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

Area of Science:

• Biotechnology
• Nutraceuticals
• Artificial Intelligence

Background:

• Fucoxanthin, a carotenoid, offers significant health benefits.
• Current extraction and quantification methods for fucoxanthin are inefficient, costly, and time-consuming, with low yields.

Purpose of the Study:

• To review the application of artificial intelligence (AI) models for optimizing fucoxanthin extraction and quantification.
• To highlight AI's potential in addressing the limitations of traditional methods.

Main Methods:

• Application of AI models like artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and support vector machine (SVM).
• Integration of metaheuristic algorithms, such as the genetic algorithm (GA), to optimize AI model parameters.
• Utilizing computer vision and regression analysis for fucoxanthin quantification.

Main Results:

• Optimized AI models (ANN, ANFIS with GA) achieved high accuracy (R²: 98.28%–99.60%) for extraction yield prediction.
• AI models (SVM, CNNs, ANN) demonstrated reliable quantification of fucoxanthin with high accuracy (R²: 66.0%–99.2%).

Conclusions:

• AI models offer a feasible and potent solution for enhancing fucoxanthin extraction and quantification.
• AI implementation can lead to reduced costs, increased yields, and accelerated development of fucoxanthin-based products.