Study on the identification of resistance of rice blast based on near infrared spectroscopy

Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case

INTRODUCTION

Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price.

OBJECTIVES

Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established.

METHODS

We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed.

RESULTS

The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model.

CONCLUSION

The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning

In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of Pleurotus geesteranus strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of Pleurotus geesteranus strains, which could further inspire new methods for the phenotypic identification of edible fungi.