Wang Jin-shui

Measurement of protein content in sesame by near-infrared spectroscopy technique

The model of determining sesame protein was built using near infrared spectroscopy (NIRS) and the FOSS system as the analyzer. The influences on the model of factors, such as the mathematics methods and optics treatment methods were studied. The results of model validation showed that the best factors were SNV only for optics treatment method and “3, 3, 3, 1” for mathematics method. The average determination coefficient of validation (RSQ) was 0.9826, the square error of cross (SEC) was 0.2313, the correlation coefficient (1-VR) was 0.7272, the square error of cross validation (SECV) was 0.9134, the average determination coefficient of validation (RSQ) was 0.896, and the standard error of prediction (SEP) was 0.827. This model could determine the protein content in sesame used as a rapid method to detect the quality of sesame seeds.

Establishment of nondestructive testing model of the protein content in wheat flour by near infrared spectroscopy

The 107 samples of wheat flour were scanned directly by NIRS and their protein contents were measured by Joe Dumars combustion method. The protein content model was built up using near infrared spectroscopy (NIRS) and the FOSS system as the analyzer. The influences on the model of factors, such as the mathematics methods and optics treatment methods were studied. Experimental results indicated that the optimal model was built by using the Modified partial least square (MPLS) under none scattering approach and two rank differential coefficient derivative preprocessing method, its standard deviation of cross validation (SECV) was 0.4112%, fore cast standard deviation(SEP) was 0.492%, predicted RSQ was 0.937. To sum up, the NIRS technique can realize fast detection of flour protein content and provide technical support for food regulator when they are monitoring the flour market.