Abstract: Hyperspectral technology has been widely used in many fields due to its excellent performance characteristics such as high sensitivity, fine resolution, multi-channel data acquisition and processing capabilities, and non-destructive testing. However, despite its outstanding performance in data acquisition and information extraction, it also faces the challenges of high requirements and costs of hardware equipment. Hardware equipment such as high-precision optics, high-quality detectors, and complex data acquisition devices required for hyperspectral imaging have become one of the key factors limiting its popularity and application. To overcome this dilemma, researchers have begun to explore the reconstruction of RGB images into hyperspectral images by extracting information from traditional RGB images through spectral reconstruction techniques at relatively low cost. This innovative approach significantly reduces the complexity and cost of spectral data acquisition and provides a brand new way for wider application of hyperspectral technology. In the study of this paper, we use surimi adulteration detection as an example to explore the performance of different spectral reconstruction algorithms in depth. By mixing the dorsal muscle of silver carp with commercially available starch in different ratios and simulating the real state of surimi when it is sold, we successfully acquired RGB images and corresponding spectral data of a series of samples. In order to ensure the scientific validity and reliability of the experiment, we adopted the spectral-physical-chemical value covariance distance method (SPXY) for the sample set and divided the data set into calibration and validation sets according to the ratio of 5:3. In the process of spectral reconstruction, we explored two mainstream methods, the multivariate polynomial least squares regression algorithm (PMLR) and the deep learning hierarchical regression network (HRNet). Comparative experimental results show that the HRNet-based reconstruction technique has relative and root-mean-square errors of 0.010 4 between the spectra and the actual spectra, respectively, while the PMLR-based algorithm corresponds to relative and root-mean-square errors of 0.012 6. These results indicate that both methods are within acceptable error ranges, which provides a subsequent experimental results provide a solid foundation. In order to verify the reliability of the reconstructed spectra in practical applications, we established models of extreme learning machine regression (ELMR), support vector machine regression (SVR), and partial least squares regression (PLSR) based on the reconstructed spectra, and combined S-G convolutional smoothing (SG), mean centering (MC), standard normal variable transformation (SNV), derivative smoothing (DE), and normalization (NOR) methods were used to preprocess the spectra with a view to improving the model performance. By using corrected correlation coefficient (R_C) corrected root mean square error (RMSE_C) predicted correlation coefficient (R_P) and root mean square error of prediction (RMSE_P) as evaluation metrics, we successfully detected the adulteration ratio of adulterated surimi. The experimental results show that the support vector machine regression model with standard normal transform (SNV) preprocessing achieves the best results with a prediction correlation coefficient (R_P) of 0.983 0 and a root mean square error of prediction (RMSE_P) of 3.954 4% in the model based on the reconstruction of spectra by the PMLR algorithm. In the model based on the reconstructed spectrum of deep learning HRNet network, the support vector machine regression model, also using SNV preprocessing, achieved the best performance, with a prediction correlation coefficient of 0.9987 and a prediction root-mean-square error of 4.080 8%. Therefore, the PMLR algorithm and HRNet network reconstruction technique based on RGB images not only realized efficient spectral reconstruction, but also provided a reliable solution for surimi adulteration detection. This research result provides new ideas and methods for quality and safety detection of food and agricultural products using low-cost hardware devices.