Rapid Detection of Total Bacterial Count of Porphyra yezoensis Based on Near Infrared Spectroscopy

To discuss the possibility of the non-destructive prediction of the total colonies of Porphyra yezoensis, this research explored a non-destructive method that using near infrared spectral imaging to predict the total colonies of Porphyra yezoensis. Porphyra yezoensis samples were measured the total colonies first, and then collected the original spectral information and total colonies of samples. Standard normal variable transformation (SNV), multiple scattering correction (MSC) and second order derivative were used to preprocess spectral data. After selecting the best pretreatment method in this study, the prediction models of total number of bacteria were established based on spectral information including mixed logistic regression (MLR), support vector regression (SVR), artificial neuro network (ANN) and convolutional neural networks (CNN). The results of the investigation showed that the second derivative method combined with standard normal variable transformation was the relative best pretreatment method. And the relative best prediction model was the CNN model which was based on the full-wave band, which the r value was 0.940. According to these results, the convolutional neural networks (CNN) could be used to predict the total number of colonies of Porphyra yezoensis.