Rapid Adulteration Detection of Fritillariae Thunbergii Bulbus: An Effective and Nondestructive Strategy Using Intelligent Algorithm‐Assisted Digital Image and FT‐NIR Spectroscopy

ABSTRACT Fritillariae Thunbergii Bulbus (FTB) is a widely‐used herb with significant nutritional and medicinal value. However, the increasing prevalence of adulterated FTB powder (FTBP) in commercial markets has compromised product quality and hindered sustainable industry development. This study developed an integrated strategy combining digital image (DI) analysis and Fourier transform near‐infrared (FT‐NIR) spectroscopy with intelligent algorithms to detect and quantify FTBP adulterants. The image and spectral data of FTB were used to establish classification models using single and fused datasets by traditional pattern recognition methods, machine learning, and deep learning algorithms. The commercial FTBs were used to validate the developed models. Quantitative regression models were developed using partial least squares (PLS) to predict the concentrations of adulterants in FTB. Traditional chemometrics revealed that DI and NIR dataset could initially distinguish FTBP and its adulterants. Particle swarm optimization‐convolutional neural network (PSO‐CNN) algorithms demonstrated superior performance by feature‐level data fusion (F‐LDF), achieving accuracy of 100%. External validation confirmed perfect discrimination of commercial FTBP using DI and NIR data, with predictive rates of 100%. For quantitative analysis, PLS regression yielded exceptional prediction performance, with the ratio of prediction to deviation values reaching 19.69, 7.37, and 6.09 for corn starch, soybean flour, and wheat flour adulteration using F‐LDF data. This study established a rapid, nondestructive, and reliable strategy for FTB authentication, with potential applications in quality control of other herbs and spices.