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

Zhongyuan Zhao, Feiyu Lian () and Yuying Jiang
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Zhongyuan Zhao: School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China
Feiyu Lian: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Yuying Jiang: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China

Agriculture, 2024, vol. 14, issue 9, 1-20

Abstract: 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.

Keywords: gas chromatography-ion mobility spectroscopy; rice flavor substance; diffusion model; generative adversarial network; transfer learning; triplet attention mechanism; knowledge distillation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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