Chinese liquor holds great cultural and economic significance globally. The accurate classification of aroma types and alcohol content is crucial for quality control in Chinese liquor production. To address limitations such as subjectivity and sensor drift in current methods, this study introduces a noninvasive, efficient, and objective approach using Near-Infrared Hyperspectral Imaging (NIR-HSI) to identify alcohol content and aroma types in Chinese liquor. Specifically, we create a comprehensive NIR hyperspectral dataset of Chinese liquor samples and train various machine learning algorithms to classify liquor samples based on their spectral features. Results show that the XGBoost model achieves the optimal performance in most of the experiments. The spectral signatures of various Chinese liquors are explored using SHapley Additive exPlanations (SHAP). We find that spectral bands, such as the band in the range of 1140-1160 nm, make important contributions to the aroma type classification, which are associated with C-H and CO bond stretching vibrations, providing valuable insights into the molecular basis of Chinese liquor analysis. Our dataset is publicly available at https://github.com/wangyunjeff/Chinese-Liquor-NIR-HSI-Dataset.

Deep learning-based unmixing methods have received great attention in recent years and achieved remarkable performance. These methods employ a data-driven approach to extract structure features from hyperspectral images; however, they tend to be less physically interpretable. Conventional unmixing methods have much more interpretability, whereas they require manually designing regularization and choosing penalty parameters. To overcome these limitations, we propose a novel unmixing method by unrolling the plug-and-play unmixing algorithm to conduct the deep architecture. Our method integrates both inner and outer priors. The carefully designed unfolding deep architecture is used to learn the spectral and spatial information from the hyperspectral image, which we refer to as inner priors. Additionally, our approach incorporates deep denoisers that have been pretrained on a large volume of image data to leverage the outer priors. Second, we design a dynamic convolution to model the multiscale information. Different scales are fused with an attention module. Experimental results of both synthetic and real datasets demonstrate that our method outperforms compared methods.

Hyperspectral unmixing aims to decompose the mixed pixels into pure spectra and calculate their corresponding fractional abundances. It holds a critical position in hyperspectral image processing. Traditional model-based unmixing methods use convex optimization to iteratively solve the unmixing problem with hand-crafted regularizers. While their performance is limited by these manually designed constraints, which may not fully capture the structural information of the data. Recently, deep learning-based unmixing methods have shown remarkable capability for this task. However, they have limited generalizability and lack interpretability. In this paper, we propose a novel hyperspectral unmixing method regularized by a diffusion model (URDM) to overcome these shortcomings. Our method leverages the advantages of both conventional optimization algorithms and deep generative models. Specifically, we formulate the unmixing objective function from a variational perspective and integrate it into a diffusion sampling process to introduce generative priors from a denoising diffusion probabilistic model (DDPM). Since the original objective function is challenging to optimize, we introduce a splitting-based strategy to decouple it into simpler subproblems. Extensive experiment results conducted on both synthetic and real datasets demonstrate the efficiency and superior performance of our proposed method.