Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Huang, Guangzao; Yuan, Lei; Shi, Wen; Chen, Xi

doi:10.1016/j.foodchem.2021.131219

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…Independent runs were repeated for the optimization due to the uncertainty of the calculation. As generally used in the literature, , one hidden layer was employed between the input layer and the bottleneck layer, as shown in Figure A. Among the possible numbers, 128, 256, and 512 were compared for the number of nodes in the hidden layers, and 30, 50, 70, 80, 90, and 100 were investigated for the number of nodes in the bottleneck layer.…”

Section: Resultsmentioning

confidence: 99%

“…The recognition performance of the method is superior to the classical soft independent modeling of the class analogy (SIMCA) method. 43 On the other hand, transfer learning, which applied learned knowledge in previous tasks to new tasks, provides an easy way for the modeling problem. By transferring the weights of the pre-trained model to a new task, minor modifications are required to build the new model.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The encoding and reconstruction errors can be used to distinguish pure and adulterated samples. The recognition performance of the method is superior to the classical soft independent modeling of the class analogy (SIMCA) method . On the other hand, transfer learning, which applied learned knowledge in previous tasks to new tasks, provides an easy way for the modeling problem.…”

Section: Introductionmentioning

confidence: 99%

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Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

Duan

Liu

Cai

et al. 2022

J. Chem. Inf. Model.

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An autoencoder architecture was adopted for near-infrared (NIR) spectral analysis by extracting the common features in the spectra. Three autoencoder-based networks with different purposes were constructed. First, a spectral encoder was established by training the network with a set of spectra as the input. The features of the spectra can be encoded by the nodes in the bottleneck layer, which in turn can be used to build a sparse and robust model. Second, taking the spectra of one instrument as the input and that of another instrument as the reference output, the common features in both spectra can be obtained in the bottleneck layer. Therefore, in the prediction step, the spectral features of the second can be predicted by taking the reverse of the decoder as the encoder. Furthermore, transfer learning was used to build the model for the spectra of more instruments by fine-tuning the trained network. NIR datasets of plant, wheat, and pharmaceutical tablets measured on multiple instruments were used to test the method. The multi-linear regression (MLR) model with the encoded features was found to have a similar or slightly better performance in prediction compared with the partial least-squares (PLS) model.

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Section: Resultsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

Duan

Liu

Cai

et al. 2022

J. Chem. Inf. Model.

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show abstract

“…These methods have clear benefits in the quality inspection of tea, such as high accuracy and quantitative analysis [18]. However, these methods are tedious, laborious, and require various chemical reagents [19], which inhibit the assessment of tea quality in a non-laboratory environment [20]. More importantly, it is very difficult to predict an adulterant mixture ratio.…”

Section: Introductionmentioning

confidence: 99%

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Kang

2022

Molecules

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Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

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“…The timely measurement information it provides can provide the basis for the identification of coal before combustion and pyrolysis, which can significantly improve efficiency and reduce the consumption of time and resources. In recent years, spectral analysis techniques have been extensively applied in the fields of soil, minerals, food, and so on. − For coal, spectroscopy is used to analyze and identify the category, composition, structure, and other characteristics of coal. Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Xiao

Yan

et al. 2022

ACS Omega

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Coal plays an indispensable role in the world’s energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.

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Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Cited by 21 publications

References 23 publications

Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

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