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Wang ZK, Ta N, Wei HC, Wang JH, Zhao J, Li M. Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine. Sci Rep 2024; 14:12598. [PMID: 38824219 PMCID: PMC11144233 DOI: 10.1038/s41598-024-63280-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
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.
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Affiliation(s)
- Zhuo-Kang Wang
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, 750021, Ningxia, China
| | - Na Ta
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, 750021, Ningxia, China
| | - Hai-Cheng Wei
- School of Medical Technology, North Minzu University, No. 204 North Wenchang Street, Yinchuan, 750021, Ningxia, China.
| | - Jin-Hang Wang
- School of Electrical and Information Engineering, North Minzu University, No. 204 North Wenchang Street, Yinchuan, 750021, Ningxia, China
| | - Jing Zhao
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
| | - Min Li
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia, China
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Johnson JB, Walsh KB, Naiker M, Ameer K. The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review. Molecules 2023; 28:molecules28073215. [PMID: 37049978 PMCID: PMC10096661 DOI: 10.3390/molecules28073215] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.
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Affiliation(s)
- Joel B Johnson
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kerry B Walsh
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Mani Naiker
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kashif Ameer
- Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan
- Department of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju 61186, Republic of Korea
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
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Shan P, Li Z, Wang Q, He Z, Wang S, Zhao Y, Wu Z, Peng S. Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process. Anal Chim Acta 2021; 1188:339205. [PMID: 34794558 DOI: 10.1016/j.aca.2021.339205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 12/01/2022]
Abstract
When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.
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Affiliation(s)
- Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhonghai He
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Shuyu Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Yuhui Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhui Wu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Silong Peng
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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