1
|
Koyun OC, Keser RK, Şahin SO, Bulut D, Yorulmaz M, Yücesoy V, Töreyin BU. RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components. ACS OMEGA 2024; 9:23241-23251. [PMID: 38854537 PMCID: PMC11154961 DOI: 10.1021/acsomega.3c09247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 06/11/2024]
Abstract
Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.
Collapse
Affiliation(s)
- Onur Can Koyun
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
| | - Reyhan Kevser Keser
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
| | | | - Damla Bulut
- ASELSAN
Inc, Yenimahalle, 06200 Ankara, Turkey
| | | | | | - Behçet Uğur Töreyin
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
| |
Collapse
|
2
|
Bertazioli D, Piazza M, Carlomagno C, Gualerzi A, Bedoni M, Messina E. An integrated computational pipeline for machine learning-driven diagnosis based on Raman spectra of saliva samples. Comput Biol Med 2024; 171:108028. [PMID: 38335817 DOI: 10.1016/j.compbiomed.2024.108028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
Raman Spectroscopy promises the ability to encode in spectral data the significant differences between biological samples belonging to patients affected by a disease and samples of healthy patients (controls). However, the decoding and interpretation of the Raman spectral fingerprint is still a difficult and time-consuming procedure even for domain experts. In this work, we test an end-to-end deep-learning diagnostic pipeline able to classify spectral data from saliva samples. The pipeline has been validated against the SARS-COV-2 Infection and for the screening of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. The proposed system can be used for the fast prototyping of promising non-invasive, cost and time-efficient diagnostic screening tests.
Collapse
Affiliation(s)
- Dario Bertazioli
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy
| | - Marco Piazza
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy.
| | - Cristiano Carlomagno
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Alice Gualerzi
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Marzia Bedoni
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Enza Messina
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy
| |
Collapse
|
3
|
Lunter D, Klang V, Kocsis D, Varga-Medveczky Z, Berkó S, Erdő F. Novel aspects of Raman spectroscopy in skin research. Exp Dermatol 2022; 31:1311-1329. [PMID: 35837832 PMCID: PMC9545633 DOI: 10.1111/exd.14645] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/07/2022] [Accepted: 07/12/2022] [Indexed: 11/27/2022]
Abstract
The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.
Collapse
Affiliation(s)
- Dominique Lunter
- University of Tübingen, Department of Pharmaceutical Technology, Institute of Pharmacy and Biochemistry, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Victoria Klang
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology and Biopharmaceutics, Faculty of Life Sciences, Vienna, Austria
| | - Dorottya Kocsis
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Zsófia Varga-Medveczky
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Szilvia Berkó
- University of Szeged, Faculty of Pharmacy, Institute of Pharmaceutical Technology and Regulatory Affairs, Szeged, Hungary
| | - Franciska Erdő
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary.,University of Tours EA 6295 Nanomédicaments et Nanosondes, Tours, France
| |
Collapse
|
4
|
Wu J, Cui X, Kang Z, Wang S, Zhu G, Yang S, Wang S, Li H, Lu C, Lv X. Rapid diagnosis of diabetes based on ResNet and Raman spectroscopy. Photodiagnosis Photodyn Ther 2022; 39:103007. [PMID: 35817371 DOI: 10.1016/j.pdpdt.2022.103007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 10/17/2022]
Abstract
Diabetes mellitus is a global public health problem, and the epidemic situation in China is particularly serious. The prevalence of the disease has been increasing in recent years, and the number of patients is the highest in the world. Diabetes has become another chronic non-communicable disease that seriously endangers the health of our people after cardiovascular and cerebrovascular diseases and tumors. In this study, urine sample data were collected from 37 diabetic patients and 37 healthy volunteers using Raman spectroscopy. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and a polynomial Savitzky-Golay smoothing algorithm. After extracting features using principal component analysis (PCA) dimensionality reduction algorithm, ResNet, support vector machine (SVM) and linear discriminant analysis (LDA) classification models were selected to classify and identify diabetic patients and healthy controls. The results show that ResNet has the best discrimination effect, and the average accuracy, recall and F1-score can reach 84.28%, 86.20% and 84.02% respectively after five cross-validations, and the area under the subject working characteristic (ROC) curve is 0.93. The experimental results show that the model established in this paper is simple to operate, highly accurate and has good reference value for rapid screening of diabetes.
