[Objective] Fresh corn is increasingly an important choice in the daily diet of consumers due to its rich nutrition and sweet taste. With the improvement of living standards, people's quality requirements for fresh corn continue to improve, among which amylose content is a key indicator affecting the taste and flavor of corn, at present, the industry mainly uses chemical detection methods to determine amylose content, which is not only time-consuming and laborious, destroys samples, but also difficult to meet the needs of rapid detection in modern agricultural production and food processing. Therefore, the development of an efficient, accurate and non-destructive rapid detection technology for amylose has become a key issue in the field of agricultural product quality control. [Methods] In this study, a non-destructive detection model for amylose content in ears of fresh corn based on near-infrared spectroscopy technology was established. Taking Jinguan 597 fresh corn as the research object, the near-infrared spectroscopic detection system independently built by the laboratory was used to collect diffuse reflectance spectral data in the middle area of the complete corn ear to ensure that the detection process did not damage the integrity of the sample. At the same time, the physical and chemical values of amylose content in samples were determined with reference, and a standard database was established. In the data preprocessing stage, the Mahalanobis Distance method was used to screen the outliers of the original spectral data, and the abnormal samples caused by operating errors or sample defects were eliminated, and finally 90 representative fresh corn samples were retained for modeling analysis. In order to optimize the model performance, the effects of five mainstream spectral pretreatment methods were compared: standard normal variable (SNV) transform to eliminate the influence of optical path difference, multiplicative scatter correction (MSC) to reduce particle scattering interference, SavitZky-Golay smoothing (SGS) to remove random noise, first-order derivative (FD) to enhance spectral characteristic peaks, and detrending (DT) to eliminate baseline drift. Based on the partial least squares regression (PLSR) algorithm, a full-band amylose prediction model was constructed, and the robustness of the model was evaluated by cross-validation. In order to further improve the efficiency of model operation, the characteristic wavelengths with the strongest correlation with amylose content were selected from the whole spectrum by innovatively combining two variable selection methods, competitive adaptive reweighted sampling (CARS) and continuous successive projections algorithm (SPA), and a simplified characteristic band prediction model was established. [Results and Discussions] The results demonstrated that among the various combined models incorporating different preprocessing and feature wavelength selection methods, the "SNV-CARS-PLSR" model, which integrated SNV preprocessing with CARS feature extraction, exhibited superior performance. This model significantly outperformed alternative modeling approaches in predictive capability. The model achieved the following performance metrics: a calibration coefficient of determination (R²_C) of 0.826, root mean square error of calibration (RMSEC) of 1.399, prediction coefficient of determination (R²_P) of 0.820, root mean square error of prediction (RMSEP) of 1.081, and residual predictive deviation (RPD) of 2.426. Comparative analysis revealed that the "SNV-CARS-PLSR" model showed a 14.0% improvement in R²_P compared to the full-band PLSR model with SNV preprocessing alone. This enhancement was primarily attributed to the CARS algorithm's effective identification of key feature wavelengths. Through its adaptive weighting and iterative optimization process, CARS successfully extracted 22 characteristic wavelengths that were strongly correlated with amylose content from the original 157 wavelength points in the full spectrum. This selective extraction process effectively eliminated redundant spectral information and noise interference, thereby significantly improving the model's predictive accuracy. [Conclusions] Combined SNV preprocessing with CARS feature selection, the study successfully established a rapid, non-destructive prediction model for amylose content in fresh maize ears utilizing near-infrared spectroscopy technology. The developed methodology demonstrated significant advantages, including rapid analysis capability and complete non destructiveness of samples. The reseach could provide technical support for rapid, non-destructive detection of amylose in fresh maize ears.

本研究不存在研究者以及与公开研究成果有关的利益冲突。