Literature DB >> 19914124

Identification of transgenic foods using NIR spectroscopy: a review.

A Alishahi1, H Farahmand, N Prieto, D Cozzolino.   

Abstract

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA. Copyright 2009 Elsevier B.V. All rights reserved.

Mesh：

Year:  2009        PMID： 19914124     DOI： 10.1016/j.saa.2009.10.001

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN： 1386-1425            Impact factor:   4.098


  10 in total

1.  Near-Infrared Spectroscopic Analysis of Total Phenolic Content and Antioxidant Activity of Berry Fruits.

Authors:  Jasenka Gajdoš Kljusurić; Kiril Mihalev; Ivana Bečić; Ivana Polović; Mariya Georgieva; Senka Djaković; Želimir Kurtanjek
Journal:  Food Technol Biotechnol       Date:  2016-06       Impact factor: 3.918

2.  Automatic discrimination of the geographical origins of milks by excitation-emission fluorescence spectrometry and chemometrics.

Authors:  Lu Xu; De-Hua Deng; Chen-Bo Cai; Hong-Wei Yang
Journal:  J Autom Methods Manag Chem       Date:  2011-08-29

3.  Automatic and rapid discrimination of cotton genotypes by near infrared spectroscopy and chemometrics.

Authors:  Hai-Feng Cui; Zi-Hong Ye; Lu Xu; Xian-Shu Fu; Cui-Wen Fan; Xiao-Ping Yu
Journal:  J Anal Methods Chem       Date:  2012-05-14       Impact factor: 2.193

4.  Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.

Authors:  Na Wu; Yu Zhang; Risu Na; Chunxiao Mi; Susu Zhu; Yong He; Chu Zhang
Journal:  RSC Adv       Date:  2019-04-25       Impact factor: 4.036

5.  Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging.

Authors:  Xuping Feng; Cheng Peng; Yue Chen; Xiaodan Liu; Xujun Feng; Yong He
Journal:  Sci Rep       Date:  2017-11-21       Impact factor: 4.379

6.  Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis.

Authors:  Xuping Feng; Yiying Zhao; Chu Zhang; Peng Cheng; Yong He
Journal:  Sensors (Basel)       Date:  2017-08-17       Impact factor: 3.576

7.  Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing-A Cost-Effective Approach.

Authors:  Nanfeng Jiang; Weiran Song; Hui Wang; Gongde Guo; Yuanyuan Liu
Journal:  Sensors (Basel)       Date:  2018-05-23       Impact factor: 3.576

Review 8.  Hyperspectral imaging for seed quality and safety inspection: a review.

Authors:  Lei Feng; Susu Zhu; Fei Liu; Yong He; Yidan Bao; Chu Zhang
Journal:  Plant Methods       Date:  2019-08-08       Impact factor: 4.993

9.  Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics.

Authors:  Yong Hao; Pei Geng; Wenhui Wu; Qinhua Wen; Min Rao
Journal:  Molecules       Date:  2019-12-13       Impact factor: 4.411

10.  Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea.

Authors:  Xiaoli Bai; Lei Zhang; Chaoyan Kang; Bingyan Quan; Yu Zheng; Xianglong Zhang; Jia Song; Ting Xia; Min Wang
Journal:  Sci Rep       Date:  2022-03-09       Impact factor: 4.379
  10 in total

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