基于高光谱技术检测小麦粉灰分含量

目的 基于高光谱技术实现对小麦粉灰分含量的准确检测。方法 利用高光谱成像技术采集小麦粉的光谱数据,建立基于偏最小二乘法(partial least squares regression,PLSR)和深度极限学习机(deep extreme learning machines,DELM)的小麦粉灰分含量预测模型;通过分析3种预处理算法和4种波长选择算法,分别选出最佳的预处理与波长选择方法,最后构建基于特征波段光谱信息的预测模型,并对结果进行比较。结果 标准正态变量校正(standard normal variable,SNV)为最佳预处理方法;连续投影算法(successive projections algorithm,SPA)相较于随机森林(random forest,RF)、无信息变量消除(uninformative variable elimination,UVE)和遗传算法(genetic algorithm,GA)选择特征波长的模型更优;DELM模型更适用于灰分含量的检测,最优模型的测试集决定系数为0.968,预测集均方根误差为0.024。结论 高光谱成像技术可以快速、精准的...

Detection of ash content in wheat flour based on hyperspectral technology

Objective Accurate detection of ash content in wheat flour was achieved based on hyperspectral technology. Methods By collecting spectral data from wheat flour based on hyperspectral imaging techniques, a model for predicting the ash content of wheat flour based on partial least squares regression (PLSR) and deep extreme learning machines (DELM) was established; By analyzing three pre-processing algorithms and four wavelength selection algorithms, the best pre-processing and wavelength selection methods were selected respectively. Ultimately, a prediction model based on the spectral information of the characteristic bands is constructed and the results were compared. Results standard normal variable (SNV) is the best pre-treatment method; successive projections algorithm (SPA) outperforms random forest (RF), uninformative variable elimination (UVE) and genetic algorithm (GA) in the selection of feature wavelengths; The DELM model is more suitable for the detection of ash content, the test set Coefficient of determination (Rp2) of the optimal model reaches 0.968, and the root mean square error (RMSEP) reaches 0.024. Conclusion Hyperspectral imaging technology allows fast and accurate non-destructive detection of ash content in wheat flour, this technology can provide a theoretical basis for the development of an online system for testing wheat flour quality.