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Mendeley readers
Attention Score in Context
| Title |
Real-Time Analysis of Potassium in Infant Formula Powder by Data-Driven Laser-Induced Breakdown Spectroscopy
|
|---|---|
| Published in |
Frontiers in Chemistry, July 2018
|
| DOI | 10.3389/fchem.2018.00325 |
| Pubmed ID | |
| Authors |
Da Chen, Jing Zong, Zhixuan Huang, Junxin Liu, Qifeng Li |
| Abstract |
Potassium represents one of the most crucial minerals in infant formula that supports healthy growth and development of infants. Here, a novel strategy for the real-time quantification of potassium in infant formula samples is introduced. Using laser-induced breakdown spectroscopy (LIBS) in a data-driven approach, a modified random frog algorithm (MRFA) is adopted in a higher-density discrete wavelet transform (HDWT) domain for the selection of the most important features related to potassium, which is named as DD-LIBS. In DD-LIBS, the HDWT oversamples the LIBS signals in both time and frequency domains by a factor of two, enhancing the spectral expandability in an approximately shift-invariant way. The MRFA is thus capable of isolating the features of potassium with experience accumulated from the collected LIBS data. Such pretreatment combined with a partial least squared (PLS) model can significantly suppress the uncontrolled shift and broadening effects on multivariate calibration, improving the capability of LIBS for accurate quantification of potassium. The present work demonstrates the feasibility of DD-LIBS for the quantification of potassium content of 90 commercial infant formula samples. A satisfactory result illustrates DD-LIBS as a feasible tool for real-time analysis of potassium content with little sample preparation. This strategy may be well extended to other element detection in the presence of uncontrolled interference. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 29% |
| Student > Bachelor | 1 | 14% |
| Other | 1 | 14% |
| Student > Master | 1 | 14% |
| Unknown | 2 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 29% |
| Chemistry | 2 | 29% |
| Psychology | 1 | 14% |
| Chemical Engineering | 1 | 14% |
| Unknown | 1 | 14% |
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 31 July 2018.
All research outputs
#20,529,173
of 23,098,660 outputs
Outputs from Frontiers in Chemistry
#2,950
of 6,040 outputs
Outputs of similar age
#287,836
of 329,833 outputs
Outputs of similar age from Frontiers in Chemistry
#116
of 190 outputs
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