Study On Ultrasonic-assisted Enzymatic Hydrolysis Of Corn Gluten Meal And Its Process In-situ Real-time Monitoring

Corn gluten meal, a main by-product of the corn wet-milling process in the preparation of corn starch. Corn gluten meal was greatly limited its application in the food industry because of poor solubility, digestion and absorption rate caused by its structural defects. However, corn gluten meal has a high content of protein, about 60%~70%, it can serve as an important source of protein. Currently, corn gluten meal being enzymatic hydrolyzed by protease for producing corn bioactive peptides with various functional activity has become a hot research. Traditional enzymatic hydrolysis of corn gluten meal has many defects, such as low utilization of protease, long reaction time, low conversion rate of substrate protein and so on. Our group has used ultrasonic pretreatment technology to improve corn gluten meal enzymatic hydrolysis, and achieved remarkable results. The purpose of present research was try to promote efficiency of enzymatic hydrolysis of corn gluten meal by ultrasonic technology. At the same time, a preliminary research on in-situ real-time monitoring during enzymatic hydrolysis process with Near infrared(NIR) reflectance spectroscopy and partial least square(PLS) is to study to provide technical support for eventual enzymatic hydrolysis process intelligent control. The main work is as follows:In the study of influence of ultrasonic technology for corn gluten meal enzymatic hydrolysis, the divergent tri-frequencies ultrasonic device was adopted to test, which has a lot of advantages including combinations of frequency, power adjustment, even distribution of sound field and so on. Take the corn gluten meal degree of hydrolysis(DH) as the indicator, a single factor test is studied which get the optimal conditions: hydrolysis reaction temperature 50℃,reaction pH 9.5, the concentration of substrate 3%, enzymatic hydrolysis time 240 min, enzymes content 4800 U/g, power density 90 W/L, combination of ultrasonic frequency 22/28/40 kHz,5 ultrasonic time 40 min, ultrasonic on-time/off-time 11 s/5 s. Under the optimal ultrasonic conditions, the DH of corn gluten meal can reach 26.90%. It is found from verification test that efficiency of ultrasonic-assisted enzymatic hydrolysis of corn gluten meal is better than corn gluten meal traditional enzymatic hydrolysis. Compared with traditional corn gluten meal enzymatic hydrolysis, DH of corn gluten meal and ACEI of hydrolysates which is enzymatic hydrolyzed with ultrasonic-assisted significantly increase 5.74%and 8.32%, respectively. It is showed that ultrasonic technology explained superiority of improving the efficiency of corn gluten meal enzymatic hydrolysis.The research also attempts using NIR spectroscopy and PLS algorithms to analyze the changes of DH and ACEI in the enzymatic hydrolysis process of corn gluten meal. Five methods of spectral pretreatment were used in calibration models optimization, mean centering(MC), multiplicative scatter correction(MSC), standard normal variate transformation(SNVT), first and second derivative(1st Der and 2nd Der). The best spectral pretreatment method is SNVT for both DH and ACEI. The correlation coefficient(Rp) between the NIR prediction and the reference results in the prediction set of DH and ACEI PLS models after SNVT pretreatment are 0.8720 and 0.8566, respectively. The results showed that: It is feasible for in-situ real-time monitoring of enzymatic hydrolysis process of corn gluten meal based on NIR spectroscopy technology and PLS algorithm.In order to build a better robustness and prediction PLS analysis model. This research attempts to optimize the selection of the feature spectral intervals from original NIR spectral in DH calibration model and ACEI calibration model. Interval partial least square(iPLS), synergy interval partial least square(Si-PLS) and backward interval partial least square(Bi-PLS) were applied to develop models in the research. And the results of models showed that: During the enzymatic hydrolysis process of corn gluten meal, Si-PLS model showed the optimal performance for both DH and ACEI. The root mean square error of prediction(RMSEP) and Rp were 2.99 and 0.9006, 11.2 and 0.9156 in the prediction set, respectively.In summary, it is feasible to get a better robustness and prediction PLS models with spectral pretreatment and selection of the feature spectral intervals. The results of research have significance to in-situ real-time monitoring of enzymatic hydrolysis process of corn gluten meal. And results laid the foundation for intelligent production of bioactive peptides.