Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy

Chen, Jiemei; Li, Mingliang; Pan, Tao; Pang, Liwen; Yao, Ling; Zhang, Jing

doi:10.1016/j.saa.2019.03.105

Cited by 44 publications

(25 citation statements)

References 34 publications

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“…Genetic markers might be used to distinguish the varieties of rice seed with high accuracy, it was to detect the difference among DNA of samples to make judgment, but it was destructive, expensive, and time‐consuming (Huang, Chen, Wang, Lu, & Chen, 2006). Recently, near infrared spectroscopy (NIRS) was used to detect different kinds of seeds, because it could express the chemical information of samples (Chen et al, 2019). However, NIRS captured only the spectral information of a small area of the sample and could not capture the image of the sample, some interference information was attached to the spectral information and some useful information was lost, so NIRS was not an ideal method.…”

Section: Introductionmentioning

confidence: 99%

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Sun

Zhang

Zhou

et al. 2021

J Food Process Engineering

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Accurate, rapid, and nondestructive identification of rice seed varieties has great significance for agriculture and food security, a method based on information fusion and artificial fish swarm algorithm (AFSA) combined with the hyperspectral imaging (HSI) of five kinds of rice seeds was proposed in this work. First, the spectral and image data were obtained from HSI, and the spectral data were preprocessed by detrending. Then, bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach, successive projections algorithm, and principal component analysis were adopted to select feature variables from the spectral and image data. Next, the support vector machine (SVM) model was constructed based on the spectral and image feature variables. In order to further improve the classification accuracy of single feature model, the model based on fused feature was developed and it was finally optimized by AFSA. The research showed that the feature variables (114 spectral variables and 11 image variables) selected by BOSS were representative, and the model accuracy based on BOSS spectral and image features reached 91.48% and 70%, respectively. The performance of the SVM model based on fused feature was improved significantly, and the model accuracy reached 97.22%. After AFSA optimization, the model accuracy finally reached 99.44%. The above result confirmed that using AFSA to optimize the model based on fused feature could be a promising method to identify rice seed varieties. Practical application Rapid and accurate identification of rice seed varieties contributes to establishment of online rice seed identification system. HSI combined with information fusion and AFSA can overcome the disadvantage of low accuracy of traditional nondestructive testing methods. The method proposed in this article can be recommended to be widely popularized in farms, grain markets, and market regulators.

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Section: Introductionmentioning

confidence: 99%

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Sun

Zhang

Zhou

et al. 2021

J Food Process Engineering

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“…Recently, nondestructive testing methods based on optical technology, such as infrared spectroscopy technology [ 4 , 5 ], machine vision, and hyperspectral technology [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], have received significant attention. Infrared spectroscopy technology identifies the spectral features of seeds [ 14 ] and analyses their internal chemical compositions (such as protein and starch) to classify them.…”

Section: Introductionmentioning

confidence: 99%

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Liu

Zeng

Guiju

et al. 2021

Sensors

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Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

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“…A chromatograph is a complicated piece of equipment that requires a professional operator, so it is not useful for rapid field detection. Although spectrometry [7]- [9] is fast and simple, it is difficult to accurately characterize the quality of samples with such techniques. Gas chromatography-mass spectrometry (GC-MS) is widely used in the field [10]- [12], but this method requires a sample to undergo pretreatment.…”

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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Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy

Cited by 44 publications

References 34 publications

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

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

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