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Onça L, Koljančić N, Furdíková K, Khvalbota L, Špánik I, Gomes AA. A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted. Food Chem 2024; 456:140075. [PMID: 38876057 DOI: 10.1016/j.foodchem.2024.140075] [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: 08/11/2023] [Revised: 06/06/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putňový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.
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Affiliation(s)
- Larisa Onça
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil
| | - Nemanja Koljančić
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Katarína Furdíková
- Institute of Biotechnology, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Liudmyla Khvalbota
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Ivan Špánik
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Adriano A Gomes
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil; Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia.
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2
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Henning S. Classification of Kudoa thyrsites infected and uninfected fish using a handheld near-infrared spectrophotometer, SIMCA and PLS-DA. JOURNAL OF FISH DISEASES 2024:e14025. [PMID: 39370681 DOI: 10.1111/jfd.14025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 09/07/2024] [Accepted: 09/12/2024] [Indexed: 10/08/2024]
Abstract
Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.
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Affiliation(s)
- S Henning
- Department of Food Technology and Science, Faculty of Applied Sciences, Cape Peninsula University of Technology, Bellville, South Africa
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3
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Durante C, Morelli L, D’Eusanio V, Tassi L, Marchetti A. Exploring the Impact of Various Wooden Barrels on the Aromatic Profile of Aceto Balsamico Tradizionale di Modena by Means of Principal Component Analysis. Molecules 2024; 29:2647. [PMID: 38893522 PMCID: PMC11173617 DOI: 10.3390/molecules29112647] [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: 04/29/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
The study examines the unique production process of Aceto Balsamico Tradizionale di Modena PDO (ABTM), emphasizing its complex phases and the impact of raw materials and artisanal skill on its flavor characteristics. Analytical tests focused on the volatile composition of vinegars from different wood barrels at different aging stages, using solid-phase micro-extraction (SPME) coupled with gas chromatography, either with mass spectrometry (GC/MS) or flame ionization detector (FID). Multivariate analysis, including principal component analysis (PCA), was employed to investigate the presence of peculiarities among the volatile profiles of samples of different barrel origin. The research focuses on characterizing the volatile composition of vinegars sourced from individual wood barrels, such as Cherry, Chestnut, Mulberry, Juniper, and Oak. Although it was not possible to identify molecules directly connected to the woody essence, some similarities emerged between vinegar samples from mulberry and cherry barrels and between those of juniper and oak. The former group is characterized by analytes with high molecular weights, such as furfural and esters, while the latter group shows more intense peaks for ethyl benzoate. Moreover, ethyl benzoate appears to predominantly influence samples from chestnut barrels. Due to the highly complex production process of ABTM, where each battery is influenced by several factors, this study's findings are specific to the current experimental conditions.
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Affiliation(s)
- Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (C.D.); (L.M.); (V.D.); (A.M.)
| | - Lorenzo Morelli
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (C.D.); (L.M.); (V.D.); (A.M.)
| | - Veronica D’Eusanio
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (C.D.); (L.M.); (V.D.); (A.M.)
- National Interuniversity Consortium of Material Science and Technology (INSTM), 50121 Florence, Italy
| | - Lorenzo Tassi
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (C.D.); (L.M.); (V.D.); (A.M.)
- National Interuniversity Consortium of Material Science and Technology (INSTM), 50121 Florence, Italy
- Interdepartmental Research Center BIOGEST-SITEIA, University of Modena and Reggio Emilia, 42121 Reggio Emilia, Italy
| | - Andrea Marchetti
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (C.D.); (L.M.); (V.D.); (A.M.)
- National Interuniversity Consortium of Material Science and Technology (INSTM), 50121 Florence, Italy
- Interdepartmental Research Center BIOGEST-SITEIA, University of Modena and Reggio Emilia, 42121 Reggio Emilia, Italy
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4
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de Oliveira Costa T, Rangel Botelho J, Helena Cassago Nascimento M, Krause M, Tereza Weitzel Dias Carneiro M, Coelho Ferreira D, Roberto Filgueiras P, de Oliveira Souza M. A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS). Food Chem 2024; 442:138268. [PMID: 38242000 DOI: 10.1016/j.foodchem.2023.138268] [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: 08/10/2023] [Revised: 11/27/2023] [Accepted: 12/22/2023] [Indexed: 01/21/2024]
Abstract
Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).
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Affiliation(s)
- Tayná de Oliveira Costa
- Laboratório de Analítica, Metabolômica e Quimiometria (LAMeQui), Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Campus Alegre (IFES), Brazil; Programa de Pós-Graduação em Ciências Naturais (PPGCN), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Brazil
| | | | | | - Maiara Krause
- Departamento de Química, Universidade Federal do Espírito Santo (UFES), Brazil
| | | | | | | | - Murilo de Oliveira Souza
- Laboratório de Analítica, Metabolômica e Quimiometria (LAMeQui), Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Campus Alegre (IFES), Brazil; Programa de Pós-Graduação em Ciências Naturais (PPGCN), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Brazil.
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5
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Wang F, Fan J, An Y, Meng G, Ji B, Li Y, Dong C. Tracing the geographical origin of endangered fungus Ophiocordyceps sinensis, especially from Nagqu, using UPLC-Q-TOF-MS. Food Chem 2024; 440:138247. [PMID: 38154283 DOI: 10.1016/j.foodchem.2023.138247] [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: 04/08/2023] [Revised: 12/08/2023] [Accepted: 12/19/2023] [Indexed: 12/30/2023]
Abstract
Ophiocordyceps sinensis (OS), known as "soft gold", played an important role in local economic development. OS from different producing areas was difficult to be discriminated by the appearance. Nagqu OS, a distinguished and safeguarded geographical indication product, commands a premium price in market. The real claim of OS geographical origins is urgently required. Here, 81 OS samples were collected from Tibetan Plateau in China to explore markers for tracing origins. OS from Xigazê can be distinguished by dark color of head of caterpillar. Then 57 samples, a fully representative training-sample set, were used to set up OPLS-DA models by nontargeted metabolomics from UPLC-QTOF-MS. Certain markers were successfully identified and validation using 21 blind test samples confirmed that the markers can trace the geographical origin of OS, especially Nagqu samples. It was affirmed that UPLC-QTOF-MS-based untargeted metabolomics coupled with OPLS-DA was a reliable strategy to trace the geographical origins of OS.
