The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
School of Biological Science, Faculty of Science, The University of Hong Kong, Hong Kong 999077, China.
Nutrients. 2021 Sep 14;13(9):3195. doi: 10.3390/nu13093195.
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training ( = 8986) and test datasets ( = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach ( = 0.84 vs. = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.
膳食纤维摄入不足在全球范围内普遍存在，并且与多种健康不良状况有关。尽管膳食纤维很重要，但在大多数国家，包装食品和饮料上膳食纤维含量的标注都是自愿的，这使得消费者和政策制定者难以监测膳食纤维的摄入量。在这里，我们开发了一种使用包装产品上常见的营养信息进行纤维含量自动和系统预测的机器学习方法。利用具有已知纤维含量信息的澳大利亚包装食品数据集，将其分为训练集（= 8986）和测试集（= 2455）。与现有的手动纤维预测方法相比，k-最近邻机器学习算法能够解释更多的纤维含量方差（= 0.84 比 = 0.68）。我们的研究结果强调了利用机器学习技术大规模、高效地预测包装产品纤维含量的机会。
