PMID- 34862682
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20220112
LR  - 20220112
IS  - 1097-0231 (Electronic)
IS  - 0951-4198 (Linking)
VI  - 36
IP  - 4
DP  - 2022 Feb 15
TI  - Untargeted selected ion flow tube mass spectrometry headspace analysis: 
      High-throughput differentiation of virgin and recycled polyethylene pellets.
PG  - e9230
LID - 10.1002/rcm.9230 [doi]
AB  - RATIONALE: Recycled plastics are increasingly used for packaging of fast-moving 
      consumer goods (FMCG). Compared with packaging made from virgin polymers, there 
      is greater risk of taints entering products due to prior use of the polymers and 
      incomplete cleaning. Increased quality assurance testing of polymer feedstock is 
      required for recycled packaging. Selected ion flow tube mass spectrometry 
      (SIFT-MS) analysis coupled with multivariate statistical data processing can 
      provide high-throughput untargeted screening of recycled polymers at low cost per 
      sample. METHODS: SIFT-MS is a direct-injection MS technique that provides 
      high-throughput automated headspace analysis of polymer samples when coupled with 
      a syringe-injection autosampler (12 incubated samples per hour). Full-scan 
      SIFT-MS data were processed using multivariate statistical analysis 
      (specifically, the soft independent modeling by class analogy (SIMCA) algorithm). 
      RESULTS: SIFT-MS full-scan data were acquired for ten replicates each of ten 
      recycled and four virgin high-density polyethylene (HDPE) pellet products from 
      multiple manufacturers. The samples varied approximately 20-fold in terms of 
      total volatile residue, while showing very high repeatability across replicates. 
      SIFT-MS scan data were dominated by aliphatic and monoterpene hydrocarbon 
      residues, and - to a lesser extent - alcohols. Application of the SIMCA algorithm 
      to the data resulted in successful classification by both individual samples and 
      manufacturers. CONCLUSIONS: Automated, untargeted SIFT-MS analysis coupled with 
      multivariate statistical data analysis has the potential to provide rapid, 
      effective screening of recycled polymer products, which would provide increased 
      quality assurance of recycled polymers used for FMCG.
CI  - (c) 2021 John Wiley & Sons Ltd.
FAU - Langford, Vaughan S
AU  - Langford VS
AUID- ORCID: 0000-0001-8059-5750
AD  - Syft Technologies Limited, Christchurch, New Zealand.
FAU - Perkins, Mark J
AU  - Perkins MJ
AD  - Anatune Limited, Cambridge, UK.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Rapid Commun Mass Spectrom
JT  - Rapid communications in mass spectrometry : RCM
JID - 8802365
SB  - IM
EDAT- 2021/12/05 06:00
MHDA- 2021/12/05 06:01
CRDT- 2021/12/04 06:18
PHST- 2021/11/24 00:00 [revised]
PHST- 2021/10/04 00:00 [received]
PHST- 2021/11/25 00:00 [accepted]
PHST- 2021/12/05 06:00 [pubmed]
PHST- 2021/12/05 06:01 [medline]
PHST- 2021/12/04 06:18 [entrez]
AID - 10.1002/rcm.9230 [doi]
PST - ppublish
SO  - Rapid Commun Mass Spectrom. 2022 Feb 15;36(4):e9230. doi: 10.1002/rcm.9230.