Evidence map›Paper›PMID 39001010›Full record

ArticleSensors (Basel, Switzerland)2024

Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy.

Rocío López-Calabozo, Ângela Liberal, Ângela Fernandes, Isabel Revilla, Isabel C F R Ferreira, Lillian Barros, Ana M Vivar-Quintana

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Rocío López-CalabozoFood Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.ORCID 0009-0004-4100-6035
Ângela LiberalFood Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.
Ângela FernandesCentro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal.ORCID 0000-0002-0157-9873
Isabel RevillaFood Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.ORCID 0000-0002-4067-3416
Isabel C F R FerreiraCentro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal.ORCID 0000-0003-4910-4882
Lillian BarrosCentro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal.ORCID 0000-0002-9050-5189
Ana M Vivar-QuintanaFood Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.ORCID 0000-0003-1543-4736

Funding

Fundação para a Ciência e Tecnologia LA/P/0007/2020Fundação para a Ciência e Tecnologia UIDB/00690/2020Fundação para a Ciência e Tecnologia UIDP/00690/2020
6 · The paper itself

Abstract

Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils.

Indexed as

CarbohydratesLens PlantSpectroscopy, Near-InfraredCalibrationFructoseLeast-Squares AnalysisStarchSucroseCarbohydratesFructoseStarchSucrosecarbohydratesfibrelentilNIRstarchsugars

Identifiers

PMID39001010
PMCPMC11244296

What OpenQuestion holds

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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.