An appearance quality classification method for Auricularia auricula based on deep learning

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored.

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics

Dendrobium officinale has drawn increasing attention as a dual-use plant with herbal medicine and food applications. The efficient quality evaluation of D. officinale is essential to ensuring its nutritional and pharmaceutical value. Given that traditional analytical methods are generally time-consuming, expensive, and laborious, this study developed a rapid and efficient approach to assess the quality of D. officinale from different geographical origins by near-infrared (NIR) spectroscopy and chemometrics. Total saponins, mannitol, and naringenin were utilized as quality indicators. Two wavelength selection methods, namely, uninformative variable elimination and competitive adaptive reweighted sampling (CARS), were utilized to enhance the prediction accuracy of the quantification model. Moreover, multiple spectral pretreatment methods were applied for model optimization. Results indicated that the partial least squares (PLS) model constructed based on the wavelengths selected by CARS exhibited superior performance in predicting the contents of the quality indicators. The coefficient of determination (RP2) and root mean square error (RMSEP) in the independent test sets were 0.8949 and 0.1250 g kg-1 for total saponins, 0.9664 and 0.2192 g kg-1 for mannitol, and 0.8570 and 0.003159 g kg-1 for naringenin, respectively. This study revealed that NIR spectroscopy and the CARS-PLS model could be used as a rapid and accurate technique to evaluate the quality of D. officinale.

Rapid Determination of Polysaccharides in Cistanche Tubulosa Using Near-Infrared Spectroscopy Combined with Machine Learning

BACKGROUND

Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in healthcare products. Polysaccharide is one of its important quality indicators.

OBJECTIVE

In this study, an analytical model based on near-infrared (NIR) spectroscopy combined with machine learning was established to predict the polysaccharide content of C. tubulosa.

METHODS

The polysaccharide content in the samples determined by the phenol-sulfuric acid method was used as a reference value, and machine learning was applied to relate the spectral information to the reference value. Dividing the samples into a calibration set and a prediction set using the Kennard-Stone algorithm. The model was optimized by various preprocessing methods, including Savitzky-Golay (SG), standard normal variate (SNV), multiple scattering correction (MSC), first-order derivative (FD), second-order derivative (SD), and combinations of them. Variable selection was performed through the successive projections algorithm (SPA) and stability competitive adaptive reweighted sampling (sCARS). Four machine learning models were used to build quantitative models, including the random forest (RF), partial least-squares (PLS), principal component regression (PCR), and support vector machine (SVM). The evaluation indexes of the model were the coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD).

RESULTS

RF performs best among the four machine learning models. R2c （calibration set coefficient of determination） and RMSEC （root mean square error of the calibration set）, %, were 0.9763. and 0.3527 for calibration, respectively. R2p （prediction set coefficient of determination）, RMSEP （root mean square error of the prediction set）, %, and RPD were 0.9230, 0.5130, and 3.33 for prediction, respectively.

CONCLUSION

The results indicate that NIR combined with the RF is an effective method applied to the quality evaluation of the polysaccharides of C. tubulosa.

HIGHLIGHTS

Four quantitative models were developed to predict the polysaccharide content in C. tubulosa, and good results were obtained. The characteristic variables were basically determined by the sCARS algorithm, and the corresponding characteristic groups were analyzed.