EC Nutrition

Background: The glycemic index (GI) of common, processed food products can only be determined experimentally. Adequate nu- merical models capable to predict the GI value of a product based on the macronutrient composition of the raw material are not available.

Objective: To develop predictive Partial Least Squares Regression (PLSR) models based on the relation between the macronutrient composition of a wide range of common, processed food products and their glycemic response, taking the processing method as selection criterion into account.

Method: Published GI data (n = 601) of common, processed food products were used for PLSR based model development. Prerequi- sites to develop reliable, predictive models are: i) the use of a proper reference product, ii) an adequate numerical description of the macronutrient composition of the processed products, iii) the use of equi-energy (1MJ) servings rather than products containing 50 g carbohydrate.

Results: After transformation of the published data into 1 MJ containing servings, they were clustered into four distinguishable product groups, each group characterized by its specific processing method. Processed legumes were recognized as a separate, fifth group. The glycemic response differs between diabetic and non-diabetic test person (P 0.05), resulting in ten different predictive PLSR models. In all cases sugar, starch and total dietary (TDF) were as the only regression factors. For the data of these ten models the correlation coefficient between the 1 MJ based and predicted glycemic values was r = 0.88.In addition it was also shown that the higher the percentage energy contained by “protein + fat”, the lower the glycemic response.

Conclusions: Relevant predictive PLSR models of common, processed food products can only than be developed if, i) equi-energetic servings rather than 50 g carbohydrate servings are used, ii) the processed food products are clustered into four product groups, each group based on major characteristics of its processing method.

keywords: Predictive models; Equi-energetic servings; Glycemic response; Common processed food products; Macronutrient compo- sition; (non-) diabetic

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