Evidence map›Paper›PMID 31687285›Full record

ArticlePeerJ2019

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in

Xiuying Liu, Chenzhou Liu, Zhaoyong Shi, Qingrui Chang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Spectral prediction of anthocyanin concentration inFrontiers in plant science · 2026
    Article
  2. Prediction of cyanidin 3-rutinoside content inFrontiers in plant science · 2024
    Article
  3. Article
  4. Article
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

4 authors at 3 institutions in 1 country.

Xiuying LiuCollege of Agriculture, Henan University of Science and Technology, Luoyang, Henan, China.
Chenzhou LiuCollege of Agriculture, Henan University of Science and Technology, Luoyang, Henan, China.
Zhaoyong ShiCollege of Agriculture, Henan University of Science and Technology, Luoyang, Henan, China.
Qingrui ChangCollege of Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province, China.
Luoyang Institute of Science and Technology · CNCentral South University of Forestry and Technology · CNNorthwest A&F University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The anthocyanin content in leaves can reveal valuable information about a plant's physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450-600 nm) in

Indexed as

Anthocyanin contentBack-propagation neural networkPartial least squares analysisPrincipal component analysisReflectance spectra

Identifiers

PMID31687285
PMCPMC6825749
OpenAlexW2982545589

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.