Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems
暂无分享,去创建一个
[1] Hongbin Pu,et al. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks , 2023, Microchemical Journal.
[2] S. Ozturk,et al. Near-infrared spectroscopy and machine learning for classification of food powders under moving conditions , 2022, Journal of Food Engineering.
[3] Yankun Peng,et al. UV-fluorescence imaging for real-time non-destructive monitoring of pork freshness. , 2022, Food chemistry.
[4] W. Zhang,et al. Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish. , 2022, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[5] Vasilios I. Kelefouras,et al. Anatomy of Deep Learning Image Classification and Object Detection on Commercial Edge Devices: A Case Study on Face Mask Detection , 2022, IEEE Access.
[6] M. Bouatia,et al. Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy , 2021, Journal of Spectroscopy.
[7] S. Kelly,et al. Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems , 2021, Trends in Food Science & Technology.
[8] I. Mporas,et al. Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware , 2021, 2021 International Carnahan Conference on Security Technology (ICCST).
[9] R. Powers,et al. The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration. , 2021, Food chemistry.
[10] Changyeun Mo,et al. Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork , 2021 .
[11] G. Nychas,et al. Data Science in the Food Industry. , 2021, Annual review of biomedical data science.
[12] Petros Spachos,et al. Deep learning and machine vision for food processing: A survey , 2021, Current research in food science.
[13] G. Nychas,et al. Quest of Intelligent Research Tools for Rapid Evaluation of Fish Quality: FTIR Spectroscopy and Multispectral Imaging Versus Microbiological Analysis , 2021, Foods.
[14] Jae Hyung Lee,et al. Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer , 2020, Sensors.
[15] Iosif Mporas,et al. Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors , 2020, IEEE Access.
[16] Luis E. Rodriguez-Saona,et al. Miniaturization of Optical Sensors and their Potential for High-Throughput Screening of Foods , 2020, Proceedings of the Virtual 2020 AOCS Annual Meeting & Expo.
[17] G. Nychas,et al. Evaluation of Fourier transform infrared spectroscopy and multispectral imaging as means of estimating the microbiological spoilage of farmed sea bream. , 2019, Food microbiology.
[18] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[19] Wei Chen,et al. Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging. , 2019, Meat science.
[20] George-John E. Nychas,et al. A unified spectra analysis workflow for the assessment of microbial contamination of ready-to-eat green salads: Comparative study and application of non-invasive sensors , 2018, Comput. Electron. Agric..
[21] M. Mossoba,et al. First use of handheld Raman spectroscopic devices and on-board chemometric analysis for the detection of milk powder adulteration , 2018, Food Control.
[22] Ya Guo,et al. Hyperspectral image-based multi-feature integration for TVB-N measurement in pork , 2018 .
[23] Jihua Wang,et al. Detection of total viable count in spiced beef using hyperspectral imaging combined with wavelet transform and multiway partial least squares algorithm , 2018 .
[24] Yoshio Makino,et al. Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances , 2018 .
[25] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] George-John E. Nychas,et al. Novel approaches for food safety management and communication , 2016 .
[28] Efstathios Z. Panagou,et al. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines , 2016 .
[29] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Hong-Ju He,et al. Microbial evaluation of raw and processed food products by Visible/Infrared, Raman and Fluorescence spectroscopy , 2015 .
[31] Benjamin Leon Bodirsky,et al. Global Food Demand Scenarios for the 21st Century , 2015, PloS one.
[32] Jun-Hu Cheng,et al. Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis , 2015 .
[33] Wei Yu,et al. The Current Status of Process Analytical Technologies in the Dairy Industry , 2015 .
[34] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[35] Arun K Bhunia,et al. One day to one hour: how quickly can foodborne pathogens be detected? , 2014, Future microbiology.
[36] Frans van den Berg,et al. Process Analytical Technology in the food industry , 2013 .
[37] G. Nychas,et al. Monitoring the succession of the biota grown on a selective medium for pseudomonads during storage of minced beef with molecular-based methods. , 2013, Food microbiology.