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Yang N, Guerin C, Kokanyan N, Perré P. Raman spectroscopy applied to online monitoring of a bioreactor: Tackling the limit of detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123343. [PMID: 37690399 DOI: 10.1016/j.saa.2023.123343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/29/2023] [Accepted: 09/02/2023] [Indexed: 09/12/2023]
Abstract
An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L-1 for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L-1. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.
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Affiliation(s)
- Ning Yang
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres 51110 Pomacle, France; CentraleSupélec, Chaire Photonique, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France; Université de Lorraine, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France.
| | - Cédric Guerin
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres 51110 Pomacle, France
| | - Ninel Kokanyan
- CentraleSupélec, Chaire Photonique, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France; Université de Lorraine, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France
| | - Patrick Perré
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres 51110 Pomacle, France; Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux (LGPM), Gif-sur-Yvette, France
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Ma ZW, Tang JW, Liu QH, Mou JY, Qiao R, Du Y, Wu CY, Tang DQ, Wang L. Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms. J Biomol Struct Dyn 2023; 41:14285-14298. [PMID: 36803175 DOI: 10.1080/07391102.2023.2180433] [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: 08/22/2022] [Accepted: 02/08/2023] [Indexed: 02/22/2023]
Abstract
The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.
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Affiliation(s)
- Zhang-Wen Ma
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, Jiangsu Province, China
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau, China
| | - Jing-Yi Mou
- The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Rui Qiao
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Department of Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Yan Du
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Chang-Yu Wu
- Department of Biomedical Engineering, School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Dao-Quan Tang
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
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Iturrioz-Rodríguez N, De Pasquale D, Fiaschi P, Ciofani G. Discrimination of glioma patient-derived cells from healthy astrocytes by exploiting Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 269:120773. [PMID: 34952436 DOI: 10.1016/j.saa.2021.120773] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/29/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. It presents a very bad prognosis with a patients' overall survival of 12-15 months; treatment failure is mainly ascribable to tumor recurrence. The development of new tools, that could help the precise detection of the tumor border, is thus an urgent need. During the last decades, different vibrational spectroscopy techniques have been developed to distinguish cancer tissue from heathy tissue; in the present work, we compared GBM cells deriving from four patients with healthy human astrocytes using Raman spectroscopy. We have shown that the region between 1000 and 1300 cm-1 is enough informative for this discrimination, indeed highlighting that peaks related to DNA/RNA and cytochrome c are increased in cancer cells. Finally, our model has been able to discriminate cancer cells from healthy cells with an average accuracy of 92.5%. We believe that this study might help to further understand which are the essential Raman peaks exploitable in the detection of cancer cells, with important perspectives under a diagnostic point of view.
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Affiliation(s)
- Nerea Iturrioz-Rodríguez
- Istituto Italiano di Tecnologia, Smart Bio-Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.
| | - Daniele De Pasquale
- Istituto Italiano di Tecnologia, Smart Bio-Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy
| | - Pietro Fiaschi
- San Martino Policlinico Hospital, Department of Neurosurgery, IRCCS for Oncology and Neurosciences, Largo Rosanna Benzi 10, 16132 Genova, Italy; University of Genoa, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), Largo Paolo Daneo 3, 16132 Genova, Italy
| | - Gianni Ciofani
- Istituto Italiano di Tecnologia, Smart Bio-Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.
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Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance. Comput Struct Biotechnol J 2022; 20:5364-5377. [PMID: 36212533 PMCID: PMC9526180 DOI: 10.1016/j.csbj.2022.09.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/21/2022] Open
Abstract
Handheld Raman spectrometer is able to generate SERS spectra with sufficient quality for Mycobacterium tuberculosis detection. It is feasible to accurately discriminate Mtb-positive sputum from Mtb-negative sputum through SERS spectrometry. Pulmonary and extra-pulmonary Mtb strains were able to be accurately distinguished via SERS spectral analysis. Profiling of antibiotic resistance of Mtb strains was successfully achieved through machine learning analysis of SERS spectra.
Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.
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Research on Task-Service Network Node Matching Method Based on Multi-Objective Optimization Model in Dynamic Hyper-Network Environment. MICROMACHINES 2021; 12:mi12111427. [PMID: 34832838 PMCID: PMC8624609 DOI: 10.3390/mi12111427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/11/2021] [Accepted: 11/15/2021] [Indexed: 11/17/2022]
Abstract
In order to reduce the cost of manufacturing and service for the Cloud 3D printing (C3DP) manufacturing grid, to solve the problem of resources optimization deployment for no-need preference under circumstance of cloud manufacturing, consider the interests of enterprises which need Cloud 3D printing resources and cloud platform operators, together with QoS and flexibility of both sides in the process of Cloud 3D printing resources configuration service, a task-service network node matching method based on Multi-Objective optimization model in dynamic hyper-network environment is built for resource allocation. This model represents interests of the above-mentioned two parties. In addition, the model examples are solved by modifying Mathematical algorithm of Node Matching and Evolutionary Solutions. Results prove that the model and the algorithm are feasible, effective and stable.
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Zhang C, Zhang C, Zhuang J, Han H, Yuan B, Liu J, Yang K, Zhuang S, Li R. Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect. MICROMACHINES 2021; 12:mi12070801. [PMID: 34357211 PMCID: PMC8305594 DOI: 10.3390/mi12070801] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/07/2021] [Accepted: 06/28/2021] [Indexed: 11/17/2022]
Abstract
Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.
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Affiliation(s)
- Chenglei Zhang
- School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
- Correspondence: (C.Z.); (J.L.)
| | - Cunshan Zhang
- School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China;
| | - Jiaojiao Zhuang
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
| | - Hu Han
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
| | - Bo Yuan
- School of Mechanical and Electrical Engineering, Wuhan City Polytechnic, Wuhan 430064, China;
| | - Jiajia Liu
- School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China; (J.Z.); (H.H.)
- Correspondence: (C.Z.); (J.L.)
| | - Kang Yang
- College of Mechanical Engineering, Anyang Institute of Technology, Anyang 455000, China;
| | - Shenle Zhuang
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
| | - Ronglan Li
- Shandong Longli Electronic Co., Ltd., Linyi 276000, China; (S.Z.); (R.L.)
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Aydın EB, Aydın M, Sezgintürk MK. A novel electrochemical immunosensor based on acetylene black/epoxy-substituted-polypyrrole polymer composite for the highly sensitive and selective detection of interleukin 6. Talanta 2021; 222:121596. [DOI: 10.1016/j.talanta.2020.121596] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/21/2020] [Accepted: 08/26/2020] [Indexed: 12/21/2022]
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