Non-destructive Detection of Water Content in <i>Porphyra</i> Based on Near-infrared Spectroscopy and Deep Learning

ZHANG Wenwen; HU Yadong; SUN Wenke; PAN Mingxuan; SHEN Zhaopeng; ZHOU Xinghu; QIAN Jin; WANG Peng

doi:10.13386/j.issn1002-0306.2023100153

Volume 45 Issue 21

Oct. 2024

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Science and Technology of Food Industry > 2024 > 45(21): 190-197. > DOI: 10.13386/j.issn1002-0306.2023100153

ZHANG Wenwen, HU Yadong, SUN Wenke, et al. Non-destructive Detection of Water Content in Porphyra Based on Near-infrared Spectroscopy and Deep Learning[J]. Science and Technology of Food Industry, 2024, 45(21): 190−197. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023100153.

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Non-destructive Detection of Water Content in Porphyra Based on Near-infrared Spectroscopy and Deep Learning

1.
Haide College, Ocean University of China, Qingdao 266104, China
2.
Jiangsu Coast Development Group Co., Ltd., Jiangsu Jinniu Industry & Trade Co., Ltd., Jiangsu Inovation Center of Marine Resources, Nanjing 210019, China
3.
College of Food Science and Engineering, Ocean University of China, Qingdao 266104, China

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Received Date: October 22, 2023
Available Online: August 23, 2024

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Abstract

Abstract

In order to explore the feasibility of combining near-infrared (NIR) spectroscopy and deep learning network for quantitative moisture detection, the dried Porphyra was divided into 479 groups, which detected the NIR spectra and moisture content. Four traditional quantitative moisture prediction models and a convolution neural networks (CNN) deep-learning moisture prediction model were finally established at full spectrum by preprocessing and analyzing the experimental data. After comparing the prediction results of the five models, it was found that the CNN model established by the preprocessing method of S-G smoothing combined with the second derivative had the best prediction effect. Its root-mean-square error of prediction (RMSEP) value was 0.456 and the coefficient of determination of prediction (R_p²) value was 0.990. After optimization, the RMSEP value of the model was reduced to 0.342 and the R_p² value could reach 0.994 (>0.8). At the same time, the ratio of performance to deviation for validation (RPD) was 6.155 (>3), which proved the possibility of practical application of the model in agriculture and food industry. The CNN model could predict the moisture content quickly, accurately, and non-destructive, improve the efficiency and accuracy of moisture detection, and provide an important reference for the quality control of related dry aquatic products.
- Porphyra,
- moisture content,
- near infrared spectroscopy,
- deep learning,
- convolution neural networks

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Science and Technology of Food Industry

Non-destructive Detection of Water Content in Porphyra Based on Near-infrared Spectroscopy and Deep Learning

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