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Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy

Overview of attention for article published in Analytical & Bioanalytical Chemistry, April 2017
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Title
Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy
Published in
Analytical & Bioanalytical Chemistry, April 2017
DOI 10.1007/s00216-017-0288-8
Pubmed ID
Authors

Gaoyang Zhang, Penghui Li, Wenfei Zhang, Jian Zhao

Abstract

Improvement of the nutritional quality of soybean is usually facilitated by a vast range of soybean germplasm with enough information about their multiple phytonutrients. In order to acquire this essential information from a huge number of soybean samples, a rapid analytic method is urgently required. Here, a nondestructive near-infrared reflectance spectroscopy (NIRS) method was developed for rapid and accurate measurement of 25 nutritional components in soybean simultaneously, including fatty acids palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid, vitamin E (VE), α-VE, γ-VE, δ-VE, saponins, isoflavonoids, and flavonoids. Modified partial least squares regression and first, second, third, and fourth derivative transformation was applied for the model development. The 1 minus variance ratio (1-VR) value of the optimal model can reach between the highest 0.95 and lowest 0.64. The predicted values of phytonutrients in soybean using NIRS technology are comparable to those obtained from using the traditional spectrum or chemical methods. A robust NIRS can be adopted as a reliable method to evaluate complex plant constituents for screening large-scale samples of soybean germplasm resources or genetic populations for improvement of nutritional qualities. Graphical Abstract ᅟ.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 45%
Student > Master 6 15%
Student > Ph. D. Student 4 10%
Professor 3 8%
Lecturer > Senior Lecturer 1 3%
Other 2 5%
Unknown 6 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 50%
Chemistry 4 10%
Engineering 3 8%
Materials Science 2 5%
Earth and Planetary Sciences 1 3%
Other 3 8%
Unknown 7 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 May 2017.
All research outputs
#15,173,117
of 25,382,440 outputs
Outputs from Analytical & Bioanalytical Chemistry
#4,598
of 9,619 outputs
Outputs of similar age
#169,580
of 324,249 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#30
of 139 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,619 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 51% of its peers.
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 324,249 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.