Two-trace two-dimensional(2T2D) correlation spectroscopy application in food safety: A review

Yang, Ran; Liu, Chunyu; Yang, Yong; Wu, Haiyun; Jin, Hao; Shan, Huiyong; Liu, Haixue

doi:10.1016/j.molstruc.2020.128219

Cited by 33 publications

(11 citation statements)

References 52 publications

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“…A set of one-dimensional MIR spectra of m samples can be converted into an m × n data matrix, where n is the number of spectral data points. Then, the synchronous (Φ) and asynchronous (ψ) two-dimensional correlation intensities between the different frequencies v 1 and v 2 can be calculated as ( Yang R. J. et al, 2014 , 2015 , 2020 )…”

Section: Methodsmentioning

confidence: 99%

“…The characteristic information of weak peak, migrating peak, and overlapping peak can be extracted effectively. Therefore, it is a beneficial alternative to standard infrared spectroscopy ( Noda, 2014a , b , 2016 , 2018 ; Yang et al, 2020 ). In recent years, 2DCOS has been widely used in the field of Chinese herbal medicine and food identification.…”

Section: Introductionmentioning

confidence: 99%

“…In this research, we collected 2,018 fruiting bodies of wild-grown boletes for four storage periods in Southwest China (mainly in Yunnan Province). Then we intercepted the 1,750– 400-, 1,450–1,000-, 1,150–1,000-cm –1 bands and used the two-trace two-dimensional (2T2D) ( Yang et al, 2020 ) method to generate the synchronous 2DCOS, asynchronous 2DCOS, and i2DCOS spectra of all samples. In this way, nine data sets and a total of 18,162 2DCOS spectral images were obtained.…”

Section: Introductionmentioning

confidence: 99%

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The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Dong

et al. 2021

Front. Microbiol.

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Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Dong

et al. 2021

Front. Microbiol.

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“…Each spectrum of sample was slightly smoothed with Omnic, version 8 (Thermo Fisher, Waltham, MA, USA). The generalized 2D correlation spectra were analyzed using 2D Pocha (Kwansei‐Gakuin University, Nishinomiya, Japan) 30,31 …”

Section: Methodsmentioning

confidence: 99%

“…The generalized 2D correlation spectra were analyzed using 2D Pocha (Kwansei-Gakuin University, Nishinomiya, Japan). 30,31 Spectral pre-processing SNV was implemented using Matlab R2019b (Mathworks Inc., Natick, MA, USA) for the collection of spectral data between 1750 and 900 cm −1 to eliminate the undesirable noise of the extracted average spectrum before model establishment. 32…”

Section: Spectral Processing and Chemometric Analysis 2d-ir Correlati...mentioning

confidence: 99%

Rapid and non‐destructive freshness evaluation of squid by FTIR coupled with chemometric techniques

Zhu

Wang

et al. 2021

J Sci Food Agric

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BACKGROUND Freshness is an important quality of squid with respect to determining the market price. The methods of evaluation of freshness fail to be widely used as a result of the lack of rapidity and quantitation. In the present study, a rapid and non‐destructive quantification of squid freshness by Fourier transform infrared spectroscopy (FTIR) spectra combined with chemometric techniques was performed. RESULTS The relatively linear content change of trimethylamine (TMA‐N) and dimethylamine (DMA‐N) of squid during storage at 4 °C indicated their feasibility as a freshness indicator, as also confirmed by sensory evaluation. The spectral changes were mainly caused by the degradation of proteins and the production of amines by two‐dimensional infrared correlation spectroscopy, among which TMA‐N, DMA‐N and putrescine were the main amines. The successive projections algorithm (SPA) was employed to select the sensitive wavenumbers to freshness for modeling prediction including partial least‐squares regression, support vector regression (SVR) and back‐propagation artificial neural network. Generally, the SPA‐SVR model of the selected characteristic wavenumber showed a higher prediction accuracy for DMA‐N (R2P = 0.951; RMSEP = 0.218), whereas both SPA‐SVR (R2P = 0.929; RMSEP = 2.602) and Full‐SVR (R2P = 0.941; RMSEP = 2.492) models had a higher predictive ability of TMA‐N. CONCLUSION The results of the present study demonstrate that FTIR spectroscopy coupled with multivariate calibration shows significant potential for the prediction of freshness in squid. © 2021 Society of Chemical Industry

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Two‐Dimensional Correlation Spectroscopy: New Developments and Applications

Jung¹,

Noda²

2022

Encyclopedia of Analytical Chemistry

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This article outlines the recent progress in 2D correlation spectroscopy (2D‐COS), especially new and noteworthy developments and its applications. 2D‐COS is a powerful technique applicable to the in‐depth analysis of various spectral data, especially those obtained in various practical spectroscopic experiments. 2D‐COS has undergone rapid developments. This article highlights new types of 2D correlation analysis, such as combination of chemometrics and 2D‐COS, moving‐window 2D analysis, projection 2D analysis, hetero‐correlation analysis, 2D codistribution spectroscopy (2D‐CDS), smooth factor analysis (SFA), two‐trace two‐dimensional (2T2D) correlation spectroscopy, as well as experimental practices, such as various perturbation methods, fields of applications, and analytical probes employed.

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Two-trace two-dimensional(2T2D) correlation spectroscopy application in food safety: A review

Cited by 33 publications

References 52 publications

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Rapid and non‐destructive freshness evaluation of squid by FTIR coupled with chemometric techniques

Two‐Dimensional Correlation Spectroscopy: New Developments and Applications

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