Our understanding of how diet affects health is limited to 150 key nutritional components that are tracked and catalogued by the United States Department of Agriculture and other national databases. Although this knowledge has been transformative for health sciences, helping unveil the role of calories, sugar, fat, vitamins and other nutritional factors in the emergence of common diseases, these nutritional components represent only a small fraction of the more than 26,000 distinct, definable biochemicals present in our food—many of which have documented effects on health but remain unquantified in any systematic fashion across different individual foods. Using new advances such as machine learning, a high-resolution library of these biochemicals could enable the systematic study of the full biochemical spectrum of our diets, opening new avenues for understanding the composition of what we eat, and how it affects health and disease.

我们对饮食如何影响健康的理解仅限于150种关键营养成分，这些成分由美国农业部和其他国家数据库进行跟踪和分类。尽管这些知识已对健康科学产生了革命性的变化，有助于揭示卡路里，糖，脂肪，维生素和其他营养因素在常见疾病中的作用，但这些营养成分仅占26,000种不同的，可定义的生物化学物质的一小部分存在于我们的食物中-其中许多已证明对健康有影响，但仍无法以系统的方式对不同的单个食物进行量化。利用机器学习等新技术，这些生物化学物质的高分辨率库可以对我们饮食中的整个生物化学光谱进行系统的研究，