Predicting Protein Content by near Infrared Reflectance Spectroscopy in Diverse Cereal Food Products

Abstract

Simultaneous determination of constituents (e.g. dietary fibre, protein, fat) by near infrared (NIR) spectroscopy would increase the speed and efficiency of nutrient analysis while substantially reducing the cost. Previous work has described the development of NIR reflectance models for the prediction of dietary fibre in a diverse group of cereal food products. While NIR spectroscopy has been used to measure protein content in cereal samples comprised of a single grain type, the utility of the NIR technique would be greatly improved if it could be expanded to cereal products derived from a diverse cross-section of grains and formulations. The present study was conducted to investigate the potential of NIR spectroscopy for the analysis of protein in a data set that included products with numerous grains, such as wheat, oats, rice, rye, corn, millet, buckwheat and with a wide range of fat, sugar and fibre contents. In addition, numerous processing techniques and food additives were represented in the data set. Nitrogen content of dry-milled cereal products was measured by combustion analysis (AOAC Method 992.23) and the range in nitrogen values was from 0.65 to 3.31% of dry weight. Milled cereal products were scanned from 1100 to 2500nm with a scanning monochromator. A nitrogen calibration was developed, using a commercial analysis program, with modified partial least squares as the regression method. The standard error of cross validation and R2 for nitrogen ( n=147 calibration samples) were 0.090% and 0.973, respectively. Independent validation samples ( n=72) were predicted with a standard error of performance of 0.079% nitrogen and r2 of 0.984. Because of the diversity of grains in the data set, crude protein was calculated using two nitrogen-to-protein conversion methods and two PLS models were developed for the prediction of crude protein. Crude protein was predicted with a similar precision to nitrogen and the results for both protein models are within the precision required for US nutrition labelling legislation. In conclusion, NIR reflectance spectroscopy can be used for rapid and accurate prediction of nitrogen and crude protein content in a heterogeneous group of cereal products comprised of a wide cross-section of grains and formulations.