A mechanistic and probabilistic model estimating micronutrient losses in industrial food processing: Vitamin C and canned green beans, a case-study. (June 2016)

• Record Type: Journal Article
• Authors: Rigaux, Clémence; Georgé, Stéphane; Albert, Isabelle; Renard, Catherine M.G.C.; Carlin, Frédéric
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 69(2016)
• Page Start: 236
• Page End: 243
• Publication Date: 2016-06
• Subjects: Process risk model -- Ascorbic acid -- Uncertainty -- Sensitivity analysis -- Scenario analysis -- Model validation; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2016.01.051
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: Changes in vitamin C concentration along the processing chain of canned green beans were predicted with a probabilistic and modular process model using vitamin C concentrations in raw green beans, constants of chemical reactions (activation energy, reaction rates and diffusivity) and process descriptors (time and temperature, dissolved oxygen concentration). The model accounts for the statistical uncertainty and/or variability of these quantities. The initial vitamin C concentration in green beans, partly established with n = 65 proper vitamin C assays, was (mean [variability interval at 95%]) 17 mg vitamin. 100 g −1 [4.1, 30.3] and markedly decreased to 7.5 mg 100 g −1 [0.6, 14.9] after blanching, to 6.2 mg 100 g −1 [0.3, 12.5] after sterilization, and to 2.3 mg 100 g −1 [0, 5.6] after storage. The model predictions were globally on agreement with independent observations that include specific vitamins C assays (n = 26) at different steps of processing. Highlights: A probabilistic and modular process model can predict vitamin C loss during canning. The model, based on published data, correctly identifies main loss steps and levels. More than 80% vitamin C disappears during canning of green beans. Predicted concentrations were overall in agreement with independent observations.