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Affiliation(s)
- Fen Wang
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Junfeng Fan
- Nagqu City Inspection and Testing Center, Nagqu City, Tibet Autonomous Region 852000, China
| | - Yabin An
- Nagqu City Inspection and Testing Center, Nagqu City, Tibet Autonomous Region 852000, China
| | - Guoliang Meng
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Bingyu Ji
- College of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
| | - Yi Li
- College of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
| | - Caihong Dong
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
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6
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Macchia E, Torricelli F, Caputo M, Sarcina L, Scandurra C, Bollella P, Catacchio M, Piscitelli M, Di Franco C, Scamarcio G, Torsi L. Point-Of-Care Ultra-Portable Single-Molecule Bioassays for One-Health. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309705. [PMID: 38108547 DOI: 10.1002/adma.202309705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/20/2023] [Indexed: 12/19/2023]
Abstract
Screening asymptomatic organisms (humans, animals, plants) with a high-diagnostic accuracy using point-of-care-testing (POCT) technologies, though still visionary holds great potential. Convenient surveillance requires easy-to-use, cost-effective, ultra-portable but highly reliable, in-vitro-diagnostic devices that are ready for use wherever they are needed. Currently, there are not yet such devices available on the market, but there are a couple more promising technologies developed at readiness-level 5: the Clustered-Regularly-Interspaced-Short-Palindromic-Repeats (CRISPR) lateral-flow-strip tests and the Single-Molecule-with-a-large-Transistor (SiMoT) bioelectronic palmar devices. They both hold key features delineated by the World-Health-Organization for POCT systems and an occurrence of false-positive and false-negative errors <1-5% resulting in diagnostic-selectivity and sensitivity >95-99%, while limit-of-detections are of few markers. CRISPR-strip is a molecular assay that, can detect down to few copies of DNA/RNA markers in blood while SiMoT immunometric and molecular test can detect down to a single oligonucleotide, protein marker, or pathogens in 0.1mL of blood, saliva, and olive-sap. These technologies can prospectively enable the systematic and reliable surveillance of asymptomatic ones prior to worsening/proliferation of illnesses allowing for timely diagnosis and swift prognosis. This could establish a proactive healthcare ecosystem that results in effective treatments for all living organisms generating diffuse and well-being at efficient costs.
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Affiliation(s)
- Eleonora Macchia
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, 70125, Italy
| | - Fabrizio Torricelli
- Dipartimento Ingegneria dell'Informazione, Università degli Studi di Brescia, Brescia, 25123, Italy
| | - Mariapia Caputo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, 70125, Italy
| | - Lucia Sarcina
- Dipartimento di Chimica and Centre for Colloid and Surface Science, Università degli Studi di Bari Aldo Moro, Bari, 20125, Italy
| | - Cecilia Scandurra
- Dipartimento di Chimica and Centre for Colloid and Surface Science, Università degli Studi di Bari Aldo Moro, Bari, 20125, Italy
| | - Paolo Bollella
- Dipartimento di Chimica and Centre for Colloid and Surface Science, Università degli Studi di Bari Aldo Moro, Bari, 20125, Italy
| | - Michele Catacchio
- Dipartimento di Chimica and Centre for Colloid and Surface Science, Università degli Studi di Bari Aldo Moro, Bari, 20125, Italy
| | - Matteo Piscitelli
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, 70125, Italy
- CNR IFN, Bari, 70126, Italy
| | | | - Gaetano Scamarcio
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, 70125, Italy
- CNR IFN, Bari, 70126, Italy
| | - Luisa Torsi
- Dipartimento di Chimica and Centre for Colloid and Surface Science, Università degli Studi di Bari Aldo Moro, Bari, 20125, Italy
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7
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Fong M, Takeshita Y, Easley R, Waters J. Detection of impurities in m-cresol purple with Soft Independent Modeling of Class Analogy for the quality control of spectrophotometric pH measurements in seawater. MARINE CHEMISTRY 2024; 259:104362. [PMID: 38414838 PMCID: PMC10895926 DOI: 10.1016/j.marchem.2024.104362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Accurate spectrophotometric pH measurements in seawater are critical to documenting long-term changes in ocean acidity and carbon chemistry, and for calibration of autonomous pH sensors. The recent development of purified indicator dyes greatly improved the accuracy of spectrophotometric pH measurements by removing interfering impurities that cause biases in pH that can grow over the seawater pH range to >0.01 above pH 8. However, some batches of purified indicators still contain significant residual impurities that lead to unacceptably large biases in pH for oceanic and estuarine climate quality measurements. While high-performance liquid chromatography (HPLC) is the standard method for verifying dye purity, alternative approaches that are simple to implement and require less specialized equipment are desirable. We developed a model to detect impurities in the pH indicator m-cresol purple (mCP) using a variant of the classification technique Soft Independent Modeling of Class Analogy (SIMCA). The classification model was trained with pure mCP spectra (350 nm to 750 nm at 1 nm resolution) at pH 12 and tested on independent samples of unpurified and purified mCP with varying levels of impurities (determined by HPLC) and measured on two different spectrophotometers. All the dyes identified as pure by the SIMCA model were sufficiently low in residual impurities that their apparent biases in pH were < 0.002 in buffered artificial seawater solutions at a salinity of 35 and over a pH range of 7.2 to 8.2. Other methods that can also detect residual impurities relevant to climate quality measurements include estimating the impurity absorption at 434 nm and assessing the apparent pH biases relative to a reference purified dye in buffered solutions or natural seawater. Laboratories that produce and distribute purified mCP should apply the SIMCA method or other suitable methods to verify that residual impurities do not significantly bias pH measurements. To apply the SIMCA method, users should download the data and model developed in this work and measure a small number of instrument standardization and model validation samples. This method represents a key step in the development of a measurement quality framework necessary to attain the uncertainty goals articulated by the Global Ocean Acidification Observing Network (GOA-ON) for climate quality measurements (i.e., ±0.003 in pH).
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Affiliation(s)
- Michael Fong
- National Institute of Standards and Technology, Chemical Sciences Division, Gaithersburg, MD USA
| | | | - Regina Easley
- National Institute of Standards and Technology, Chemical Sciences Division, Gaithersburg, MD USA
| | - Jason Waters
- National Institute of Standards and Technology, Chemical Sciences Division, Gaithersburg, MD USA
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8
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Palermo C, Mentana A, Tomaiuolo M, Campaniello M, Iammarino M, Centonze D, Zianni R. Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry and Chemometric Approach for the Study of Volatile Profile in X-ray Irradiated Surface-Ripened Cheeses. Foods 2024; 13:416. [PMID: 38338551 PMCID: PMC10855764 DOI: 10.3390/foods13030416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
X-ray irradiation is an emerging non-thermal technology that is used as a preservation and sanitization technique to inactivate pathogens and spoilage organisms, increasing the shelf life of products. In this work, two different types of surface-ripened cheeses, Brie and Camembert, produced with cow milk, were treated with X-rays at three dose levels, 2.0, 4.0 and 6.0 kGy, to evaluate the irradiation effects on the volatile profile using a volatolomic approach. The headspace solid-phase microextraction (HS-SPME) technique combined with gas chromatography-mass spectrometry (GC-MS) was used to extract and analyze the volatile fraction from these dairy matrices. The HS-SPME method was optimized by a central composite design in combination with a desirability optimization methodology. The Carboxen/PDMS fiber, 50 °C for extraction temperature and 60 min for time extraction were found to be the best parameter settings and were applied for this investigation. The obtained fingerprints demonstrated that the irradiation-induced changes are dose dependent. The X-ray irradiation produced many new volatiles not found in the non-irradiated samples, but it also varied the amount of some volatiles already present in the control. Specifically, aldehydes and hydrocarbons increased with the irradiation dose, whereas alcohols, carboxylic acids, esters, methyl esters, ketones, lactones and sulfur-containing compounds showed a non-linear dependence on the dose levels; indeed, they increased up to 4.0 kGy, and then decreased slightly at 6.0 kGy. This trend, more evident in the Camembert profile, is probably due to the fact that these compounds are involved in different oxidation mechanisms of lipids and proteins, which were induced by the radiation treatment. In these oxidative chemical changes, the production and degradation processes of the volatiles are competitive, but at higher doses, the decomposition reactions exceed those of formation. A principal component analysis and partial least square discriminant analysis were used to discriminate between the treated and untreated samples. Moreover, this study allowed for the identification of potential markers of X-ray treatment for the two cheeses, confirming this approach as a useful tool for the control of irradiated surface-ripened cheeses.
