Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation

Rice bran, a by-product of the rice milling process, has emerged as a functional food and being used in formulation of healthy food and drinks. However, rice bran is often contaminated with numerous mycotoxins. In this study, a method to simultaneous detection of aflatoxins (AFB1, AFB2, AFG1, and AF...

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Main Authors: Akmal Salim, Sofiyatul, Sukor, Rashidah, Ismail, Mohd Nazri, Selamat, Jinap
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Online Access:http://psasir.upm.edu.my/id/eprint/96758/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/96758/
https://www.mdpi.com/2072-6651/13/4/280
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spelling my.upm.eprints.967582022-12-01T04:35:49Z http://psasir.upm.edu.my/id/eprint/96758/ Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation Akmal Salim, Sofiyatul Sukor, Rashidah Ismail, Mohd Nazri Selamat, Jinap Rice bran, a by-product of the rice milling process, has emerged as a functional food and being used in formulation of healthy food and drinks. However, rice bran is often contaminated with numerous mycotoxins. In this study, a method to simultaneous detection of aflatoxins (AFB1, AFB2, AFG1, and AFG2), ochratoxin A (OTA), deoxynivalenol (DON), fumonisins (FB1 and FB2), sterigmatocystin (STG), T-2 toxin, HT-2 toxin, diacetoxyscirpenol (DAS) and zearalenone (ZEA) in rice bran was developed, optimized and validated using dispersive liquid–liquid microextraction (DLLME) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). In DLLME, using a solvent mixture of methanol/water (80:20, v/v) as the dispersive solvent and chloroform as the extraction solvent with the addition of 5% salt improved the extraction recoveries (63–120%). The developed method was further optimized using the response surface methodology (RSM) combined with Box–Behnken Design (BBD). Under the optimized experimental conditions, good linearity was obtained with a correlation coefficient (r2) ≥ 0.990 and a limit of detection (LOD) between 0.5 to 50 ng g−1. The recoveries ranged from 70.2% to 99.4% with an RSD below 1.28%. The proposed method was successfully applied to analyze multi-mycotoxin in 24 rice bran samples. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/96758/1/ABSTRACT.pdf Akmal Salim, Sofiyatul and Sukor, Rashidah and Ismail, Mohd Nazri and Selamat, Jinap (2021) Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation. Toxins, 13 (4). pp. 1-21. ISSN 2072-6651 https://www.mdpi.com/2072-6651/13/4/280 10.3390/toxins13040280
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Rice bran, a by-product of the rice milling process, has emerged as a functional food and being used in formulation of healthy food and drinks. However, rice bran is often contaminated with numerous mycotoxins. In this study, a method to simultaneous detection of aflatoxins (AFB1, AFB2, AFG1, and AFG2), ochratoxin A (OTA), deoxynivalenol (DON), fumonisins (FB1 and FB2), sterigmatocystin (STG), T-2 toxin, HT-2 toxin, diacetoxyscirpenol (DAS) and zearalenone (ZEA) in rice bran was developed, optimized and validated using dispersive liquid–liquid microextraction (DLLME) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). In DLLME, using a solvent mixture of methanol/water (80:20, v/v) as the dispersive solvent and chloroform as the extraction solvent with the addition of 5% salt improved the extraction recoveries (63–120%). The developed method was further optimized using the response surface methodology (RSM) combined with Box–Behnken Design (BBD). Under the optimized experimental conditions, good linearity was obtained with a correlation coefficient (r2) ≥ 0.990 and a limit of detection (LOD) between 0.5 to 50 ng g−1. The recoveries ranged from 70.2% to 99.4% with an RSD below 1.28%. The proposed method was successfully applied to analyze multi-mycotoxin in 24 rice bran samples.
format Article
author Akmal Salim, Sofiyatul
Sukor, Rashidah
Ismail, Mohd Nazri
Selamat, Jinap
spellingShingle Akmal Salim, Sofiyatul
Sukor, Rashidah
Ismail, Mohd Nazri
Selamat, Jinap
Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
author_facet Akmal Salim, Sofiyatul
Sukor, Rashidah
Ismail, Mohd Nazri
Selamat, Jinap
author_sort Akmal Salim, Sofiyatul
title Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
title_short Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
title_full Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
title_fullStr Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
title_full_unstemmed Dispersive liquid-liquid microextraction DLLME and LC-MS/MS analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
title_sort dispersive liquid-liquid microextraction dllme and lc-ms/ms analysis for multi-mycotoxin in rice bran: method development, optimisation and validation
publisher Multidisciplinary Digital Publishing Institute
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/96758/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/96758/
https://www.mdpi.com/2072-6651/13/4/280
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score 13.160551