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

Safflower seed oil is expensive, and there are issues in the market such as the adulteration with cheaper edible oils, leading to inferior products. Near-infrared spectroscopy (NIR) is a non-destructive analytical method with the advantages of being fast, non-destructive, and pollution-free. However, in the case of low-concentration adulterated oil, the main components and their contents are nearly identical to those in pure oil, and the spectral feature peaks of the samples are similar, making it difficult for conventional analysis methods to select effective characteristic variables. Extreme Learning Machine (ELM), as a single-hidden-layer feedforward neural network model, possesses strong feature extraction and model representation capabilities. However, its random initialization of weights and biases leads to the issue of blind training. The Sparrow Search Algorithm (SSA) can effectively optimize the random initialization problem in ELM, but it is prone to issues such as being trapped in local optima and slower convergence. This study proposes a novel quantitative adulteration analysis model for safflower seed oil (ISSA-ELM), which combines ELM with an improved Sparrow Search Algorithm (ISSA). In the experiment, safflower seed oil was used as the base oil, and peanut oil, corn oil, and soybean oil were gradually added to prepare adulterated oil samples. To ensure the resolution of experimental samples and accurately capture spectral variations in the low-concentration range, a concentration gradient of 2% was used for adulteration levels between 2% and 70%, while a 5% gradient was applied for concentrations exceeding 70%. Each type of adulterant had 42 gradient concentrations, with 6 samples per concentration, totaling 252 samples. Original spectral data were collected, and the samples were randomly divided into a training set (70%) and a testing set (30%). Different preprocessing methods and feature extraction techniques were combined to discuss the results of three adulteration analysis models: PLS, SSA-ELM and ISSA-ELM. The final experimental results show that compared to the safflower seed oil adulteration prediction model based on SSA-ELM (<i>R</i>² = 0.8938, RMSE = 0.0835, RPD = 2.9018)and PLS(<i>R</i>_P² = 0.9015, RMSEP = 0.0876, RPD = 3.0128), the model based on ISSA-ELM (<i>R</i>_P² = 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.