1
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Su R, Li S, Su Y, Wang Z, Gao M. Ultrasensitive detection of contaminants in milk using a novel NMS-Ag modified water-resistant paper substrate. Food Chem 2024; 461:140843. [PMID: 39178549 DOI: 10.1016/j.foodchem.2024.140843] [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: 04/19/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024]
Abstract
Rapid and precise detection of harmful substances in food products is essential for ensuring public health and safety. This study introduces a novel surface-enhanced Raman spectroscopy (SERS) substrate, composed of a molybdenum disulfide‑silver nanocomposite, applied to flexible, water-resistant filter paper for detecting melamine and bisphenol A (BPA) in milk. Optimized molybdenum disulfide (NMS) nanoflowers (NFs) were synthesized through hydrothermal methods and high-temperature annealing, then modified with silver (Ag) nanoparticles to form the NMS-Ag nanocomposite (NMSA6). This substrate greatly enhances the Raman signal, achieving an enhancement factor of approximately 1.49 × 107 and a detection limit as low as 10-11 M for simultaneous multi-component analysis. Finite-difference time-domain (FDTD) simulations confirm the enhancement mechanism. The NMSA6 substrate demonstrates remarkably low detection limits for BPA and melamine, facilitating the analysis of various hazardous substances. These findings highlight the substrate's potential for highly sensitive, label-free detection, presenting a viable tool for food safety monitoring.
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Affiliation(s)
- Rui Su
- College of Physics, Jilin Normal University, Siping 136000, PR China; National Demonstration Centre for Experimental Physics Education, Jilin Normal University, Siping 136000, PR China; Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education, Jilin Normal University, Changchun 130103, PR China
| | - Siqi Li
- College of Physics, Jilin Normal University, Siping 136000, PR China
| | - Yugang Su
- College of Physics, Jilin Normal University, Siping 136000, PR China.
| | - Zhong Wang
- College of Physics, Jilin Normal University, Siping 136000, PR China; National Demonstration Centre for Experimental Physics Education, Jilin Normal University, Siping 136000, PR China; Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education, Jilin Normal University, Changchun 130103, PR China.
| | - Ming Gao
- College of Physics, Jilin Normal University, Siping 136000, PR China; National Demonstration Centre for Experimental Physics Education, Jilin Normal University, Siping 136000, PR China; Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education, Jilin Normal University, Changchun 130103, PR China.
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2
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Yang X, Sima Y, Luo X, Li Y, He M. Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin. J Pharm Anal 2024; 14:100936. [PMID: 38655399 PMCID: PMC11036100 DOI: 10.1016/j.jpha.2024.01.004] [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: 07/25/2023] [Revised: 12/24/2023] [Accepted: 01/11/2024] [Indexed: 04/26/2024] Open
Abstract
This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.
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Affiliation(s)
- Xinyue Yang
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Yingyu Sima
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, 410082, China
| | - Xuhuai Luo
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Yaping Li
- Department of Quality Control, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Min He
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China
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3
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Milani NBL, van Gilst E, Pirok BWJ, Schoenmakers PJ. Comprehensive two-dimensional gas chromatography- A discussion on recent innovations. J Sep Sci 2023; 46:e2300304. [PMID: 37654057 DOI: 10.1002/jssc.202300304] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/16/2023] [Accepted: 08/19/2023] [Indexed: 09/02/2023]
Abstract
Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.
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Affiliation(s)
- Nino B L Milani
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Eric van Gilst
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
| | - Peter J Schoenmakers
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Amsterdam, the Netherlands
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4
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Caratti A, Squara S, Bicchi C, Tao Q, Geschwender D, Reichenbach SE, Ferrero F, Borreani G, Cordero C. Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome. J Chromatogr A 2023; 1699:464010. [PMID: 37116300 DOI: 10.1016/j.chroma.2023.464010] [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: 03/13/2023] [Revised: 04/18/2023] [Accepted: 04/20/2023] [Indexed: 04/30/2023]
Abstract
Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.
