The development of rapid and intelligent methods is urgently needed for wheat quality evaluation. Using the prediction of wet gluten content as a case study, this work systematically investigated the performance of various machine learning algorithms and their optimization for content prediction, based on hyperspectral data from the visible and near-infrared ranges of wheat grains and flour. The results revealed that the random forest regression (RFR) algorithm delivered the best predictive performance under two conditions: first, when applied directly to visible spectra; and second, when applied to fused visible and near-infrared spectral data. This held true for both grains and flour. Conversely, its direct application to NIR spectra alone yielded relatively worse performance. Following data optimization, the first-derivative (FD) visible spectra of wheat grains were smoothed using a Savitzky–Golay (SG) filter and subsequently used as input for the RFR model. This optimized approach achieved a coefficient of determination (r²) of 0.8579, a root mean square error (RMSE) of 0.0216, and a relative percent deviation (RPD) of 2.6978. Under the same conditions, for wheat flour, the corresponding values were 0.8383, 0.0231, and 2.5293, respectively. Similarly, for wheat flour, the RFR model was applied to the SG-filtered FD spectra derived from the fused visible and near-infrared data, yielding an r² of 0.8474, an RMSE of 0.0224, and an RPD of 2.6034. Under the same conditions, wheat grains yielded an r² of 0.8494, an RMSE of 0.0223, and an RPD of 2.6208. This efficient and rapid intelligent prediction scheme demonstrates considerable potential for the quality assessment and control of relevant food products.

Cereal foods occupy a central position in the global food consumption structure. Staple foods such as wheat, rice, and corn provide essential nutrients like carbohydrates and proteins for billions of people. Long-term intake of these foods can reduce the risk of cardiovascular and cerebrovascular diseases. However, the development of modern agriculture has caused some quality and safety issues such as pesticide residues, mycotoxin contamination, heavy metal residues, and illegal additives in the production, processing, and storage of cereal foods. Traditional detection techniques such as chromatography and mass spectroscopy have limitations including time-consuming procedures and high costs. Surface-enhanced Raman spectroscopy (SERS), with advantages of non-complex pretreatment, rapid detection, and high sensitivity, can accurately detect factors affecting cereal quality. This paper reviews the principles and substrate types of SERS first. Secondly, it systematically summarizes the research progress in the applications of SERS technology in cereal quality control from multiple aspects, including the detection of microbial contamination, pesticide residues, and heavy metal residues. Finally, it provides an outlook on SERS technology. SERS is expected to further improve the accuracy and efficiency of quality control for cereal foods through the development of new substrates, combination with other detection technologies, and intelligent data analysis methods.