Hyperspectral imaging testing of tobacco spice content based on dimensionality reduction via neighborhood structure collaborative preservation embedding

Quantitative testing of spice content in tobacco leaves is crucial for maintaining cigarette quality during manufacturing. While hyperspectral imaging (HSI) offers a non-destructive testing (NDT) alternative to traditional contact-based offline measurements, its redundant high-dimensional data with significant nonlinearity poses a major challenge for accurate spice content testing. To address this, we developed a novel dimensionality reduction algorithm neighborhood structure collaborative preservation embedding (NSCPE) for improving the HSI nondestructive testing system. Lying in the theoretical fusion of manifold learning and collaborative representation, NSCPE constructs a neighborhood structure-aware collaborative representation (NSaCR) model to adaptively capture the precise local correlations of samples within neighborhoods of varying density. And this local structural information is preserved through a dual collaborative graph embedding framework, which maintains both point-to-point and set-to-set neighborhood structures in embedding simultaneously. This adaptive mining and comprehensive preservation mechanism for local structures mitigates data nonlinearity and extracts intrinsic features highly relevant to spice content variations. In experiments, NSCPE achieved superior performance, with the optimal results of R 2 = 0.958 and RMSE = 0.349 on LanzhouTob and R 2 = 0.861 and RMSE = 0.637 on ShandongTob data sets, outperforming several state-of-the-art methods and showing its potential in real-time quality monitoring in cigarette industry.