Next Article in Journal
Inhibitory Effects of Aqueous Ethanol Extracts of Poplar-Type Propolis on Advanced Glycation End Products and Protein Oxidation
Next Article in Special Issue
Enhancing Food Image Recognition by Multi-Level Fusion and the Attention Mechanism
Previous Article in Journal
Rheological Properties and Sensory Profile of Yoghurt Produced with Novel Combination of Probiotic Cultures
Previous Article in Special Issue
Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 13
Issue 19
10.3390/foods13193023
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy

by
Wenwen Zhang
1,
Mingxuan Pan
2,
Peng Wang
3,
Jiao Xue
2,
Xinghu Zhou
2,
Wenke Sun
3,
Yadong Hu
2 and
Zhaopeng Shen
3,*
1
Haide College, Ocean University of China, Qingdao 266003, China
2
Jiangsu Coast Development Group Co., Ltd., Nanjing 210019, China
3
College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
*
Author to whom correspondence should be addressed.
Foods 2024, 13(19), 3023; https://doi.org/10.3390/foods13193023
Submission received: 24 August 2024 / Revised: 17 September 2024 / Accepted: 18 September 2024 / Published: 24 September 2024
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
Browse Figures
Review Reports Versions Notes

Abstract

This study explored the performance and reliability of three predictive models—extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)—for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model’s high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety.
Keywords: Porphyra yezoensis; near-infrared (NIR) spectroscopy; moisture content; wavelength selection; prediction performance Porphyra yezoensis; near-infrared (NIR) spectroscopy; moisture content; wavelength selection; prediction performance

Share and Cite

MDPI and ACS Style

Zhang, W.; Pan, M.; Wang, P.; Xue, J.; Zhou, X.; Sun, W.; Hu, Y.; Shen, Z. Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy. Foods 2024, 13, 3023. https://doi.org/10.3390/foods13193023

AMA Style

Zhang W, Pan M, Wang P, Xue J, Zhou X, Sun W, Hu Y, Shen Z. Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy. Foods. 2024; 13(19):3023. https://doi.org/10.3390/foods13193023

Chicago/Turabian Style

Zhang, Wenwen, Mingxuan Pan, Peng Wang, Jiao Xue, Xinghu Zhou, Wenke Sun, Yadong Hu, and Zhaopeng Shen. 2024. "Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy" Foods 13, no. 19: 3023. https://doi.org/10.3390/foods13193023

APA Style

Zhang, W., Pan, M., Wang, P., Xue, J., Zhou, X., Sun, W., Hu, Y., & Shen, Z. (2024). Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy. Foods, 13(19), 3023. https://doi.org/10.3390/foods13193023

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop