Pacu fish are an important source of revenue and sustenance in Argentina. Previously, the fish were tank-raised in very controlled conditions that ensured a high-quality food product. More recently, the farming of the pacu fish has been incorporated into an integrated system with rice fields so that insects and weeds can be controlled, and the fields can continue to produce both rice and fish. While this agricultural approach may be economically beneficial, it raises concerns that herbicides, pesticides, or other environmental background conditions used for maintaining the rice fields may alter the fish metabolome prior to human consumption. At a more general level, the long-term adoption of this approach raises concerns that fall under the realm of forensics enquiry as the farmed fish integrated system may not be tightly controlled. To study the impact on the pacu fish metabolome, we employ comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS) to samples of tank-raised and farmed pacu fish (two sample classes). The GC×GC-TOFMS data set is then analyzed by tile-based Fisher ratio (F-ratio) analysis, and ANOVA based computational approach, for the non-targeted discovery of analytes with significantly different concentrations between these two sample classes. A total of 110 hits were discovered using F-ratio analysis, of which 70 had a significantly different concentration between classes (p &lt; 0.05). A majority of these significant hits were metabolites critical to regular biological functioning and were downregulated in farmed fish, indicating that an integrated farming system may negatively impact the fish metabolome. Ultimately, the workflow using GC×GC-TOFMS coupled with F-ratio analysis is well suited to discover and then monitor chemical changes that may relate to human practices, whether done incidentally or on purpose.

Development of a chemical analysis platform to understand the farming environment impact on raising Argentinian pacu fish: Implications for forensics analysis applications

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