Spectral reconstruction from RGB image to hyperspectral image: Take the detection of glutamic acid index in beef as an example

Fujia Dong; Ying Xu; Yingkun Shi; Yingjie Feng; Zhaoyang Ma; Hui Li; Zhongxiong Zhang; Guangxian Wang; Yue Chen; Jinhua Xian; Shichang Wang; Songlei Wang; Weiguo Yi

doi:10.1016/j.foodchem.2024.141543

Spectral reconstruction from RGB image to hyperspectral image: Take the detection of glutamic acid index in beef as an example

Food Chem. 2024 Oct 5;463(Pt 4):141543. doi: 10.1016/j.foodchem.2024.141543. Online ahead of print.

Authors

Fujia Dong¹, Ying Xu², Yingkun Shi³, Yingjie Feng³, Zhaoyang Ma³, Hui Li³, Zhongxiong Zhang³, Guangxian Wang³, Yue Chen³, Jinhua Xian³, Shichang Wang², Songlei Wang⁴, Weiguo Yi⁵

Affiliations

¹ School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
² College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
³ School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
⁴ School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China. Electronic address: Wangsonglei163@126.com.
⁵ School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China. Electronic address: ywg@nxu.edu.cn.

PMID: 39395351
DOI: 10.1016/j.foodchem.2024.141543

Abstract

The use of spectral reconstruction (SR) to recovery RGB images to full-scene hyperspectral image (HSI) is an important measure to achieve real-time and low-cost HSI applications. Taking the detection of glutamic acid index for 360 beef samples as an example, the feasibility of using 11 state-of-the-art reconstruction algorithms to achieve RGB to HSI in complex food systems was investigated. The multivariate correlation analysis was used to prove that RGB is a projection of three-channel comprehensive coverage wide-band information. The comprehensive quality attributes (PSNR-Params-FLOPS) was proposed to determine the optimal reconstruction model (MST++, MST, MIRNet, and MPRNet). Moreover, SSIM values and t-SNE were introduced to evaluate the consistency of the reconstruction results. Finally, Lightweight Transformer was used to establish the detection models of Raw-HSI, RGB and SR-HSI for the prediction of glutamic acid index for beef. The results showed that the MST++ model exhibited the best performance in SR, with RMSE, PSNR, and SSIM values of 0.015, 36.70, and 0.9253, respectively. Meanwhile, the prediction effect of MST++ (R²_P = 0.8422 and RPD = 2.46) reconstructed was close to the Raw-HSI (R²_P = 0.8526 and RPD = 2.69). The results provide practical application scenarios and detailed analysis ideas for RGB-to-HSI.

Keywords: Hyperspectral imaging; Lightweight transformer; Multivariate evaluation; Quantitative analysis; RGB images; Spectral reconstruction.