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Affiliation(s)
- Andrea Caratti
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | - Simone Squara
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | | | | | - Stephen E Reichenbach
- GC Image LLC, Lincoln, NE, USA; Computer Science and Engineering Department, University of Nebraska - Lincoln, Lincoln, NE, USA
| | - Francesco Ferrero
- Department of Agricultural, Forestry and Food Sciences, Università di Torino, Grugliasco TO, Italy
| | - Giorgio Borreani
- Department of Agricultural, Forestry and Food Sciences, Università di Torino, Grugliasco TO, Italy
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy.
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5
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Squara S, Manig F, Henle T, Hellwig M, Caratti A, Bicchi C, Reichenbach SE, Tao Q, Collino M, Cordero C. Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data. Anal Bioanal Chem 2023; 415:2493-2509. [PMID: 36631574 PMCID: PMC10149478 DOI: 10.1007/s00216-023-04516-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.
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Affiliation(s)
- Simone Squara
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Friederike Manig
- Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Thomas Henle
- Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Michael Hellwig
- Special Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Andrea Caratti
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Carlo Bicchi
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Stephen E Reichenbach
- Computer Science and Engineering Department, University of Nebraska, Lincoln, NE, USA.,GC Image LLC, Lincoln, NE, USA
| | | | - Massimo Collino
- Dipartimento Di Neuroscienze "Rita Levi Montalcini", University of Turin, Turin, Italy.
| | - Chiara Cordero
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy.
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6
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Manousi N, Kalogiouri N, Ferracane A, Zachariadis GA, Samanidou VF, Tranchida PQ, Mondello L, Rosenberg E. Solid-phase microextraction Arrow combined with comprehensive two-dimensional gas chromatography-mass spectrometry for the elucidation of the volatile composition of honey samples. Anal Bioanal Chem 2023; 415:2547-2560. [PMID: 36629895 DOI: 10.1007/s00216-023-04513-0] [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: 11/13/2022] [Revised: 12/23/2022] [Accepted: 01/02/2023] [Indexed: 01/12/2023]
Abstract
In this work, a solid-phase microextraction (SPME) Arrow method combined with comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) was developed for the elucidation of the volatile composition of honey samples. The sample preparation protocol was optimized to ensure high extraction efficiency of the volatile organic compounds (VOCs) which are directly associated with the organoleptic properties of honey and its acceptance by the consumers. Following its optimization, SPME Arrow was compared to conventional SPME in terms of sensitivity, precision, and number of extracted VOCs. The utilization of SPME Arrow fibers enabled the determination of 203, 147, and 149 compounds in honeydew honey, flower honey, and pine honey, respectively, while a significantly lower number of compounds (124, 94, and 111 for honeydew honey, flower honey, and pine honey, respectively) was determined using conventional SPME. At the same time, the utilization of SPME Arrow resulted in enhanced sensitivity and precision. All things considered, SPME Arrow and GC × GC-MS can be considered as highly suitable for the elucidation of the volatile composition of complex food samples resulting in high sensitivity and separation efficiency.
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Affiliation(s)
- Natalia Manousi
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.,Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, 1060, Vienna, Austria
| | - Natasa Kalogiouri
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.,Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, 1060, Vienna, Austria
| | - Antonio Ferracane
- Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, 1060, Vienna, Austria. .,Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy.
