Near infrared reflectance spectroscopy of pasticceria foodstuff as protein content predicting method

Background: The authors evaluated the potential of NIR spectroscopy for the analysis of protein of pasticceria food, and it have analyzed 120 samples.Methods: Protein content reference was obtained by standard lab procedure. The authors used spectra and reference data, partial least squares regression analysis that was applied to calculate a NIR method model (n=120), divided into calibration (n=84) and validation (n=36), to predict protein content in fed.Results: The study shows that using NIR technique spectra and using PLS regression, a model could be developed having a root mean square standard error of performance of R2 value of 0.81. The regression coefficients show that analytically useful absorptions for the original PLS model are between 4000 and 4500nm.Conclusion: a NIR model was developed for the prediction of protein in pasticceria products, which is sufficiently accurate for screening samples. Screening can be accomplished rapidly without the use of chemicals.