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Omics Technologies Applied to Agriculture and Food

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies Federico Stilo, Erica Liberto, Stephen E. Reichenbach, Qingping Tao, Carlo Bicchi, and Chiara Emilia Irma Cordero J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b01661 • Publication Date (Web): 17 Apr 2019 Downloaded from http://pubs.acs.org on April 23, 2019

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Journal of Agricultural and Food Chemistry

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies

Federico Stilo1, Erica Liberto1, Stephen E. Reichenbach2,3, Qingping Tao3, Carlo Bicchi1 and Chiara Cordero1*

1Dipartimento 2Computer 3GC

di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Turin, Italy

Science and Engineering Department, University of Nebraska, Lincoln, NE, USA

Image LLC, Lincoln, NE, USA

*Corresponding author: Dr. Chiara Cordero - Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, I-10125 Torino, Italy – e-mail: [email protected]; phone: +39 011 6707172; fax: +39 011 2367662

1 ACS Paragon Plus Environment


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Abstract

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Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection

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(GC×GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC×GC-

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MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables

5

related to either the analytical platform or to the experimental parameters adopted for the analysis.

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This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak

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patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-

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climatic, harvest year or shelf-life changes are studied. 2D-pattern misalignments are forced by changing

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chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by

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template matching, are analyzed and a rational workflow defined to accurately re-align patterns and analytes

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metadata.

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Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor

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threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform

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parameters are key for effective template matching. In targeted analysis (supervised workflow) with

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optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined

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untargeted and targeted (UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9 %. Response

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normalization also is examined, evidencing good performance of multiple internal standard normalization

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that effectively compensates for discriminations occurring during injection of highly volatile compounds. The

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resulting workflow is simple, effective, and time efficient.

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Key words

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Comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry; extra-

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virgin olive oil volatiles; template matching; combined untargeted and targeted (UT) fingerprinting; data

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alignment in long-term studies


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Introduction

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One of the most important features of -omics approaches applied to food is the multiplicity of

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encrypted information that they can provide.1 Because of the complexity of the food field, reliable results

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often can be obtained only with projects spread over years. Clear examples are monitoring chemical key

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characteristics of food crops whose cultivation has been moved in non-native countries or continents or

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submitted to the effect of climate changing. The possibility of comparing and/or merging data obtained over

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wide time-ranges, not necessarily resulting from rigorously standardized conditions (or improved analytical

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instrumentation), is therefore mandatory to achieve reliable and useful results.

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Comprehensive two-dimensional gas chromatography (GC×GC) is one of the most informative

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separation techniques for chemical characterization of complex fractions of volatiles from food. 2–4 It enables

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highly effective fingerprinting 5 and, when combined with mass spectrometric detection (GC×GC-MS), it has

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the intrinsic potential to provide a detailed profiling, giving access to higher level information encrypted in

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complex patterns of volatiles, for example: sample origin, technological signature, and aroma.3,6–9

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Each analytical run produces dense and multidimensional data, so that elaboration and

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interpretation of chemical information is challenging. Moreover, 2D-peak patterns representing the sample

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2D fingerprint, are defined by a series of variables also related to the analytical platform and to the

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experimental parameters adopted for the analysis. The choice of flow modulation instead of

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thermal/cryogenic modulation, MS detection by fast scanning quadrupoles vs. time-of-flight MS, low-

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resolution MS vs. high-resolution MS as well as GC×GC stationary phase combination, columns lengths and

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diameters, carrier gas linear-velocities, modulation period (PM) and oven temperature programming greatly

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impact on 2D-patterns signature and informational density.10

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Although most of these parameters, once fixed after method development and optimization, are

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kept constant (e.g., column set-up, carrier gas flows, and modulation parameters) or can be standardized as

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the MS tuning and optimization, some others are a source of random variability that must be considered

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when fingerprinting and pattern recognition studies extend over time and/or across different platforms.



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For mono-dimensional (1D) GC-MS applications, possible strategies for chromatographic alignment

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and data normalization are: (a) linear retention indexing (van Den Dool and Kratz or Kovats indices) or

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retention time locking methods based on pressure/flow adjustments (i.e., retention time locking)

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accurately locate target analytes along the analytical run; (b) chromatographic realignment; 14–16 (c) internal

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standardization for response normalization by single or multiple Internal Standards (IS) addition; and (d)

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external standard normalization by adopting single or multiple External Standards (ES). These strategies are

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effective and routinely adopted in peak-features based applications5. However, for GC×GC, these 1D-GC

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strategies may be ineffective especially for retention inconsistencies that result from two, almost

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independent, separation steps. On the other hand, the peculiar nature of 2D-peak patterns offers the

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possibility of exploiting pattern recognition algorithms for fast and effective fingerprinting. So, strategies for

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pattern alignment and normalization are needed.

11–13

to

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Pattern recognition approaches based on peak-region features,5 implemented with the smart

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template concept, 17 use different transform functions to facilitate recognition of 2D peak patterns based on

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retention-times coordinates and establish correspondences between 2D-peaks, or 2D-peak-regions, from a

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reference pattern to those in an analyzed pattern even in presence of retention times shifts17–19 and/or when

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severe misalignments occur because of different modulation regimes. 20,21

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Pattern correspondences are at the basis of the re-alignment of untargeted/targeted 2D-peaks or

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2D-peak-regions across a samples-set to enable fingerprinting investigations.3 Furthermore, the specificity

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and reliability of pattern matching can be improved by including constraint functions, operating on the third

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dimension of the data, i.e., the MS signature (i.e. EI-MS fragmentation pattern). Typical functions are those

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that limit positive correspondences to 2D-peaks with spectral similarity match above a certain threshold or,

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more simply, for 2D-peaks that comply for specific m/z relative ratios between informative fragments of the

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spectrum.

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In a scenario that food chemical fingerprinting must be extended over long time-frames, as for

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example to cover different harvest years or shelf-life modifications, strategies and tools for data re-alignment


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and normalization are necessary22,23 together with more rational strategies and intuitive operative

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protocols/workflows to guide analysts over the present limits deriving from analytical data misalignment.

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This study addresses, for the first time, 2D-peak patterns variations occurring in long-term studies

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that might impact the effectiveness of combined untargeted and targeted fingerprinting (UT fingerprinting).

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The complex 2D patterns of volatiles from Extra Virgin Olive oil of different quality are here studied

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as model application. The comprehensive profiling and fingerprinting of olive oil volatiles can be strategically

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extended over wide-time frames, e.g. over harvest years or across the shelf-life of the product, to build

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databases for authenticity and valorisation of products. A concrete challenge faced through the collection of

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profiling data from Italian extra-virgin olive oils within the Violin project.24

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In spite of the great potential of GC×GC in exploiting the chemical dimensionality of olive oil volatile

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fractions, just a few studies are available in this field and none of them addresses challenges posed by long

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time-frame studies. Vaz Freire et al.

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distributions of volatiles. An open-source image analysis software (Image J, National Institutes of Health) was

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used to extract detector response information from 2D regions over the separation space. Image-features

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with a high discrimination potential were selected by Principal Component Analysis (PCA) and targeted

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profiling was then combined to locate known analytes within most informative 2D regions.

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adopted an image-features approach26 to investigate characteristic

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Studies aimed at defining geographical origin indicators or cultivar markers include those by Cajka et

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al.27 who adopted GC×GC-TOF-MS to identify 44 compounds able to discriminate extra-virgin olive oils based

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on their different geographical origin and production year, and by Lukić et al. 28 who applied a peak-features

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approach to reveal compositional differences between oils obtained by different olive cultivars and

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geographical areas. They considered, as potentially informative, both untargeted and targeted analytes as

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they were extracted from the raw data-set on the basis of relative retention and spectral features. Magagna

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et al. 8 first developed an integrated strategy for UT fingerprinting based on template matching, to define

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olive ripening indicators, while Purcaro et al.9 combined targeted and untargeted analysis to delineate

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chemical blueprints of olive oil aroma defects.



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The olive oil volatiles 2D-patterns considered were obtained in a one-year study during which

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misalignments and inconsistencies were introduced by varying column lengths and restrictions, modulation

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period (PM), and operating with the time-of-flight (TOF) MS with differently optimized parameters. The

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processing steps, preceding template matching, are analyzed to define a rational workflow enabling

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consistent pattern recognition and to provide solid foundation for data processing procedures to be adopted

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in such challenging scenarios.