| | - George A Zachariadis
- 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
| | - Peter Q Tranchida
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy.,Chromaleont S.R.L., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy.,Department of Sciences and Technologies for Human and Environment, University Campus Bio-Medico of Rome, Rome, Italy
| | - Erwin Rosenberg
- Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, 1060, Vienna, Austria
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7
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Elkhateeb Y, Fadel M. Studies on red-pigment production by Talaromyces atroroseus TRP-NRC mutant II from wheat bran via solid-state fermentation. EGYPTIAN PHARMACEUTICAL JOURNAL 2023. [DOI: 10.4103/epj.epj_60_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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8
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Comprehensive Controller for Super Sonic Molecular Beam Gas Chromatograph Mass Spectrometer. SEPARATIONS 2022. [DOI: 10.3390/separations9120417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
This paper presents a new, comprehensive digital circuit used for the control of a novel gas chromatograph mass spectrometer (GC-MS) interface that is based on supersonic molecular beam (SMB). The circuit includes a Texas Instruments 150 MHz digital signal controller (DSC), high voltage amplifiers for 8 independent channels and 4 independent channels of high resolution pulse width modulation (PWM). The circuit, along with a sophisticated embedded program and a custom made personal computer (PC) application, control all aspects of the interface: smart filament emission-current stabilization, static and scanning mass-dependent ion-source voltages, transfer-line heater proportional integral differential (PID) controls with thermocouple feedbacks, on/off valves, relays and several peripheral device controls that enable the full operation of a turbo-molecular vacuum pump, and of gas flow and pressure controllers. All aspects of this comprehensive controller were successfully tested. The signal for the 450 Th ion (C32H66) for example increased by 123% which is a significant increase. It is obvious that correctly tuned dynamic voltages can guarantee the optimal signal for each mass.
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9
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Creydt M, Fischer M. Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing. Electrophoresis 2022; 43:2334-2350. [PMID: 36104152 DOI: 10.1002/elps.202200126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/10/2022] [Accepted: 09/05/2022] [Indexed: 12/14/2022]
Abstract
The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.
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Affiliation(s)
- Marina Creydt
- Hamburg School of Food Science - Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science - Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
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10
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Pua A, Goh RMV, Huang Y, Tang VCY, Ee KH, Cornuz M, Liu SQ, Lassabliere B, Yu B. Recent advances in analytical strategies for coffee volatile studies: Opportunities and challenges. Food Chem 2022; 388:132971. [PMID: 35462220 DOI: 10.1016/j.foodchem.2022.132971] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/08/2022] [Accepted: 04/10/2022] [Indexed: 11/29/2022]
Abstract
Coffee has attracted significant research interest owing to its complex volatile composition and aroma, which imparts a pleasant sensorial experience that remains challenging to analyse and interpret. This review summarises analytical challenges associated with coffee's volatile and matrix complexity, and recent developments in instrumental techniques to resolve them. The benefits of state-of-the-art analytical techniques applied to coffee volatile analysis from experimental design to sample preparation, separation, detection, and data analysis are evaluated. Complementary method selection coupled with progressive experimental design and data analysis are vital to unravel the increasing comprehensiveness of coffee volatile datasets. Considering this, analytical workflows for conventional, targeted, and untargeted coffee volatile analyses are thus proposed considering the trends towards sorptive extraction, multidimensional gas chromatography, and high-resolution mass spectrometry. In conclusion, no single analytical method addresses coffee's complexity in its entirely, and volatile analysis must be tailored to the key objectives and concerns of the analyst.
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Affiliation(s)
- Aileen Pua
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore; Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Sigapore
| | - Rui Min Vivian Goh
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore
| | - Yunle Huang
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore; Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Sigapore
| | - Vivien Chia Yen Tang
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore
| | - Kim-Huey Ee
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore
| | - Maurin Cornuz
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore
| | - Shao Quan Liu
- Department of Food Science and Technology, National University of Singapore, S14 Level 5, Science Drive 2, Singapore 117542, Sigapore.
| | - Benjamin Lassabliere
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore
| | - Bin Yu
- Mane SEA Pte Ltd, 3 Biopolis Drive, #07-17/18/19 Synapse, Singapore 138623, Sigapore.
