1
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Capobianco G, Pronti L, Gorga E, Romani M, Cestelli-Guidi M, Serranti S, Bonifazi G. Methodological approach for the automatic discrimination of pictorial materials using fused hyperspectral imaging data from the visible to mid-infrared range coupled with machine learning methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123412. [PMID: 37741099 DOI: 10.1016/j.saa.2023.123412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
Hyperspectral imaging represents a powerful tool for the study of artwork's materials since it permits to obtain simultaneously information about the spectral behavior of the materials and their spatial distribution. By combining hyperspectral images performed on several spectral intervals (visible, near infrared and mid-infrared ranges) through chemometric methods it is possible to clearly identify most of the materials used in painting (i.e., pigments, dyes, varnishes, and binders). Moreover, in the last decade, the development of machine learning algorithms coupled with comprehensive and continuously updated databases opens new perspective on the automatic recognition of pictorial materials. In this work, we propose a novel procedure to support the automatic discrimination of pictorial materials consisting in a mid-level data fusion on imaging datasets coming from two commercial hyperspectral cameras, in the 400-1000 nm and 1000-2500 nm spectral ranges, respectively, and a MAcroscopic Fourier Transform InfRared scanning in reflection mode (MA-rFTIR), in the 7000 to 350 cm-1 (1428 nm - 28 μm) spectral range. The automatic recognition of 102 pictorial mock-ups from the fused data is performed by testing the performance of ECOC-SVM (error-correcting output coding and support vector machine) model obtaining a good predictive result with only few pixels that are confused with other classes. The methodology described in this paper demonstrates that an accurate paint layer multiclass recognition is feasible, and the use of chemometric approaches solves some challenges involving the study of materials.
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Affiliation(s)
- G Capobianco
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
| | - Lucilla Pronti
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy.
| | - E Gorga
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - M Romani
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - M Cestelli-Guidi
- National Laboratories of Frascati - INFN, via Enrico Fermi 54, 00044 Frascati, Rome, Italy
| | - Silvia Serranti
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
| | - G Bonifazi
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
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2
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Jin G, Zhu Y, Cui C, Yang C, Hu S, Cai H, Ning J, Wei C, Li A, Hou R. Tracing the origin of Taiping Houkui green tea using 1H NMR and HS-SPME-GC-MS chemical fingerprints, data fusion and chemometrics. Food Chem 2023; 425:136538. [PMID: 37300997 DOI: 10.1016/j.foodchem.2023.136538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/17/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
The narrow geographical traceability of green tea is both important and challenging. This study aimed to establish multi-technology metabolomic and chemometric approaches to finely discriminate the geographic origins of green teas. Taiping Houkui green tea samples were analyzed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and 1H NMR of polar (D2O) and non-polar (CDCl3). Common dimension, low-level and mid-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of samples from different origins. In assessments of tea from six origins, the single instrument data test set results in 40.00% to 80.00% accuracy. Data fusion improved single-instrument performance classification with mid-level data fusion to obtain 93.33% accuracy in the test set. These results provide comprehensive metabolomic insights into the origin of TPHK fingerprinting and open up new metabolomic approaches for quality control in the tea industry.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Yuanyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chuanjian Cui
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chen Yang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Shaode Hu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Huimei Cai
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Chaoling Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China
| | - Aoxia Li
- Anhui Lanxiang Houkui Tea Co., Ltd., Huangshan, Anhui 245703, China
| | - Ruyan Hou
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei 230036, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, Hefei 230036, China.
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3
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Hayes E, Greene D, O’Donnell C, O’Shea N, Fenelon MA. Spectroscopic technologies and data fusion: Applications for the dairy industry. Front Nutr 2023; 9:1074688. [PMID: 36712542 PMCID: PMC9875022 DOI: 10.3389/fnut.2022.1074688] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.
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Affiliation(s)
- Elena Hayes
- University College Dublin (UCD) School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland,Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
| | - Derek Greene
- University College Dublin (UCD) School of Computer Science, University College Dublin, Dublin, Ireland
| | - Colm O’Donnell
- University College Dublin (UCD) School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland
| | - Norah O’Shea
- Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
| | - Mark A. Fenelon
- University College Dublin (UCD) School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland,Teagasc Food Research Centre, Moorepark, Fermoy, Ireland,*Correspondence: Mark A. Fenelon,
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4
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Xu Y, Zhang J, Wang Y. Recent trends of multi-source and non-destructive information for quality authentication of herbs and spices. Food Chem 2023; 398:133939. [DOI: 10.1016/j.foodchem.2022.133939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/19/2022] [Accepted: 08/10/2022] [Indexed: 11/15/2022]
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5
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Mialon N, Roig B, Capodanno E, Cadiere A. Untargeted metabolomic approaches in food authenticity: a review that showcases biomarkers. Food Chem 2022; 398:133856. [DOI: 10.1016/j.foodchem.2022.133856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 11/26/2022]
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Abstract
The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations.