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Materials and methods

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Reference compounds and solvents

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Pure reference standards of α- and β-thujone and methyl-2-octynoate used as Internal Standards

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(ISs), n-alkane standards (n-C7 to n-C25) used for linear retention index (IT) calibration and pure reference

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compounds for targeted analytes identity confirmation were supplied by Merk (Sigma-Aldrich srl Italy, Milan,

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Italy). Cyclohexane (HPLC grade) for n-alkane standards and pure dibutyl phthalate for ISTDs working

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solutions were from Merk.

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Olive Oil samples

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Extra Virgin Olive oils (EVO oils), supplied by the University of Granada (Spain), Prof. Luis Cuadros-

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Rodríguez, were obtained from olives of the Picual cultivar, harvested in the regions of Granada Altipiano

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named Baza and Benamaurel, and grown under differing production and irrigation practices. 8 Samples were

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obtained by mixing olives from five different trees from the same plot in duplicate batches (A and B). Olives

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for oil production were collected at four different ripening stages: November 10-12, 2014; November 24-28,

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2014; December 16-17, 2014; and January 12-15, 2015, and classified by oil quality (Extra Virgin - EVOO,

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Virgin-VOO or Lampante- LOO). Samples acronyms and characteristics are summarized in Table 1. Oil

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qualifications were by a certified laboratory (ISO 17025:2018)

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(EEC) No 2568/91 of 11 July 1991 and IOC Standard COI/T.15/NC No 3/Rev. 12. Some quality indices are

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reported in Table 1, including the sensory panel test results.

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and according to Commission Regulation


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Headspace solid-phase microextraction sampling devices and conditions

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Volatiles were sampled from samples by headspace (HS) solid-phase microextraction (SPME).

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DVB/CAR/PDMS df 50/30 μm 2 cm length fiber (Supelco, Belle-fonte, PA, USA) was chosen based on previous

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studies 9 and conditioned before use as recommended by the manufacturer. The ISs were pre-loaded onto

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the SPME device,

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methyl-2-octynoate at 100 mg L-1 in dibutyl phtalate standard solution. ISs were equilibrated at 40°C and pre-

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loaded by exposing the SPME device for 5 min.

29,30

before sampling, in a 20 mL headspace vial containing a 5.0 μL of α/β-thujone and

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Sampling was carried out on 0.100 ± 0.005 g of oil samples precisely weighed in 20 mL headspace

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vials. The very low amount of sample was chosen to comply with HS linearity conditions for most of the key-

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analytes responsible of samples discrimination. 8 Sampling was at 40°C for 60 min. After extraction, the SPME

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device was automatically transferred to the Split/Splitless (S/SL) injection port of the GC×GC system kept at

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260°C for 5 min. Each sample was analyzed in triplicate.

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Instrument set-up and analysis conditions

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GC×GC analyses were performed on an Agilent 7890 GC chromatograph (Agilent Technologies,

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Wilmington DE, USA) coupled to a Markes BenchTOF-Select™ mass spectrometer featuring Tandem

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ionization™ (Markes International, Llantrisant, UK). The GC transfer line was at 270°C. TOF MS tuning

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parameters were set for single ionization at 70 eV and for tandem ionization at 70 and 12 eV; the scan range

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was set at 35-350 m/z with a spectra acquisition frequency of 100 Hz for single eV and 50 Hz/channel for

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tandem ionization.

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The system was equipped with a two-stage KT 2004 loop type thermal modulator (Zoex Corporation,

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Houston, TX) cooled with liquid nitrogen controlled by Optimode v2.0 (SRA Intruments, Cernusco sul Naviglio,

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Milan, Italy). Modulation periods (PM) and hot jet pulse times are detailed in Table 2, along with other

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parameters. A Mass Flow Controller (MFC) reduced the cold-jet stream from 45% to 8% of the total flow with

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a linear function along the run duration. A fused silica capillary loop (1.0 m × 0.1 mm id) was used in the

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modulator slit.



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The column set was configured as follows: 1D SolGel-Wax column (100% polyethylene glycol; 30 m ×

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0.25 mm dc × 0.25 μm df) from Trajan Analytical Science (Ringwood, Australia) coupled with a 2D OV1701

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column (86% polydimethylsiloxane, 7% phenyl, 7% cyanopropyl; 1 m × 0.1 mm dc × 0.10 μm df) from Mega

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(Legnano, Milan, Italy). A capillary restriction towards the MS was used to generate a differential pressure-

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drop influencing actual carrier gas linear velocities along the column in Set-up 2 (1 m × 0.1 mm dc deactivated

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silica). The GC Split/Splitless (S/SL) injector port was set at 260°C and operated in split mode with a split ratio

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1:20.

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The carrier gas was helium at a constant nominal flow of 1.3 mL/min. The oven temperature

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programming was set as follows: 40°C (2 min) to 240°C (10 min) at 3.5°C/min. Carrier gas average linear

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velocities (1ū and 2ū), pressure settings, and hold-up times are reported in Table 2 and were obtained by basic

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calculations with a reference temperature of 60°C.

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For IT determination, 1μL of the n-alkanes sample solution was injected with a split ratio 1:50.

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Data were acquired by TOF-DS™ (Markes International, Llantrisant, UK) and processed by GC Image ver 2.8

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(GC Image, LLC Lincoln, NE, USA).

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UT fingerprinting workflow

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The distribution of detectable analytes over the 2D chromatographic space in a GC×GC separation is

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at the core of pattern recognition based on the smart template concept. 31 The template is a pattern of 2D-

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peaks and/or graphic objects (geometrical objects delineating 2D-peak contour) built over a reference

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image(s) (single or composite image) 32 and then used to recognize similar patterns of 2D-peaks in an analyzed

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image(s).33 Each template object (2D-peak and/or graphic) can carry various metadata including: compound

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chemical name, retention times, IT, mass spectrum, informative ions and their relative ratios, constraint

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functions to limit peak correspondences above certain thresholds, and qualifier functions examining

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metadata information by script functions.

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Peak-region features34 are of great help in the presence of temporal inconsistencies and detector

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fluctuations. Peak-regions attempt to define one chromatographic region around each analyte peak, thereby

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achieving the one-feature-to-one-analyte selectivity but with greater robustness than can be achieved with 8 ACS Paragon Plus Environment

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single 2D-peak detection.26 2D-peaks and peak-regions are features adopted in the combined targeted and

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untargeted fingerprinting (UT fingerprinting) strategy.8,20,32,35

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UT fingerprinting establishes a group of reliable peaks, positively matched across all or most-of

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chromatograms in set,36 and then uses them to align chromatograms18 for their combination into a single,

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composite chromatogram. From the composite chromatogram, 2D-peaks (i.e., the combination of the re-

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aligned responses in the 2D retention-time plane) are detected and their outlines are recorded to define

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peak-region objects. The set of reliable 2D-peaks and peak-regions objects are collected in the feature

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template, or consensus template, covering the whole sample-set variability and capable of cross-

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corresponding chemical feature patterns among samples. Within all detected analytes, the subset of targeted

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compounds can be highlighted by completing their metadata fields (compound name, ion ratios, IT) and

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computed together with untargeted features during the data processing.

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A schematic of the UT fingerprinting process is illustrated in the Supplementary Material –

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Supplementary Figure 1 together with some details on targeted and untargeted 2D peaks and peak-regions.

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Results and discussion

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This section discusses all steps, supervised by the analyst, aiming at selecting key parameters to generate

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a targeted template or a reliable or consensus template covering the entire volatile fraction, and its chemical

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dimensionality,6 of extra-virgin olive oil. In particular, some critical parameters are examined for their impact

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on template matching efficacy: (a) 2D-peaks response thresholds (Signal-to-Noise ratio - SNR and Volume-

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to-Noise ratio – VNR); (b) MS reference signature to be used for spectral constraints; (c) MS similarity

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thresholds; and (d) the template matching transform and settings to compensate for 2D-peak patterns

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variability to achieve an effective, reliable and comprehensive chemical fingerprinting.

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The next paragraphs illustrate the strategy adopted to generate misaligned patterns and the

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subsequent workflow designed to re-align templates. Results are critically discussed in term of accuracy (i.e.,

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true positive matches) and data inter-batch transferability (i.e., response normalization).



