A model comparing and combining NIR and THz spectroscopy to enhance the prediction accuracy of components in food samples

Qingxia Li; Da-Wen Sun

doi:10.1117/12.3010848

9 May 2024 A model comparing and combining NIR and THz spectroscopy to enhance the prediction accuracy of components in food samples

Qingxia Li, Da-Wen Sun

Author Affiliations +

Proceedings Volume 12946, Active and Passive Smart Structures and Integrated Systems XVIII; 1294621 (2024) https://doi.org/10.1117/12.3010848
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Conference Poster

Abstract

This study aimed to investigate the accuracy and reliability of near-infrared (NIR) spectroscopy combined with terahertz (THz) spectroscopy for quantitative analysis of the chemical composition of food samples. Traditionally, NIR and THz spectroscopy have been used separately in food analysis. While NIR spectroscopy has advantages in terms of nondestructive and speed, THz spectroscopy is prized for its ability to penetrate deep into the sample, making it suitable for detecting internal quality. A detailed comparison of NIR and THz spectral data from commercially available biscuit and sunflower seed samples was conducted to evaluate the effectiveness of the combined use of the two techniques in improving the accuracy of ingredient detection and to explore how to effectively integrate the two spectral data to maximize their benefits in food analysis. The experimental results showed that NIR spectra showed high prediction accuracy in biscuit samples but were relatively weak in sunflower seed samples. Considering the characteristics of THz spectroscopy and recent research progress, THz spectroscopy may provide more accurate predictions than NIR spectroscopy in the chemical composition of sunflower seed analysis. This study demonstrates a new food detection strategy and provides the food industry with an effective method to ensure the quality and safety of food for the public. It also provides new perspectives and tools for analyzing other materials and compounds.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingxia Li and Da-Wen Sun "A model comparing and combining NIR and THz spectroscopy to enhance the prediction accuracy of components in food samples", Proc. SPIE 12946, Active and Passive Smart Structures and Integrated Systems XVIII, 1294621 (9 May 2024); https://doi.org/10.1117/12.3010848

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