Literature DB >> 15969804

Detection of apple juice adulteration using near-infrared transflectance spectroscopy.

Lorenzo León1, J Daniel Kelly, Gerard Downey.   

Abstract

Near-infrared transflectance spectroscopy was used to detect adulteration of apple juice samples. A total of 150 apple samples from 19 different varieties were collected in two consecutive years from orchards throughout the main cultivation areas in Ireland. Adulterant samples at 10, 20, 30, and 40% w/w were prepared using two types of adulterants: a high fructose corn syrup (HFCS) with 45% fructose and 55% glucose, and a sugars solution (SUGARS) made with 60% fructose, 25% glucose, and 15% sucrose (the average content of these sugars in apple juice). The results show that NIR analysis can be used to predict adulteration of apple juices by added sugars with a detection limit of 9.5% for samples adulterated with HFCS, 18.5% for samples adulterated with SUGARS, and 17% for the combined (HFCS + SUGARS) adulterants. Discriminant partial least squares (PLS) regression can detect authentic apple juice with an accuracy of 86-100% and adulterant apple juice with an accuracy of 91-100% depending on the adulterant type and level of adulteration considered. This method could provide a rapid screening technique for the detection of this type of apple juice adulteration, although further work is required to demonstrate model robustness.

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Year:  2005        PMID: 15969804     DOI: 10.1366/0003702053945921

Source DB:  PubMed          Journal:  Appl Spectrosc        ISSN: 0003-7028            Impact factor:   2.388


  5 in total

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Authors:  Li-juan Xie; Xing-qian Ye; Dong-hong Liu; Yi-bin Ying
Journal:  J Zhejiang Univ Sci B       Date:  2008-12       Impact factor: 3.066

2.  Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data.

Authors:  José Luis P Calle; Marta Barea-Sepúlveda; Ana Ruiz-Rodríguez; José Ángel Álvarez; Marta Ferreiro-González; Miguel Palma
Journal:  Sensors (Basel)       Date:  2022-05-19       Impact factor: 3.847

3.  FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples.

Authors:  Najeeb Ur Rehman; Ahmed Al-Harrasi; Ricard Boqué; Fazal Mabood; Muhammed Al-Broumi; Javid Hussain; Saif Alameri
Journal:  Foods       Date:  2020-06-24

4.  Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine.

Authors:  Enguang Zuo; Lei Sun; Junyi Yan; Cheng Chen; Chen Chen; Xiaoyi Lv
Journal:  Sci Rep       Date:  2022-08-10       Impact factor: 4.996

5.  A hybrid sensing approach for pure and adulterated honey classification.

Authors:  Norazian Subari; Junita Mohamad Saleh; Ali Yeon Md Shakaff; Ammar Zakaria
Journal:  Sensors (Basel)       Date:  2012-10-17       Impact factor: 3.576

  5 in total

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