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Pattern misalignment challenges

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Chromatographic pattern distortions and misalignments were induced, generating worst-case

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scenarios, by changing the following parameters: (a) columns were from different commercial batches; (b) a

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post-column restriction was added generating a pressure drop between column inlet and outlet, influencing

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carrier gas linear velocities in both analytical dimensions; and (c) the PM was set at 4 or 3.5 s generating an

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absolute 2D retention misalignment. All the other parameters, carrier gas nominal flow, oven temperature

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programming and injection conditions were kept constant. Analytical conditions for the two resulting set-ups

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(i.e., Set-up 1 and Set-up 2) are summarized in Table 2. Figure 1 shows the colorized plot of an oil sample

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obtained from olives at an early ripening stage (Baza-1-A) analyzed by the two set-ups (Figure 1A for Set-up

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1 and Figure 1B for Set-up 2). Pattern differences are visible and are related to the different chromatographic

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efficiencies (peak-width – 1W and 2W – Table 2 data) that impact resolution and to the absolute retentions

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that affect system orthogonality.37–39.

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The global misalignment between peaks patterns is visualized in Figure 2 and evaluated by calculating

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analyte relative retention in both separation dimensions against reference peaks.21 (Z)-3-hexen-1-ol acetate,

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which elutes in the middle area of the chromatographic plane, was arbitrarily chosen as reference/centroid,

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while phenol, the last-eluting marker, was used to normalize each analyte’s relative position i.

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The 1D relative retention (1D RR) is calculated by Equation 1:

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1D

RR = (1tRi - 1tR(Z)-3-Hexen-1-ol acetate) / 1tRphenol

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where 1tRi corresponds to the first-dimension retention time expressed in minutes for the targeted peak i,

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(Z)-3-Hexen-1-ol acetate is the reference peak, and phenol is the last eluting peak. The 2D relative retention

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(2D RR) is calculated through Equation 2:

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2D

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where 2tRi corresponds to the second dimension retention time expressed in minutes for the targeted peak i,

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(Z)-3-hexen-1-ol acetate is the reference peak, and PM the modulation time. 21

RR = (2tRi - 2tR(Z)-3-Hexen-1-ol acetate) / PM

Eq. 1

Eq. 2

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As visualized in Figure 2, there is a dramatic impact on the 2D absolute and relative retention. This

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effect is due primarily to the different PM applied (4 vs. 3.5 seconds) and to the actual carrier gas linear 10 ACS Paragon Plus Environment

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velocities (1ū and 2ū) and operative pressures (initial head-pressure and mid-point pressure). Analytes falling

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in the third quadrant show an higher 2D k in Set-up 1, whereas this effect decreases as a function of increasing

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1D

k (retention).

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Interestingly, the two patterns, although misaligned on the normalized retention times space, keep

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coherent the group-type separation for homologous series. Normal alkanes (n-alkanes), shown with green

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indicators, mostly in the second quadrant; linear aldehydes, shown with orange indicators, spanning mostly

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across the first and third quadrants; and short chain fatty acids, shown with cyan indicators, appearing in the

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forth quadrant, all are rationalized over the 2D space.

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The next step of the study addresses detector response variations and examines threshold parameters for 2D-peaks descriptors to adopt for consistent template matching.

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Supervised workflow for reliable targeted template construction

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MS detector response fluctuations due to tuning, optimization, and/or other factors directly impact

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on absolute response and background noise intensity. Such performance issues also may affect template

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matching effectiveness and analyte identity confirmation, as a consequence of variable peak detection and

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the varying quality and reliability of 2D-peak spectra adopted as reference for matching. In this study, TOF

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MS was set differently: in Set-up 1, it multiplexed between high and low ionization energies (Tandem

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Ionization ™ - 70 and 12 eV) at 50 Hz acquisition rate for each channel, whereas in Set-up 2, TOF MS operated

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in single electron energy acquisition mode (70 eV) at 100 Hz. Therefore, MS was tuned differently

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output signals exhibited different absolute responses (total ion current) and background noise intensities.

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and

The signals resulting from the same sample (Baza-1-A), whose patterns are illustrated in Figure 1A

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and 1B, have the following characteristics:

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

Background noise sampled in the middle of the chromatogram within a 50x50 acquisition-point window

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reported an average absolute total intensity of 75,500 counts (RSD% 1D 0.6 and 2D 5.98) for Set-up 1 and

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167,000 counts (RSD% 1D 0.5 and 2D 1.57) for Set-up 2. Supplementary data visually illustrates

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performance evaluation operations (Supplementary material – Supplementary Figure 2 – SF2). 11 ACS Paragon Plus Environment


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

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After background subtraction41, the average intensity was 38,000 counts (RSD% 1D 1.19 and 2D 8.9) for Set-up 1 and 88,000 counts (RSD% 1D 0.9 and 2D 2.49) for Set-up 2.

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

The number of detected 2D-peaks above SNR 15 were 770 for Set-up 1 and 500 for Set-up 2.

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

Within the detected 2D-peaks, SNR values ranged between 15-13,000 in Set-up 1 and between 15-3,000

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in Set-up 2. 

Volume-to-noise ratio (VNR) values ranged between 100-14,100 in Set-up 1 and 100-6,700 in Set-up 2.

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Experimental results indicate that MS, operating with a single ionization energy at 70 eV, results in

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greater absolute and relative background noise (e.g., 1.9 times) compared to the tandem ionization settings.

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Interestingly, the noise fluctuations are greater along the 1D (RSD% values) where column bleeding increases

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as a function of temperature programming. Background noise subtraction has almost the same effect, in

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terms of noise suppression, and in both cases, signal intensity is halved compared to the initial values.

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With respect to peak detection, Set-up 1 exhibited better chromatographic efficiency (Table 3 1W0.1

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and 2W0.1) and resulted in a larger number of detected peaks over SNR≥15, with a wide range of variation,

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i.e., 15-13,000 SNR, whereas in Set-up 2 maximum SNR achieved only a value of 3,000. On the other hand,

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VNR, which corresponds to the ratio of analyte 2D-peak volume to the standard error (SE (σ/n)), is not so

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influenced by peak-width as SNR. It informs about the dynamic range of the MS response42 and with Set-up

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1 this appears 10 times greater (up to 14,100 vs. up to 6,700 VNR) compared to Set-up 2. Although with Set-

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up 1 the number of detected peaks over a SNR of 15 was higher, a greater volume standard deviation (σ) was

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computed.

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Based on the differences observed in the absolute response, spectral quality fluctuations were

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expected especially for low-intensity or threshold peaks. Therefore, the next step was to define threshold

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parameters for template construction with the objective of achieving 100% true-positive matches (accuracy)

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in presence of random variability over the application of the method in a short term and with the same

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method set-up. Therefore, these tests were performed between analytical replicates of the same oil sample

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to define benchmark values and then validated over a new sample acquired in the same conditions. Tests


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were done on the three analytical replicates of Baza-4-A acquired by Set-up 1; validation was on the three

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replicates of Bena-4-A acquired by the same set-up.

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Threshold values for candidate peaks populating a template were set for SNR and NIST Similarity

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match factor (direct match factor - DMF); as reference, spectral signatures were tested for the average

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spectrum (named blob spectrum) and the highest modulation spectrum (named peak spectrum). SNR values

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were varied step-wise in a range between 10 and 100, covering 2D peaks with a percent response between

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0.01 and 0.04, while the DMF threshold was set at 800 or 700.43 Templates were built with ten 2D-peaks with

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SNR within the fixed range and homogeneously distributed over the 2D space. Experimental results,

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expressed as % of true-positive matches, are reported in Table 4.

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2D Peaks with SNR values below 50 are connoted by inconsistent MS spectral signatures resulting in

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false-negative matches even when DMF threshold is lowered from 800 to 700. For these peak, neither the

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blob spectrum nor the peak spectrum are sufficiently reliable to carry consistent information for identity

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confirmation. For 2D-peaks with a SNR within 50-100 in the reference chromatogram, the rate of positive

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matches increases from 10% to 70% when MS constraints are kept at 800 DMF and blob spectrum considered.

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The rate of true-positive matches reaches 100% with the combination of lower DMF threshold at 700 and

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peak spectrum taken as reference MS. Note, in these cases no false positives were revealed, meaning that

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correspondences were just established between peaks generated by the same chemical entity.

295

Results suggest that a SNR cut-off should be defined, based on 2D data particulars, to limit

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inconsistencies at targeted identity confirmation level. The validation of these settings was by applying the

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templates resulting from the reference sample Baza-4-A to Bena-4-A replicates on the same column set-up.

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Results, reported in Table 4, confirmed the need for a SNR threshold of at least 50 with better performances

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at DMF threshold of 700 with a reference template spectrum collected from the highest modulation (peak

300

spectrum). In this case, some template peaks were unmatched (true-negative matches) because they were

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not detected in the analyzed sample (below method Limit of Detection LOD).