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Affiliation(s)
- Carmen Palermo
- Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, Via Napoli 25, 71122 Foggia, Italy;
| | - Annalisa Mentana
- Laboratorio Nazionale di Riferimento per il Trattamento degli Alimenti e dei Loro Ingredienti con Radiazioni Ionizzanti, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy; (M.T.); (M.C.); (M.I.); (R.Z.)
| | - Michele Tomaiuolo
- Laboratorio Nazionale di Riferimento per il Trattamento degli Alimenti e dei Loro Ingredienti con Radiazioni Ionizzanti, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy; (M.T.); (M.C.); (M.I.); (R.Z.)
| | - Maria Campaniello
- Laboratorio Nazionale di Riferimento per il Trattamento degli Alimenti e dei Loro Ingredienti con Radiazioni Ionizzanti, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy; (M.T.); (M.C.); (M.I.); (R.Z.)
| | - Marco Iammarino
- Laboratorio Nazionale di Riferimento per il Trattamento degli Alimenti e dei Loro Ingredienti con Radiazioni Ionizzanti, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy; (M.T.); (M.C.); (M.I.); (R.Z.)
| | - Diego Centonze
- Dipartimento di Scienze Mediche e Chirurgiche, Università di Foggia, Via Napoli 25, 71122 Foggia, Italy;
| | - Rosalia Zianni
- Laboratorio Nazionale di Riferimento per il Trattamento degli Alimenti e dei Loro Ingredienti con Radiazioni Ionizzanti, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Via Manfredonia 20, 71121 Foggia, Italy; (M.T.); (M.C.); (M.I.); (R.Z.)
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9
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Ciaccheri L, De Girolamo A, Cervellieri S, Lippolis V, Mencaglia AA, Pascale M, Mignani AG. Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran. Molecules 2023; 28:7808. [PMID: 38067538 PMCID: PMC10708224 DOI: 10.3390/molecules28237808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40-1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.
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Affiliation(s)
- Leonardo Ciaccheri
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
| | - Annalisa De Girolamo
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Salvatore Cervellieri
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Vincenzo Lippolis
- CNR—Istituto di Scienze delle Produzioni Alimentari (ISPA), Via G. Amendola, 122/O, 70126 Bari, Italy; (S.C.); (V.L.)
| | - Andrea Azelio Mencaglia
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
| | - Michelangelo Pascale
- CNR—Istituto di Scienze dell’Alimentazione (ISA), Via Roma, 64, 83100 Avellino, Italy;
| | - Anna Grazia Mignani
- CNR—Istituto di Fisica Applicata “Nello Carrara” (IFAC), Via Madonna del Piano, 10, Sesto Fiorentino, 50019 Florence, Italy; (A.A.M.); (A.G.M.)
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10
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Liu S, Liu H, Li J, Wang Y. Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata. Food Sci Nutr 2023; 11:6249-6259. [PMID: 37823161 PMCID: PMC10563693 DOI: 10.1002/fsn3.3565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 10/13/2023] Open
Abstract
To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.
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Affiliation(s)
- Shuai Liu
- College of Agronomy and BiotechnologyYunnan Agricultural UniversityKunmingChina
- Medicinal Plants Research InstituteYunnan Academy of Agricultural SciencesKunmingChina
| | - Honggao Liu
- Yunnan Key Laboratory of Gastrodia and Fungi Symbiotic BiologyZhaotong UniversityZhaotongChina
| | - Jieqing Li
- College of Agronomy and BiotechnologyYunnan Agricultural UniversityKunmingChina
| | - Yuanzhong Wang
- Medicinal Plants Research InstituteYunnan Academy of Agricultural SciencesKunmingChina
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11
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Vitale R, Cocchi M, Biancolillo A, Ruckebusch C, Marini F. Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial. Anal Chim Acta 2023; 1270:341304. [PMID: 37311606 DOI: 10.1016/j.aca.2023.341304] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 06/15/2023]
Abstract
This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: "why employing SIMCA?", "when employing SIMCA?" and "how employing/not employing SIMCA?". With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available.
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Affiliation(s)
- Raffaele Vitale
- U. Lille, CNRS, LASIRE, Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Cité Scientifique, F-59000 Lille, France.
| | - Marina Cocchi
- Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Alessandra Biancolillo
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Via Vetoio, 67100, L'Aquila, Italy
| | - Cyril Ruckebusch
- U. Lille, CNRS, LASIRE, Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l'Environnement, Cité Scientifique, F-59000 Lille, France
| | - Federico Marini
- Dipartimento di Chimica, Università degli Studi di Roma "La Sapienza", Piazzale Aldo Moro 5, 00185, Roma, Italy
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12
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Yang Z, Nagashima H, Arakawa H. Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging. MARINE POLLUTION BULLETIN 2023; 193:115200. [PMID: 37364340 DOI: 10.1016/j.marpolbul.2023.115200] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/14/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023]
Abstract
In this study, an automated identification workflow for Raman micro-imaging (RMI) was developed, and the performance was evaluated by artificial samples of microplastic (MP) microsphere with different sizes and types. Theoretical detection rate and estimated particle size were derived and compared with experimental data. Results show that the proposed workflow can identify plastic types and estimate the size of the MP microspheres under different conditions for most cases. However, size of laser spot and discrepancy between sample surface and focal plane can influence RMI results in two ways. Firstly, small particles are more likely to be detected. Secondly, estimated sizes of particles are more likely to be overestimated. The derived uncertainties can serve as a reference for future experimental design and further investigation of more complex situations. The workflow is accessible online, and interested researchers can adjust the parameter values as necessary to suit their specific circumstances.
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Affiliation(s)
- Zijiang Yang
- Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Konan 4-5-7, Minato-Ku, Tokyo 108-8477, Japan.
| | - Hiroya Nagashima
- Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Konan 4-5-7, Minato-Ku, Tokyo 108-8477, Japan.
| | - Hisayuki Arakawa
- Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Konan 4-5-7, Minato-Ku, Tokyo 108-8477, Japan.
