Analysis of Microplastics in Takeaway Food Containers in China Using FPA-FTIR Whole Filter Analysis

With the rapid development and popularization of the internet and smartphone industry for ordering and delivery, the consumption of takeaway food is increasing globally, especially in China. However, there is little information about microplastics in takeaway food containers, so their potential risks to human health remain unknown. This study explored the possibility of using focal plane array (FPA)-based micro-FT-IR imaging to detect microplastics released from food containers and evaluated their contents using an automated database matching analysis method. We investigated microplastics in seven types of food containers widely used in China. The most common plastic types observed were polyamide (PA), polyurethane (PU) and polystyrene (PS), which were found to comprise 22.8%, 18.2%, and 8.5% (number of particles) of all microplastics, respectively. Microplastics were found in all seven types of food containers, and the content excluding cellulose was 29–552 items/container. Our research shows that microplastics in takeaway food containers might originate from atmospheric sediment or flakes from the inside surface of the container. According to the content of microplastics in takeaway food containers, people who order takeaway food 5–10 times a month might consume 145–5520 microplastic pieces from food containers.

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24.