Quality control of entomopathogenic nematodes through infrared spectroscopy (FTIR-ATR, 2D-COS): Tracing, modelling...

Entomopathogenic nematodes (EPNs) are widely used in biological pest control, yet their efficacy in field applications is highly dependent on physiological quality during storage; an area lacking direct, predictive quality control tools. Current assessments rely solely on labor-intensive infectivity tests using insect hosts like Galleria mellonella. This study breaks new ground by combining Fourier Transform Infrared Spectroscopy (FTIR-ATR), two-dimensional correlation spectroscopy (2D-COS), and machine learning to deliver a rapid, non-destructive method for evaluating EPNs viability under thermal stress. This study examines the biochemical responses of Heterorhabditis indica and Steinernema riobrave subjected to thermal stress at 10 °C, 20 °C, and 30 °C over an 8 week. Infectivity responses varied significantly between species under thermal stress. S. riobrave declined substantially after six weeks at 30 °C (-55 %), whereas H. indica retained considerably higher infectivity at the same temperature, demonstrating superior thermal tolerance. FTIR spectra revealed temperature- and species-dependent changes across lipid, carbohydrate, and protein spectral regions, indicating distinct biochemical adaptation strategies. Glycogen and triglyceride levels dropped sharply in S. riobrave at 30 °C (up to -65 %), while H. indica exhibited elevated trehalose accumulation, particularly at 20 °C, suggesting enhanced protective mechanisms. Machine learning models showed strong predictive performance., with Support Vector Machine algorithms trained on spectral data accurately predicting infectivity (R² = 0.93, RMSE = 0.04). Furthermore, 2D-COS analysis highlighted trehalose and protein amide I spectral shifts as early diagnostic markers of thermal stress, preceding observable infectivity decline. Together, these findings introduce a novel, integrative platform for EPNs quality control that is rapid, sensitive, and predictive. This approach offers a transformative advance for commercial biocontrol production, allowing early detection of stress-induced viability loss and optimization of storage protocols to ensure field performance.