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13
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Avanzi Barbosa Mascareli V, Galvan D, Craveiro de Andrade J, Lelis C, Adam Conte-Junior C, Michelino Gaeta Lopes G, César de Macedo Júnior F, Aparecida Spinosa W. Spectralprint techniques coupled with chemometric tools for vinegar classifications. Food Chem 2023; 410:135373. [PMID: 36608560 DOI: 10.1016/j.foodchem.2022.135373] [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: 09/12/2022] [Revised: 11/29/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022]
Abstract
Vinegar is a versatile product used for food preservation, cooking, healthcare, and cleaning. In this study, 80 vinegar of different raw materials, aging time, and for the first time by the agronomic method of raw material cultivation were classified by spectralprint techniques with chemometrics. Datasets were obtained by proton nuclear magnetic resonance (1H NMR), Fourier transforms mid-infrared (FT-IR), near-infrared (NIR), and ultraviolet-visible (UV-vis); then evaluated by common dimension (ComDim) and partial least squares-discriminant analysis (PLS-DA). NMR with PLS-DA had the best prediction performance compared to other techniques, with accuracy values between 92.3 and 100 %, followed by FT-IR and UV-vis of 80.8 and 96.0 % and NIR between 69.2 and 84.0 %. The results indicated that the classification of vinegar according to the agronomic cultivation method is more complex than aging time or raw material. However, any of these spectralprint techniques have demonstrated that they can be used in the classification of vinegar.
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Affiliation(s)
| | - Diego Galvan
- Institute of Chemistry, Federal University of Mato Grosso do Sul (UFMS), Campo Grande, MS 79.070-900, Brazil.
| | - Jelmir Craveiro de Andrade
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil; Graduate Program in Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil
| | - Carini Lelis
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil; Graduate Program in Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil
| | - Carlos Adam Conte-Junior
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil; Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-598, Brazil; Graduate Program in Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21.941-909, Brazil
| | | | | | - Wilma Aparecida Spinosa
- Department of Food Science and Technology, State University of Londrina (UEL), Londrina, PR 86.057-970, Brazil
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14
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Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure. Food Chem 2023; 404:134543. [DOI: 10.1016/j.foodchem.2022.134543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/25/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
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15
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da Silva Medeiros ML, Brasil YL, Cruz-Tirado LJP, Lima AF, Godoy HT, Barbin DF. Portable NIR spectrometer and chemometric tools for predicting quality attributes and adulteration levels in butteroil. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Tessaro L, Mutz YDS, Andrade JCD, Aquino A, Belem NKR, Silva FGS, Conte-Junior CA. ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121883. [PMID: 36126622 PMCID: PMC9473138 DOI: 10.1016/j.saa.2022.121883] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/29/2022] [Accepted: 09/10/2022] [Indexed: 06/15/2023]
Abstract
Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.
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Affiliation(s)
- Leticia Tessaro
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21941-909, Brazil; COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro, RJ 21941-598, Brazil; Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil; Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil; Post-Graduation Program of Chemistry (PGQu), Institute of chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil.
| | - Yhan da Silva Mutz
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21941-909, Brazil; COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro, RJ 21941-598, Brazil; Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil; Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Jelmir Craveiro de Andrade
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21941-909, Brazil; COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro, RJ 21941-598, Brazil; Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil; Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil; Post-Graduation Program of Chemistry (PGQu), Institute of chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21941-909, Brazil; COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro, RJ 21941-598, Brazil; Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil; Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Natasha Kilsy Rocha Belem
- Laboratory of Immunogenetics and Molecular Biology of the General Hospital and Maternity Hospital of Cuiabá, Brazil
| | | | - Carlos Adam Conte-Junior
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ 21941-909, Brazil; COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Cidade Universitária, Rio de Janeiro, RJ 21941-598, Brazil; Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil; Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil; Post-Graduation Program of Chemistry (PGQu), Institute of chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil.
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17
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Forooghi E, Vali Zade S, Sahebi H, Abdollahi H, Sadeghi N, Jannat B. Authentication and Discrimination of Tissue Origin of Bovine Gelatin using Combined Supervised Pattern Recognition Strategies. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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18
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Tessaro L, da Silva Mutz Y, Lelis CA, Andrade JCD, Aquino A, Panzenhagen P, Ochioni AC, Sousa Vieira IR, Conte-Junior CA. Combination of RT-LAMP and fluorescence spectroscopy using chemometric techniques for an ultra-sensitive and rapid alternative for the detection of SARS-CoV-2. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:4922-4930. [PMID: 36426753 DOI: 10.1039/d2ay01502a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The increased spread of COVID-19 caused by SARS-CoV-2 has made it necessary to develop more efficient, fast, accurate, specific, sensitive and easy-to-use detection platforms to overcome the disadvantages of gold standard methods (RT-qPCR). Here an approach was developed for the detection of the SARS-CoV-2 virus using the loop-mediated isothermal amplification (LAMP) technique for SARS-CoV-2 RNA target amplification in samples of nasopharyngeal swabs. The discrimination between positive and negative SARS-CoV-2 samples was achieved by using fluorescence spectra generated by the excitation of the LAMP's DNA intercalator dye at λ497 nm in a fluorescence spectrophotometer and chemometric tools. Exploratory analysis of the 83 sample spectra using principal component analysis (PCA) indicated a trend in differentiation between positive and negative samples resulting from the peak emission of the fluorescent dye. The classification was performed by partial least squares discriminant analysis (PLS-DA) achieving a sensitivity, a specificity and an accuracy of 100%, 95% and 89%, respectively for the discrimination between negative and positive samples from 1.58 to 0.25 ng L-1 after LAMP amplification. Therefore, this study indicates that the use of the LAMP technique in fluorescence spectroscopy may offer a fast (<1 hour), sensitive and low-cost method.
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Affiliation(s)
- Leticia Tessaro
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
- Post-Graduation Program of Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil
| | - Yhan da Silva Mutz
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Carini Aparecida Lelis
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Jelmir Craveiro de Andrade
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
- Post-Graduation Program of Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
- Post-Graduation Program of Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil
| | - Pedro Panzenhagen
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Alan Clavelland Ochioni
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Italo Rennan Sousa Vieira
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Carlos Adam Conte-Junior
- Analytical and Molecular Laboratorial Center (CLAn), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, 21941-909, Brazil
- COVID-19 Research Group, Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), University City, Rio de Janeiro RJ, 21941-598, Brazil
- Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro RJ, Brazil
- Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
- Post-Graduation Program of Chemistry (PGQu), Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), University City, Rio de Janeiro, RJ, Brazil
- Department of Biochemistry, Institute of Chemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, 21941-901, Brazil
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19
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Ordoudi SA, Özdikicierler O, Tsimidou MZ. Detection of ternary mixtures of virgin olive oil with canola, hazelnut or safflower oils via non-targeted ATR-FTIR fingerprinting and chemometrics. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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20
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Hoffman LC, Ingle P, Khole AH, Zhang S, Yang Z, Beya M, Bureš D, Cozzolino D. Characterisation and Identification of Individual Intact Goat Muscle Samples ( Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics. Foods 2022; 11:foods11182894. [PMID: 36141022 PMCID: PMC9498649 DOI: 10.3390/foods11182894] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 09/07/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.
