Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

Wu, Bin; Tang, Wenbo; Zhou, Jin; Jia, Hongwen; Shen, Hualei; Qi, Zuxuan

doi:10.1016/j.heliyon.2024.e27732

Cited by 2 publications

References 32 publications

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Altitude and Regional Differentiation of Dark Tea Using Non-Targeted Metabolomics Based on UHPLC-Q-Exactive Orbitrap MS

Zhang,

Han,

et al. 2024

Preprint

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Dark tea, a fermented tea variety, is closely linked to its geographical origin in terms of quality and market value. Thus, accurately identifying the geographical origin of dark tea is crucial for ensuring its quality and determining its market price. This study established a non-targeted metabolomics approach using ultra-performance liquid chromatography-quadrupole-electrostatic field Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) to identify differential chemical components of dark tea from various geographical regions. Chemometric modeling was employed to predict the tea's origin. From the non-targeted metabolomics analysis of 47 dark tea samples, 12 key metabolites were selected, primarily based on altitude. Using these differential metabolites, an orthogonal partial least squares-discriminant analysis (OPLS-DA) validation model was developed. Furthermore, a method incorporating geographical factors, particularly altitude, was established, and OPLS-DA validation models were constructed for each region. After model fitting, validation, and discrimination training, the results showed no overfitting, and the accuracy rates for both the training and validation sets reached 100%. The method established in this study shows significant potential for distinguishing the geographical origin of dark tea and provides a strong foundation for origin identification in fermented foods.

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Altitude and Regional Differentiation of Dark Tea Using Non-Targeted Metabolomics Based on UHPLC-Q-Exactive Orbitrap MS

Zhang,

Han,

et al. 2024

Preprint

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Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

Liang,

Liu,

Zhan

et al. 2024

Actuators

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Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance.

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Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

Cited by 2 publications

References 32 publications

Altitude and Regional Differentiation of Dark Tea Using Non-Targeted Metabolomics Based on UHPLC-Q-Exactive Orbitrap MS

Altitude and Regional Differentiation of Dark Tea Using Non-Targeted Metabolomics Based on UHPLC-Q-Exactive Orbitrap MS

Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

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