Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). (15th April 2016) - Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zhu, Zhiwei; Pu, Hongbin
Highlights: HSI combined with wavelet analysis was firstly used to predict the TVB-N of prawns. Three prediction models based on PLSR, BP-NN and LS-SVM were established. Spectrum was de-noised by wavelet analysis and uninformative variable elimination. A non-linear relationship was found between the wavelet features and TVB-N value. The distribution maps of TVB-N values were created and visualized. Abstract: A visible/near-infrared hyperspectral imaging (HSI) system (400–1000 nm) coupled with wavelet analysis was used to determine the total volatile basic nitrogen (TVB-N) contents of prawns during cold storage. Spectral information was denoised by conducting wavelet analysis and uninformative variable elimination (UVE) algorithm, and then three wavelet features (energy, entropy and modulus maxima) were extracted. Quantitative models were established between the wavelet features and the reference TVB-N contents by using three regression algorithms. As a result, the LS-SVM model with modulus maxima features was considered as the best model for determining the TVB-N contents of prawns, with an excellent R P 2 of 0.9547, RMSEP = 0.7213 mg N/100 g and RPD = 4.799. Finally, an image processing algorithm was developed for generating a TVB-N distribution map. This study demonstrated the possibility of applying the HSI imaging system in combination with wavelet analysis to the monitoring of TVB-N values in prawns.