Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems

Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.

[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.