Estimate of protein content in rice semolina by applying regressions in the near infrared region.
DOI:
https://doi.org/10.15517/am.v27i2.21153Keywords:
principal components regression, partial least squares, near infrared spectroscopy, Bootstrap.Abstract
The objective of this study was the empirically compare the partial least squares (PLS) regression model and the principal components regression (PCR) model to predict the protein percentage in rice semolina. The estimates were carried out using the absorbance values in the near infrared region. 135 samples of rice semolina were collected between 2004 and 2012 from several pet food plants in Costa Rica. The convergence of the results was validated through Bootstrapping techniques. The observations were split in two groups: one data set to estimate the best regression model (n=120) and a data set of validation (n=15). The models estimated in the data set showed difficulties with outliers, consequently an observation was removed to obtain the best PLS model. In the validation of the regression model, the goodness of fit referred to statistics of the mean standard error of prediction (MSEP), the root mean square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of performance to deviation (RPD), and the graphics of observed against predicted values confirmed better adjustments for the PLS regression (SEP=0.304) in comparison to the PCR model (SEP=0.312). The simulation method showed a better convergence in the results of the PLS regression technique, to predict the percentage of protein in rice semolina.
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References
Beebe, K. 1987. An introduction to multivariate calibration and analysis. Anal. Chem. 59:1007A-1017A.
Blanco, M. 2002. NIR spectroscopy: a rapid-response analytical tool. Anal. Chem. 21:240-250.
Bro, R. 1996. Multiway calibration - Multilinear PLS. J. Chemom. 10:47-61.
Büning-Pfaue, H. 2003. Analysis of water in food by near infrared spectroscopy. Food Chem. 82:107-115.
Campabadal, C., y M. Murillo. 1985a. Efecto de la adulteración de la semolina de arroz con carbonato de calcio en la alimentación de pollos de engorde. Agron. Costarricense 9(1):13-20.
Campabadal, C., y M. Murillo. 1985b. Utilización de la semolina de arroz en la alimentación de gallinas en desarrollo de postura. Agron. Costarricense 9(1):13-20.
Campabadal, C., M. Murillo, y J. Solís. 1982. Utilización de la semolina de arroz en dietas para pollos parrilleros con y sin suplementación de grasa. Agron. Costarricense 6(1/2):73-79.
Carmiol, G., C. Campabadal, y M. Zumbado. 1982. Utilización de la semolina de arroz en la alimentación de pollos parrilleros. Adulteración con cascarilla de arroz. Agron. Costarricense 6(1/2):65-72.
Dos Santos, R. 2009. Développement de nouvelles méthodes chimiométriques d’analyse. Ph.D. Tesis. Agro Paris Tech., Paris, FRA.
Dubernet, M., et M. Dubernet. 2000. Utilisation de l’analyse infrarouge à transformée de Fourier pour l’analyse oenologique de routine. Française d’ Œnologie 181:3914-3917.
Ergon, R. 2003. Constrained numerical optimization of PCR/PLSR predictors. Chemom. Intell. Lab. Syst. 65:293-303.
Ergon, R. 2006. Reduced PCR/PLSR models by subspace projections. Chemom. Intell. Lab. Syst. 81:68-73.
Ergon, R., and K. Esbensen. 2002. PCR/PLSR optimization based on noise covariance estimation and Kalman filtering theory. J. Chemom. 16:401-407.
Efron, B., and G. Gong. 1983. A leisurely look at the Bootstrap, the jackknife, and cross-validation. Am. Stat. 37:36-48.
Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regression (with discussion). Ann. Stat. 32:407-499.
FAO. 2010. L’état de l’insécurité alimentaire dans le monde. Roma. http://www.fao.org/docrep/013/i1683f/i1683f.pdf (consulté 20 ago. 2015).
Guyomard, H., D. Aubert, et S. Jumel. 2006. Entreprises et filières agro-alimentaires face à de nouveaux enjeux. Département Sciences Sociales, Agriculture et Alimentation, espace et environnement de l’Institut National de la Recherche Agronomique, FRA.
Kubista, M., R. Sjoback, and B. Albinsson. 1993. Determination of equiIibrium constants by chemometric analysis of spectroscopic Data. Anal. Chem. 65:994-998.
Martens, H., and T. Naes. 1989. Multivariate calibration. Wiley, NY, USA.
Mevik, B., and H. Cederkvist. 2004. Mean squared error of prediction (MSEP) estimates for principal component regression PCR and partial least squares regression (PLSR). J. Chemom. 18:422-429.
Neter, J., M. Kutner, C. Nachtsheim, and W. Wasserman. 1996. Applied linear statistical models. 4th ed. McGraw-Hill, NY, USA.
Otto, M. 2007. Chemometrics: statistics and computer application in analytical chemistry. 2nd ed. Wiley, NY, USA.
Rouessac, F. 2004. Analyse chimique: méthodes et techniques instrumentales modernes. 6me éd. Dunod, Paris, FRA.
Rouessac, F., et P. Rouessac. 1998. Analyse chimique: méthodes et techniques instrumentales modernes. Cours et exercices résolus. 4me éd. Dunod, Paris, FRA.
Sauvant, D. 2005. Principes généraux de l’alimentation animale. Institut National Agronomique Paris-Grigno (INAP-G), Paris, FRA.
Tenenhaus, M. 1995. A partial least squares approach to multiple regression, redundancy analysis, and canonical analysis. Les cahiers de la recherche de HEC, Paris, FRA.
Tenenhaus, M. 1998. La régression PLS: Théorie et pratique. Editions Thechnip, Paris, FRA.
Tippens, P. 2001. Física. Conceptos y aplicaciones. 6th ed. McGraw Hill, NY, USA.
Workman, J., M. Koch, and D. Veltkamp. 2003. Process analytical chemistry. Anal. Chem. 75:2859-2876.
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