Literature DB >> 31708344

High throughput nutritional profiling of pea seeds using Fourier transform mid-infrared spectroscopy.

Chithra Karunakaran1, Perumal Vijayan2, Jarvis Stobbs1, Ramandeep Kaur Bamrah2, Gene Arganosa2, Thomas D Warkentin3.   

Abstract

Seed samples from 117 genetically diverse pea breeding lines were used to determine the robustness of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid nutritional profiling of seeds. The FT-MIR results were compared to wet chemistry methods for assessing the concentrations of total protein, starch, fiber, phytic acid, and carotenoids in pea seed samples. Of the five partial least square regression models (PLSR) developed, protein, fiber and phytic acid concentrations predicted by the models exhibited correlation coefficients greater than 0.83 when compared with data obtained using the wet chemistry methods for both the calibration and validation sets. The starch PLSR model had a correlation greater than 0.75, and carotenoids had correlation of 0.71 for the validation sets. The methods implemented in this research show the novelty and usefulness of FT-MIR as a simple, fast, and cost-effective technique to determine multiple seed constituents simultaneously.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Carotenoids; Fiber; Mid-infrared spectroscopy; Pea seeds; Phytic acid; Protein; Seed quality; Starch

Mesh:

Substances:

Year:  2019        PMID: 31708344     DOI: 10.1016/j.foodchem.2019.125585

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


  2 in total

1.  Loss factor and moisture diffusivity property estimation of lentil crop during microwave processing.

Authors:  Mohamad Mehdi Heydari; Tahereh Najib; Oon-Doo Baik; Kaiyang Tu; Venkatesh Meda
Journal:  Curr Res Food Sci       Date:  2021-12-25

2.  Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.

Authors:  Hongyan Zhu; Jun-Li Xu
Journal:  Molecules       Date:  2020-10-28       Impact factor: 4.411

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.