Nutrient Composition of Cereal, Pseudo-Cereals and Legumes Flours : an Overview of Literature Data

Liew, Ning Min (2020) Nutrient Composition of Cereal, Pseudo-Cereals and Legumes Flours : an Overview of Literature Data. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Flour is a food material that highly susceptible for adulteration, particularly high quality flours are adulterated with less expensive substitutes such as cheaper flour, other food or non-food substances. For this reason, identification is needed. This study aims to review and analyzed the published data on the general main nutrient composition of the selected cereal (wheat), pseudo-cereals (amaranth, buckwheat and quinoa) and legumes (chickpea, lentil and soybean) flours in the literature using overview and secondary data analysis approach. Overall, 171 papers and 2007 number of individual data were reviewed. Geographical origins contributed to higher significant impacts on the nutrient compositions than variety factor. The nutrient compositions of fat, protein, total carbohydrate and ash contents of the flours varied significantly according to the different types of seed grains. Protein is one of the most important content in flour and soybean flour had significantly highest protein content of 41.29 ± 0.61%, followed by lentil (26.11 ± 0.49%) and chickpea (21.79 ± 0.46%) as compared to cereal flour (12.62 ± 0.26% of protein content in wheat flour) and pseudo-cereals flours (15.67 ± 0.21% of protein content in amaranth flour, 11.46 ± 0.24% of protein content in buckwheat flour and 14.74 ± 0.30% of protein content in quinoa flour) from the published data (P < 0.05). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied in the classification of seven types of the seed grain flours based on the nutrient composition which obeyed the assumptions of each analysis. DA showed highest classification power than HCA and PCA. Discriminant analysis (DA) achieved 87.5% correct classification and 86.5% correct prediction with fat, protein, total carbohydrate and moisture were the most discriminant characters for classifying the types of seed grain flour. The classification of different types of seed grain flour helps in identification to reduce fraudulence in flours.