Abstract: The texture is an important index that indicates the noodle quality. In this research, the relationship between hardness and texture with respect to potato noodles was demonstrated by using 108 noodle samples with different amounts of potatoes. Noodle images would be captured by hyperspectral imaging technology and textures would be analyzed during that process. A calibration and cross-validation model were established based on the least squares regression method. 72 noodles as training samples were used to establish the model and the other 36 samples were used to verify the calibration model. The coefficient of determination (R²_C and R²_cv) values of the calibration model and cross-validation model were 0.877 and 0.842 while the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) values were 11.405 and 13.166. The coefficient of determination (R²_P) of the prediction set was 0.988 and the root mean square error of prediction (RMSEP) was 3.585. Results showed that the proposed determination method of potato noodle hardness was of high robustness and stability and could be used as a non-destructive detection technology for detecting the noodle hardness.

Key words: nondestructive determination, hyperspectral imaging, potato noodle hardness, partial least squares regression