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Rules for template peaks thresholds and reference spectra were then applied to build a reference

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targeted template of known analytes. This fully supervised approach aimed at characterizing the distribution 13 ACS Paragon Plus Environment


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of marker compounds known for their role in defining olive oil sensory quality or to refer about olives ripening

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status8,9. It was also the complement of the UT fingerprinting process and included targeted peaks listed in

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Table 3. Analytes identifications were by combining 1D retention data (IT) with MS fragmentation pattern

307

similarity above 900 DMF by adopting commercial

308

authentic standards.

43,44

and in-house databases or, when possible, by

309

The template of 126 2D-peaks was built by inspecting samples patterns obtained with Set-up 1;

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reference peaks inclusion was limited to those analytes showing a SNR≥100, the reference spectra was from

311

the highest modulation (e.g., peak spectrum), and the MS constraint was set at 700 DMF and 700 Reverse

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Match Factor (RMF).43

313

Results for targeted template matching are summarized in Table 5. The average matching within Set-

314

up 1 samples achieved 97% (122 over 126 peaks). Further comments will follow for template matching for

315

Set-up 2 patterns.

316 317

Template transformation

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Once template construction was established with simple rules for confident identification and

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effective matching, the next step was the selection of matching algorithm (transform) and related

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parameters to effectively transform the template to match the peak pattern showing severe misalignment.

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To approach this challenge, global polynomial transformations were tested, as it was successful in complex

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re-alignment problems such as those posed by method translation from thermal to differential flow

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modulated platforms.

324

polynomials) are successful when a sufficient number of alignment points, at least 10 for affine and 30 for

325

second-degree polynomial, are available to guide the pattern re-alignment.18

18,21

Global, low-degree transformation functions (second-degree or third-degree

326

The strategy here applied included the re-alignment of the targeted template built over Set-up 1

327

analyses to chromatograms from Set-up 2. The first step included the adjustment of the distance threshold

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parameters for the 1D and 2D, which correspond to the horizontal and vertical distance threshold that limits

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the after-transformation distance between template and candidate 2D-peaks. These distances are expressed 14 ACS Paragon Plus Environment

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in inter-sample distances (i.e., pixel dimensions). To compensate for the greater 2D misalignment the 2D

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distance threshold was step-wise incremented from a minimum value of 5 up to 25. On the 1D, where the

332

misalignment was minimal, a threshold of 10 was sufficient to avoid false-negative matches.

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At the same time, affine and second-degree polynomial matching transforms were tested for

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performance.18 These funtions are available in the current version of GC Image (i.e., v2.8). The reader

335

interested in a deeper insight on transform functions and their effectiveness to re-align complex patterns can

336

refer to previous articles by Reichenbach and co-workers.18,31,36

337

The Set-up 1 targeted template was applied to the first (arbitrarily chosen) sample pattern (i.e., Baza-

338

4-A) obtained with Set-up 2. At first, the number of true-positive matches was higher for the second-degree

339

transform, so the iterative process of match-and-transform was continued. Iterating the process of matching

340

and template transform allows the template to be adapted to the actual pattern while increasing, step-by-

341

step, the number of matched peaks up to the maximum number that corresponds to all targeted analytes

342

actually detected/confirmed in the analyzed pattern. In practice, this operation increments the number of

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alignment points available step-by-step, thus improving the quality of the global template transformation at

344

each step.

345

Experimental results for the application of the targeted template adapted to Set-up 1 over the

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patterns of Set-up 2 resulted in a 65 positive matches over 126 template peaks (51% - 5.4% RSD); after

347

transformation by taking these 65 alignment points, the successive matching step achieved 95 positive

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matches with a 75% of matched peaks; then, the next step matched 110 peaks, 87%. The maximum number

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of matched peaks, shown in Table 5, was 121 (96%) and was achieved after one additional matching step. In

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practice, for a full and effective re-alignment of the targeted template, a variable number of iterations

351

between 3 and 5 was applied.

352

All such results are listed in Table 5; benchmark values for maximum template matching performance

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are those corresponding to the application of the targeted template to Set-up 1 samples (first column). On

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average matching performance was better for Set-up 1 patterns (97% of true-positive matches), although the

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loss of accuracy on Set-up 2 pattern was just 5% (92.46% vs. 97.02%). These results present a solid foundation 15 ACS Paragon Plus Environment


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for the application of this experimented strategy to a fully-unsupervised approach as that for the reliable

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template construction.

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The next section illustrates the process of feature template construction over samples patterns from

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Set-up 1 and its successive alignment over Set-up 2 patterns. Accuracy results are discussed as % of true-

360

positive matches. 45

361

UT fingerprinting: feature template construction

362

The feature template was built over a subset of Set-up 1 chromatograms with the first analytical

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replicate of all analyzed samples. The 2D chromatograms were pre-processed for rasterization, background

364

subtraction, and 2D peaks detection above a SNR threshold of 100. Data processing was then conducted

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within a component of the GC Image software suite (Image InvestigatorTM) using the previously validated

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settings:

367



SNR≥100 as threshold value for template peaks;

368



peak spectrum as MS reference to upload in the template;

369



DMF similarity threshold at 700;

370



matching transform parameters with 1D distance threshold of 10 and 2D of 25.

371

Additional settings, specific for this process, included an option for reliable peak inclusion that was set as

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most relaxed: with this setting the algorithm considers as reliable peaks all 2D-peaks that match across at

373

least half of the chromatograms. Reliable peaks are fundamental as they are used as alignment points for the

374

transform function when the feature template is used to cross-align a large set of chromatograms including

375

those obtained with a different set-up.

376

The final feature template accounted for 257 reliable peaks and 1500 peak-regions. The reliable

377

template built over Set-up 1 chromatograms is shown in Supplementary material – Supplementary Figure 3

378

– SF3. Matching constraints for MS spectrum similarity were applied on 2D-peak features and results are

379

summarized in Table 5. The average % of matching for Set-up 1 chromatograms was 99.75 (±0.45 RSD%);

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when the feature template is transformed to match for Set-up 2 patterns, the % of matching is slightly lower

381

97.95 (±0.99 RSD%) but evidences the high accuracy of the process. 16 ACS Paragon Plus Environment

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Once re-aligned almost all chemical features are detected in all samples patterns, proceeding in a

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sort of data fusion, the final step aimed at defining the best 2D-peak response descriptor for cross-sample

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analysis.

385 386

Response normalization and samples clustering

387

As evidenced by signal intensity evaluation and by 2D-peaks statistics (SNR and VNR distribution),

388

pattern cross-alignment is not sufficient to compensate for random variations across measurement sessions

389

and impacting detector response. Response normalization is mandatory to allow consistent cross-

390

comparison of data set. The removal of unwanted intensity variation (i.e., normalization) is referred to as

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signal drift correction, removal of batch effect, scaling, and matrix effects removal, and can be approached

392

differently as function of the study objectives.23 Normalization is, in fact, a fundamental step because it may

393

affect the outcomes of a study; the meaningfulness of differentially abundant analytes may vary depending

394

on the normalization method.23,41

395

In this study, we tested three simple approaches within those generally adopted in volatiles profiling

396

studies.3,46 The first included multiple IS normalization with α and β tujone and methyl octinoate that were

397

pre-loaded into the SPME device before headspace sampling of olive oils.30 As an alternative method, the

398

analyte % response (calculated on the 2D-Volume) was considered. It was obtained by: (a) normalizing

399

analyte 2D-Volume over all detected 2D-peaks above fixed thresholds or (b) normalizing analyte 2D-Volume

400

over all UT peaks included in the feature template. In this last approach 2D-peaks from column bleeding and

401

from interferents were excluded.

402

Results are illustrated, for a selection of informative analytes covering different volatilities, polarities

403

and amounts, in Figure 3. The bias is computed as Error %, according to Equation 3, and between 2D peaks

404

response (Normalized 2D Volume over IS, % response over all 2D peaks detected or over UT peaks) taking

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Set-up 2 as reference.

