Literature DB >> 32543218

Detection of adulteration in food based on nondestructive analysis techniques: a review.

Yong He1,2, Xiulin Bai1,2, Qinlin Xiao1,2, Fei Liu1,2, Lei Zhou1,2, Chu Zhang1,2.   

Abstract

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Entities:  

Keywords:  Adulteration; detection; food safety; nondestructive techniques

Mesh:

Year:  2020        PMID: 32543218     DOI: 10.1080/10408398.2020.1777526

Source DB:  PubMed          Journal:  Crit Rev Food Sci Nutr        ISSN: 1040-8398            Impact factor:   11.176


  4 in total

1.  Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology.

Authors:  Lian Li; ZhiTian Zuo; YuanZhong Wang
Journal:  Front Microbiol       Date:  2022-07-08       Impact factor: 6.064

2.  Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach.

Authors:  Fatema Ahmmed; Daniel P Killeen; Keith C Gordon; Sara J Fraser-Miller
Journal:  Molecules       Date:  2022-07-15       Impact factor: 4.927

3.  Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants.

Authors:  Xihui Bian; Deyun Wu; Kui Zhang; Peng Liu; Huibing Shi; Xiaoyao Tan; Zhigang Wang
Journal:  Biosensors (Basel)       Date:  2022-08-01

4.  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

  4 in total

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