Identification of Rice Varieties and Adulteration Using Gas Chromatography-Ion Mobility Spectrometry

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.

Effect of NPS Fertilizer, Spraying Nano-NPK+TE and Cerium Oxide NPs on the Qualitative Properties and Bioactive Components of Rice Grains (Oryza Sativa L.)

“…Rice grain samples were extracted using a modified version of the method proposed by reference [28,29], Firstly, (5 g) of dried powder of rice grains from every treatment were added to (5 ml) of 50% ethanol and constantly stirred. The mixture was filtered with filter sheets, and the filtrate was collected while the sediment was re-extracted using ethanol.…”

Using a Light-Weight CNN for Perfume Identification with An Integrated Handheld Electronic Nose

“…Existing electronic techniques that can be used for PI mainly include EN [3,4], fluctuationenhanced sensing (FES) [5,6], gas chromatography (GC) [7], and GC with mass spectrometry (GC-MS) [8,9]. Typical ENs are mainly composed of the measurement acquisition hardware and data processing software.…”

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data

“…13,14 In recent years, NIR spectroscopy combined with SIMCA has been widely used in sample classification. 14 Suhandy 15 Currently, rice varieties are mainly identified by manual or gas chromatography 19 analytical techniques, both of which have issues with difficult operation, arduous processing, and poor detection accuracy. As a quick, environmentally friendly, and nondestructive analytical technique, NIR spectroscopy analytical technique can quickly and easily gather vital data on experimental samples that contain hydrogen, such as transmittance, absorbance, reflectance, and chemical bond strength.…”