When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

Abstract

Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food–drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.

The Future of Food

“…Retail formats that are expected to expand include e-commerce, driven by convenience and personalization, as well as sustainable and ethical retail emphasizing eco-friendly practices. Technology-driven stores and direct-to-consumer brands also represent growth areas in the retail industry [42][43][44][45][46][50][51][52][53].…”

“…On the other hand, using AI concepts, machines, and deep learning methods has become increasingly prevalent in studies aimed at analyzing vast food databases to explore aspects like flavors, food compositions, and chemical compounds [52]. Additionally, sensory analysis and taste testing conducted by AI systems may play a role in assessing food quality and tailoring recommendations to individual preferences.…”

Quimioinformática, Inteligencia Artificial y la Química de Alimentos

“…Sin embargo, como se verá, en esta revisión, no todos los tipos de IA en la Química de Alimentos son utilizados para tareas culinarias (Tseng et al, 2023).…”

Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

“…Recent studies have investigated the utility of cANN using physicochemical data [18]. Mathematical models that capture the associations between physicochemically derived descriptors and diverse sample types can be constructed using a range of machine learning algorithms [19]. In this investigation, cANN was chosen based on its established efficacy in previous studies [20].…”