Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Abstract

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R² accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R² accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Keywords: Fucoxanthin; artificial intelligence; digital images; extraction; identification; machine learning; microalgae; quantification; response surface methodology; statistical data.

Figure 1.

Keyword co-occurrence network map of AI application in the extraction and quantification of fucoxanthin from microalgae. source link: https://www.vosviewer.com/.

Figure 2.

An overview methodology pipeline for the configuration of AI models to be incorporated into the extraction of fucoxanthin from microalgae. The first key step involves selection of input variables and output variable, followed by data pre-processing to divide the data into the desired proportion of training, testing, and validation data prior data normalization. The third key step involves the appropriate model selection and optimization to improve the accuracy and precision of the respective model. lastly, model evaluation to determine the robustness and accuracy of the model based on testing or unseen dataset.

Figure 3.

Methodology for incorporating machine learning and deep learning into quantification of fucoxanthin. The first step begins with data acquisition by injecting sample into either HPLC or UV-vis spectrophotometry for the signal processing of chromatogram. If valid, then proceed for sensitive analysis or feature selection to select the desired parameters or otherwise, return to the first step. The data acquired will be divided into training and testing dataset, in which training dataset will be used for model development, while testing dataset for model validation. If training of dataset is completed, proceed for model validation or otherwise, return to model development. If model validation is successful, the model is completely built, otherwise return to model validation.