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11
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The Chemistry of Green and Roasted Coffee by Selectable 1D/2D Gas Chromatography Mass Spectrometry with Spectral Deconvolution. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27165328. [PMID: 36014566 PMCID: PMC9414832 DOI: 10.3390/molecules27165328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022]
Abstract
Gas chromatography/mass spectrometry (GC/MS) is a long-standing technique for the analysis of volatile organic compounds (VOCs). When coupled with the Ion Analytics software, GC/MS provides unmatched selectivity in the analysis of complex mixtures and it reduces the reliance on high-resolution chromatography to obtain clean mass spectra. Here, we present an application of spectral deconvolution, with mass spectral subtraction, to identify a wide array of VOCs in green and roasted coffees. Automated sequential, two-dimensional GC-GC/MS of a roasted coffee sample produced the retention index and spectrum of 750 compounds. These initial analytes served as targets for subsequent coffee analysis by GC/MS. The workflow resulted in the quantitation of 511 compounds detected in two different green and roasted coffees. Of these, over 100 compounds serve as candidate differentiators of coffee quality, AAA vs. AA, as designated by the Coopedota cooperative in Costa Rica. Of these, 72 compounds survive the roasting process and can be used to discriminate green coffee quality after roasting.
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12
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Trinklein TJ, Synovec RE. Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis. J Chromatogr A 2022; 1677:463321. [PMID: 35853427 DOI: 10.1016/j.chroma.2022.463321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/01/2022] [Accepted: 07/07/2022] [Indexed: 10/17/2022]
Abstract
Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δr>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, αe,2D, and within-class relative standard deviation in peak area, RSDwc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δr>, αe,2D, and RSDwc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δr>, αe,2D, and RSDwc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.
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Affiliation(s)
- Timothy J Trinklein
- Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, USA
| | - Robert E Synovec
- Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, USA.
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13
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Ferracane A, Manousi N, Tranchida PQ, Zachariadis GA, Mondello L, Rosenberg E. Exploring the volatile profile of whiskey samples using solid-phase microextraction Arrow and comprehensive two-dimensional gas chromatography-mass spectrometry. J Chromatogr A 2022; 1676:463241. [PMID: 35763950 DOI: 10.1016/j.chroma.2022.463241] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/10/2022] [Accepted: 06/11/2022] [Indexed: 12/28/2022]
Abstract
We present a novel sample preparation method for the extraction and preconcentration of volatile organic compounds from whiskey samples prior to their determination by comprehensive two-dimensional gas chromatography (GC × GC) coupled to mass spectrometry (MS). Sample preparation of the volatile compounds, important for the organoleptic characteristics of different whiskeys and their acceptance and liking by the consumers, is based on the use of the solid-phase microextraction (SPME) Arrow. After optimization, the proposed method was compared with conventional SPME regarding the analysis of different types of whiskey (i.e., Irish whiskey, single malt Scotch whiskey and blended Scotch whiskey) and was shown to exhibit an up to a factor of six higher sensitivity and better repeatability by a factor of up to five, depending on the compound class. A total of 167 volatile organic compounds, including terpenes, alcohols, esters, carboxylic acids, ketones, were tentatively-identified using the SPME Arrow technique, while a significantly lower number of compounds (126) were determined by means of conventional SPME. SPME Arrow combined with GC × GC-MS was demonstrated to be a powerful analytical tool for the exploration of the volatile profile of complex samples, allowing to identify differences in important flavour compounds for the three different types of whiskey investigated.
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Affiliation(s)
- Antonio Ferracane
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, Vienna 1060, Austria
| | - Natalia Manousi
- Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, Vienna 1060, Austria; Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Peter Q Tranchida
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - George A Zachariadis
- Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; Chromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; Department of Sciences and Technologies for Human and Environment, University Campus Bio-Medico of Rome, Rome, Italy
| | - Erwin Rosenberg
- Institute of Chemical Technology and Analytics, Vienna University of Technology, Getreidemarkt 9/164, Vienna 1060, Austria.
