Literature DB >> 25148988

Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy.

Leiqing Pan1, Qibing Zhu2, Renfu Lu3, J Mitchell McGrath4.   

Abstract

Visible and near-infrared spectra in interactance mode were acquired for intact and sliced beet samples, using two portable spectrometers for the spectral regions of 400-1100 nm and 900-1600 nm, respectively. Sucrose prediction models for intact and sliced beets were developed and then validated. The spectrometer for 400-1100 nm was able to predict the sucrose content with correlations of prediction (rp) of 0.80 and 0.88 and standard errors of prediction (SEPs) of 0.89% and 0.70%, for intact beets and beet slices, respectively. The spectrometer for 900-1600 nm had rp values of 0.74 and 0.88 and SEPs of 1.02% and 0.69% for intact beets and beet slices. These results showed the feasibility of using the portable spectrometer to predict the sucrose content of beet slices. Using simple correlation analysis, the study also identified important wavelengths that had strong correlation with the sucrose content. Published by Elsevier Ltd.

Entities:  

Keywords:  Detection; High-performance liquid chromatography; Near-infrared spectroscopy; Sucrose content; Sugar beet

Mesh:

Substances:

Year:  2014        PMID: 25148988     DOI: 10.1016/j.foodchem.2014.06.117

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  3 in total

1.  Rapid Starch Evaluation in Fresh Cassava Root Using a Developed Portable Visible and Near-Infrared Spectrometer.

Authors:  Yuranan Bantadjan; Ronnarit Rittiron; Kritsanun Malithong; Sureeporn Narongwongwattana
Journal:  ACS Omega       Date:  2020-05-07

2.  Microbial Inoculation for Productivity Improvements and Potential Biological Control in Sugar Beet Crops.

Authors:  Gonzalo Sacristán-Pérez-Minayo; Domingo Javier López-Robles; Carlos Rad; Luis Miranda-Barroso
Journal:  Front Plant Sci       Date:  2020-12-22       Impact factor: 5.753

3.  Band width selection data from Near Infra-red Spectral (NIRS) quantitative modelling of energy storage components (protein, lipid, glycogen) for single and multi-bivalve species models.

Authors:  Jill K Bartlett; William A Maher; Matthew B J Purss
Journal:  Data Brief       Date:  2018-04-22
  3 in total

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