Proof-of-Concept: NIR Spectroscopy Can Detect Rice Pathogen, Burkholderia glumae, in Artificially Inoculated Rice Seeds

Authors: MAGHIRANG, ELIZABETH - Retired ARS Employee; YAPTENCO, KEVIN - University Of The Philippines Los Banos; VERA CRUZ, CASIANA - International Rice Research Institute; NGUYEN, MARIAN HANNA - Retired Non ARS Employee; ARMSTRONG, PAUL - Retired ARS Employee; Pordesimo, Lester; SCHEPLER-LUU, VAN - International Rice Research Institute; Scully, Erin

Submitted to: Plant Health Progress
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/24/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Burkholderia glumae is a seed-borne rice pathogen that causes bacterial panicle blight (BPB). Although this pathogen has caused significant losses to global rice production, the use of antibiotics and other chemicals to control this pathogen is not currently allowed in the U.S. One way to manage this disease is the removal of infected seeds from any seed lots prior to planting. Thus, there is a critical need to develop fast, non-destructive, and reliable techniques to detect this pathogen in seeds. We developed a mathematical model based on NIR reflectance spectroscopy to identify seeds that were infected with B. glumae, which correctly classified 94.7% of uninfected and 100% of infected seeds. Independent model validation with test data that were not used to train the model resulted in correct classification of 90% and 100% uninfected and infected seeds, respectively. Moreover, we were able to detect infections in rice seeds treated at B. glumae dilution levels of 0.001 to 0.8. These results indicate that NIR-based technique can be used to control the occurrence of bacterial leaf blight in U.S. rice fields by identifying infected seeds and removing them from seed lots prior to planting. This rapid test could also serve as an important screening tool for plant breeders in the selection of rice varieties with resistance to this pathogen.

Technical Abstract: Strong evidence that near-infrared transmittance and reflectance spectroscopy can detect B. glumae contamination in bacterial suspensions and in individual rice seeds, respectively, was shown in this proof-of-concept study. For B. glumae suspension (0.0 to 1.42 x 104 CFU mL-1), NIR transmittance spectroscopy using partial least squares (PLS) regression calibration model (1000-1650 nm) showed a coefficient of determination (R2) = 0.984 and root mean square error (RMSE) = 0.031. For rice seeds treated in varying dilutions of B. glumae (0.0 to 4.52 log10 CFU/mL bacterial load in seeds), NIR reflectance spectroscopy using a selected PLS second derivative with Savitzky-Golay smoothing calibration model (1000-1650 nm) resulted in coefficient of determination of calibration (R2Cal) = 0.97, root mean square error of calibration (RMSEC) = 0.06, coefficient of determination of cross-validation (R2CV) = 0.93, standard error of cross validation (SECV) = 0.08 at 7 factors; the independent validation showed coefficient of determination of validation (R2Val) = 0.83 and standard error of prediction (SEP) = 0.11. Two-category qualitative PLS calibration model (1000-1650 nm) correctly classified 94.7% of uninoculated and 100% of inoculated seeds with independent validation of 90% and 100%, respectively. Predictions may be attributable to differences in aliphatic hydrocarbons, cellulose, amide, oil, protein, and starch contents across rice seeds that are uninoculated and inoculated at varying dilution levels. Developing NIR-based instrumentation for individual seed segregation of healthy from contaminated rice seeds can support a clean seed program and can be a useful tool for quarantine officers for seed exchange and farmers' seed production.