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14
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Sudol PE, Ochoa GS, Cain CN, Synovec RE. Tile-based variance rank initiated-unsupervised sample indexing for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Anal Chim Acta 2022; 1209:339847. [DOI: 10.1016/j.aca.2022.339847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/13/2022] [Accepted: 04/16/2022] [Indexed: 11/30/2022]
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15
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Cain CN, Trinklein TJ, Ochoa GS, Synovec RE. Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification. Anal Chem 2022; 94:5658-5666. [PMID: 35347985 DOI: 10.1021/acs.analchem.2c00223] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.
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Affiliation(s)
- Caitlin N Cain
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Timothy J Trinklein
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Grant S Ochoa
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Robert E Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
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16
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Sudol PE, Galletta M, Tranchida PQ, Zoccali M, Mondello L, Synovec RE. Untargeted profiling and differentiation of geographical variants of wine samples using headspace solid-phase microextraction flow-modulated comprehensive two-dimensional gas chromatography with the support of tile-based Fisher ratio analysis. J Chromatogr A 2021; 1662:462735. [PMID: 34936905 DOI: 10.1016/j.chroma.2021.462735] [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: 10/12/2021] [Revised: 11/29/2021] [Accepted: 12/02/2021] [Indexed: 12/25/2022]
Abstract
The volatile fraction of food, also called the food volatilome, is increasingly used to develop new fingerprinting approaches. The characterization of the food volatilome is important to achieve desired flavor profiles in food production processes, or to differentiate different products, with winemaking being one popular area of interest. In the present research, headspace solid-phase microextraction (HS SPME) coupled to flow-modulated comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (FM GC×GC-TOFMS) was used to characterize geographical-based differences in the volatilome of five white "Grillo" wines (of Sicilian origin), comprising the five sample classes. All wines were produced with the same vinification method in 2019. To minimize the influence of minor bottle-to-bottle differences, three bottles of the same wine were randomly selected, and three samples were collected per bottle, resulting in nine sample replicates per wine. Particular emphasis was devoted to the operational conditions of a novel low duty cycle flow modulator. A fast FM GC×GC-TOFMS method with a modulation period of 700 ms and a re-injection period of 80 ms was developed. Following, the instrumental software was exploited to identify class-distinguishing analytes in the dataset via tile-based Fisher ratio analysis (i.e., ChromaTOF Tile). A tile size of 10 modulations (7 s) on the first dimension and 45 spectra (300 ms) on the second dimension was used to encompass average peak widths and to account for minor retention time shifting. Off-line software was used to apply an ANOVA test. A p-value of 0.01 was applied in order to select the most important class-distinguishing analytes, which were input to principal component analysis (PCA). The PCA scores plot showed distinct clustering of the wines according to geographical origin, although the loadings revealed that only a few analytes were necessary to differentiate the wines. However, a comprehensive flavor profile assessment underscored the importance of all the information output by the ChromaTOF Tile software.
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Affiliation(s)
- Paige E Sudol
- Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, United States of America
| | - Micaela Galletta
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Peter Q Tranchida
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Mariosimone Zoccali
- Department of Mathematical and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy.
