基于深度学习网络框架的高光谱和低场核磁共振检测腌制海参水分含量

摘要：海参加工过程中水分含量（（ MC）的精准控制有利于改善海参的口感、保持其营养价值，直接关系到海参的品质和 保质期。本研究旨在探索利用深度学习（（ DL）实现基于高光谱成像（（ HSI）和低场核磁共振（（ LF-NMR）数据快速无损检 测腌制海参中MC的可行性。首先，利用HSI和LF-NMR数据，分别将三种布谷鸟搜索（（CS）降维算法（（Traditional-CS、 Binary-CS 和 Chaotic-CS）与 DL 框架相结合，建立预测模型，证明了 DL 框架在预测海参 MC 方面的可行性，并选择 Chaotic-CS 算法作为最优降维算法。然后分别基于 HSI 和LF-NMR数据实现 MC 可视化,检测 MC 的迁移和减少。最后 利用 HSI 和 LF-NMR 数据,探讨基于 Fusion-net DL(FDL)框架的模型的优势,该模型比单一数据模型表现出更好的性 能,R=0.9929,RMSE2C=0.0016,RP2C=0.9936,RPD=12.5041。综上所述,基于DL框架的HSI和LF-NMR数据可以实现盐渍 海参中MC的快速无损检测,验证了基于FDL框架的数据融合检测的优势。

关键词：高光谱成像 低场核磁共振 海参 深度学习网络

期刊：Food Research International

DOI：https://DOI.org/10.1016/j.foodres.2022.111174

设备：GaiaSorter

Detection of moisture content in salted sea cucumbers by hyperspectral and low field nuclear magnetic resonance based on deep learning network framework

Abstract：The accurate control of moisture content (MC) during the processing of sea cucumber is beneficial to improve the taste of sea cucumber and maintain its nutritional value, which is directly related to the quality and shelf life of sea cucumber. The purpose of this study is to explore the feasibility using deep learning (DL) to realize rapid nondestructive detection of MC in salted sea cucumbers based on hyperspectral imaging (HSI) and low field nuclear magnetic resonance (LF-NMR) data. Firstly, three Cuckoo Search (CS) dimensionality reduction algo rithms (Traditional-CS, Binary-CS and Chaotic-CS) were combined with DL framework respectively using HSI and LF-NMR data to establish prediction models, which proved the feasibility of DL framework in predicting the MC of sea cucumbers, and Chaotic-CS algorithm was selected as the optimal dimensionality reduction algorithm. Then, the MC visualization based on HSI and LF-NMR data was realized respectively to detect the migration and decrease of MC. Finally, using both HSI and LF-NMR data, the advantages of the models based on Fusion-net DL (FDL) framework were discussed, which showed better performance than the single-data models, with R 0.9929, RMSE 2 C of 0.0016, R P 2 C of of 0.9936 and RPD of 12.5041. In summary, the rapid nondestructive detection of MC in salted sea cucumbers could be realized by HSI and LF-NMR data based on DL framework, and the advantage of data fusion detection based on FDL framework was verified.

Keywords：Hyperspectral imaging Low field nuclear magnetic resonance Sea cucumber Deep learning network