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Galletta M, Zoccali M, Malegori C, Oliveri P, Tranchida PQ, Mondello L, Mondello M. Flow-modulation comprehensive two-dimensional enantio-gas chromatography: A valid and flexible alternative to heart-cutting multidimensional enantio-gas chromatography. Talanta 2024; 275:126137. [PMID: 38677163 DOI: 10.1016/j.talanta.2024.126137] [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: 02/27/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
The present research is focused on the proposal of use of flow-modulation comprehensive two-dimensional enantio-gas chromatography (FM eGC × GC) as a valid, flexible, and possibly superior alternative to heart-cutting multidimensional enantio-GC (eMDGC). The latter, a technique of demonstrated utility, is used specifically for the targeted separation of chiral compounds, whereas FM eGC × GC can produce both targeted and high-resolution untargeted information in a single run. It is clearly possible to use eMDGC for untargeted analysis, often with a flame ionization detector (stand-by analysis), to monitor a first-dimension (1D) separation, of much lower peak capacity compared to FM eGC × GC. If eMDGC is used with mass spectrometry (MS), it is normally exploited to monitor the second-dimension (2D) separation. The analytical instrument consisted of automated solid-phase microextraction (SPME), and a low duty-cycle FM eGC × GC system (with time-of-flight MS), equipped with an enantioselective 1D column (2,3-di-O-methyl-6-t-butyl silyl β-cyclodextrin derivative) and a 2D polyethylene glycol one. Ten Marsala wines were subjected to analysis, for the determination of chiral lactones (many at the low ppb level, due to the high concentration capacity of SPME) and for general analyte profiling. In many instances, highly complex chromatograms were attained, with statistical analysis (ANOVA-simultaneous component analysis and partial least squares discriminant analysis) used for sample differentiation.
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Affiliation(s)
- Micaela Galletta
- Messina Institute of Technology c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168, Messina, Italy.
| | - Mariosimone Zoccali
- Department of Mathematical and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Viale Ferdinando Stagno d'Alcontres 31, 98166, Messina, Italy
| | - Cristina Malegori
- Department of Pharmacy, University of Genova, Viale Cembrano 4, I-16148, Genova, Italy
| | - Paolo Oliveri
- Department of Pharmacy, University of Genova, Viale Cembrano 4, I-16148, Genova, Italy
| | - Peter Q Tranchida
- Messina Institute of Technology c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168, Messina, Italy.
| | - Luigi Mondello
- Messina Institute of Technology c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168, Messina, Italy; Chromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168, Messina, Italy
| | - Monica Mondello
- Chromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Former Veterinary School, University of Messina, Viale G. Palatucci snc, 98168, Messina, Italy
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Koljančić N, Onça L, Khvalbota L, Vyviurska O, Gomes AA, Špánik I. Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography. Curr Res Food Sci 2024; 8:100725. [PMID: 38590691 PMCID: PMC11000173 DOI: 10.1016/j.crfs.2024.100725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/10/2024] Open
Abstract
This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.
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Affiliation(s)
- Nemanja Koljančić
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37, Bratislava, Slovakia
| | - Larissa Onça
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37, Bratislava, Slovakia
- Instituto de Química, Universidade Federal Do Rio Grande Do Sul, Avenida Bento Gonçalves, 9500, 91501-970, Porto Alegre, RS, Brazil
| | - Liudmyla Khvalbota
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37, Bratislava, Slovakia
| | - Olga Vyviurska
- 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
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37, Bratislava, Slovakia
- Instituto de Química, Universidade Federal Do Rio Grande Do Sul, Avenida Bento Gonçalves, 9500, 91501-970, Porto Alegre, RS, Brazil
| | - Ivan Špánik
- 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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Chen Y, Xie X, Wen Z, Zuo Y, Bai Z, Wu Q. Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches. Heliyon 2023; 9:e21920. [PMID: 38027626 PMCID: PMC10654251 DOI: 10.1016/j.heliyon.2023.e21920] [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: 05/27/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Matcha has been globally valued by consumers for its distinctive fragrance and flavor since ancient times. Currently, the protected designation of origin (PDO) certified matcha, characterized by unique sensory attributes, has garnered renewed interest from consumers and the industry. Given the challenges associated with assessing sensory perceptions, the origin of PDO-certified matcha samples from Guizhou was determined using NIRS and LC-MS platforms. Notably, the accuracy of our established attribute models, based on informative wavelengths selected by the CARS-PLS method, exceeds 0.9 for five sensory attributes, particularly the particle homogeneity attribute (with a validation correlation coefficient of 0.9668). Moreover, an LC-MS method was utilized to analyze non-target matcha metabolites to identify the primary flavor compounds associated with each flavor attribute and to pinpoint the key constituents responsible for variations in grade and flavor intensity. Additionally, high three-way intercorrelations between descriptive sensory attributes, metabolites, and the selected informative wavelengths were observed through network analysis, with correlation coefficients calculated to quantify these relationships. In this research, the integration of matcha chemical composition and sensory panel data was utilized to develop predictive models for assessing the flavor profile of matcha based on its chemical properties.
