Food structure, function and artificial intelligence. (May 2022)

• Record Type: Journal Article
• Authors: Mengucci, Carlo; Ferranti, Pasquale; Romano, Annalisa; Masi, Paolo; Picone, Gianfranco; Capozzi, Francesco
• Is Part Of: Trends in food science & technology. Volume 123(2022)
• Page Start: 251
• Page End: 263
• Publication Date: 2022-05
• Subjects: Food structure -- Bio-accessibility -- Bioavailability -- Functional food -- Digital twin -- In silico food -- Digestion; AGE Advanced Glycation End-products -- AI Artificial Intelligence -- ATR Attenuated Total Reflectance -- DLS Dynamic Light Scattering -- DSC Differential Scanning Calorimetry -- ESEM Environmental Scanning Electron Microscopy -- GI Glycemic Index -- GIT Gastrointestinal Tract -- KPCA Kernel Principal Component Analysis -- LM Optical or Light Microscopy -- MRI Magnetic Resonance Imaging -- NIR Near-Infrared Reflectance -- NMR Nuclear Magnetic Resonance -- PCA Principal Component Analysis -- QSAR Quantitative Structure-Activity Relationship -- RBF Radial Base Function -- SDS Page Sodium Dodecyl Sulphate - PolyAcrylamide Gel Electrophoresis -- SEM Scanning Electron Microscopy -- SFM Standardized Food Model -- TGA Thermogravimetric Analysis -- TD - NMR Time Domain - Nuclear Magnetic Resonance -- TEM Transmission Electron Microscopy; Food industry and trade -- Periodicals; Food -- Biotechnology -- Periodicals; 664.005
• DOI: 10.1016/j.tifs.2022.03.015
• Languages: English
• ISSNs: 0924-2244

Abstract

Abstract: Background: The complexity of food structure is such as to hinder its inclusion in mathematical models predicting food properties and transformations, although a considerable impulse is being determined by using artificial intelligence. As a matter of fact, food definition currently neglects the structural description, even in those fields for which structure is demonstrated to have a decisive role, such as nutrition. Scope and approach: This review aims to analyse the current knowledge about the structure of foods and its potential use to numerically define the sensory and nutritional quality, as well as the stability properties. Starting from this information, a possible methodology is explored to build, even in an automated way, mathematical models for simulating and predicting the properties of food. A model pipeline has been proposed and applied to pasta, in particular exploiting the description of the structural changes occurring upon cooking. Key findings and conclusions: Foods may be designed in silico, based on automated pipelines for direct extraction of information on rheological and sensory properties as derived from structure images and from data on the dynamic state of the water. The ultimate goal of these approaches is to make more limited use of expensive and time-consuming experiments on physically prepared foods to get to use digital twins of foods designed in the laboratory. Highlights: Supramolecular structure is important for in-silico design of functional foods. Models based on artificial intelligence may predict optimal food structures. Water-matrix interactions and structure must be included in digital twin of food.