Artificial Intelligence Applied to Flavonoid Data in Food Matrices

Abstract

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

“…Accordingly, the results showed that STEPW and VIP are useful in phenotyping plants, as reported in previous studies [ 31 , 33 , 37 ]. However, it has been noted that these methods have limitations, such as low accuracy and biases in the output.…”

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

“…The data were divided into calibration (70%) and validation (30%) sets, with the calibration set of 270 samples and the validation set of 90 samples achieving high accuracy and precision [ 4 , 42 ]. The results highlighted robust generation models based on R 2 , offset, RMSE, RPD, bias, and weight coefficients, although estimating anthocyanins (AnC) and flavonoids (Flv) proved more challenging [ 40 , 43 ]. Despite the variation between C 3 and C 4 plants, the highest prediction values were obtained for AnC and Flv, with a full spectra method applied to crop analysis [ 13 , 34 ].…”

“…While many reflectance indices have demonstrated superior performance in estimating certain plant traits compared to existing vegetation indices [ 47 ], there is still a need for a more robust model [ 15 , 48 ]. Specifically, we lack a system that adequately incorporates data related to anthocyanins and flavonoids in plants of agronomic importance [ 43 , 48 ]. Enhancing the current models to incorporate these pigment compounds could provide a more comprehensive assessment of plant health and development, thus further optimizing high-throughput plant pigment phenotyping platforms [ 36 , 41 , 47 ].…”

African Sorghum-Based Fermented Foods: Past, Current and Future Prospects

“…The authors were able to optimize bioethanol production from sorghum grains, and indicated the effectiveness of the approach in reducing cost, time and effort associated with experimental techniques [186]. Further detailed description, classification and use of these AI and ML techniques is available in the literature, and can be consulted for further reading [187][188][189][190][191]. The specific and potential immediate application of AI and ML to sorghum-based fermented foods include predictive product development and optimization of fermentation processes.…”