Recognition of Rice Species Based on Gas Chromatography-Ion Mobility Spectrometry and Deep Learning

• Published in: Agriculture (Basel) Vol. 14; no. 9; p. 1552
• Main Authors: Zhao, Zhongyuan, Lian, Feiyu, Jiang, Yuying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; Artificial neural networks; Biometric recognition systems; Chromatography; Deep learning; diffusion model; Distillation; Efficiency; Electric fields; Feature extraction; Gas chromatography; gas chromatography-ion mobility spectroscopy; generative adversarial network; Generative adversarial networks; Ion mobility spectroscopy; Ionic mobility; Machine learning; Mass spectrometry; Neural networks; Parameter identification; Prediction models; Principal components analysis; Rice; rice flavor substance; Scientific imaging; Software; Spectral analysis; Spectrometry; Spectroscopy; Spectrum analysis; Support vector machines; Transfer learning; triplet attention mechanism; VOCs; Volatile organic compounds; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14091552

To address the challenge of relying on complex biochemical methods for identifying rice species, a prediction model that combines gas chromatography-ion mobility spectroscopy (GC-IMS) with a convolutional neural network (CNN) was developed. The model utilizes the GC-IMS fingerprint data of each rice variety sample, and an improved CNN structure is employed to increase the recognition accuracy. First, an improved generative adversarial network based on the diffusion model (DGAN) is used for data enhancement to expand the dataset size. Then, on the basis of a residual network called ResNet50, a transfer learning method is introduced to improve the training effect of the model under the condition of a small sample. In addition, a new attention mechanism called Triplet is introduced to further highlight useful features and improve the feature extraction performance of the model. Finally, to reduce the number of model parameters and improve the efficiency of the model, a method called knowledge distillation is used to compress the model. The results of our experiments revealed that the recognition accuracy for identifying the 10 rice varieties was close to 96%; hence, the proposed model significantly outperformed traditional models such as principal component analysis and support vector machine. Furthermore, compared to the traditional CNN, our model reduced the number of parameters and number of computations by 53% and 55%, respectively, without compromising classification accuracy. The study also suggests that the combination of GC-IMS and our proposed deep learning method had better discrimination abilities for rice varieties than traditional chromatography and other spectral analysis methods and that it effectively identified rice varieties.