OBJECTIVE To propose a rapid detection method based on the data fusion of near-infrared spectroscopy and colorimetry, to achieve efficient and non-destructive assessment of the sporoderm-broken rate of Ganoderma lucidum spore powder during the processing, thereby ensuring product quality.

METHODS First, near-infrared(NIR) spectra and colorimetric data of Ganoderma lucidum spore powder samples with different sporoderm-broken rate were collected. Subsequently, preliminary quantitative models were established by applying various preprocessing methods to each type of single data individually. Then, the performance of quantitative models based on low-level, mid-level, and high-level data fusion strategies was systematically compared. Finally, the optimal model for rapid sporoderm-broken rate prediction was selected to provide technical support for the real-time monitoring of the sporoderm-broken rate process.

RESULTS The LLF-SG-PLSR model was identified as the best-performing predictor, yielding an RMSEP of 0.0689, R² of 0.9420, and RPD of 4.154, confirming its precision in sporoderm-broken rate evaluation.

CONCLUSION The optimized model enables rapid and non-destructive detection of the sporoderm-broken rate during Ganoderma lucidum spore powder processing, providing a data-driven approach for quality control in traditional Chinese medicine production.