Collapse
Affiliation(s)
- Jianying Wu
- Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi, Xinjiang 830054, China
| | - Xinyue Cui
- Shihezi University, Shihezi, Xinjiang 832003, China
| | - Zhenping Kang
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
| | - Shanshan Wang
- Xinjiang Medical University, Urumqi, Xinjiang 830054, China
| | - Guoqiang Zhu
- Xinjiang Medical University, Urumqi, Xinjiang 830054, China
| | - Shufen Yang
- Department of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang 830001, China
| | - Shun Wang
- Department of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang 830001, China
| | - Hongtao Li
- Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, Xinjiang 830011, China
| | - Chen Lu
- Department of Nephrology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang 830011, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, Xinjiang 830046, China.
| |
Collapse
|
5
|
Farag MA, Abib B, Tawfik S, Shafik N, Khattab AR. Caviar and fish roe substitutes: Current status of their nutritive value, bio-chemical diversity, authenticity and quality control methods with future perspectives. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
6
|
Martinez L, He L. Detection of Mycotoxins in Food Using Surface-Enhanced Raman Spectroscopy: A Review. ACS APPLIED BIO MATERIALS 2021; 4:295-310. [PMID: 35014285 DOI: 10.1021/acsabm.0c01349] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Mycotoxins are toxic metabolites produced by fungi that contaminate many important crops worldwide. Humans are commonly exposed to mycotoxins through the consumption of contaminated food products. Mycotoxin contamination is unpredictable and unavoidable; it occurs at any point in the food production system under favorable conditions, and they cannot be destroyed by common heat treatments, because of their high thermal stability. Early and fast detection plays an essential role in this unique challenge to monitor the presence of these compounds in the food chain. Surface-enhanced Raman spectroscopy (SERS) is an advanced spectroscopic technique that integrates Raman spectroscopic molecular fingerprinting and enhanced sensitivity based on nanotechnology to meet the requirement of sensitivity and selectivity, but that can also be performed in a cost-effective and straightforward manner. This Review focuses on the SERS methodologies applied to date for qualitative and quantitative analysis of mycotoxins based on a variety of SERS substrates, as well as our perspectives on current limitations and future trends for applying this technique to mycotoxin analyses.
Collapse
Affiliation(s)
- Lourdes Martinez
- Department of Food Science, University of Massachusetts, Amherst, Massachusetts United States
| | - Lili He
- Department of Food Science, University of Massachusetts, Amherst, Massachusetts United States
| |
Collapse
|
7
|
Zhang H, Cheng C, Gao R, Yan Z, Zhu Z, Yang B, Chen C, Lv X, Li H, Huang Z. Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms. Photodiagnosis Photodyn Ther 2020; 33:102104. [PMID: 33212265 DOI: 10.1016/j.pdpdt.2020.102104] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/03/2020] [Accepted: 11/09/2020] [Indexed: 11/17/2022]
Abstract
Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.
Collapse
Affiliation(s)
- Huiting Zhang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Cheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Ziwei Yan
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Zhimin Zhu
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 840046, China.
| | - Hongyi Li
- Quality of Products Supervision and Inspection Institute, Urumqi 830011, Xinjiang, China
| | | |
Collapse
|
8
|
Ma S, Li LH, Hao SX, Yang XQ, Huang H, Cen JW, Wang YQ. Fatty-acid Profiles and Fingerprints of Seven Types of Fish Roes as Determined by Chemometric Methods. J Oleo Sci 2020; 69:1199-1208. [PMID: 32908092 DOI: 10.5650/jos.ess20061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The fatty acids in seven species of fish roes were determined by GC-MS in combination with principal component and cluster analyses in order to derive their fatty-acid profiles and fingerprints. Twenty-three common chromatography peaks were identified in the fatty-acid fingerprints of the seven fish roes. A total of 19 typical fatty acids were identified in the fish roes studied. The fatty acid contents of the roes were significantly different, with saturated-fatty-acid contents in the seven roes ranging from 26.69% to 41.81%, and the unsaturated-fatty-acid contents ranging from 57.65% to 72.21%, the total EPA and DHA content (37.20%) is high in E. cypselurus roe, especially. The seven roe species were clearly distinguished according to fatty-acid composition and content by principal component analysis (PCA) and divided into two groups by cluster analysis (CA). PCA of the fatty acid data yielded three significant PCs , which together account for 94% of the total variance; with PC1 contributing 54% of the total.
Collapse
Affiliation(s)
- Shuang Ma
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences.,College of Food Science and Technology, Shanghai Ocean University
| | - Lai Hao Li
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| | - Shu Xian Hao
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| | - Xian Qing Yang
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| | - Hui Huang
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| | - Jian Wei Cen
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| | - Yue Qi Wang
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs; Guangdong Provincial Key Laboratory of Fishery Ecology Environment; South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
| |
Collapse
|
9
|
Rocha WFDC, do Prado CB, Blonder N. Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods. Molecules 2020; 25:E3025. [PMID: 32630676 PMCID: PMC7411792 DOI: 10.3390/molecules25133025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/25/2020] [Accepted: 06/29/2020] [Indexed: 11/16/2022] Open
Abstract
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.
