Selective Adsorption of Essential Oil Compounds by <i>Waxy/Amylose Extender</i> (<i>wx/ae</i>) Double‐Mutant Rice Starch Revealed by Gas Chromatography

Takagi, Hiroki; Suzuki, Shiho; Kitamura, Shinichi

doi:10.1002/star.201700301

Cited by 1 publication

(1 citation statement)

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“…As a new detection method, an electronic nose [1], [2] has shortcomings such as sensor drift, low accuracy, and poor consistency in its results, and its applications in the fields of food quality and safety inspection are unsatisfactory. Chromatography [3]- [6] has been used to detect adulterated rice, and it offers high efficiency, sensitivity, accuracy, and relative immunity to interference factors. However, due to the presence of different rice varieties and methods of processing rice (each rice variety has a unique composition of starch, protein, and fatty acids), using a chromatograph also has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

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

Lian

et al. 2021

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%

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

Lian

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

Selective Adsorption of Essential Oil Compounds by Waxy/Amylose Extender (wx/ae) Double‐Mutant Rice Starch Revealed by Gas Chromatography

Cited by 1 publication

References 26 publications

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

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

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