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Affiliation(s)
- Louwrens C. Hoffman
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Prasheek Ingle
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ankita Hemant Khole
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Shuxin Zhang
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Zhiyin Yang
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Michel Beya
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Daniel Bureš
- Institute of Animal Science, Přátelství 815, 104 00 Prague, Czech Republic
- Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
| | - Daniel Cozzolino
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Brisbane, QLD 4072, Australia
- Correspondence:
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21
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Bunaciu AA, Fleschin S, Aboul-Enein HY. Determination of some edible oils adulteration with paraffin oil using infrared spectroscopy. PHARMACIA 2022. [DOI: 10.3897/pharmacia.69.e76175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The spectroscopy of molecular vibrations using mid-infrared or near-infrared techniques was used more and more to characterize different compounds, including edible oil, in order to monitor any changes and to detect fraudulent modifications. This article presents a new method for quantitative adulteration of extra virgin olive oil (EVOO) or corn germ oil (CGO) with a mineral oil, such as paraffin oil (PO). A Fourier transform infrared (FT-IR) spectrometric method, using ATR spectra, was developed for the rapid, direct measurement of edible oils adulteration. The results indicate the efficiency of the proposed method for the detection of paraffin oil in adulteration of EVOO and CGO with RSD (< 3.0%).
Graphical abstract:
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22
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Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares. Food Chem 2022; 382:132271. [PMID: 35189444 DOI: 10.1016/j.foodchem.2022.132271] [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: 08/03/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 11/23/2022]
Abstract
New approach to deal with food authentication by modelling methods based on data recorded from different sources is proposed and called OC-PLS, combines an orthogonalization step between the different data sets to eliminate redundant information followed by definition of an acceptance area for a target class by OC-PLS. The proposed method was evaluated in two case studies. The first study used a controlled scenario with simulated data. In the second case study, the approach was applied using UV-VIS and IR data, in order to differentiate Slovak Tokaj Selection wines of high quality from other lower market value wines from the Slovak Tokaj wine region. In both cases, better results were reached than when individual blocks of data were achieved. The proposed method proved to be effective in properly exploring common and distinct information in each data block. The best compromise between sensitivity and selectivity in the prediction step was achieved.
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23
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Brito ALB, Brüggen C, Ildiz GO, Fausto R. Investigation of menopause-induced changes on hair by Raman spectroscopy and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 275:121175. [PMID: 35344858 DOI: 10.1016/j.saa.2022.121175] [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: 01/11/2022] [Revised: 03/10/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
The ending of estrogen production in the ovaries after menopause results in a series of important physiologic changes, including hair texture and growth. In this study we demonstrate that Raman spectroscopy can be used successfully as a tool to probe menopause-induced changes on hair, in particular when coupled with suitable chemometrics approaches. The detailed analysis of the average Raman spectra (in particular of the Amide I and νS-S stretching spectral regions) of the hair samples of women pre- and post-menopause allowed to estimate that absence of estrogen in post-menopause women leads to an average reduction of ∼12% in the thickness of the hair cuticle, compared to that of pre-menopause women, and revealed the strong prevalence of disulphide bonds in the most stable gauche-gauche-gauche conformation in the hair cuticle. From the analysis of the νS-S stretching spectral region it could also be concluded that the amount of α-helix keratin is slightly higher for post-menopause than for pre-menopause women. A series of statistical models were developed in order to classify the hair samples. Outperforming the traditional PCA-LDA (principal component analysis - linear discriminant analysis) approach, in the present study a GA-LDA (genetic algorithm - linear discriminant analysis) strategy was used for variable reduction/selection and samples' classification. This strategy allowed to develop of a statistical model (L16), which has exceptional prediction capability (total accuracy of 96.6%, with excellent sensitivity and selectivity) and can be used as an efficient instrument for the hair samples' classification. In addition, a new chemometrics approach is here presented, which allows to overcome the intrinsic limitations of the GA algorithm and that can be used to develop statistical models that use GA as the variable reduction/selection method, but superseding its stochastic nature. Three suitable models for classification of the hair samples according to the menopause status of the women were developed using this novel approach (LV17, BLV20 and PLS7 models), which are based on the Fisher's and Bayers' LDA approaches and the PLS-DA method. The followed new chemometrics approach uses the results of a large set of GA-LDA runs over the full data matrix for the selection of the reduced data matrices. The criterion for the selection of the variables is their statistical significance in terms of number of occurrences as solutions of the whole set of GA-LDA runs.
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Affiliation(s)
- Anna Luiza B Brito
- CQC-IMS, Department of Chemistry, University of Coimbra, P-3004-535 Coimbra, Portugal.
| | - Carlotta Brüggen
- Biochemistry Center, Heidelberg University, P-69120 Heidelberg, Germany
| | - Gulce Ogruc Ildiz
- CQC-IMS, Department of Chemistry, University of Coimbra, P-3004-535 Coimbra, Portugal; Faculty of Science and Letters, Department of Physics, Istanbul Kultur University, Atakoy Campus, Bakirkoy 34156, Istanbul, Turkey
| | - Rui Fausto
- CQC-IMS, Department of Chemistry, University of Coimbra, P-3004-535 Coimbra, Portugal
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24
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In-depth chemometric strategy to detect up to four adulterants in cashew nuts by IR spectroscopic techniques. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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25
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Hoffman LC, Ni D, Dayananda B, Abdul Ghafar N, Cozzolino D. Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy. SENSORS 2022; 22:s22134988. [PMID: 35808484 PMCID: PMC9269732 DOI: 10.3390/s22134988] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/29/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022]
Abstract
Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.
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Affiliation(s)
- Louwrens Christiaan Hoffman
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
| | - Dongdong Ni
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
| | - Buddhi Dayananda
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (B.D.); (N.A.G.)
| | - N Abdul Ghafar
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (B.D.); (N.A.G.)
| | - Daniel Cozzolino
- Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia; (L.C.H.); (D.N.)
- Correspondence:
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26
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Rodionova OY, Titova AV, Godin FY, Balyklova KS, Pomerantsev AL, Rutledge DN. Monitoring of the natural aging of Diclofenac tablets, NIR and MIR-ATR spectroscopy coupled with chemometrics data analysis. J Pharm Biomed Anal 2022; 219:114917. [PMID: 35803016 DOI: 10.1016/j.jpba.2022.114917] [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: 03/14/2022] [Revised: 06/11/2022] [Accepted: 06/25/2022] [Indexed: 10/17/2022]
Abstract
This study presents the analysis of the natural long-term aging of both the intact tablets and the active pharmaceutical ingredient. No forced aging conditions were applied to the samples. It is shown that the near infrared spectroscopy of the intact tablets packed in plastic blisters, supported by chemometrics, is a reliable method for detection of even slight deviations of the medicine from its regular state. Independent components analysis helps to extract source signals from spectra of the composite object "a coated tablet sealed in polyvinylchloride blister". Further analysis of the near infrared and attenuated total reflectance infrared spectra of the pure substance confirmed that the aging process detected by the analysis of the intact tablets is directly related to the degradation of the active pharmaceutical ingredient.
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Affiliation(s)
- O Ye Rodionova
- Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Roszdravnadzor, Slavyanskay sq., 4-1, 109074 Moscow, Russia; Semenov Federal Research Center for Chemical Physics RAS, Kosygin 4, 119991 Moscow, Russia
| | - A V Titova
- Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Roszdravnadzor, Slavyanskay sq., 4-1, 109074 Moscow, Russia
| | - F Y Godin
- Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Roszdravnadzor, Slavyanskay sq., 4-1, 109074 Moscow, Russia
| | - K S Balyklova
- Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Roszdravnadzor, Slavyanskay sq., 4-1, 109074 Moscow, Russia; I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya Str., 2-4, 119991 Moscow, Russia
| | - A L Pomerantsev
- Semenov Federal Research Center for Chemical Physics RAS, Kosygin 4, 119991 Moscow, Russia
| | - D N Rutledge
- National Wine and Grape Industry Centre, Charles Sturt University, 2650 Wagga Wagga, Australia; ChemHouse Research Group, Montpellier, France.