406 407

Error % = ((Responsei Set-up 2 - Responsei Set-up 1)/ Responsei Set-up 2)*100

Eq. 3 17

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Normalization by IS(s) performs, on average, more effectively than those on % response (Average

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Error % 11.2 vs. 16.2); it better compensates for response fluctuations derived by S/SL injection

410

discriminations here impacting on highly volatiles (heptane and acetone) and due to the different operative

411

head-pressures applied to the two set-up. For highly volatile analytes, the Error % computed for IS

412

normalization drops below 16 while for % response it reaches 50-60%. On the other hand, % response

413

indicators well compensate for detection variability on less abundant analytes as 1-octen-3 ol (0.01%), 2-

414

butanone (0.01%), 3-penten-2-one (0.02%), 1-octanol (0.04%), and octanal (0.06%). Note, response

415

variations also are influenced by chromatographic performance; 2D-peaks showing long tails (carbonyl

416

derivatives or unsaturated alcohols) or distorted by overloading phenomena may be split into multiple 2D-

417

peaks. In these cases, supervision is needed to merge all 2D-peaks belonging to the same analytes in a single

418

one.

419

Although ISs normalization gave better results for analytes heavily discriminated, it requires a

420

dedicated sample preparation with ISs pre-loading before sampling that may impact on the global analysis

421

time. In addition, a careful selection of standards is necessary by focusing on compounds not present in the

422

samples under study while covering the suitable range of volatility and polarity. In this scenario, % response

423

normalization is attractive being simpler and less time consuming although it does not rule out the use of

424

quality control procedures as for example external standardization (ES) or multiple quality control samples

425

analysis.

426

As a proof of concept, a PCA was run on normalized responses obtained from samples analyzed by

427

the two set-ups. The data matrix consisted of 126 chemical variables corresponding to the targeted analytes

428

listed in Table 2, and 16 samples corresponding to 2 plots × 4 ripening stages × 2 set-up. Technical replicates

429

were averaged; therefore, the final data matrix was 126 × 16 dimensioned. Results are shown in the score

430

plot of Figure 4A; observations (samples) are rationally distributed over the Cartesian plane according to the

431

ripening stage of olives (visible along the F1 from right to left) and in accordance to oil quality (i.e. extra-

432

virgin, virgin or lampante). Results are in agreement with those from the original study by Magagna et al.8

433

confirming the informative role of several analytes related to positive aroma attributes (i.e., (E)-2-Hexen-118 ACS Paragon Plus Environment

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ol, (Z)-3-Hexen-1-ol and (E,E)-2,4-Hexadienal) with a relevant role in early ripening stages and 6-Methyl-5-

435

hepten-2-one with higher impact on later stages were oils were classified as lampante or virgin.

436

Measurements from the two set-ups are homogeneously distributed over the Cartesian plane

437

confirming the effectiveness of response normalization on the “batch effect”. The latter is evident in Figure

438

4B where the PCA was conducted on analytes 2D volumes without normalization. Here measurements from

439

the two set-ups are independently clustered, as also indicated by confidence ellipses, and well discriminated

440

along the F1. On the other side, as a conformation on the consistency of the measurements obtained by the

441

two set-up, along F2 for both clusters, the ripening stage is coherently distributed from the upper-side of the

442

graph to bottom.

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Acknowledgements

445

The research was supported by Progetto Ager – Fondazioni in rete per la ricerca agroalimentare. Project

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acronym Violin - Valorization of Italian olive products through innovative analytical tools.

447 448

Compliance with ethical standards Notes

449

Prof. Stephen E. Reichenbach and Dr. Qingping Tao have a financial interest in GC Image, LLC.

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Caption to Figures Figure 1: colorized plot of the Baza-1-A sample analyzed with Set-up 1 (1A) and Set-up 2 (1B). Red colored areas indicate the available separation space while green areas include all targeted peaks elution area. For details, see the text.

Figure 2: dispersion graph resulting from the relative position of targeted peak analytes (white indicators) from the two set-ups in the normalized retention times space: homologous series n-alkanes - green indicators, linear saturated aldehydes - orange indicators, an short chain fatty acids - cyan indicators.

Figure 3: Error %, according to Equation 3, calculated between 2D peaks response (Normalized 2D Volume over IS, % response over all 2D peaks detected or over UT peaks) taking Set-up 2 as reference for a selection of informative analytes. Red dotted lines indicate boundaries for acceptance at ± 20%.

Figure 4: PCA score plots based on normalized responses for 126 targeted analytes (Table 2) resulting from samples (Baza and Benamaurel plots) analyzed with set-up 1 and set-up 2 (4A), colors corresponds to different ripening stages of olives (1-4). The scores plot in 4B is based on analytes absolute responses without normalization while confidence ellipses clearly highlight the samples clusters influenced by batch effect (setup 1 red indicators and set-up 2 black indicators.



Page 26 of 40

Caption to Tables Table 1: List of samples together with acronym, harvest region, harvest stage, quality parameters according to COMMISSION REGULATION (EEC) No 2568/91 of 11 July 1991 and IOC Standard COI/T.15/NC No 3/Rev. 12, sensory evaluation results (Md: median of defects – Mf: median of fruity notes) and commercial classification.

Table 2: Set-up 1 and 2 columns characteristics, settings and operative pressures.

Table 3: List of all targeted analytes together with their experimental IT. Identification criterion: (a) authentic reference compound – putative identification or (b) IT ± 20 and spectral similarity direct match ≥ 850 – tentative identification. Elution order (#Rank) is reported for the two set-up as for retention times (1tR and 2t

R)

and relative standard deviation (RSD%) calculated over all analyzed samples, peak-width at 10% of peak

height estimated on each dimension (1W0.1 (min) and 2W0.1 (sec)).

Table 4: template matching results based on Set-up 1 templates including 2D peaks with SNR ranging from 10 to 100. Similarity DMF threshold applied is 800 or 700 and reference spectrum is blob (average 2D-peak spectrum) or peak (highest modulation spectrum). The upper part of the table refers to benckmark values obtained by applying Set-up 1 templates over replicated analyses of the same sample (i.e., Baza-4-A); the lower part of the table refers about results of Set-up 1 templates over replicated analyses of another sample (i.e., Bena-4-A).

Table 5: template matching results for targeted template (supervised workflow) from Set-up 1 applied to Setup 2 samples and for feature template (unsupervised and automatic workflow) built over Set-up 1 samples and applied on Set-up 2 samples.


Page 27 of 40


Table 1: List of samples together with acronym, harvest region, harvest stage, quality parameters according to COMMISSION REGULATION (EEC) No 2568/91 of 11 July 1991 and IOC Standard COI/T.15/NC No 3/Rev. 12, sensory evaluation results (Md: median of defects – Mf: median of fruity notes) and commercial classification.

Sample Acronym

Region

Harvest stage

Baza-1-A Baza-2-A Baza-3-A Baza-4-A Baza-1-B Baza-2-B Baza-3-B Baza-4-B Bena-1-A Bena-2-A Bena-3-A Bena-4-A Bena-1-B Bena-2-B Bena-3-B Bena-4-B

Baza Baza Baza Baza Baza Baza Baza Baza Benamaurel Benamaurel Benamaurel Benamaurel Benamaurel Benamaurel Benamaurel Benamaurel

November 10-12 November 24-28 December 16-17 January 12-15 November 10-12 November 24-28 December 16-17 January 12-15 November 10-12 November 24-28 December 16-17 January 12-15 November 10-12 November 24-28 December 16-17 January 12-15

Acidity Peroxide index (%) (mEq O2/kg) 0.20 0.20 0.20 0.40 0.20 0.10 0.20 0.40 0.20 0.20 0.20 0.40 0.10 0.20 0.20 0.20

5 3 5 11 4 3 6 13 5 4 8 19 4 3 6 14

K232

K270

ΔK

Md

Mf

Classification

1.84 1.60 1.17 1.11 1.92 1.65 1.28 1.12 1.61 1.53 1.19 1.05 1.64 1.48 1.51 1.05

0.20 0.20 0.20 0.10 0.20 0.20 0.10 0.10 0.20 0.20 0.10 0.10 0.40 0.20 0.10 0.10

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0 0 > 0.00 > 0.00 0 0 > 0.00 > 0.00 0 0 0 > 0.00 0 0 0 > 0.00

5 4.1 1.3 0 5.2 3.8 1.7 0 4.4 4.3 3.1 0 4.2 4.3 2.9 0

EVOO EVOO VOO LOO EVOO EVOO VOO LOO EVOO EVOO EVOO LOO EVOO EVOO EVOO LOO



Page 28 of 40

Table 2: Set-up 1 and 2 columns characteristics, settings and operative pressures.

