Innovative AI methods for monitoring front-of-package information: A case study on infant foods

Abstract

Front-of-package (FOP) is one of the most direct communication channels connecting manufacturers and consumers, as it displays crucial information such as certification, nutrition, and health. Traditional methods for obtaining information from FOPs often involved manual collection and analysis. To overcome these labor-intensive characteristics, new methods using two artificial intelligence (AI) approaches were applied for information monitoring of FOPs. In order to provide practical implementations, a case study was conducted on infant food products. First, FOP images were collected from Amazon.com. Then, from the FOP images, 1) the certification usage status of the infant food group was obtained by recognizing the certification marks using object detection. Moreover, 2) the nutrition and health-related texts written on the images were automatically extracted based on optical character recognition (OCR), and the associations between health-related texts were identified by network analysis. The model attained a 94.9% accuracy in identifying certification marks, unveiling prevalent certifications like Kosher. Frequency and network analysis revealed common nutrients and health associations, providing valuable insights into consumer perception. These methods enable fast and efficient monitoring capabilities, which can significantly benefit various food industries. Moreover, the AI-based approaches used in the study are believed to offer insights for related industries regarding the swift transformations in product information status.