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Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms

Overview of attention for article published in Food Analytical Methods, January 2019
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Mentioned by

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1 tweeter

Readers on

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3 Mendeley
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Title
Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms
Published in
Food Analytical Methods, January 2019
DOI 10.1007/s12161-018-01429-9
Authors

Lei Feng, Min Zhang, Benu Adhikari, Zhimei Guo

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 33%
Student > Master 1 33%
Student > Ph. D. Student 1 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 33%
Chemistry 1 33%
Engineering 1 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 January 2019.
All research outputs
#11,745,865
of 13,232,126 outputs
Outputs from Food Analytical Methods
#131
of 242 outputs
Outputs of similar age
#235,259
of 277,514 outputs
Outputs of similar age from Food Analytical Methods
#10
of 25 outputs
Altmetric has tracked 13,232,126 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 242 research outputs from this source. They receive a mean Attention Score of 1.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,514 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.