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7
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Acho L, Pujol-Vázquez G. Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems. SENSORS 2021; 21:s21248437. [PMID: 34960534 PMCID: PMC8708564 DOI: 10.3390/s21248437] [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: 11/07/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 12/25/2022]
Abstract
In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.
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8
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Schackart KE, Yoon JY. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. SENSORS (BASEL, SWITZERLAND) 2021; 21:5519. [PMID: 34450960 PMCID: PMC8401027 DOI: 10.3390/s21165519] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/09/2021] [Accepted: 08/13/2021] [Indexed: 01/06/2023]
Abstract
Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.
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Affiliation(s)
- Kenneth E. Schackart
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
| | - Jeong-Yeol Yoon
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
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9
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Biancolillo A, Battistoni S, Presutto R, Marini F. Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations. Molecules 2021; 26:molecules26164944. [PMID: 34443532 PMCID: PMC8398775 DOI: 10.3390/molecules26164944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/07/2021] [Accepted: 08/13/2021] [Indexed: 11/25/2022] Open
Abstract
The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.
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Affiliation(s)
- Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, Via Vetoio, 67100 L’Aquila, Italy
- Correspondence: (A.B.); (F.M.)
| | - Stefano Battistoni
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy; (S.B.); (R.P.)
| | - Regina Presutto
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy; (S.B.); (R.P.)
| | - Federico Marini
- Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185 Rome, Italy; (S.B.); (R.P.)
- Correspondence: (A.B.); (F.M.)
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10
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Luong NDM, Membré JM, Coroller L, Zagorec M, Poirier S, Chaillou S, Desmonts MH, Werner D, Cariou V, Guillou S. Application of a path-modelling approach for deciphering causality relationships between microbiota, volatile organic compounds and off-odour profiles during meat spoilage. Int J Food Microbiol 2021; 348:109208. [PMID: 33940536 DOI: 10.1016/j.ijfoodmicro.2021.109208] [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: 12/18/2020] [Revised: 03/26/2021] [Accepted: 04/18/2021] [Indexed: 12/01/2022]
Abstract
Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.
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Affiliation(s)
| | | | - Louis Coroller
- Univ Brest, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne (LUBEM), UMT Alter'ix, Quimper, France.
| | | | - Simon Poirier
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, F78352 Jouy-en-Josas, France.
| | - Stéphane Chaillou
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, F78352 Jouy-en-Josas, France.
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11
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Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041709] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.
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12
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Tortorella S, Cinti S. How Can Chemometrics Support the Development of Point of Need Devices? Anal Chem 2021; 93:2713-2722. [DOI: 10.1021/acs.analchem.0c04151] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sara Tortorella
- Molecular Horizon srl, Via Montelino 30, 06084 Bettona, Perugia, Italy
| | - Stefano Cinti
- Department of Pharmacy, University of Naples “Federico II”, Via Domenico Montesano 49, 80131 Naples, Italy
- BAT Center−Interuniversity Center for Studies on Bioinspired Agro-Environmental Technology, University of Napoli “Federico II”, 80055 Portici, Naples, Italy
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13
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Ríos-Reina R, Camiña JM, Callejón RM, Azcarate SM. Spectralprint techniques for wine and vinegar characterization, authentication and quality control: Advances and projections. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116121] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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14
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Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods: A Comprehensive Review of Recent Technological Advances. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196802] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested.
