Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 22;12(17):3145.
doi: 10.3390/foods12173145.

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation

Tianhao Li  1   2 , Wensong Wei  3   4 , Shujuan Xing  3   4 , Weiqing Min  1   2 , Chunjiang Zhang  3   4 , Shuqiang Jiang  1   2
Affiliations

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation

Tianhao Li et al. Foods. .

Abstract

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.

Keywords: deep learning; food nutrition estimation; near-infrared hyperspectral imaging; wavelength selection.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The process of hyperspectral image acquisition spectra extraction.
Figure 2
Figure 2
(a) The architecture of our proposed OptmWave framework. (b) Detailed design of SPGN. (c) Detailed design of PN.
Figure 3
Figure 3
Spectral characterization for the dataset and the visualization of experiment results. (a) Reflectance spectra of all samples. (b) Mean spectrum and standard deviation of the spectral data. The blue line shows the mean spectrum, while the shaded area represents the standard deviation of the reflectance values at each wavelength. (c) Reference versus predicted values for protein content from OptmWave. (d) Visualization of effective wavelength selection result by SPA, CARS, and our proposed SPGN.
See this image and copyright information in PMC

References

    1. Miller V., Webb P., Cudhea F., Shi P., Zhang J., Reedy J., Erndt-Marino J., Coates J., Mozaffarian D. Global dietary quality in 185 countries from 1990 to 2018 show wide differences by nation, age, education, and urbanicity. Nat. Food. 2022;3:694–702. doi: 10.1038/s43016-022-00594-9. - DOI - PMC - PubMed
    1. O’Hearn M., Erndt-Marino J., Gerber S., Lauren B.N., Economos C., Wong J.B., Blumberg J.B., Mozaffarian D. Validation of Food Compass with a healthy diet, cardiometabolic health, and mortality among US adults, 1999–2018. Nat. Commun. 2022;13:7066. - PMC - PubMed
    1. Min W., Jiang S., Liu L., Rui Y., Jain R. A survey on food computing. ACM Comput. Surv. CSUR. 2019;52:1–36. doi: 10.1145/3329168. - DOI
    1. Wang W., Min W., Li T., Dong X., Li H., Jiang S. A review on vision-based analysis for automatic dietary assessment. Trends Food Sci. Technol. 2022;122:223–237. doi: 10.1016/j.tifs.2022.02.017. - DOI
    1. Mirtchouk M., Merck C., Kleinberg S. Automated estimation of food type and amount consumed from body-worn audio and motion sensors; Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing; Heidelberg, Germany. 12–16 September 2016; pp. 451–462.

LinkOut - more resources