Evidence map›Paper›PMID 37365431›Full record

ArticleLasers in medical science2023

Evaluation of medicinal plants using laser-induced breakdown spectroscopy (LIBS) combined with chemometric techniques.

Muhammad Nouman Khan, Qianqian Wang, Bushra Sana Idrees, Rijah Waheed, Ajaz Ul Haq, Muhammad Abrar, Yasir Jamil

Abstract read
PubMed Publisher
In one paragraph

Article in Lasers in medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 34% of its field
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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Review
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

7 authors at 4 institutions in 2 countries.

Muhammad Nouman KhanSchool of Optics and Photonics, Beijing Institute of Technology, Beijing, China. noumankhan@bit.edu.cn.
Qianqian WangSchool of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
Bushra Sana IdreesSchool of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
Rijah WaheedDepartment of Physics, Hazara University, Mansehra, Pakistan.
Ajaz Ul HaqDepartment of Physics, Hazara University, Mansehra, Pakistan.
Muhammad AbrarDepartment of Physics, Hazara University, Mansehra, Pakistan.
Yasir JamilDepartment of Physics, University of Agriculture Faisalabad, Faisalabad, Pakistan.
Hazara University · PKMinistry of Industry and Information Technology · CNBeijing Institute of Technology · CNUniversity of Agriculture Faisalabad · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medicinal plants play a vital role in herbal medical field and allopathic medicine field industry. Chemical and spectroscopic studies of Taraxacum officinale, Hyoscyamus niger, Ajuga bracteosa, Elaeagnus angustifolia, Camellia sinensis, and Berberis lyceum are conducted in this paper by using a 532-nm Nd:YAG laser in an open air environment. These medicinal plant's leaves, roots, seed, and flowers are used to treat a range of diseases by the locals. It is crucial to be able to distinguish between beneficial and detrimental metal elements in these plants. We demonstrated how various elements are categorized and how roots, leaves, seeds and flowers of same plants differ from each other on the basis of elemental analysis. Furthermore, for classification purpose, different classification models, partial least square discriminant analysis (PLS-DA), k-nearest neighbors (kNN), and principal component analysis (PCA) are used. We found silicon (Si), aluminum (Al), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), manganese (Mn), phosphorous (P), and vanadium (V) in all of the medicinal plant samples with a molecular form of carbon and nitrogen band. We detected Ca, Mg, Si, and P as primary components in all of the plant samples, as well as V, Fe, Mn, Al, and Ti as essential medicinal metals, and additional trace elements like Si, Sr, and Al. The result's findings show that the PLS-DA classification model with single normal variate (SNV) preprocessing method is the most effective classification model for different types of plant samples. The average correct classification rate obtained for PLS-DA with SNV is 95%. Moreover, laser-induced breakdown spectroscopy (LIBS) was successfully employed to perform rapid, sensitive, and quantitative trace element analysis on medicinal herbs and plant samples.

Indexed as

Lasers, Solid-StatePlants, MedicinalTrace ElementsCalciumChemometricsMagnesiumSodiumSpectrum AnalysisCalciumMagnesiumSodiumTrace ElementsAllopathic medicineClassificationLIBSPartially least square discriminantPreprocessingQuantitativeTaraxacum officinale

Identifiers

PMID37365431
OpenAlexW4382047701

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.