PMID- 34677322
OWN - NLM
STAT- MEDLINE
DCOM- 20211115
LR  - 20211115
IS  - 2079-6374 (Electronic)
IS  - 2079-6374 (Linking)
VI  - 11
IP  - 10
DP  - 2021 Sep 30
TI  - An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using 
      Sensor Fusion.
LID - 10.3390/bios11100366 [doi]
LID - 366
AB  - The continuous development of more accurate and selective bio- and chemo-sensors 
      has led to a growing use of sensor arrays in different fields, such as health 
      monitoring, cell culture analysis, bio-signals processing, or food quality 
      tracking. The analysis and information extraction from the amount of data 
      provided by these sensor arrays is possible based on Machine Learning techniques 
      applied to sensor fusion. However, most of these computing solutions are 
      implemented on costly and bulky computers, limiting its use in in-situ scenarios 
      outside complex laboratory facilities. This work presents the application of 
      machine learning techniques in food quality assessment using a single Field 
      Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power 
      consumption as well as low-size allow the application of the proposed solution 
      even in space constrained places, as in food manufacturing chains. As an example, 
      the proposed system is tested on an e-nose developed for beef classification and 
      microbial population prediction.
FAU - Eneriz, Daniel
AU  - Eneriz D
AUID- ORCID: 0000-0001-5709-1183
AD  - Faculty of Science, University of Zaragoza, 50009 Zaragoza, Spain.
FAU - Medrano, Nicolas
AU  - Medrano N
AUID- ORCID: 0000-0002-5380-3013
AD  - Faculty of Science, University of Zaragoza, 50009 Zaragoza, Spain.
FAU - Calvo, Belen
AU  - Calvo B
AUID- ORCID: 0000-0003-2361-1077
AD  - Faculty of Science, University of Zaragoza, 50009 Zaragoza, Spain.
LA  - eng
GR  - PID2019-106570 RB-I00/Ministerio de Ciencia, Innovacion y Universidades/
GR  - BOA20201210014/Gobierno de Aragon/
PT  - Journal Article
DEP - 20210930
PL  - Switzerland
TA  - Biosensors (Basel)
JT  - Biosensors
JID - 101609191
SB  - IM
MH  - Computers
MH  - Electronic Nose
MH  - Equipment Design
MH  - *Food Analysis
MH  - Food Quality
MH  - Humans
MH  - Machine Learning
MH  - *Signal Processing, Computer-Assisted
PMC - PMC8534206
OTO - NOTNLM
OT  - FPGA
OT  - TVC
OT  - e-nose
OT  - food quality
OT  - neural networks
OT  - sensor fusion
COIS- The authors declare no conflict of interest.
EDAT- 2021/10/23 06:00
MHDA- 2021/11/16 06:00
PMCR- 2021/09/30
CRDT- 2021/10/22 12:18
PHST- 2021/08/31 00:00 [received]
PHST- 2021/09/27 00:00 [revised]
PHST- 2021/09/29 00:00 [accepted]
PHST- 2021/10/22 12:18 [entrez]
PHST- 2021/10/23 06:00 [pubmed]
PHST- 2021/11/16 06:00 [medline]
PHST- 2021/09/30 00:00 [pmc-release]
AID - bios11100366 [pii]
AID - biosensors-11-00366 [pii]
AID - 10.3390/bios11100366 [doi]
PST - epublish
SO  - Biosensors (Basel). 2021 Sep 30;11(10):366. doi: 10.3390/bios11100366.