Rapid and non-destructive determination of oil content in fuzzy cottonseeds via near-infrared reflectance spectroscopy

Abstract

In China, enterprises specializing in cottonseed processing predominantly acquire fuzzy cottonseeds. However, the expeditious determination of oil content within these fuzzy cottonseeds remains a major challenge. This particular challenge considerably influences the economic efficiency of enterprises engaged in cottonseed processing. To address this concern, this research acquired near-infrared spectral (NIRS) information from fuzzy cottonseeds and applied the Standard Normal Variate (SNV) pretreatment to eliminate data noise. Subsequently, the Uninformative Variable Elimination (UVE) was employed to select feature wavelength points reflecting fuzzy cottonseed oil content. To further accurately extract the feature wavelengths of fuzzy cottonseeds, Competitive Adaptive Reweighted Sampling (CARS) and Random Frog (RF) were utilized for a second round of feature wavelength extraction. Finally, prediction models for fuzzy cottonseed oil content were established using partial least squares regression (PLSR), Support Vector Regression (SVR), and Least Squares Support Vector Machines (LSSVM). Results revealed that the LSSVM model constructed using the feature selected by the UVE-CARS had the best performance in predicting the oil content of fuzzy cottonseeds. The values of Coefficient of Determination (R2), Root Mean Square Error (RMSE), Residual Error Ratio (RER), and Residual Predictive Deviation (RPD) were 0.8605, 0.0075, 12.8116, and 3.8154, respectively. The LSSVM model built on the feature wavelength points selected by the UVE-RF demonstrated the second-best prediction performance with an R2 of 0.8329, an RMSE of 0.0091, an RER of 10.4861, and an RPD of 3.1228. The validation experiments corroborated these findings, it was found that the R2 was 0.8122, the RMSE was 0.0063, The RER was 7.6190, and the RPD was 2.9036. This underscores the effectiveness of the method employed in this study for detecting the oil content in fuzzy cottonseeds. The approach presented in this study serves as a crucial reference for the swift and accurate assessment of oil content in fuzzy cottonseeds, providing an impactful technical solution for cottonseed processing enterprises to enhance their economic outcomes.