D Columns

1

gas

D Columns

2

D Carrier settings 2

Modulation

Set-up 2

D: SolGelWax™ (30 m, 0.25mm dc, 0.25 μm df) Batch N° 1238274C06 He carrier @ 1.3 mL/min - constant flow conditions Average velocity (1ū): 15.3 cm/s Initial head-pressure (relative) 234 kPa Outlet pressure (absolute) 285 kPa Hold-up 3.27 min 2D: OV1701 Mega (1.0 m, 0.10 mm d , 0.10 μm d ) c f Loop-capillary: deactivated fused silica (1.0 m, 0.10 mm dc,) Restriction toward MS: none He carrier @ 1.3 mL/min - constant flow conditions Average velocity (2ū): 157 cm/s Mid-point pressure (relative) 184 kPa Hold-up 1.28 s PM: 3.5 s - Hot-Jet pulse time: 250 ms

1D: SolGelWax™ (30 m, 0.25mm d , 0.25 μm d ) c f Batch N° 1315621E03 He carrier @ 1.3 mL/min - constant flow conditions Average velocity (1ū): 12.8 cm/s Initial head-pressure (relative) 290 kPa Outlet pressure (absolute) 349 kPa Hold-up 3.89 min 2D: OV1701 Mega (1.0 m, 0.10 mm d , 0.10 μm d ) c f Loop-capillary: deactivated fused silica (1.0 m, 0.10 mm dc,) Restriction toward MS: deactivated fused silica (1 m, 0.10 mm dc,) He carrier @ 1.3 mL/min - constant flow conditions Average velocity (2ū): 128 cm/s Mid-point pressure (relative) 248 kPa Hold-up 2.35 s PM: 4s - Hot-Jet pulse time: 250 ms

1

1

D Carrier settings

Set-up 1

gas


Page 29 of 40


Table 3: List of all targeted analytes together with their experimental IT. Identification criterion: (a) authentic reference compound – putative identification or (b) IT ± 20 and spectral similarity direct match ≥ 850 – tentative identification. Elution order (#Rank) is reported for the two set-up as for retention times (1tR and 2tR) and relative standard deviation (RSD%) calculated over all analyzed samples, peak-width at 10% of peak height estimated on each dimension (1W0.1 (min) and 2W (sec)). 0.1

Compounds Hexane 2,2,4-Trimethylpentane Heptane Cyclohexane 1,3-Pentadiene Propanal Octane Acetone 1-Octene Tetrahydrofuran Butanal Ethyl acetate 2,3-Dimethylheptane 2-Methylbutanal Ethanol 1-Methoxyhexane 2-Ethylfurane 2-Methylnonane 2,3-Butanedione Pentanal 3-Methylnonane Acetonitrile (Z)-1-Methoxy-3-hexene Decane 1-Penten-3-one Propan-1-ol α-Pinene (E)-2-Butenal Toluene 1-Decene 2,3-Pentanedione 4-Methyldecane Hexanal

Exp IT

Identification

778 789 795 802 813 819 830 831 850 858 867 873 893 895 913 932 932 947 955 956 960 980 991 997 1000 1011 1011 1017 1020 1032 1033 1051 1059

a b a b b a a a b b a a b a a b b b a a b b b a a a a a a b a b a

# Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

1t R

(min) 4.36 4.63 4.78 4.96 5.25 5.44 5.70 5.74 6.26 6.46 6.67 6.86 7.43 7.44 7.78 8.42 8.42 8.85 8.94 9.03 9.20 9.69 10.01 10.18 10.25 10.65 10.66 10.95 10.99 11.41 11.50 12.04 12.39

RSD% 0.77 0.73 0.00 0.00 0.00 0.62 0.59 0.59 0.54 0.52 0.51 0.49 0.45 0.01 0.43 0.40 0.40 0.38 0.38 0.00 0.37 0.00 0.34 0.01 0.33 0.00 0.00 1.11 0.31 0.00 0.00 0.56 0.27

Set-up 1 2t R RSD% (sec) 0.33 3.53 0.56 0.00 0.58 0.00 0.49 2.34 0.42 0.00 0.28 0.00 1.06 0.00 0.29 4.03 1.01 1.15 0.48 0.00 0.42 0.00 0.43 2.71 1.76 1.14 0.60 0.21 0.26 0.00 1.15 1.01 0.54 0.00 2.29 2.02 0.37 3.09 0.65 0.02 2.33 0.50 0.27 0.04 1.03 1.12 2.51 0.03 0.56 0.00 0.29 0.04 1.62 0.01 0.50 4.00 0.68 2.94 2.06 0.01 0.52 0.00 2.83 0.41 0.86 0.00

1W

0.1

(min) 0.12 0.10 0.10 0.08 0.37 0.25 0.10 0.25 0.12 0.23 0.23 0.18 0.23 0.22 0.19 0.14 0.29 0.31 0.37 0.30 0.31 0.15 0.14 0.18 0.21 0.19 0.20 0.21 0.18 0.17 0.21 0.29 0.21

2W

0.1

(sec) 0.05 0.06 0.05 0.04 0.17 0.12 0.09 0.11 0.08 0.16 0.13 0.05 0.17 0.07 0.09 0.09 0.07 0.24 0.05 0.23 0.27 0.10 0.09 0.08 0.06 0.08 0.06 0.15 0.07 0.05 0.06 0.31 0.07

# Rank 1 2 3 6 4 5 8 10 7 9 11 13 12 15 14 17 18 16 19 21 20 22 24 23 25 28 27 26 29 30 32 31 33

1t R

(min) 4.80 5.44 5.53 5.88 5.60 5.82 6.28 6.67 6.26 6.33 7.47 7.74 7.73 8.27 7.78 9.09 9.11 9.04 9.18 10.00 9.65 10.27 10.66 10.48 10.96 11.43 11.18 11.05 11.61 11.98 12.19 12.02 13.09

RSD% 8.45 3.53 3.61 3.49 0.00 0.00 1.18 0.00 0.62 1.82 2.36 0.01 0.86 8.41 2.47 0.43 0.74 0.43 3.71 0.04 0.69 0.03 0.36 0.06 0.70 0.02 0.01 1.51 0.67 0.38 0.32 0.56 1.28

Set-up 2 2t R (sec) 1.06 0.83 0.60 0.96 0.54 0.52 1.68 0.74 1.01 0.48 0.91 1.08 1.99 1.16 0.38 2.21 1.10 2.85 0.45 1.26 2.48 0.86 1.99 3.31 1.11 0.65 2.77 0.59 1.37 2.98 1.16 2.93 1.72

RSD% 5.80 3.07 5.38 7.99 4.26 7.62 1.04 8.48 3.78 13.50 8.41 0.05 11.30 8.24 3.53 2.66 3.64 2.84 11.80 0.05 12.00 0.29 4.07 0.09 3.74 0.11 0.17 6.55 4.68 0.53 0.27 2.44 6.23

1W

0.1

(min) 0.40 0.36 0.38 0.29 0.53 0.40 0.33 0.53 0.40 0.36 0.42 0.33 0.38 0.43 0.42 0.29 0.38 0.49 0.42 0.37 0.51 0.30 0.24 0.32 0.42 0.34 0.28 0.40 0.33 0.43 0.36 0.53 0.42

2W

0.1

(sec) 0.15 0.13 0.16 0.11 0.17 0.15 0.15 0.37 0.23 0.18 0.23 0.16 0.13 0.24 0.29 0.31 0.17 0.23 0.13 0.16 0.19 0.32 0.24 0.15 0.23 0.18 0.16 0.15 0.17 0.20 0.23 0.38 0.19



Isobutyl alcohol 2,4,6-Trimethyldecane β-Pinene Undecane 3-Penten-2-one 1-Methoxy-2-propanol 3-Methylbutyl acetate 1-Methoxy-1-propanol Ethylbenzene 1,4-Dimethylbenzene 1-Butanol 1,3-Dimethylbenzene Butyl 2-methylpropanoate 1-Penten-3-ol 2-Methylpropyl butyrate ß-Myrcene Heptanal 1,3-Xylene 2-Ehylhexanal 3-Methyl-2-butenal 3-Methyl-1-butanol 2-Methyl-1-butanol Limonene Eucalyptol (E)-2-Hexenal Dodecane Terpinene (E)-ß-Ocimene 1-Pentanol Styrene 1-Dodecene Hexyl acetate 3-Hydroxy-2-butanone 1,2,3-Trimethylbenzene 2-Ethyl-2-hexenal (Z)-3-Hexenyl acetate N,N-Dimethylformamide (Z)-2-Heptenal 6-Methyl-5-hepten-2-one (Z)-3-Hexen-1-ol (E,E)-2,4-Hexadienal Nonanal (E)-2-Hexen-1-ol