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27
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Inside the Egg—Demonstrating Provenance Without the Cracking Using Near Infrared Spectroscopy. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02348-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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28
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Noviana E, Indrayanto G, Rohman A. Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines. Front Pharmacol 2022; 13:853023. [PMID: 35721184 PMCID: PMC9201489 DOI: 10.3389/fphar.2022.853023] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/26/2022] [Indexed: 01/01/2023] Open
Abstract
Herbal drugs or herbal medicines (HMs) have a long-standing history as natural remedies for preventing and curing diseases. HMs have garnered greater interest during the past decades due to their broad, synergistic actions on the physiological systems and relatively lower incidence of adverse events, compared to synthetic drugs. However, assuring reproducible quality, efficacy, and safety from herbal drugs remains a challenging task. HMs typically consist of many constituents whose presence and quantity may vary among different sources of materials. Fingerprint analysis has emerged as a very useful technique to assess the quality of herbal drug materials and formulations for establishing standardized herbal products. Rather than using a single or two marker(s), fingerprinting techniques take great consideration of the complexity of herbal drugs by evaluating the whole chemical profile and extracting a common pattern to be set as a criterion for assessing the individual material or formulation. In this review, we described and assessed various fingerprinting techniques reported to date, which are applicable to the standardization and quality control of HMs. We also evaluated the application of multivariate data analysis or chemometrics in assisting the analysis of the complex datasets from the determination of HMs. To ensure that these methods yield reliable results, we reviewed the validation status of the methods and provided perspectives on those. Finally, we concluded by highlighting major accomplishments and presenting a gap analysis between the existing techniques and what is needed to continue moving forward.
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Affiliation(s)
- Eka Noviana
- Departement of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | | | - Abdul Rohman
- Departement of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia.,Center of Excellence, Institute for Halal Industry and Systems, Universitas Gadjah Mada, Yogyakarta, Indonesia
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29
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Yan Z, Liu H, Li T, Li J, Wang Y. Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113490] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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30
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Wulandari L, Idroes R, Noviandy TR, Indrayanto G. Application of chemometrics using direct spectroscopic methods as a QC tool in pharmaceutical industry and their validation. PROFILES OF DRUG SUBSTANCES, EXCIPIENTS, AND RELATED METHODOLOGY 2022; 47:327-379. [PMID: 35396015 DOI: 10.1016/bs.podrm.2021.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This present review described the application of chemometrics using direct spectroscopic methods at the quality control (QC) laboratory of Pharmaceutical Industries. Using chemometrics methods, all QC assessments during the fabrication processes of the drug preparations can be well performed. Chemometrics methods have some advantages compared to the conventional methods, i.e., non-destructive, can be performed directly to intake samples without any extractions, unnecessary performing stability studies, and cost-effective. To achieve reliable results of analyses, all methods must be validated first prior to routine applications. According to the current Pharmacopeia, the validation parameters are specificity/selectivity, accuracy, repeatability, intermediate precision, range, detection limit, quantification limit and robustness. These validation data must meet the acceptance criteria, that have been described by the analytical target profile (ATP) of the drug preparations.
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Affiliation(s)
| | - Rinaldi Idroes
- Department of Pharmacy, Banda Aceh, Indonesia; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Syiah Kuala University, Banda Aceh, Indonesia
| | - Teuku Rizky Noviandy
- Department of Informatics, Faculty of Mathematics and Natural Sciences, Syiah Kuala University, Banda Aceh, Indonesia
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31
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A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers. Sci Rep 2022; 12:5630. [PMID: 35379877 PMCID: PMC8980006 DOI: 10.1038/s41598-022-09632-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/22/2022] [Indexed: 11/09/2022] Open
Abstract
In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.
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32
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Lelis CA, Galvan D, Tessaro L, de Andrade JC, Mutz YS, Conte-Junior CA. Fluorescence spectroscopy in tandem with chemometric tools applied to milk quality control. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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33
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Souza MA, Santos AS, da Silva SW, Braga JWB, Sousa MH. Raman spectroscopy of fingerprints and chemometric analysis for forensic sex determination in humans. Forensic Chem 2022. [DOI: 10.1016/j.forc.2021.100395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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34
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Weishaupt I, Neubauer P, Schneider J. Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. Food Sci Nutr 2022; 10:800-812. [PMID: 35311170 PMCID: PMC8907734 DOI: 10.1002/fsn3.2709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/22/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022] Open
Abstract
The feasibility of inline classification and characterization of seven fruit juice varieties was investigated by the application of near-infrared spectroscopy (NIRS) combined with chemometrics. The findings are intended to be used to optimize the flash pasteurization of liquid foods. More precise information of the kind of product in real time had to be achieved to enable a more product-specific process. Using the method of partial least squares discriminant analysis, the fruit juice varieties were classified, showing a classification rate of 100% regarding an internal and 69% regarding an external test sets. A characterization by the extract content, pH value, turbidity, and viscosity was made by fitting a partial least squares regression model. The percentage prediction error of the pH value was <3% for internal and external test sets, and for the Brix value prediction errors were about 4% (internal) and 20% (external). The parameters viscosity and turbidity were found to be unsuitable. Despite this, the strategy applied to gain more product-specific information in real time showed to be feasible. By linking the results to a database containing potentially harmful microorganisms for various types of fruit juices, a more product-specific calculation of the necessary heat input can be performed. To demonstrate the practical relevance, a comparison between conventional and product-adapted process control was performed using two fruit varieties as examples in case of Alicyclobacillus acidoterrestris. Thus, with more accurate product information, achieved through the use of NIRS with chemometrics, a more precise calculation of the heat input can be achieved.
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Affiliation(s)
- Imke Weishaupt
- Institute for Life Science Technologies ILT.NRWDepartment of Life Science TechnologiesOWL University of Applied Sciences and ArtsLemgoGermany
| | - Peter Neubauer
- Bioprocess EngineeringDepartment of BiotechnologyTechnische Universität BerlinBerlinGermany
| | - Jan Schneider
- Institute for Life Science Technologies ILT.NRWDepartment of Life Science TechnologiesOWL University of Applied Sciences and ArtsLemgoGermany
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35
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Chen H, He Y. Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2022; 50:91-131. [PMID: 34931589 DOI: 10.1142/s0192415x22500045] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.
