Abstract
To solve the problems existing in traditional biochemical methods, such as complex sample pretreatment requirements, tedious detection processes and low detection accuracies with respect to rice species and adulteration, the volatile flavor substances of five kinds of rice are detected using headspace-gas chromatography-ion mobility spectrometry (HGC-IMS) to effectively identify the quality of rice and adulterated rice. The ion migration fingerprint spectra of five kinds of rice are identified using a semi-supervised generative adversarial network (SSGAN). We replace the output layer of the discriminator in a GAN with a softmax classifier, thus extending the GAN to a semi-supervised GAN. We define additional category tags for generated samples to guide the training process. Semi-supervised training is used to optimize the network parameters, and the trained discriminant network is used for classifying HGC-IMS images. The experimental results show that the prediction accuracy of the model reaches 98.00%, which is significantly higher than the rates achieved by other models, such as a decision tree, a support vector machine (SVM), improved SVM models (LS-SVM and PCA-SVM) and local geometric structure Fisher analysis (LGSFA); 98.00% is also higher than the prediction accuracies of the VGGNet, ResNet and Fast RCNN deep learning models. The experimental results also show that the accuracy of HGC-IMS image classification for identifying adulterated rice reaches 97.30%, which is higher than those of traditional chromatographic or spectral methods. The proposed method overcomes the shortcomings of some intelligent algorithms regarding the application of ion migration spectra and is feasible for accurately predicting rice varieties and adulterated rice.
Highlights
Rice is a common staple food in Asia
The above algorithms have achieved good results in terms of the analysis and recognition of spectral data, but the recognition accuracy of the image spectrum obtained by a shallow neural network still needs to be improved, and a convolutional neural network needs a large number of training samples to achieve high accuracy. Due to these limiting conditions, we obtained fewer ion mobility spectrum samples than in previous studies, and we propose a semi-supervised Gas chromatography-ion migration spectrometry (GC-IMS) fingerprint classification method based on a generative adversarial network [30]
We used a small number of labeled samples and a large number of unlabeled samples generated by the GAN and used a semi-supervised learning method to train an improved supervised generative adversarial network (SSGAN) model
Summary
Rice is a common staple food in Asia. Different kinds of rice have different flavors and tastes. Thai fragrant rice contains a compound called acetyl-pyrroline (2-acetyl1-pyrroline), which gives this rice a pleasant aroma, and Chinese Wuchang rice has an aroma, sweetness and good taste. These high-quality varieties of rice are usually expensive. Asia often use Vietnamese or Cambodian rice as Thai fragrant rice (because these varieties have a similar appearance); in mainland China, rice is usually mixed, and other ordinary types of rice with similar appearances are used fraudulently. The use of Wuchang rice can increase profits. Determining the origin and variety of rice is very important in the grain market
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