Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

Lu, Bingxu; Tian, Feng; Chen, Cheng; Wu, Wei; Tian, Xuecong; Chen, Chen; Lv, Xiaoyi

doi:10.1016/j.saa.2023.122355

Cited by 19 publications

(2 citation statements)

References 53 publications

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“…In the formula,

is the sequence number of the layer,

is the

-th feature output of the

layer,

is the output of the

layer and the input of the

layer,

is the convolutional filter of the i -th layer,

is the deviation, and

is the set of input feature maps.

is an activation function ( Lu et al, 2023 , Dian et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Li,

Qing,

Wang

et al. 2024

Current Research in Food Science

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“…In the formula,

is the sequence number of the layer,

is the

-th feature output of the

layer,

is the output of the

layer and the input of the

layer,

is the convolutional filter of the i -th layer,

is the deviation, and

is the set of input feature maps.

is an activation function ( Lu et al, 2023 , Dian et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Li,

Qing,

Wang

et al. 2024

Current Research in Food Science

View full text Add to dashboard Cite

“…Spectral technology has been widely applied in wine origin identification, wine quality evaluation 7 , and various food research 8 , 9 due to its simplicity, high sensitivity, no need for sample pretreatment, and no need for experimental reagents. Lu et al identified corresponding biomarkers by searching for Raman spectra of red wine, analyzed Raman spectra using PCA, and established a red wine origin recognition model by combining dimensionality reduction data with deep learning, achieving a more accurate classification of red wine origins 10 . Tana et al used ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry to identify characteristic substances in wine from different regions, screened out the characteristic bands of near-infrared spectroscopy of wine, and accurately divided wine samples from six regions 11 .…”

Section: Introductionmentioning

confidence: 99%

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

Wang,

Ta,

Wei

et al. 2024

Sci Rep

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To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

show abstract

A Novel Custom Deep Learning Network Combining 1D-Convolution and LSTM for Rapid Wine Quality Detection in Small and Average-Scale Applications

Nguyen,

Le,

Duong

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

Cited by 19 publications

References 53 publications

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

A Novel Custom Deep Learning Network Combining 1D-Convolution and LSTM for Rapid Wine Quality Detection in Small and Average-Scale Applications

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