Navegando por Assunto "Espectroscopia no infravermelho próximo"

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Acesso aberto (Open Access)
Discriminação da origem geográfica e quantificação de parâmetros de qualidade em sementes sementes de cacau (Theobroma cacao) cultivadas na amazônia utilizando espectroscopia no infravermelho próximo e ferramentas quimiométricas
(Universidade Federal do Pará, 2022-06-03) FERREIRA, Fabielle Negrão; OLIVEIRA, Marcos Enê Chaves; http://lattes.cnpq.br/9052059910078575; LOPES, Alessandra Santos; http://lattes.cnpq.br/8156697119235191; https://orcid.org/0000-0002-8584-5859
This work discusses the application of the Near Infrared Vibrational Spectroscopy (NIR) technique in cocoa samples from different regions of the state of Pará (Medicilandia, Tucumă and Tomé-açu) with the aim of predicting major components and discriminating these different geographic regions. The NIR technique was associated with exploratory analysis of spectral data, testing different regression and discrimination models. This research is divided into three chapters. The first chapter reviews the concepts related to the NIR technique, and explores the most diverse articles on the application of the NIR technique in cocoa for different evaluations, such as, for analysis of major components, identification of fraud and discrimination of cocoa in different regions, from the planet. In the second chapter, prediction models were developed for nitrogen, moisture and total lipids in non-fermented samples, since certain cocoa components are not altered during fermentation, so these major components of the seeds could be quickly predicted by FT spectroscopy-NIR using PLS regression. The third article presents the application of NIR associated with exploratory analysis of spectral data by principal components, for discrimination of cocoa beans under five different treatment conditions (raw, dry fermented, dry unfermented and fermented defatted), from the three different regions mentioned geographic areas, and the best results were obtained when evaluating the fermented cocoa samples from the three regions.
Acesso aberto (Open Access)
Farinha de mandioca (Manihot esculenta) e tucupi: uma abordagem analítica utilizando espectroscopia no unfravermelho próximo (NIRS) e ferramentas quimiométricas
(Universidade Federal do Pará, 2022-04-25) POMPEU, Darly Rodrigues; SOUZA, Jesus Nazareno Silva de; http://lattes.cnpq.br/3640438725903079; PENA, Rosinelson da Silva; http://lattes.cnpq.br/3452623210043423
The 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.