parameters using the partial least square regression method. The performance of models was evaluated by the root mean standard error of calibration (RMSEC) and validation (RMSEV), and R2 values. The aW (RMSEC = RMSEV = 0.05), moisture content (RMSEC = 0.35%; RMSEV = 0.45%) and pH (RMSEC = 0.16; RMSEV = 0.18) could be predicted (R2 > 0.727) by NIRS coupled to multivariate analysis. NIRS coupled to Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA) was also used to investigate the classification of fermented and unfermented CF. The use of NIRS spectra allows to obtain better performance parameters (training accuracy: 86.3–93.8%; validation accuracy: 84.6–96.2%) to discriminate fermented and unfermented CF than the use of the physicochemical properties (training accuracy: 80%; validation accuracy: 84.6%). NIRS was also used to predict nine quality physicochemical properties of tucupi Sixty-five samples of tucupi were used in this study. The performance of models was evaluated by the R2, RMSEC, root mean standard error of cross-validation (RMSECV) and RMSEV values. The total soluble solids contents could be predicted (R2 > 0.727; RMSEC = 0.184%; RMSECV = 0.411%; RMSEV = 0.338%) by NIRS coupled to multivariate analysis. NIRS and chemometrics proved to be a powerful tool to predict quality parameters in CF and tucupi as well as to discriminate fermented and non-fermented CF.Acesso AbertoAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/Espectroscopia no infravermelho próximoQuimiometriaPrediçãoDiscriminaçãoFarinha de mandiocaTucupiNear infrared spectroscopyChemometricsPredictionDiscriminationCassava flourTucupiTeseCNPQ::CIENCIAS AGRARIAS::CIENCIA E TECNOLOGIA DE ALIMENTOSPROPRIEDADES QUÍMICAS, BIOQUÍMICAS E MICROBIOLÓGICAS DOS ALIMENTOS E COMPOSTOS BIOATIVOSAPROVEITAMENTO DE RESÍDUOS AGROINDUSTRIAIS E DESENVOLVIMENTO DE PRODUTOSANÁLISES E DESENVOLVIMENTO DE PROCESSOS DE SEPARAÇÃO E TRANSFORMAÇÃO APLICADAS EM ALIMENTOSCIÊNCIA E TECNOLOGIA DE ALIMENTOS