Evidence map›Paper›PMID 31357626›Full record

ArticleFoods (Basel, Switzerland)2019

Headspace Volatile Evaluation of Carrot Samples-Comparison of GC/MS and AuNPs-hpDNA-Based E-Nose.

Sara Gaggiotti, Marcello Mascini, Paola Pittia, Flavio Della Pelle, Dario Compagnone

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sara GaggiottiFaculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, via Renato Balzarini 1, 64100 Teramo, Italy.
Marcello MasciniFaculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, via Renato Balzarini 1, 64100 Teramo, Italy.ORCID 0000-0003-2508-5680
Paola PittiaFaculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, via Renato Balzarini 1, 64100 Teramo, Italy.
Flavio Della PelleFaculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, via Renato Balzarini 1, 64100 Teramo, Italy. fdellapelle@unite.it.ORCID 0000-0002-8877-7580
Dario CompagnoneFaculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, via Renato Balzarini 1, 64100 Teramo, Italy. dcompagnone@unite.it.ORCID 0000-0001-7849-8943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The performances of a quartz crystal microbalances (QCMs) based on an electronic nose (E-nose), modified with hairpin-DNA (hpDNA) for carrot aroma profiling has been evaluated. Solid phase micro-extraction (SPME) headspace sampling, combined with gas chromatography (GC), was used as a reference method. The changes in carrot aroma profiles stored at different temperatures (-18 °C, 4 °C, 25 °C, and 40 °C) were monitored during time up to 26 days. The principal component analysis of the data evidenced the different aroma patterns related to the presence of different key compounds. The output data achieved with the hpDNA-based E-nose were able to detect aroma patterns similar to gas chromatography with mass spectrometry (GC-MS). This work demonstrates that hpDNA has different sizes of loops that can be used for the development of sensor arrays able to detect aroma patterns in food and their changes with advantages in terms of easiness of usage, rapidity, and cost of analysis versus classical methods.

Indexed as

carrotselectronic nosegold nanoparticles-hairpin-DNA (AuNPs-hpDNA)quartz crystal microbalances (QCMs)sensor arrayvolatile compounds analysis

Identifiers

PMID31357626
PMCPMC6722575

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.