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Affiliation(s)
- Haiyang Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
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36
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De Géa Neves M, Poppi RJ, Breitkreitz MC. Authentication of plant-based protein powders and classification of adulterants as whey, soy protein, and wheat using FT-NIR in tandem with OC-PLS and PLS-DA models. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108489] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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37
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Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106839] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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38
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Kalogiouri NP, Samanidou VF. Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of wine phenolic composition as it is affected by environmental factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:59150-59164. [PMID: 32577971 DOI: 10.1007/s11356-020-09681-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
The guarantee of wine authenticity arises great concern because of its nutritional and economic importance. Phenolic fingerprints have been used as a source of chemical information for various authentication issues, including botanical and geographical origin, as well as vintage age. The local environment affects wine production and especially its phenolic metabolites. Integrated analytical methodologies combined with chemometrics can be applied in wine fingerprinting studies for the determination and establishment of phenolic markers that contain comprehensive and standardized information about the wine profile and how it can be affected by various environmental factors. This review summarizes all the recent trends in the generation of chemometric models that have been developed for treating chromatographic data and have been used for the investigation of critical wine authenticity issues, revealing phenolic markers responsible for the botanical, geographical, and vintage age classification of wines. Overall, the current review suggests that chromatographic methodologies are promising and powerful techniques that can be used for the accurate determination of phenolic compounds in difficult matrices like wine, highlighting the advantages of the applications of supervised chemometric tools over unsupervised for the construction of prediction models that have been successfully used for the classification based on their territorial and botanical origin.
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Affiliation(s)
- Natasa P Kalogiouri
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
| | - Victoria F Samanidou
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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39
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Pomerantsev AL, Rodionova OY. New trends in qualitative analysis: Performance, optimization, and validation of multi-class and soft models. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116372] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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Zhong P, Wei X, Xu Y, Zhang L, Koidis A, Liu Y, Lei Y, Wu S, Lei H. Integration of Untargeted and Pseudotargeted Metabolomics for Authentication of Three Shrimp Species Using UHPLC-Q-Orbitrap. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:8861-8873. [PMID: 34319107 DOI: 10.1021/acs.jafc.1c02630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this work, an untargeted and pseudotargeted metabolomics combination approach was used for authentication of three shrimp species (Litopenaeus vanmamei, Penaeus japonicus, and Penaeus monodon). The monophasic extraction-based untargeted metabolomics approach enabled comprehensive-coverage and high-throughput analysis of shrimp tissue and revealed 26 potential markers. The pseudotargeted metabolomics approach confirmed 21 markers (including 9 key markers), which realized at least putative identification. The 21 confirmed markers, as well as 9 key markers, were used to develop PLS-DA models, correctly classifying 60/60 testing samples. Furthermore, DD-SIMCA and PLS-DA models were integrated based on the 9 key markers, with 59/60 and 20/20 samples of the species that were involved and uninvolved in model training correctly classified. The results demonstrated the potential of this untargeted and pseudotargeted metabolomics combination approach for shrimp species authentication.
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Affiliation(s)
- Peng Zhong
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoqun Wei
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Yi Xu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Lulu Zhang
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Anastasios Koidis
- Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DJ, United Kingdom
| | - Yunle Liu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Yi Lei
- Guangdong Institute of Food Inspection, Guangzhou 510435, China
| | - Shaozong Wu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Hongtao Lei
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
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Wang L, Li J, Li T, Liu H, Wang Y. Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus. ACS OMEGA 2021; 6:19665-19674. [PMID: 34368554 PMCID: PMC8340397 DOI: 10.1021/acsomega.1c02317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/09/2021] [Indexed: 05/07/2023]
Abstract
The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0-2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1-2 days, 0-48 h, recommended for consumption), category II (3-4 days, 48-96 h, recommended for consumption), and category III (5-6 days, 96-144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.
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Affiliation(s)
- Li Wang
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
| | - Jieqing Li
- College
of Resources and Environment, Yunnan Agricultural
University, Kunming 650201, China
| | - Tao Li
- College
of Resources and Environment, Yuxi Normal
University, Yuxi 653199, China
| | - Honggao Liu
- College
of Agronomy and Life Sciences, Zhaotong
University, Zhaotong 657000, China
| | - Yuanzhong Wang
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
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42
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Wang L, Wang Q, Wang Y, Wang Y. Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia cocos with Different Data Fusion Approaches. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:5818999. [PMID: 34336360 PMCID: PMC8321747 DOI: 10.1155/2021/5818999] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/11/2021] [Indexed: 06/13/2023]
Abstract
Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method-Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis-were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.
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Affiliation(s)
- Li Wang
- Quality Standards and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China
| | - Qinqin Wang
- The First Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming 650021, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Yunmei Wang
- Quality Standards and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
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Ruiz S, Sarabia LA, Sánchez MS, Ortiz MC. Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to Non-Defective Ones. Front Chem 2021; 9:681958. [PMID: 34327191 PMCID: PMC8313983 DOI: 10.3389/fchem.2021.681958] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/14/2021] [Indexed: 11/30/2022] Open
Abstract
In the context of binary class-modelling techniques, the paper presents the computation in the input space of linear boundaries of a class-model constructed with given values of sensitivity and specificity. This is done by inversion of a decision threshold, set with these values of sensitivity and specificity, in the probabilistic class-models computed by means of PLS-CM (Partial Least Squares for Class-Modelling). The characterization of the boundary hyperplanes, in the latent space (space spanned by the selected latent variables of the fitted PLS model) or in the input space, makes it possible to calculate directions that can be followed to move objects toward the class-model of interest. Different points computed along these directions will show how to modify the input variables (provided they can be manipulated) so that, eventually, a computed ‘object’ would be inside the class-model, in terms of the prediction with the PLS model. When the class of interest is that of “adequate” objects, as for example in some process control or product formulation, the proposed procedure helps in answering the question about how to modify the input variables so that a defective object would be inside the class-model of the adequate (non-defective) ones. This is the situation illustrated with some examples, taken from the literature when modelling the class of adequate objects.
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Affiliation(s)
- Santiago Ruiz
- Department Matemáticas y Computación, Facultad de Ciencias, Universidad de Burgos, Burgos, Spain
| | - Luis Antonio Sarabia
- Department Matemáticas y Computación, Facultad de Ciencias, Universidad de Burgos, Burgos, Spain
| | - María Sagrario Sánchez
- Department Matemáticas y Computación, Facultad de Ciencias, Universidad de Burgos, Burgos, Spain
| | - María Cruz Ortiz
- Department Química, Facultad de Ciencias, Universidad de Burgos, Burgos, Spain
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Zhang Y, Han Y, Hu W, Pan Q, Liu Z, Ling G, Shi Q, Weng R. Diacylglycerols ions as novel marker indicators for the classification of edible oils using ultrahigh resolution mass spectrometry. Food Res Int 2021; 145:110422. [PMID: 34112424 DOI: 10.1016/j.foodres.2021.110422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/28/2021] [Accepted: 05/11/2021] [Indexed: 12/01/2022]
Abstract
Diacylglycerols (DAGs) ions, instead of triacylglycerols (TAGs) ions, were established as marker indicators for an improved classification of edible oils using ultrahigh resolution mass spectrometry (UHRMS). DAGs ions can be used not only to identify triacylglycerols (TAGs) and their embedded fatty acids (FAs), but also to distinguish positional isomers of TAGs. In this work, DAGs ions were determined in edible oils by direct infusion atmospheric pressure chemical ionization-ultrahigh resolution mass spectrometry (APCI-UHRMS), where the ultrahigh resolving power up to 500,000 FWHM (full width at half maximum) can provide accurate molecular compositions and detailed fingerprints MS spectra in a minute. A total of 146 samples belonging to 22 species of plant oils and animal fats, were characterized. Chemometric analyses were performed using principal component analysis, partial least square-discriminant analysis and orthogonal partial least squares-discriminant analysis. DAGs ions were proved to be better than TAGs ions as marker indicators in the chemometric analyses. An overall correct rate of 93.40% was achieved for the classification of tested samples. In addition, blend oils and gutter oils were also characterized by this developed method.