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; Chromaleont s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; BeSep s.r.l., c/o Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy; Unit of Food Science and Nutrition, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy
| | - Robert E Synovec
- Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, United States of America
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17
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Hu W, Zhou W, Wang C, Liu Z, Chen Z. Rapid Analysis of Biological Samples Using Monolithic Polymer-Based In-Tube Solid-Phase Microextraction with Direct Mass Spectrometry. ACS APPLIED BIO MATERIALS 2021; 4:6236-6243. [DOI: 10.1021/acsabm.1c00551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Wei Hu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Hubei Province Engineering and Technology Research Center for Fluorinated Pharmaceuticals, Wuhan University School of Pharmaceutical Sciences, No. 185 Donghu Road, Wuchang District, Wuhan 430071, China
- State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Beijing 100080, China
| | - Wei Zhou
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Hubei Province Engineering and Technology Research Center for Fluorinated Pharmaceuticals, Wuhan University School of Pharmaceutical Sciences, No. 185 Donghu Road, Wuchang District, Wuhan 430071, China
- State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Beijing 100080, China
| | - Chenlu Wang
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Hubei Province Engineering and Technology Research Center for Fluorinated Pharmaceuticals, Wuhan University School of Pharmaceutical Sciences, No. 185 Donghu Road, Wuchang District, Wuhan 430071, China
| | - Zichun Liu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Hubei Province Engineering and Technology Research Center for Fluorinated Pharmaceuticals, Wuhan University School of Pharmaceutical Sciences, No. 185 Donghu Road, Wuchang District, Wuhan 430071, China
| | - Zilin Chen
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Hubei Province Engineering and Technology Research Center for Fluorinated Pharmaceuticals, Wuhan University School of Pharmaceutical Sciences, No. 185 Donghu Road, Wuchang District, Wuhan 430071, China
- State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Beijing 100080, China
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18
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Stilo F, Jiménez-Carvelo AM, Liberto E, Bicchi C, Reichenbach SE, Cuadros-Rodríguez L, Cordero C. Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:8874-8889. [PMID: 34319731 PMCID: PMC8389832 DOI: 10.1021/acs.jafc.1c02981] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 05/21/2023]
Abstract
The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.
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Affiliation(s)
- Federico Stilo
- Dipartimento
di Scienza e Tecnologia del Farmaco, Università
degli Studi di Torino, Via Pietro Giuria 9, Torino I-10125, Italy
| | - Ana M. Jiménez-Carvelo
- Department
of Analytical Chemistry, Faculty of Science, University of Granada, Av. Fuentenueva S/N, Granada E-18071, Spain
- . Phone: +39 011 6707172
| | - Erica Liberto
- Dipartimento
di Scienza e Tecnologia del Farmaco, Università
degli Studi di Torino, Via Pietro Giuria 9, Torino I-10125, Italy
| | - Carlo Bicchi
- Dipartimento
di Scienza e Tecnologia del Farmaco, Università
degli Studi di Torino, Via Pietro Giuria 9, Torino I-10125, Italy
| | - Stephen E. Reichenbach
- University
of Nebraska, Lincoln, Nebraska 68588, United
States
- GC
Image LLC, Lincoln, Nebraska 68508, United
States
| | - Luis Cuadros-Rodríguez
- Department
of Analytical Chemistry, Faculty of Science, University of Granada, Av. Fuentenueva S/N, Granada E-18071, Spain
| | - Chiara Cordero
- Dipartimento
di Scienza e Tecnologia del Farmaco, Università
degli Studi di Torino, Via Pietro Giuria 9, Torino I-10125, Italy
- . Phone: +34 958240797
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19
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20
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Bressanello D, Marengo A, Cordero C, Strocchi G, Rubiolo P, Pellegrino G, Ruosi MR, Bicchi C, Liberto E. Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:4550-4560. [PMID: 33823588 DOI: 10.1021/acs.jafc.1c00509] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.
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Affiliation(s)
- D Bressanello
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - A Marengo
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - C Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - G Strocchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - P Rubiolo
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - G Pellegrino
- Lavazza S.p.A., Strada Settimo 410, 10156 Turin, Italy
| | - M R Ruosi
- Lavazza S.p.A., Strada Settimo 410, 10156 Turin, Italy
| | - C Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
| | - E Liberto
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy
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21
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Stilo F, Bicchi C, Reichenbach SE, Cordero C. Comprehensive two‐dimensional gas chromatography as a boosting technology in food‐omic investigations. J Sep Sci 2021; 44:1592-1611. [DOI: 10.1002/jssc.202100017] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/11/2021] [Accepted: 02/11/2021] [Indexed: 12/25/2022]
Affiliation(s)
- Federico Stilo
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino Torino Italy
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino Torino Italy
| | - Stephen E. Reichenbach
- Computer Science and Engineering Department University of Nebraska–Lincoln Lincoln Nebraska USA
- GC Image Lincoln Nebraska USA
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino Torino Italy
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