Introduction
The growing demand for sustainable agricultural practices has increased interest in utilizing agro-industrial waste as a valuable resource. Onion waste, rich in anthocyanins, presents significant potential for food, pharmaceutical, and nutraceutical applications. Rapid and reliable assessment methods are essential to unlock this potential, and the integration of spectroscopy with machine learning offers an innovative solution for efficient compound quantification.
Anthocyanins in Onion Waste
Anthocyanins are natural pigments responsible for antioxidant and anti-inflammatory activities. Onion waste contains considerable concentrations of these compounds, making it an underutilized source of functional ingredients. Accurate measurement of anthocyanin content is crucial for determining its industrial applicability and economic value.
Spectroscopic Techniques for Rapid Analysis
Visible–Near-Short-Wave and Mid-Infrared spectroscopy provide fast, non-destructive tools for analyzing chemical composition. These techniques capture spectral fingerprints linked to anthocyanin structures, enabling rapid screening without extensive sample preparation or chemical reagents.
Integration of Machine Learning Models
Machine learning algorithms enhance the predictive power of spectroscopic data by identifying complex, non-linear relationships between spectra and anthocyanin concentration. Models such as regression and pattern recognition improve accuracy, reproducibility, and scalability of analytical methods.
Advantages of Non-Destructive Assessment
Compared to conventional laboratory analyses, spectroscopic and machine-learning-based approaches reduce time, cost, and environmental impact. These methods support high-throughput screening and real-time decision-making, aligning with the principles of smart agriculture and sustainable food systems.
Implications for Sustainable Agri-Food Systems
The rapid assessment of anthocyanins in onion waste promotes waste valorization and circular bioeconomy concepts. By transforming agricultural by-products into valuable resources, this research contributes to sustainable innovation, improved resource efficiency, and data-driven agri-food industries.
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