Random and system errors in nutrient analysis: An application of adaptive neural-network protocols

摘要

USDA's nutritional reference database is the primary database used by several nutrient analyses software. Despite the use of one database, variations among systems emerge impacting nutrient estimation. Consequently, this paper explores these critical differences by classifying sources of variations leading to computational errors. Learning Vector Quantization and Bayesian (neural-network) methods were applied to characterize pattern of errors. As a result, two common errors were identified to be prevalent in nutrient analyses. Random errors (37.1% of total variance) stemming from selection process (Operator) because of the enormity of food items listed in the database and system errors (59.5% of total variance) due to measurements (units, tools and procedures) variations. Computerized classification of random and system errors are of significant interest in the field of nutrition and food engineering because, it provides practical solutions to database query and optimization of inherent variations due to measurement errors that are beneficial to research methodologies.