Quantitative analysis of safflower seed oil adulteration based on near-infrared spectroscopy combined with improved sparrow algorithm optimization model ISSA-ELM

= 0.9934, RMSEP = 0.0207, RPD = 10.1457) offers higher prediction accuracy and stability, and the error detection limit of ISSA-ELM can reach 2%. The ISSA optimized the SSA's tendency to reduce population diversity, slow convergence, and get stuck in local optima in the later stages of iteration, greatly enhancing the global optimization ability of the algorithm. Therefore, ISSA-ELM can effectively identify adulterated safflower seed oil, providing a technological pathway and basis for research into the adulteration of safflower seed oil.