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Affiliation(s)
- Yanfen Zhang
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
| | - Yehua Han
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China.
| | - Wenya Hu
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
| | - Qiong Pan
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
| | - Zhanfang Liu
- Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
| | - Guannan Ling
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
| | - Quan Shi
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
| | - Rui Weng
- Key Laboratory of Agro-food Safety and Quality of Ministry of Agriculture and Rural Affairs, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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dos Santos Pereira EV, de Sousa Fernandes DD, de Araújo MCU, Diniz PHGD, Maciel MIS. In-situ authentication of goat milk in terms of its adulteration with cow milk using a low-cost portable NIR spectrophotometer. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105885] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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46
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Oliveri P, Malegori C, Mustorgi E, Casale M. Qualitative pattern recognition in chemistry: Theoretical background and practical guidelines. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105725] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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47
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Cáceres-Nevado JM, Garrido-Varo A, De Pedro-Sanz E, Tejerina-Barrado D, Pérez-Marín DC. Non-destructive Near Infrared Spectroscopy for the labelling of frozen Iberian pork loins. Meat Sci 2021; 175:108440. [PMID: 33497852 DOI: 10.1016/j.meatsci.2021.108440] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 12/17/2020] [Accepted: 01/10/2021] [Indexed: 11/18/2022]
Abstract
Iberian pigs fed on acorns and pasture were slaughtered from January until March of 2018 and 2019. The meat from those Iberian pigs is a seasonal food that only can be found fresh, at the marketplace, during a limit period of the year. Selling frozen-thawed meat is a legal practice, but consumers must be informed about it on the product label. However, to declare as fresh meat, meat previously frozen, is one of the most frequent meat frauds. The present study compares the performance of two rather different Near Infrared Spectroscopy instruments, based on Fourier Transform and Linear Variable Filter technologies, for the in-situ detection of fresh and frozen-thawed acorns-fed Iberian pig loins using Partial Least Discriminant Analysis (PLS-DA). The performance of the models developed for both instruments offered a very high discriminant ability. Furthermore, the models showed consistent results and interpretation when were evaluated with several scalars and graphical methods.
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Affiliation(s)
- J M Cáceres-Nevado
- Faculty of Agricultural and Forestry Engineering, University of Córdoba, Campus Rabanales, N-IV, km 396, Córdoba 14014, Spain.
| | - A Garrido-Varo
- Faculty of Agricultural and Forestry Engineering, University of Córdoba, Campus Rabanales, N-IV, km 396, Córdoba 14014, Spain
| | - E De Pedro-Sanz
- Faculty of Agricultural and Forestry Engineering, University of Córdoba, Campus Rabanales, N-IV, km 396, Córdoba 14014, Spain
| | - D Tejerina-Barrado
- Meat Quality Area, Centro de Investigaciones Científicas y Tecnológicas of Extremadura (CICYTEX-La Orden), Junta de Extremadura, Guadajira, Badajoz, Spain
| | - D C Pérez-Marín
- Faculty of Agricultural and Forestry Engineering, University of Córdoba, Campus Rabanales, N-IV, km 396, Córdoba 14014, Spain
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48
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Pérez-Marín D, Fearn T, Riccioli C, De Pedro E, Garrido A. Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy. Talanta 2021; 222:121511. [PMID: 33167222 DOI: 10.1016/j.talanta.2020.121511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/22/2020] [Accepted: 08/03/2020] [Indexed: 10/23/2022]
Abstract
Iberian pig ham is one of several high value European food products that are the subject of significant attempts at fraud because of the high price differences between commercial categories. Iberian pig products are classified by the Spanish regulations into different categories, mainly depending on the feeding regime during the fattening phase and the race involved, being of Premium quality those products obtained from the animals fed with acorns and other natural resources. Most of the previous NIRS studies related to the Iberian pig have involved the use of at-line instruments to predict quantitative quality parameters. This paper explores the use of the NIR spectra (369 for training and 199 for validation) to classify samples according to the categories Premium (animals fed with acorn) and Non Premium (animals fed with compound feeds), using a MicroNIR™ Pro1700 microspectrometer to analyse individual carcasses in situ at the slaughterhouse line. Four discriminant methods were explored: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Kernel Bayes and Logistic Regression. These are all discriminant methods that naturally produce classification probabilities to quantify the uncertainty of the results. Rules were tuned and methods compared using both classification error rates and a probability scoring rule. LDA gave the best results, attaining an overall accuracy of 93% and providing well-calibrated classification probabilities.
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Affiliation(s)
- Dolores Pérez-Marín
- Department of Animal Production, E.T.S.I.A.M., Universidad de Córdoba, Campus Rabanales, 14071, Córdoba, Spain.
| | - Tom Fearn
- Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 6BT, London, UK
| | - Cecilia Riccioli
- Department of Animal Production, E.T.S.I.A.M., Universidad de Córdoba, Campus Rabanales, 14071, Córdoba, Spain
| | - Emiliano De Pedro
- Department of Animal Production, E.T.S.I.A.M., Universidad de Córdoba, Campus Rabanales, 14071, Córdoba, Spain
| | - Ana Garrido
- Department of Animal Production, E.T.S.I.A.M., Universidad de Córdoba, Campus Rabanales, 14071, Córdoba, Spain
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49
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Segers K, Slosse A, Viaene J, Bannier MAGE, Van de Kant KDG, Dompeling E, Van Eeckhaut A, Vercammen J, Vander Heyden Y. Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques. Talanta 2021; 225:122080. [PMID: 33592793 DOI: 10.1016/j.talanta.2021.122080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 12/29/2020] [Accepted: 12/31/2020] [Indexed: 01/26/2023]
Abstract
Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H3O+, NO+ and O2+. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.
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Affiliation(s)
- Karen Segers
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium; Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Amorn Slosse
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Johan Viaene
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Michiel A G E Bannier
- Department of Paediatric Respiratory Medicine, School for Public Health and Primary Care, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Kim D G Van de Kant
- Department of Paediatric Respiratory Medicine, School for Public Health and Primary Care, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Edward Dompeling
- Department of Paediatric Respiratory Medicine, School for Public Health and Primary Care, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Ann Van Eeckhaut
- Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Joeri Vercammen
- Interscience Expert Center (IS-X), Avenue Jean-Etienne Lenoir 2, 1348, Louvain-la-Neuve, Belgium; Industrial Catalysis and Adsorption Technology (INCAT), Faculty of Engineering and Architecture, Ghent University, Valentin Vaerwyckweg 1, 9000, Ghent, Belgium.
| | - Yvan Vander Heyden
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090, Brussels, Belgium.
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50
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de Oliveira Moreira AC, Braga JWB. Authenticity Identification of Copaiba Oil Using a Handheld NIR Spectrometer and DD-SIMCA. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01933-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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