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Affiliation(s)
- Yan Chen
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Xiaoyao Xie
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Zhirui Wen
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
| | - Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei, 442000, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd., Huaxi District, Guiyang, Guizhou, 550001, China
| | - Qing Wu
- Guizhou Key Laboratory of Information and Computing Science, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou, 550001, China
- Innovation Laboratory, The Third Experiment Middle School in Guiyang, Guiyang, Guizhou, 550001, China
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Techniques for Food Authentication: Trends and Emerging Approaches. Foods 2023; 12:foods12061134. [PMID: 36981061 PMCID: PMC10048066 DOI: 10.3390/foods12061134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/03/2023] [Indexed: 03/10/2023] Open
Abstract
Food producers and retailers are obliged to provide correct food information to consumers; however, despite national and international legislation, food labels frequently contain false or misleading statements regarding food composition, quality, geographic origin, and/or processing [...]
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Nolvachai Y, Amaral MSS, Marriott PJ. Foods and Contaminants Analysis Using Multidimensional Gas Chromatography: An Update of Recent Studies, Technology, and Applications. Anal Chem 2023; 95:238-263. [PMID: 36625115 DOI: 10.1021/acs.analchem.2c04680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Yada Nolvachai
- Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - Michelle S S Amaral
- Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - Philip J Marriott
- Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
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Vyviurska O, Koljančić N, Gomes AA, Špánik I. Optimization of enantiomer separation in flow-modulated comprehensive two-dimensional gas chromatography by response surface methodology coupled to artificial neural networks: Wine analysis case study. J Chromatogr A 2022; 1675:463189. [PMID: 35667220 DOI: 10.1016/j.chroma.2022.463189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022]
Abstract
In spite of extensive applications of flow modulated comprehensive two-dimensional gas chromatography (FM-GG × GC) in different research areas, its application in the field of chiral separation is very limited. From a practical point of view, the establishment of experimental parameters for enantiomer separations is possibly more demanding in this case. Since the carrier gas flows in both dimensions, it affects not only the separation parameters, but also the fill/flush volumes of the modulator and its working efficiency. In this context, a multivariate design of experiment was applied to find the optimum experimental parameters of a reversed fill/flush (RFF) modulator for enantiomer separation of organic compounds present in botrytized wine samples. The results were described both with response surface methodology and artificial neural networks (ANN). The enantiomeric composition of chiral compounds present in the botrytized wines was used to identify their geographical origin, by principal component analysis (PCA). In addition, the developed one-class partial least squares (OC-PLS) model enabled recognition of the wine samples from the Tokaj wine region with 93% effectiveness in the presence of other samples.
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Affiliation(s)
- Olga Vyviurska
- Faculty of Chemical and Food Technology, Institute of Analytical Chemistry, Slovak University of Technology in Bratislava, Bratislava 81237, Slovak Republic
| | - Nemanja Koljančić
- Faculty of Chemical and Food Technology, Institute of Analytical Chemistry, Slovak University of Technology in Bratislava, Bratislava 81237, Slovak Republic
| | - Adriano A Gomes
- Faculty of Chemical and Food Technology, Institute of Analytical Chemistry, Slovak University of Technology in Bratislava, Bratislava 81237, Slovak Republic; Institute of Chemistry, Federal University of Rio Grande do Sul, Bento Gonçalves Avenue, 9500, Porto Alegre, RS 91501-970, Brazil
| | - Ivan Špánik
- Faculty of Chemical and Food Technology, Institute of Analytical Chemistry, Slovak University of Technology in Bratislava, Bratislava 81237, Slovak Republic.
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