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Article

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

by
Yurong Zhang
1,2,3,
Shuxian Liu
1,2,3,
Xianqing Zhou
1,2,3,* and
Jun-Hu Cheng
4,*
1
School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China
2
Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
3
Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
4
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
*
Authors to whom correspondence should be addressed.
Molecules 2024, 29(13), 2968; https://doi.org/10.3390/molecules29132968
Submission received: 23 May 2024 / Revised: 18 June 2024 / Accepted: 19 June 2024 / Published: 21 June 2024
(This article belongs to the Special Issue Food Chemistry in Asia—2nd Edition)

Abstract

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.
Keywords: corn; fatty acid values; freshness; hyperspectral imaging; chemical information visualization corn; fatty acid values; freshness; hyperspectral imaging; chemical information visualization

Share and Cite

MDPI and ACS Style

Zhang, Y.; Liu, S.; Zhou, X.; Cheng, J.-H. Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology. Molecules 2024, 29, 2968. https://doi.org/10.3390/molecules29132968

AMA Style

Zhang Y, Liu S, Zhou X, Cheng J-H. Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology. Molecules. 2024; 29(13):2968. https://doi.org/10.3390/molecules29132968

Chicago/Turabian Style

Zhang, Yurong, Shuxian Liu, Xianqing Zhou, and Jun-Hu Cheng. 2024. "Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology" Molecules 29, no. 13: 2968. https://doi.org/10.3390/molecules29132968

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