Performing Department
(N/A)
Non Technical Summary
Food can coatings serve to retard or inhibit corrosion by preventing contact between the metal of the can and the can contents, especially when the can contents are aggressive (or problematic). Bisphenol A (BPA)-based epoxy coatings are the industry standard, exhibiting excellent adhesion to metal; can fabrication properties; and end product protection, regardless of the food formulation and thermal preservation process. However, due to anticipated changes in consumer sentiment and in the regulatory environment, the packaging coatings industry began developing non-BPA coatings over a decade ago, wherein the non-BPA designation indicates the coating technology platform is based on polymeric materials not derived from BPA. At present, the United States Food and Drug Administration states that the available information from their safety review of scientific evidence continues to support the safety of BPA for currently approved uses in food containers and packaging. Regardless, the material degradation and corrosion phenomena associated with novel non-BPA packaging coatings merit further study. With that said, food-packaging interactions are often multi-factorial and difficult to model deterministically. Additionally, the number of possible combinations of food and coating formulations to be tested is overwhelming and practically infeasible. To effectively approach this complex problem, we will evaluate systematically designed non-BPA coatings using advanced materials characterization techniques before and after thermal processing to determine the corrosivity of various food matrices and coating performance and properties. These data will be used as inputs in machine learning models to elucidate interactions between food constituents and novel food can coatings, develop accelerated methods and tools to screen coatings, assess compatibility of foods and coatings, and predict product shelf life to improve food safety and food security.
Animal Health Component
50%
Research Effort Categories
Basic
40%
Applied
50%
Developmental
10%
Goals / Objectives
The overarchinggoal is to reduce corrosion failure mechanisms inlight metal packaging lined with non-BPA coatings, thereby improving food safety, reducing food waste, and extending product shelf life. To achieve this goal, we set three objectives: (1) determine the corrosivity of food ingredients that are in contact with systematically designed food can coatings, (2) determine the effect of and mechanism by which coating properties influence the migration of food components through the food can coatings, and(3) select and implement a suitable machine learning approach for food-packaging interactions and their corresponding coating performance.
Project Methods
Failure mechanisms in canned foods include the mass transport of food components through coatings, influenced by properties of the polymer (e.g., glass transition temperature) and the penetrant (e.g., diffusivity and solubility), coating morphology (e.g., film thickness and surface roughness), and processing parameters (e.g., time-temperature combinations for retort sterilization and ageing); lack of adhesion of coatings on metal, affected by the coating chemistry and application, metal substrate preparation and pretreatment, and mechanically-induced stress from can manufacture; and corrosion, which depends on the metal substrate, oxygen conditions, and food matrix. Methods to investigate these processes include:Electrochemical impedance spectroscopy (EIS) to rapidly detect microscopic defects in coatings and to monitor and evaluate the barrier performance of coating systems during exposure to various electrolyte solutions by providing quantitative kinetic and mechanistic information on coating degradation and underlying electrochemical processes;Differential scanning calorimetry to determine the glass transition temperature (Tg) of the polymer coatings, wherein a lower Tg may suggest polymer degradation by a reduction in molecular weight;Optical profilometry (OP), specifically white light interferometry, to generate 3-dimensional surface topographical maps of the test coupons and to measure film thickness and surface roughness of the coating and metal substrate;Non-contact oxygen measurements through the transparent glass of paint test cells using autoclavable optical trace oxygen sensor spots;Solid-state nuclear magnetic resonance spectroscopy to characterize polymer degradation mechanisms;X-ray diffractometry to determine the extent of metal dissolution (e.g., iron and tin);X-ray fluorescence to quantify the migration of food components into coatings (e.g., K+ and Cl-);Active learning to select new samples to acquire that are expected to provide the maximal new information (i.e., by implicitly modeling correlation between factors) for data-driven materials design and improved designs of experiments; andConvolutional neural network to obtain quantitative feature vectors from micrographs of coatings captured using OP, allowing these image data to serve as inputs for predictive modeling tasks (e.g., anomaly detection andtexture classification for theclassification of pristine vs. degraded coatings based on coating chemistry and food simulant compositions, objective assessments of the severity of coating failures, prediction of coating performance and food-packaging compatibility, and further understanding of microstructures on coatings and the extent of and mechanism by which polymer coatings degrade)