LAUSR

Abstract The Gram-positive, anaerobic, spore-forming bacterium, Clostridium perfringens (C. perfringens) causes a variety of diseases in humans and other animals. Spore germination is thought to be the first stage of infection by C. perfringens. AGFK, a mixture of l -asparagine, d -glucose, d -fructose, and potassium ions, is an effective nutrient germinant. The objective of this study was to investigate the effects of different AGFK concentrations (0, 50, 100, 200 mM/mL) on C. perfringens spore germination. This paper proposes a novel rapid method for the measurement of spore germinability based on microscopic hyperspectral imaging technology (HSIT). The spore germination rate (Srate), the OD600% and Ca2+-DPA% of C. perfringens were determined by chemical methods under different concentrations of AGFK. The results showed that spores have a maximum germination rate of 94.59% after 80 min with 100 mM/mL AGFK. Microscopic HSIT revealed that the spectral and spatial characteristics of spores varied during the spore germination process. Multivariate analyses (GA-siPLS and GA-PLS) and the gray symbiotic matrix (GLCM) were used to extract highly correlated spectral and spatial descriptors from the time-series data from microscopic HSIT, respectively. Single spectral, spatial signals and data fusion of spectral and spatial information were then used to predict the Srate, the OD600% and Ca2+-DPA % by GA-PLS, respectively. The result show that the Srate calibration was built by GA-PLS using data fusion variables and yielded acceptable results (Rc = 0.96, RMSEC = 0.64, Rcv = 0.93, RMSEP = 0.87, Rp = 0.94). The OD600% optimal model was built by GA-PLS using image variables and yielded acceptable results (Rc = 0.93, RMSEC = 19.36, Rcv = 0.91, RMSEP = 24.36, Rp = 0.89). For Ca2+-DPA %, the model based on the fusion of spectral and imaging data was optimal. The Ca2+-DPA % calibration yielded acceptable results (Rc = 0.95, RMSEC = 49.83, Rcv = 0.93, RMSEP = 58.98, Rp = 0.92). This work demonstrates the potential of microscopic HSIT for the non-destructive detection of spore germinability. The data fusion models also significantly improved the prediction of spore germinability. In conclusion, microscopic HSIT exhibits considerable promise for nondestructive diagnostics of spore germination.