Machine learning models to predict micronutrient profile in food after processing

Naravane, Tarini; Tagkopoulos, Ilias

doi:10.1016/j.crfs.2023.100500

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…These studies contribute to a vast body of research on food composition and transformative mechanisms. Integrating this experimental body requires a standardized language like this, and such large datasets have the potential for knowledge modeling, as evidenced by the models developed on traditional nutrition-focused datasets (Naravane and Tagkopoulos, 2023 ).…”

Section: Methodology and Resultsmentioning

confidence: 99%

Ontological how and why: action and objective of planned processes in the food domain

Dooley

Naravane

2023

Front. Artif. Intell.

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The computational modeling of food processing, aimed at various applications including industrial automation, robotics, food safety, preservation, energy conservation, and recipe nutrition estimation, has been ongoing for decades within food science research labs, industry, and regulatory agencies. The datasets from this prior work have the potential to advance the field of data-driven modeling if they can be harmonized, but this requires a standardized language as a starting point. Our primary goal is to explore two interdependent aspects of this language: the granularity of process modeling sub-parts and parameter details and the substitution of compatible inputs and processes. A delicate semantic distinction—categorizing planned processes based on the objectives they seek to fulfill vs. categorizing them by the actions or mechanisms they utilize—helps organize and facilitate this endeavor. To bring an ontological lens to process modeling, we employ the Open Biological and Biomedical Ontology Foundry ontological framework to organize two main classes of the FoodOn upper-level material processing hierarchy according to objective and mechanism, respectively. We include examples of material processing by mechanism, ranging from abstract ones such as “application of energy” down to specific classes such as “heating by microwave.” Similarly, material processing by objective—often a transformation to bring about materials with certain qualities or composition—can, for example, range from “material processing by heating threshold” to “steaming rice”.

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Section: Methodology and Resultsmentioning

confidence: 99%

Ontological how and why: action and objective of planned processes in the food domain

Dooley

Naravane

2023

Front. Artif. Intell.

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“…Data-driven approaches, especially machine learning, can accelerate the discovery of the VOCs related to smoke taint and the predictive modeling of the smoke taint index. In recent years, machine learning algorithms as a part of the Artificial Intelligence (AI) have increasingly been applied in food science and agriculture for a sustainable food system, including predicting micronutrients, − creating food ontologies and knowledge bases, precision agriculture, and crop and animal management . Although VOCs in smoke-affected grapes and wine have been reported, the levels contributing to the smoke taint effect of VOCs have been evaluated, and a few studies that model the smoke flavor based on chemical composition have been published recently, the number of studies focusing on data-driven approaches, especially predictive modeling of smoke taint based on VOC concentrations, are still limited.…”

Section: Introductionmentioning

confidence: 99%

Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine

Tan,

Neupane,

Wen

et al. 2024

J. Agric. Food Chem.

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Smoke taint in wine has become a critical issue in the wine industry due to its significant negative impact on wine quality. Data-driven approaches including univariate analysis and predictive modeling are applied to a data set containing concentrations of 20 VOCs in 48 grape samples and 56 corresponding wine samples with a taster-evaluated smoke taint index. The resulting models for predicting the smoke taint index of wines are highly predictive when using as inputs VOC concentrations after log conversion in both grapes and wines (Pearson Correlation Coefficient PCC = 0.82; R 2 = 0.68) and less so when only grape VOCs are used (Pearson Correlation Coefficient PCC = 0.76; R 2 = 0.56), and the classification models also show the capacity for detecting smoke-tainted wines using both wine and grape VOC concentrations (Recall = 0.76; Precision = 0.92; F1 = 0.82) or using only grape VOC concentrations (Recall = 0.74; Precision = 0.92; F1 = 0.80). The performance of the predictive model shows the possibility of predicting the smoke taint index of the wine and grape samples before fermentation. The corresponding code of data analysis and predictive modeling of smoke taint in wine is available in the Github repository ().

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“…Machine learning has various applications in the food processing industry. It can be used to predict the micronutrient profile in cooked food from raw food composition, improving precision and generality [16]. Machine learning algorithms can accurately predict the degree of processing for any food, indicating that a significant portion of the US food supply is ultra-processed [17].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

Sarpong,

Akowuah,

Darko

et al. 2024

Preprint

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Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of gari. This domain remains unexplored thus far. A total of 196 graters were studied. Key variables studied included tooth diameter (TD), tooth height (TH), inter-tooth spacing (ITS), drum speed (DS), clearance (C), and moisture content of cassava (MC). Geometric mean diameter (GMD) represented mash quality. Feature importance rankings emphasized TH (0.488784), C (0.243284), TD (0.112682), ITS (0.103547), DS (0.036261), and MC (0.015442) in determining particle size (GMD) of grated mash. Machine learning models efficiently interpreted these attributes, including gradient boost regressor, linear regression, neural network, and random forest. The gradient boost regressor was the best predictive model, achieving 95.34% accuracy, RMSE (0.3291), and MAE (0.2303). The study provides a GMD predictive equation and optimized parameters for specific gari size production, offering valuable insights for tailored machinery in the cassava grating activity.

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Machine learning models to predict micronutrient profile in food after processing

Cited by 5 publications

References 31 publications

Ontological how and why: action and objective of planned processes in the food domain

Ontological how and why: action and objective of planned processes in the food domain

Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

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