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Development of an analytical method for guazatine pesticides in agricultural products using LC-MS/MS.

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Title: Development of an analytical method for guazatine pesticides in agricultural products using LC-MS/MS.
Authors: Park, So Hee1 (AUTHOR), Oh, Dogyung1 (AUTHOR), Baek, Eun Joo1 (AUTHOR), Na, Tae Woong2 (AUTHOR) naratw@korea.kr
Source: Food Additives & Contaminants. Part A: Chemistry, Analysis, Control, Exposure & Risk Assessment. May2025, Vol. 42 Issue 5, p594-602. 9p.
Subjects: Farm produce, Microbiological synthesis, Lipid synthesis, Matrix effect, Standard deviations
Abstract: Guazatine, a non-invasive disinfectant used for seed treatment and post-harvest citrus protection, inhibits bacterial lipid synthesis and consists of about 13 components. While it may occasionally be detected in fruits and vegetables such as citrus, its use as a pesticide has been banned in several countries due to health and environmental concerns. To ensure safe export of domestic produce, a simultaneous method of analysis for guazatine components was developed, focusing on GG, GGG, GGN, and GNG as indicators. Using LC-MS/MS, samples were extracted with a 3% formic acid/acetone mixture and purified with hexane. The method was validated across five agricultural products (mandarin, pepper, potato, brown rice, and soybean), achieving an r2 above 0.995, and detection and quantitation limits of 0.8 and 2.5 µg/L, respectively. Ion suppression was high, with matrix effects ranging from −84.2 to −89.8%. Recovery rates were 70.2–99.6%, with relative standard deviations under 6.8%. Cross-laboratory validation showed recovery rates of 70.3–106.7% and relative standard deviations below 15.0%, meeting CODEX guidelines. [ABSTRACT FROM AUTHOR]
Copyright of Food Additives & Contaminants. Part A: Chemistry, Analysis, Control, Exposure & Risk Assessment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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PubTypeId: academicJournal
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  Data: Development of an analytical method for guazatine pesticides in agricultural products using LC-MS/MS.
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  Data: <searchLink fieldCode="JN" term="%22Food+Additives+%26+Contaminants%2E+Part+A%3A+Chemistry%2C+Analysis%2C+Control%2C+Exposure+%26+Risk+Assessment%22">Food Additives & Contaminants. Part A: Chemistry, Analysis, Control, Exposure & Risk Assessment</searchLink>. May2025, Vol. 42 Issue 5, p594-602. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Farm+produce%22">Farm produce</searchLink><br /><searchLink fieldCode="DE" term="%22Microbiological+synthesis%22">Microbiological synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Lipid+synthesis%22">Lipid synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+effect%22">Matrix effect</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
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  Label: Abstract
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  Data: Guazatine, a non-invasive disinfectant used for seed treatment and post-harvest citrus protection, inhibits bacterial lipid synthesis and consists of about 13 components. While it may occasionally be detected in fruits and vegetables such as citrus, its use as a pesticide has been banned in several countries due to health and environmental concerns. To ensure safe export of domestic produce, a simultaneous method of analysis for guazatine components was developed, focusing on GG, GGG, GGN, and GNG as indicators. Using LC-MS/MS, samples were extracted with a 3% formic acid/acetone mixture and purified with hexane. The method was validated across five agricultural products (mandarin, pepper, potato, brown rice, and soybean), achieving an r2 above 0.995, and detection and quantitation limits of 0.8 and 2.5 µg/L, respectively. Ion suppression was high, with matrix effects ranging from −84.2 to −89.8%. Recovery rates were 70.2–99.6%, with relative standard deviations under 6.8%. Cross-laboratory validation showed recovery rates of 70.3–106.7% and relative standard deviations below 15.0%, meeting CODEX guidelines. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Food Additives & Contaminants. Part A: Chemistry, Analysis, Control, Exposure & Risk Assessment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/19440049.2025.2467625
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      – Code: eng
        Text: English
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      – SubjectFull: Farm produce
        Type: general
      – SubjectFull: Microbiological synthesis
        Type: general
      – SubjectFull: Lipid synthesis
        Type: general
      – SubjectFull: Matrix effect
        Type: general
      – SubjectFull: Standard deviations
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            NameFull: Park, So Hee
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            NameFull: Oh, Dogyung
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            NameFull: Baek, Eun Joo
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            NameFull: Na, Tae Woong
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            – D: 01
              M: 05
              Text: May2025
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              Y: 2025
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