1061 1090 1093 1097 1100 1101 1101 1103 1106 1115 1115 1121 1128 1130 1141 1147 1162 1163 1166 1172 1177 1177 1182 1190 1193 1197 1229 1234 1236 1244 1249 1250 1256 1261 1278 1300 1302 1306 1319 1346 1369 1370 1373

a b a a a a a b b b a b b a a a a b a a b b a a a a a a a b b a a b a a b a a a a a a

34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

12.46 13.26 13.59 13.75 13.88 13.90 13.90 13.98 14.12 14.41 14.41 14.64 14.94 15.01 15.40 15.65 16.28 16.33 16.45 16.68 16.86 16.86 17.09 17.37 17.50 17.68 18.92 19.08 19.15 19.57 19.74 19.78 19.93 20.18 20.77 21.43 21.53 21.66 22.17 23.74 24.38 24.44 24.56

0.27 0.25 0.00 0.00 0.00 0.01 0.24 0.24 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.18 0.01 0.17 0.00 0.17 0.00 0.00 0.16 0.00 0.16 0.00 0.00 0.00 0.00 0.00

0.34 3.11 1.58 2.85 0.60 0.39 1.02 0.39 0.86 0.87 0.36 0.84 1.36 0.36 1.33 1.33 1.02 0.83 1.33 0.61 0.79 0.41 1.41 1.54 0.81 3.02 1.35 1.27 0.11 0.62 2.67 1.13 0.63 0.96 1.14 0.94 0.50 0.90 0.91 0.42 0.58 1.24 0.40

0.00 0.37 0.00 0.01 0.00 0.03 0.00 2.94 0.00 1.32 0.00 0.00 0.02 0.00 0.87 0.87 0.00 1.39 0.87 1.88 1.47 0.03 0.82 0.01 1.43 0.01 0.85 1.82 6.74 0.01 0.87 1.02 1.84 0.00 0.02 0.00 0.00 0.00 1.26 0.00 0.00 1.61 0.00

0.16 0.29 0.17 0.19 0.27 0.38 0.14 0.39 0.16 0.16 0.21 0.14 0.12 0.16 0.31 0.14 0.19 0.18 0.14 0.16 0.16 0.16 0.16 0.32 0.31 0.19 0.12 0.16 0.33 0.31 0.27 0.18 0.37 0.27 0.17 0.14 0.21 0.25 0.18 0.21 0.18 0.29 0.21

0.04 0.33 0.05 0.09 0.14 0.09 0.10 0.16 0.07 0.17 0.06 0.06 0.08 0.05 0.21 0.09 0.08 0.11 0.09 0.07 0.07 0.08 0.09 0.15 0.12 0.09 0.09 0.11 0.17 0.22 0.32 0.09 0.21 0.09 0.09 0.09 0.15 0.08 0.08 0.11 0.05 0.10 0.09

Page 30 of 40

34 38 36 42 35 39 37 40 41 43 44 45 47 46 48 49 52 50 53 51 54 56 55 58 57 59 60 62 61 63 64 67 66 65 68 69 71 70 72 73 76 75 74

13.10 13.99 13.77 14.30 13.73 14.10 13.95 14.12 14.24 14.59 14.98 15.09 15.62 15.53 15.74 15.74 17.07 16.61 17.17 16.98 17.50 17.69 17.57 17.94 17.80 18.18 19.20 19.49 19.33 19.98 19.98 20.59 20.58 20.35 21.34 21.95 22.28 22.24 22.86 23.73 25.39 24.77 24.71

0.88 1.93 0.01 0.01 1.70 0.02 3.01 1.84 1.51 0.53 1.81 0.44 0.01 0.86 1.30 1.07 2.64 0.97 3.19 2.24 3.19 0.01 0.44 0.01 2.43 0.01 0.34 0.35 2.60 0.01 1.75 0.97 1.16 2.01 0.02 0.47 1.95 1.55 0.45 0.85 0.26 1.48 2.20

0.78 3.26 1.70 3.61 0.69 0.48 1.34 0.66 1.31 0.95 1.22 1.75 2.12 0.99 1.55 1.55 2.00 1.34 2.30 0.97 1.45 1.10 2.87 2.43 1.50 3.68 1.71 2.64 0.92 1.39 2.74 2.35 1.02 1.06 2.13 1.98 1.47 1.38 1.87 1.03 1.22 1.77 0.91

6.51 8.52 0.22 0.09 6.19 0.74 9.21 10.90 7.54 10.60 8.66 6.31 0.05 12.50 8.44 6.74 6.10 4.40 13.60 14.10 13.60 0.25 19.40 0.12 3.66 0.08 3.07 5.06 7.57 0.10 12.80 15.40 4.55 3.73 0.08 1.01 13.30 5.59 1.22 6.28 2.84 14.10 3.37

0.29 0.44 0.25 0.32 0.42 0.41 0.69 0.36 0.42 0.47 0.36 0.64 0.33 0.36 0.38 0.34 0.44 0.44 0.51 0.47 0.51 0.38 0.36 0.37 0.31 0.33 0.20 0.27 0.69 0.58 0.33 0.42 0.36 0.38 0.48 0.27 0.36 0.29 0.20 0.22 0.33 0.49 0.31

0.17 0.35 0.12 0.16 0.22 0.22 0.18 0.32 0.30 0.25 0.23 0.35 0.15 0.24 0.15 0.32 0.18 0.29 0.29 0.32 0.29 0.25 0.26 0.24 0.19 0.16 0.30 0.21 0.31 0.43 0.30 0.22 0.21 0.27 0.32 0.33 0.37 0.29 0.29 0.24 0.49 0.31 0.31


Page 31 of 40

(E)-3-Octen-2-one α-Thujone (ISTD) (E)-2-Octenal 1-Octen-3-ol β-Thujone (ISTD) 1-Heptanol 1-Ethenyl-4-ethyl-benzene Furfural (E,E)-2,4-Heptadienal 5-Methyl-2-(1-methylethyl)cyclohexanone Butyl-2-ethylhexanoate 2-Ethyl-1-hexanol (Z)-Hepten-4-ol Decanal (E)-2-Hepten-1-ol 3,5-Octadien-2-one Benzaldehyde 6-Undecanone Propanoic acid (E)-2-Nonenal Linaloool 1-Octanol Nonyl acetate 5-Methyl-2(5H)-furanone Undecanal Butanoic acid Butyrolactone 1-Nonanol Ethyl benzoate 5-Ethyl-2(5H)-furanone (Z)-3-Nonen-1-ol Dodecanal α-Muurolene 3,4-Dimethyl-2,5-furandione Pentadecanoic acid 1,4-Cyclohex-2-enedione α-Farnesene (E,E)-2,4-Decadienal Methyl salicylate 2-(2-Butoxyethoxy)-ethanol δ-Pentalactone 3-Phenyl-2-propenal


1382 1402 1415 1417 1424 1429 1430 1434 1437 1446

a a a a a a b a a b

77 78 79 80 81 82 83 84 85

24.85 25.55 25.73 25.78 26.26 26.43 26.48 26.77 26.89

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.96 1.24 0.98 0.34 1.21 0.53 0.86 0.34 0.69

0.01 0.02 2.04 0.00 0.01 2.19 0.00 0.00 1.67

0.28 0.25 0.27 0.18 0.15 0.18 0.37 0.17 0.14

0.10 0.16 0.14 0.08 0.09 0.05 0.13 0.06 0.06

77 79 78 81 80 84 82 86 87

25.41 26.47 25.83 26.83 26.59 27.19 26.87 27.54 27.80

0.01 0.02 1.13 0.01 0.01 0.28 0.72 0.01 1.06

1.82 2.20 1.01 1.15 2.21 1.17 1.81 1.17 1.44

0.03 0.13 11.90 0.09 0.10 2.65 1.68 0.22 0.00

0.50 0.31 0.80 0.20 0.21 0.27 0.51 0.32 0.62

0.33 0.31 0.24 0.29 0.20 0.30 0.25 0.30 0.25

86

27.13

0.00

1.31

0.88

0.21

0.13

83

27.05

0.28

1.37

2.41

0.33

0.25

1453 1460 1469 1474 1476 1485 1494 1505 1506 1509 1514 1525 1567 1589 1598 1610 1611 1628 1637 1643 1647 1684 1701 1701 1710 1710 1725 1732 1743 1754 1767 1794

a a a a a a a a a a a a b b a a a a a b a a a b a b a a b a b b

87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118

27.42 27.72 28.18 28.35 28.41 28.88 29.03 29.38 29.40 29.58 29.75 30.16 31.15 31.46 32.08 32.57 32.61 33.66 34.03 34.28 34.44 35.64 35.99 35.99 36.16 36.16 36.98 37.20 37.57 37.92 38.33 39.20

