A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei)
Authors:
- Xinjie YuXYXinjie Yu
- Jianping WangJWJianping Wang
- Shiting WenSWShiting Wen
- Jinqiu YangJYJinqiu Yang
- Fengfeng ZhangFZFengfeng Zhang
- 1 February 2019
- journal article
- research article
- Published by Elsevier in Biosystems Engineering
- Vol. 178, 244-255
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61802344)
- Zhejiang Provincial Natural Science Foundation of China (LY16F030012, LY15F030016)
- Ningbo Science and Technology Special Project of China (2017C110002)
- Natural Science Foundation of Ningbo Municipality (2017A610118)
This publication has 31 references indexed in Scilit:
- Nondestructive measurement of total volatile basic nitrogen (TVB-N) content in salted pork in jelly using a hyperspectral imaging technique combined with efficient hypercube processing algorithmsAnalytical Methods, 2013
- Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon fleshTalanta, 2013
- Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging systemAnalytica Chimica Acta, 2012
- Model fusion for prediction of apple firmness using hyperspectral scattering imageComputers and Electronics in Agriculture, 2012
- Real-Time Monitoring of Shrimp Spoilage Using On-Package Sticker Sensor Based on Natural Dye of CurcuminFood Analytical Methods, 2011
- Evaluation of internal defect and surface color of whole pickles using hyperspectral imagingJournal of Food Engineering, 2010
- Melanosis and Quality Changes of Pacific White Shrimp (Litopenaeus vannamei) Treated with Catechin during Iced StorageJournal of Agricultural and Food Chemistry, 2009
- Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samplesBiosystems Engineering, 2009
- Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrationsEnvironmental Modelling & Software, 2007
- Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopyGeoderma, 2006