PMID- 36230198
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221017
IS  - 2304-8158 (Print)
IS  - 2304-8158 (Electronic)
IS  - 2304-8158 (Linking)
VI  - 11
IP  - 19
DP  - 2022 Oct 7
TI  - Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR 
      Hyperspectral Imaging and FTIR Spectroscopy.
LID - 10.3390/foods11193122 [doi]
LID - 3122
AB  - It has been reported that some brands of roasted ground coffee, whose ingredients 
      are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. 
      Thus, the objective of this research was to test whether near-infrared 
      spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared 
      spectroscopy (FTIRs) could be used to test whether samples of coffee were pure 
      Arabica or whether they contained Robusta, and if so, what were the levels of 
      Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs 
      techniques were established with support vector machine classification (SVMC). 
      Results showed that the highest levels of accuracy in the prediction set were 
      98.04 and 97.06%, respectively. Quantitative models of both techniques for 
      predicting the concentration of Robusta in the samples of Arabica with Robusta 
      were established using support vector machine regression (SVMR), which gave the 
      highest levels of accuracy in the prediction set with a coefficient of 
      determination for prediction (R(p)(2)) of 0.964 and 0.956 and root mean square 
      error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore 
      concluded that the results showed that both techniques (NIR-HSI and FTIRs) have 
      the potential for use in the inspection of roasted ground coffee to classify and 
      determine the respective levels of Arabica and Robusta within the mixture.
FAU - Sahachairungrueng, Woranitta
AU  - Sahachairungrueng W
AD  - Department of Food Science, School of Food-Industry, King Mongkut's Institute of 
      Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand.
FAU - Meechan, Chanyanuch
AU  - Meechan C
AD  - Department of Food Process Engineering, School of Food-Industry, King Mongkut's 
      Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand.
FAU - Veerachat, Nutchaya
AU  - Veerachat N
AD  - Department of Food Process Engineering, School of Food-Industry, King Mongkut's 
      Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand.
FAU - Thompson, Anthony Keith
AU  - Thompson AK
AD  - Department of Postharvest Technology, Cranfield University, College Road, Bedford 
      MK43 0AL, UK.
FAU - Teerachaichayut, Sontisuk
AU  - Teerachaichayut S
AD  - Department of Food Process Engineering, School of Food-Industry, King Mongkut's 
      Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand.
LA  - eng
GR  - 2563-01-07005/School of Food Industry, King Mongkut's Institute of Technology 
      Ladkrabang/
GR  - King Mongkut's Institute of Technology Ladkrabang/
PT  - Journal Article
DEP - 20221007
PL  - Switzerland
TA  - Foods
JT  - Foods (Basel, Switzerland)
JID - 101670569
PMC - PMC9562924
OTO - NOTNLM
OT  - classification
OT  - detection
OT  - qualitative
OT  - quantitative
OT  - spectra
COIS- The authors declare no conflict of interest.
EDAT- 2022/10/15 06:00
MHDA- 2022/10/15 06:01
PMCR- 2022/10/07
CRDT- 2022/10/14 01:49
PHST- 2022/09/02 00:00 [received]
PHST- 2022/09/30 00:00 [revised]
PHST- 2022/10/03 00:00 [accepted]
PHST- 2022/10/14 01:49 [entrez]
PHST- 2022/10/15 06:00 [pubmed]
PHST- 2022/10/15 06:01 [medline]
PHST- 2022/10/07 00:00 [pmc-release]
AID - foods11193122 [pii]
AID - foods-11-03122 [pii]
AID - 10.3390/foods11193122 [doi]
PST - epublish
SO  - Foods. 2022 Oct 7;11(19):3122. doi: 10.3390/foods11193122.