用机器学习提升食品加工分级，让分类更客观精准。
Informatics for Food Processing
- 用随机森林模型基于营养数据推断加工程度，生成连续评分
- 大语言模型可处理缺失信息，实现食品描述的语义嵌入预测
- 融合结构与非结构数据，实现大规模食品分类新范式
本文探讨食品加工的演变、分类及其健康影响，强调机器学习、人工智能和数据科学在推动食品信息学发展中的变革作用。回顾了NOVA、Nutri-Score和SIGA等传统分类框架，指出其主观性与可重复性差的问题，限制了流行病学研究与公共政策制定。为此，提出FoodProX——一个基于营养成分数据训练的随机森林模型，用于推断加工水平并生成连续的FPro评分。同时探索BERT和BioBERT等大语言模型对食品描述与配料表进行语义嵌入，支持在数据缺失情况下的预测任务。关键贡献是利用Open Food Facts数据库开展案例研究，展示多模态AI如何整合结构化与非结构化数据，实现食品加工级别的规模化分类，为公共卫生与研究提供新方法。
原文摘要 · Abstract (English)
This chapter explores the evolution, classification, and health implications of food processing, while emphasizing the transformative role of machine learning, artificial intelligence (AI), and data science in advancing food informatics. It begins with a historical overview and a critical review of traditional classification frameworks such as NOVA, Nutri-Score, and SIGA, highlighting their strengths and limitations, particularly the subjectivity and reproducibility challenges that hinder epidemiological research and public policy. To address these issues, the chapter presents novel computational approaches, including FoodProX, a random forest model trained on nutrient composition data to infer processing levels and generate a continuous FPro score. It also explores how large language models like BERT and BioBERT can semantically embed food descriptions and ingredient lists for predictive tasks, even in the presence of missing data. A key contribution of the chapter is a novel case study using the Open Food Facts database, showcasing how multimodal AI models can integrate structured and unstructured data to classify foods at scale, offering a new paradigm for food processing assessment in public health and research.
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