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15
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Marzetti E, Guerra F, Calvani R, Marini F, Biancolillo A, Gervasoni J, Primiano A, Coelho-Júnior HJ, Landi F, Bernabei R, Bucci C, Picca A. Circulating Mitochondrial-Derived Vesicles, Inflammatory Biomarkers and Amino Acids in Older Adults With Physical Frailty and Sarcopenia: A Preliminary BIOSPHERE Multi-Marker Study Using Sequential and Orthogonalized Covariance Selection - Linear Discriminant Analysis. Front Cell Dev Biol 2020; 8:564417. [PMID: 33072749 PMCID: PMC7536309 DOI: 10.3389/fcell.2020.564417] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/13/2020] [Indexed: 01/04/2023] Open
Abstract
Physical frailty and sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. The multifaceted pathophysiology of this condition recapitulates all hallmarks of aging making the identification of specific biomarkers challenging. In the present study, we explored the relationship among three processes that are thought to be involved in PF&S (i.e., systemic inflammation, amino acid dysmetabolism, and mitochondrial dysfunction). We took advantage of the well-characterized cohort of older adults recruited in the “BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons” (BIOSPHERE) study to preliminarily combine in a multi-platform analytical approach inflammatory biomolecules, amino acids and derivatives, and mitochondrial-derived vesicle (MDV) cargo molecules to evaluate their performance as possible biomarkers for PF&S. Eleven older adults aged 70 years and older with PF&S and 10 non-sarcopenic non-frail controls were included in the analysis based on the availability of the three categories of biomolecules. A sequential and orthogonalized covariance selection—linear discriminant analysis (SO-CovSel–LDA) approach was used for biomarkers selection. Of the 75 analytes assayed, 16 had concentrations below the detection limit. Within the remaining 59 biomolecules, So-CovSel–LDA selected a set comprising two amino acids (phosphoethanolamine and tryptophan), two cytokines (interleukin 1 receptor antagonist and macrophage inflammatory protein 1β), and MDV-derived nicotinamide adenine dinucleotide reduced form:ubiquinone oxidoreductase subunit S3 as the best predictors for discriminating older people with and without PF&S. The evaluation of these biomarkers in larger cohorts and their changes over time or in response to interventions may unveil specific pathogenetic pathways of PF&S and identify new biological targets for drug development.
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Affiliation(s)
- Emanuele Marzetti
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Flora Guerra
- Department of Biological and Environmental Sciences and Technologies, Università del Salento, Lecce, Italy
| | - Riccardo Calvani
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Federico Marini
- Department of Chemistry, Sapienza Università di Roma, Rome, Italy
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, Università degli Studi dell'Aquila, L'Aquila, Italy
| | - Jacopo Gervasoni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | | | | | - Francesco Landi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Roberto Bernabei
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Cecilia Bucci
- Department of Biological and Environmental Sciences and Technologies, Università del Salento, Lecce, Italy
| | - Anna Picca
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
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16
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Biancolillo A, Preys S, Gaci B, Le-Quere JL, Laboure H, Deuscher Z, Cheynier V, Sommerer N, Fayeulle N, Costet P, Hue C, Boulanger R, Alary K, Lebrun M, Christine Lahon M, Morel G, Maraval I, Davrieux F, Roger JM. Multi-block classification of chocolate and cocoa samples into sensory poles. Food Chem 2020; 340:127904. [PMID: 32890856 DOI: 10.1016/j.foodchem.2020.127904] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/20/2020] [Accepted: 08/19/2020] [Indexed: 02/03/2023]
Abstract
The present study aims at developing an analytical methodology which allows correlating sensory poles of chocolate to their chemical characteristics and, eventually, to those of the cocoa beans used for its preparation. Trained panelists investigated several samples of chocolate, and they divided them into four sensorial poles (characterized by 36 different descriptors) attributable to chocolate flavor. The same samples were analyzed by six different techniques: Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), Solid Phase Micro Extraction-Gas Chromatography-Mass Spectroscopy (SPME-GC-MS), High-Performance Liquid Chromatography (HPLC) (for the quantification of eight organic acids), Ultra High Performance Liquid Chromatography coupled to triple-quadrupole Mass Spectrometry (UHPLC-QqQ-MS) for polyphenol quantification, 3D front face fluorescence Spectroscopy and Near Infrared Spectroscopy (NIRS). A multi-block classification approach (Sequential and Orthogonalized-Partial Least Squares - SO-PLS) has been used, in order to exploit the chemical information to predict the sensorial poles of samples. Among thirty-one test samples, only two were misclassified.
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Affiliation(s)
- Alessandra Biancolillo
- University of L'Aquila, Department of Physical and Chemical Sciences, Via Vetoio 67100, Coppito, L'Aquila, Italy.
