## 6 Advances in Analytical Strategies and Future Directions
### Future Trends in Carotenoids Analysis  
Recent machine learning (ML) innovations have significantly impacted carotenoid analysis, particularly in non-invasive detection and data processing. Traditional techniques such as UPLC-MS/MS, while highly sensitive, are time-consuming and require complex sample preparation. ML-based approaches offer an alternative by improving predictive modeling, classification, and real-time quantification [133].  
One of the most promising applications is deep learning-based spectral analysis. Machine learning algorithms process chromaticity measurements (L, a, b* values) to estimate carotenoid concentrations with high accuracy**, eliminating the need for destructive chemical extractions. For example, long short-term memory (LSTM) networks have been trained to predict carotenoid levels in aquaculture species, demonstrating accuracy improvements of over 450 times compared to conventional methods [133].  
Additionally, ML has been applied in human plasma studies, correlating beta-carotene levels with lifestyle factors such as diet, smoking, and alcohol consumption. Rough set theory and decision trees have successfully classified plasma carotenoid levels with >90% accuracy, providing insights into how carotenoid bioavailability is affected by external variables [134]. These applications suggest that ML can be integrated into large-scale nutritional and clinical studies, making carotenoid quantification more accessible and data-driven.  
Alongside AI-driven techniques, lab-on-a-chip (LOC) technologies have emerged as a revolutionary approach to miniaturizing and automating carotenoid analysis. LOC systems integrate sample preparation, separation, and detection into a single microfluidic platform, reducing reagent consumption and analysis time while enabling on-site and portable applications [135].  
Recent optofluidic LOC devices have been optimized for carotenoid detection in biological samples, leveraging microchannel-based light absorption measurements. These systems have been used to analyze microalgae pigments, demonstrating high sensitivity and the ability to handle small sample volumes [135].  
Additionally, miniaturized Raman spectroscopy has been integrated into LOC platforms, enhancing non-destructive, high-throughput screening of carotenoids. Portable Raman instruments have successfully detected carotenoids in complex matrices under extreme conditions, such as remote desert environments, showcasing the potential for field applications in food safety, agriculture, and biomedical research [136].  
By integrating Raman spectroscopy with LOC systems, researchers aim to enhance detection capabilities while maintaining low-cost, high-throughput analysis [137]. Future developments in laser-based miniaturized systems and on-chip spectrometric detection could lead to highly portable platforms for carotenoid analysis, improving accessibility and applicability in diverse fields such as food science, agriculture, and biomedical research.  
While LOC and ML approaches offer transformative improvements, challenges remain in standardizing these technologies for regulatory acceptance and ensuring compatibility with existing chromatographic protocols. Continued research is needed to validate these emerging methods against conventional techniques before they can be fully integrated into industrial and regulatory settings.