High Throughput Residue Analysis of Indaziflam and its Metabolites in Palm Oil Using Liquid Chromatography-Tandem
Norizah Halim1, Ainie Kuntom1, Raviraj Shinde2
1Malaysian Palm Oil Board, No. 6 Pesiaran Institusi Bandar Baru Bangi, 43000 Kajang Selangor, Malaysia.
Background:
Indaziflam (IND) is a herbicide that is used in palm oil plantations for broad spectrum management of weeds. Until now, no validated method has been available for residue estimation of this herbicide in palm oil products.
Objective:
In this study, we report a rapid method for the residue analysis of IND and its metabolites, viz., IND-carboxylic acid, diaminotriazine, and triazine indanone in a wide range of palm oil matrices using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Method:
The optimized sample preparation workflows included two options: (1) acetonitrile extraction (QuEChERS workflow), followed by freezing at -80°C and (2) acetonitrile extraction, followed by cleanup through a C18 solid phase extraction (SPE) cartridge. The optimized LC runtime was 7 min. All these analytes were estimated by LC-MS/MS multiple reaction monitoring.
Results:
Both sample preparation methods provided similar method performance and acceptable results. The limit of quantification (LOQ) of IND, IND-carboxylic acid, and triazine indanone was 0.001 mg/kg. For diaminotriazine, the LOQ was 0.005 mg/kg. The method accuracy and precision complied with the SANTE/12682/2019 guidelines of analytical quality control.
Conclusions:
The potentiality of the method lies in a high throughput analysis of IND and its metabolites in a single chromatographic run with high selectivity and sensitivity. Considering its fit-for-purpose performance, the method can be implemented in regulatory testing of IND residues in a wide range of palm oil matrices that are consumed and traded worldwide.
Highlights:
This work has provided a validated method for simultaneous residue analysis of indaziflam and its metabolites in crude palm oil and its derived matrices with high sensitivity, selectivity, and throughput.
