Showing results for Machine Learning

Machine learning models to predict micronutrient profile in food after processing

The information on nutritional profile of cooked foods is important to both food manufacturers and consumers, and a major challenge to obtaining precise information is the inherent variation in composition across biological samples of any given raw ingredient. The ideal solution would address precision and generability, but the current solutions are limited in their capabilities; analytical methods are too costly to scale, retention-factor based methods are scalable but approximate, and kinetic models are bespoke to a food and nutrient. We provide an alternate solution that predicts the micronutrient profile in cooked food from the raw food composition, and for multiple foods. The prediction model is trained on an existing food composition dataset and has a 31% lower error on average (across all foods, processes and nutrients) than predictions obtained using the baseline method of retention-factors. Our results argue that data scaling and transformation prior to training the models is important to mitigate any yield bias. This study demonstrates the potential of machine learning methods over current solutions, and additionally provides….

Semi-automated construction of food composition knowledge base

A food composition knowledge base, which stores the essential phyto-, micro-, and macro-nutrients of foods is useful for both research and industrial applications. Although many existing knowledge bases attempt to curate such information, they are often limited by time-consuming manual curation processes. Outside of the food science domain, natural language processing methods that utilize pre-trained language models have recently shown promising results for extracting knowledge from unstructured text. In this work, we propose a semi-automated framework for constructing a knowledge base of food composition from the scientific literature available online. To this end, we utilize a pre-trained BioBERT language model in an active learning setup that allows the optimal use of limited training data. Our work demonstrates how human-in-the-loop models are a step toward AI-assisted food systems that scale well to the ever-increasing big data.

Algorithmic lifestyle optimization

Objective: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual.
Materials and methods: We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group.
Results: Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to…

Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes

We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R² = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer…

Chardonnay marc as a new model for upcycled co-products in the food industry: concentration of diverse natural products chemistry for consumer health and sensory benefits

Research continues to provide compelling insights into potential health benefits associated with diets rich in plant-based natural products (PBNPs). Coupled with evidence from dietary intervention trials, dietary recommendations increasingly include higher intakes of PBNPs. In addition to health benefits, PBNPs can drive flavor and sensory perceptions in foods and beverages. Chardonnay marc (pomace) is a byproduct of winemaking obtained after fruit pressing that has not undergone fermentation. Recent research has revealed that PBNP diversity within Chardonnay marc has potential relevance to human health and desirable sensory attributes in food and beverage products. This review explores the potential of Chardonnay marc as a valuable new PBNP…

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