The determination of protein, fat and moisture in bread by near infrared reflectance spectroscopy
- 1 August 1984
- journal article
- research article
- Published by Wiley in Journal of the Science of Food and Agriculture
- Vol. 35 (8) , 940-945
- https://doi.org/10.1002/jsfa.2740350820
Abstract
Near infrared (n.i.r.) reflectance spectroscopy has been employed for the determination of protein, fat and moisture in sliced white bread. N.i.r. reflectance at six wavelengths was measured using circular samples from each of six alternate slices taken from one half of each of 30 loaves of different composition. The six readings for each loaf at each wavelength were averaged and used to produce calibrations which, on prediction of the compositions of a further 30 loaves sampled in the same way, gave rise to standard deviations of differences between n.i.r. and standard procedures of 0.20% for protein, 0.18% for fat and 0.51% for moisture. Calibrations derived from the other halves of the loaves, which had been air‐dried and ground to a powder, resulted in similar standard deviation of differences for protein and fat.Keywords
This publication has 7 references indexed in Scilit:
- Application of near infrared reflectance spectroscopy to the compositional analysis of biscuits and biscuit doughsJournal of the Science of Food and Agriculture, 1984
- Measurement of fat and sucrose in dry cake mixes by near infrared reflectance spectroscopyInternational Journal of Food Science & Technology, 1983
- Collaborative evaluation of universal calibrations for the measurement of protein and moisture in flour by near infrared reflectanceInternational Journal of Food Science & Technology, 1983
- The application of near infrared reflectance analysis to rapid flour testingInternational Journal of Food Science & Technology, 1982
- Principles and practice of near infra‐red (NIR) reflectance analysisInternational Journal of Food Science & Technology, 1981
- Detection of Influential Observation in Linear RegressionTechnometrics, 1977
- Simultaneous Inference and the Choice of Variable Subsets in Multiple RegressionTechnometrics, 1974