Collapse
Affiliation(s)
- Werickson Fortunato de Carvalho Rocha
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
| | - Charles Bezerra do Prado
- National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil; (W.F.C.R.); (C.B.d.P.)
| | - Niksa Blonder
- National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390 Gaithersburg, MD 20899, USA
| |
Collapse
|
10
|
Lussier F, Thibault V, Charron B, Wallace GQ, Masson JF. Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2019.115796] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
11
|
Huang L, Song Y, Kamal T, Li Y, Xia K, Lin Z, Qi L, Cheng S, Zhu BW, Tan M. A non-invasive method based on low-field NMR to analyze the quality changes in caviar from hybrid sturgeon (Huso dauricus, Acipenser schrenckiid
). J FOOD PROCESS PRES 2017. [DOI: 10.1111/jfpp.13256] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Linlin Huang
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Yukun Song
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Tariq Kamal
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
| | - Yan Li
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Kexin Xia
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Zhuyi Lin
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Libo Qi
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
| | - Shasha Cheng
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Bei-Wei Zhu
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| | - Mingqian Tan
- School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood; Dalian 116034 China
- Engineering Research Center of Seafood of Ministry of Education of China; Dalian 116034 China
- Liaoning Key Laboratory of Seafood Science and Technology; Dalian 116034 China
| |
Collapse
|
12
|
Xing Z, Du C, Tian K, Ma F, Shen Y, Zhou J. Application of FTIR-PAS and Raman spectroscopies for the determination of organic matter in farmland soils. Talanta 2016; 158:262-269. [DOI: 10.1016/j.talanta.2016.05.076] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/22/2016] [Accepted: 05/29/2016] [Indexed: 11/15/2022]
|
13
|
Li X, Yang T, Li S, Jin L, Wang D, Guan D, Ding J. Noninvasive liver diseases detection based on serum surface enhanced Raman spectroscopy and statistical analysis. OPTICS EXPRESS 2015; 23:18361-72. [PMID: 26191894 DOI: 10.1364/oe.23.018361] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) of blood serum to discriminate liver cancer and liver cirrhosis patients from normal people. Serum taken from 44 healthy people, 45 liver cancer patients, 42 post-treatment liver cancer patients and 45 liver cirrhosis patients was measured. SERS peaks from these groups were compared and the assignments and biomedical meanings were analyzed and explained. In addition, support vector machine (SVM), partial least square-discriminant analysis (PLS-DA) and artificial neural networks (ANN) was used on the obtained SERS spectra to identify its diagnostic potential for liver diseases. PLS-SVM, PLS-DA and PLS-ANN indicated 91.5%, 89.2% and 90.3% accuracy, respectively. This preliminary study demonstrates that serum SERS can be used for liver cancer screening.
Collapse
|
14
|
Boyaci IH, Temiz HT, Geniş HE, Acar Soykut E, Yazgan NN, Güven B, Uysal RS, Bozkurt AG, İlaslan K, Torun O, Dudak Şeker FC. Dispersive and FT-Raman spectroscopic methods in food analysis. RSC Adv 2015. [DOI: 10.1039/c4ra12463d] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Raman spectroscopy is a powerful technique for molecular analysis of food samples.
Collapse
Affiliation(s)
- Ismail Hakki Boyaci
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Havva Tümay Temiz
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Hüseyin Efe Geniş
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | | | - Nazife Nur Yazgan
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Burcu Güven
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Reyhan Selin Uysal
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Akif Göktuğ Bozkurt
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Kerem İlaslan
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | - Ozlem Torun
- Department of Food Engineering
- Faculty of Engineering
- Hacettepe University
- 06800 Ankara
- Turkey
| | | |
Collapse
|
15
|
|
16
|
Ooi HL, Ng SC, Lim E, Salamonsen RF, Avolio AP, Lovell NH. Robust Aortic Valve Non-Opening Detection for Different Cardiac Conditions. Artif Organs 2014; 38:E57-67. [DOI: 10.1111/aor.12220] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Hui-Lee Ooi
- Department of Biomedical Engineering; University of Malaya; Kuala Lumpur Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering; University of Malaya; Kuala Lumpur Malaysia
| | - Einly Lim
- Department of Biomedical Engineering; University of Malaya; Kuala Lumpur Malaysia
| | - Robert F. Salamonsen
- Department of Epidemiology and Preventive Medicine; Monash University; Melbourne Australia
| | - Alberto P. Avolio
- Australian School of Advanced Medicine; Macquaries University; Sydney NSW Australia
| | - Nigel H. Lovell
- Graduate School of Biomedical Engineering; University of New South Wales; Sydney NSW Australia
| |
Collapse
|