| | | | - Belal Gaci
- ITAP, Inrae, Montpellier SupAgro, University of Montpellier, Montpellier, France; ChemHouse Research Group, Montpellier, France
| | - Jean-Luc Le-Quere
- Centre des Sciences du Goût et de l'Alimentation (CSGA), AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Helene Laboure
- Centre des Sciences du Goût et de l'Alimentation (CSGA), AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Zoe Deuscher
- Centre des Sciences du Goût et de l'Alimentation (CSGA), AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, F-21000 Dijon, France; CIRAD, UMR Qualisud, F-34398 Montpellier, France
| | - Veronique Cheynier
- SPO, INRAE, Univ Montpellier, Montpellier Supagro, 34060 Montpellier, France
| | - Nicolas Sommerer
- SPO, INRAE, Univ Montpellier, Montpellier Supagro, 34060 Montpellier, France
| | - Noemie Fayeulle
- SPO, INRAE, Univ Montpellier, Montpellier Supagro, 34060 Montpellier, France
| | - Pierre Costet
- Chocolaterie Valrhona, 14 avenue du Président Roosevelt, 26600 Tain L'Hermitage, France
| | - Clotilde Hue
- Chocolaterie Valrhona, 14 avenue du Président Roosevelt, 26600 Tain L'Hermitage, France
| | - Renaud Boulanger
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Karine Alary
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Marc Lebrun
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Marie Christine Lahon
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Gilles Morel
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Isabelle Maraval
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Fabrice Davrieux
- CIRAD, UMR Qualisud, F-34398 Montpellier, France; CIRAD, UMR Qualisud, F-97490 Sainte-Clotilde, Réunion, France
| | - Jean-Michel Roger
- ITAP, Inrae, Montpellier SupAgro, University of Montpellier, Montpellier, France; ChemHouse Research Group, Montpellier, France
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17
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Ríos-Reina R, Azcarate SM, Camiña JM, Goicoechea HC. Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines. Anal Chim Acta 2020; 1126:52-62. [PMID: 32736724 DOI: 10.1016/j.aca.2020.06.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 11/28/2022]
Abstract
Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.
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Affiliation(s)
- Rocío Ríos-Reina
- Área de Nutrición y Bromatología, Fac. Farmacia, Univ. Sevilla, C/P. García González No. 2, E-41012, Sevilla, Spain
| | - Silvana M Azcarate
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa-CONICET, Instituto de Ciencias de La Tierra y Ambientales de La Pampa (INCITAP), Av. Uruguay 151, 6300, Santa Rosa, La Pampa, Argentina.
| | - José M Camiña
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa-CONICET, Instituto de Ciencias de La Tierra y Ambientales de La Pampa (INCITAP), Av. Uruguay 151, 6300, Santa Rosa, La Pampa, Argentina
| | - Héctor C Goicoechea
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional Del Litoral-CONICET, Ciudad Universitaria, Santa Fe, S3000ZAA, Argentina
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Hassoun A, Måge I, Schmidt WF, Temiz HT, Li L, Kim HY, Nilsen H, Biancolillo A, Aït-Kaddour A, Sikorski M, Sikorska E, Grassi S, Cozzolino D. Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years. Foods 2020; 9:E1069. [PMID: 32781687 PMCID: PMC7466239 DOI: 10.3390/foods9081069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.
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Affiliation(s)
- Abdo Hassoun
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Ingrid Måge
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Walter F. Schmidt
- United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA;
| | - Havva Tümay Temiz
- Department of Food Engineering, Bingol University, 12000 Bingol, Turkey;
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China;
| | - Hae-Yeong Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea;
| | - Heidi Nilsen
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy;
| | | | - Marek Sikorski
- Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland;
| | - Ewa Sikorska
- Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland;
| | - Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy;
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 39 Kessels Rd, Coopers Plains, QLD 4108, Australia;
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ANOVA-Simultaneous Component analysis modelling of low-level-fused spectroscopic data: A food chemistry case-study. Anal Chim Acta 2020; 1125:308-314. [PMID: 32674778 DOI: 10.1016/j.aca.2020.05.059] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/20/2020] [Accepted: 05/23/2020] [Indexed: 11/22/2022]
Abstract
Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.
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Desta F, Buxton M, Jansen J. Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20051472. [PMID: 32156030 PMCID: PMC7085633 DOI: 10.3390/s20051472] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 02/25/2020] [Accepted: 03/04/2020] [Indexed: 06/01/2023]
Abstract
Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO2, Al2O3, and Fe2O3 concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO2, Al2O3, and Fe2O3 mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications.
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Affiliation(s)
- Feven Desta
- Resource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands;
| | - Mike Buxton
- Resource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands;
| | - Jeroen Jansen
- Department of Analytical Chemistry: Chemometrics, Faculty of Science, Radboud University, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands;
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The “Metabolic biomarkers of frailty in older people with type 2 diabetes mellitus” (MetaboFrail) study: Rationale, design and methods. Exp Gerontol 2020; 129:110782. [DOI: 10.1016/j.exger.2019.110782] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 11/14/2019] [Indexed: 12/19/2022]
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