Potential feed-to-milk transfer of tridemorph-fungicide detected by untargeted LC-HRMS-based metabolomics screening
January 1, 2026·,,,,,,
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S. Amaliyah
F.S. Aurum
Ema Damayanti
D. Dwiyitno
M.F. Karimy
V.T. Rosyida
N. Fitrianto
Dr. Nodali Ndraha
T.H. Siregar
P. Pourazad
Siska Aditya
Abstract
The transfer of agrochemical residues into dairy products is a potential food safety concern, particularly in regions where residue monitoring remains limited. This study applied untargeted metabolomic LC-HRMS screening to investigate chemical features in dairy feed and corresponding milk samples collected from smallholder farms in East Java, Indonesia. A tridemorph-related feature was detected in both matrices, supported by accurate mass measurements (≤ ±5 ppm), consistent retention times (ΔRT = 0.025 min), stable mass errors within ±5 ppm, and MS2 spectral matching, corresponding to Schymanski Level 2 confidence. Semi-quantitative analysis based on relative peak areas showed significantly higher values in milk than in feed (p < 0.0001), with a median milk-to-feed ratio of 2.32. However, no significant farm-level correlation was observed between matrices (ρ = −0.19, p = 0.523), suggesting that transfer dynamics may be complex rather than directly proportional. Although absolute quantification was not performed, the co-occurrence of consistent tridemorph-related signals in feed and milk is compatible with the possibility of feed-to-milk transfer. These findings highlight the value of high-resolution untargeted screening approaches for proactive surveillance of agrochemical residues in dairy production systems.
Type
Publication
Research in Veterinary Science

Authors
Dr. Nodali Ndraha
(he/him)
Food Science Researcher
Welcome! I am a food safety researcher working at the intersection of wet-lab molecular microbiology and computational risk modeling. My work focuses on tracking and controlling foodborne pathogens—from investigating biofilm resistance and fermentation ecology to developing rapid diagnostic assays and Quantitative Microbial Risk Assessment (QMRA) models. I am passionate about open science, reproducible data workflows in R, and advancing global food safety standards.