Ultrasound treatment can improve enzymolysis efficiency by changing the amounts of sulfhydryl groups (SH) and disulfide bonds (SS) in protein. This paper proposes an in-situ and real-time monitoring method for SH and SS during ultrasound application processes using a miniature near-infrared (NIR) optical fiber spectrometer and a chemometrics model to determine the endpoint of ultrasonic treatment. The results show that SH and SS contents fluctuated greatly with the extension of ultrasonic time. The optimal spectral intervals for SH content were 869⁻947, 1207⁻1284, 1458⁻1536 and 2205⁻2274 nm, the optimal spectral intervals of SS content were 933⁻992, 1388⁻1446, 2091⁻2148 and 2217⁻2274 nm. According to the optimal spectral intervals, the synergy interval partial least squares (Si-PLS) and error back propagation neural network (BP-ANN) for SH, SS contents were established. The BP-ANN model was better than the Si-PLS model. The correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) for the BP-ANN model of SH were 0.9113 and 0.38 μmol/g, respectively, the Rp² and residual prediction deviation of SH were 0.8305 and 2.91, respectively. For the BP-ANN model of SS, the Rp and the RMSEP were 0.7523 and 6.56 μmol/g, respectively. The Rp² and residual prediction deviation (RPD) of SS were 0.8305 and 2.91, respectively. However, the Rp² and RPD of SS was 0.5660 and 1.64, respectively. This work demonstrated that the miniature NIR combined with BP-ANN algorithms has high potential for in-situ monitoring of SH during the ultrasonic treatment process, while the spectral prediction model of SS needs to be further developed.
Keywords: disulfide bonds; in-situ monitoring; sulfhydryl groups; ultrasonic treatment; wheat gluten.