Characterization of ash content in wheat flour using data fusion

Abstract

Ash detection played an important role in wheat flour quality testing. In this work, we proposed a method for the ash content detection in wheat flour, which based on hyperspectral (HSI) method fused with terahertz (THz) technology. Standard Normal Variable Correction (SNV) and Multiple Scattering Correction (MSC) were used for the pretreatment of HSI and THz data. Moreover, feature wavelengths were extracted by the Successive Projections Algorithm (SPA) and Feature weighting algorithms (Relieff), respectively. Hierarchical Extreme Learning Machine (H-ELM) model were used for the analysis of two spectral data, which combined with data fusion strategy. The prediction coefficient of determination (r2) and the root mean square error of prediction (RMSEP) were employed to evaluate the performance of the model effectively. Results suggested that the model accuracy of data fusion was better than that of single spectrum model. Compared to other fused method, this model obtained better results at r2 = 0.989 and RMSEP = 0.015, which based on the SPA-selected HSI feature data fused with the Relieff-selected THz feature data. This study demonstrates that data fusion analysis offers a way to detect ash content in wheat flour, which based on HSI method and THz technology.