0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.01 0.00 0.00 0.00 0.00 0.11 0.00 0.10 0.00 0.00 0.10 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00

1.89 0.60 0.46 1.30 0.44 0.77 0.49 1.53 0.14 1.05 0.67 0.57 1.33 0.46 1.37 0.16 0.44 0.63 0.67 0.41 0.57 1.42 1.61 0.54 0.17 0.43 1.43 0.91 0.57 0.52 0.52 0.52

1.22 0.00 0.02 1.54 0.02 0.01 2.34 0.01 0.06 1.10 1.73 2.01 0.01 0.00 1.69 0.00 0.00 1.82 1.71 10.20 0.02 1.41 1.89 0.02 0.21 0.03 0.02 2.55 2.04 0.00 0.00 0.00

0.39 0.21 0.19 0.25 0.41 0.21 0.18 0.38 0.35 0.41 0.18 0.14 0.25 0.54 0.53 0.39 0.35 0.16 0.12 0.25 0.21 0.51 0.16 0.25 0.32 0.33 0.15 0.37 0.25 0.25 0.39 0.21

0.29 0.11 0.08 0.09 0.16 0.12 0.04 0.14 0.09 0.17 0.06 0.05 0.16 0.07 0.28 0.09 0.09 0.05 0.06 0.16 0.07 0.21 0.13 0.11 0.20 0.19 0.06 0.20 0.11 0.16 0.16 0.07

85 88 90 89 91 92 95 96 97 93 94 98 99 100 102 101 103 104 105 106 107 108 110 111 109 112 113 114 115 116 117 118

27.33 28.06 28.93 28.60 29.20 29.52 29.95 29.96 30.29 29.66 29.90 30.69 30.96 32.49 32.76 32.64 32.78 34.22 34.53 35.04 35.04 36.25 36.86 36.96 36.80 37.13 37.13 37.64 38.11 38.39 39.36 40.26

0.88 0.02 0.01 1.50 0.00 0.02 0.47 0.01 0.02 1.42 0.26 0.25 0.00 0.24 0.12 0.42 0.12 0.11 0.84 0.61 0.01 0.38 0.28 0.01 0.01 0.00 0.01 0.10 1.57 0.70 0.00 0.10

1.97 1.46 1.17 1.89 0.97 1.56 1.06 2.30 0.96 1.16 0.72 1.57 1.29 1.02 2.72 1.45 0.94 1.43 1.35 1.24 1.11 2.49 2.52 1.41 0.94 1.28 2.16 0.95 1.16 1.31 1.18 1.14

7.99 0.23 0.23 14.30 0.03 0.10 15.40 0.05 0.12 16.50 2.90 2.40 0.06 3.08 0.71 7.74 3.34 2.20 10.90 8.38 0.26 8.17 12.80 0.12 0.14 0.21 0.11 3.20 9.73 5.33 2.74 3.51

0.40 0.34 0.39 0.33 0.55 0.60 0.44 0.44 0.47 0.36 0.38 0.16 0.40 0.62 0.78 0.47 0.24 0.20 0.40 0.36 0.44 0.47 0.40 0.55 0.60 0.49 0.28 0.64 0.56 0.31 0.60 0.40

0.19 0.20 0.15 0.25 0.33 0.31 0.22 0.25 0.23 0.15 0.12 0.22 0.28 0.25 0.50 0.23 0.27 0.23 0.28 0.33 0.30 0.36 0.30 0.28 0.34 0.22 0.15 0.35 0.41 0.35 0.32 0.29



Hexadecanoic acid (Z)-6,10-Dimethyl-5,9undecadien-2-one Butyl benzoate Benzyl alcohol Phenylethyl alcohol 4-Phenyl-3-buten-2-one 1-Dodecanol Phenol Average Min Max

Page 32 of 40

1805 1821

a b

119

39.51

0.09

0.20

0.00

0.31

0.22

119

40.47

0.10

0.83

7.78

0.58

0.43

120

40.02

0.00

1.13

2.05

0.35

0.20

120

40.70

0.09

2.12

0.99

0.31

0.29

1830 1832 1869 1914 1928 1956

b a a b a a

121 122 123 124 125 126 -

40.31 40.37 41.44 42.82 43.23 44.04 -

0.00 0.00 0.16 0.00 0.00 0.00 0.12 0.00 1.11

0.82 0.28 0.34 0.56 0.81 1.27 -

0.02 0.00 0.00 0.00 1.42 3.97 0.86 0.00 10.20

0.41 0.28 0.29 0.40 0.23 0.43 0.24 0.08 0.54

0.27 0.14 0.11 0.14 0.13 0.34 0.12 0.04 0.34

121 122 123 124 125 126 -

41.27 41.29 41.42 43.77 43.89 44.84 -

0.00 0.00 0.16 0.09 0.32 1.51 0.90 0.00 8.45

1.69 0.99 0.83 1.12 1.68 1.66 -

0.14 0.26 12.00 1.61 8.43 18.30 5.03 0.00 19.40

0.59 0.46 0.56 0.64 0.38 0.53 0.42 0.16 1.04

0.30 0.27 0.34 0.25 0.31 0.41 0.25 0.11 0.50


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Table 4: template matching results based on Set-up 1 templates including 2D peaks with SNR ranging from 10 to 100. Similarity DMF threshold applied are 800 or 700 while reference spectrum is blob (average 2D-peak spectrum) or peak (highest modulation spectrum). The upper part of the table refers to benchmark values obtained by applying Set-up 1 templates over replicated analyses of the same sample (i.e., Baza-4-A); lower part of the table refers about results of Setup 1 templates over replicated analyses of another sample (i.e., Bena-4-A). Baza-4-A Set-up 1 – three replicates SNR 10 ± 2 30 ± 2 50 ± 2 70 ± 2 90 ± 2 100 ± 2

% Response 0.01 0.02 0.02 0.03 0.04 0.04

Similarity threshold 800 – Blob MS Peaks n° % Matching Matched peaks n° Column bleeding or interferences 10 10.00 % 1 10 10.00 % 1 10 40.00 % 4 10 40.00 % 4 10 70.00 % 7

Baza 4-A Set-up 1 over Bena-4-A Set-up 1 SNR 10 ± 2 30 ± 2 50 ± 2 70 ± 2 90 ± 2 100 ± 2

% Response 0.01 0.02 0.02 0.03 0.04 0.04

Similarity threshold 800 – Blob MS Peaks n° % Matching Matched peaks n° Column bleeding or interferences 10 10.00 % 1 10 10.00 % 1 8 25.00 % 2 5 40.00 % 2 7 42.86 % 3

Similarity threshold 700 – Peak MS % Matching Matched peaks n° 40.00 % 90.00 % 100.00 % 100.00 % 100.00 %

4 9 10 10 10

Similarity threshold 700 – Peak MS % Matching Matched peaks n° 10.00 % 20.00 % 87.50 % 100.00 % 100.00 %

1 1 7 5 7



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Table 5: template matching results for targeted template (supervised workflow) from Set-up 1 applied to Set-up 2 samples and for feature template (unsupervised and automatic workflow) built over Set-up 1 samples and applied on Set-up 2 samples.

Samples Baza-1 Baza-2 Baza-3 Baza-4 Bena-1 Bena-2 Bena-3 Bena-4 Average RSD%

Targeted template (126 peaks) Set-up 1 Set-up 2 % Peaks n° % Peaks n° 94.44 119 91.27 115 97.62 123 92.06 116 98.41 124 95.24 120 100.00 126 96.03 121 93.65 118 88.89 112 96.03 121 91.27 115 97.62 123 92.06 116 98.41 124 92.86 117 97.02 122 92.46 116 2.22 2.22 2.47 2.47

Feature template (257 reliable peaks) Set-up 1 Set-up 2 % Peaks n° % Peaks n° 100.00 257 97.28 250 100.00 257 98.44 253 100.00 257 98.83 254 99.22 255 96.86 247 100.00 257 98.44 253 100.00 257 96.50 248 98.83 254 99.22 256 100.00 257 98.05 252 99.75 256 97.95 252 0.45 0.46 0.99 1.22


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Figure 1

A

B



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Figure 2


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Figure 3

30 20 10

Error %

0 -10 -20 -30 -40 -50 -60 -70

Bias % volume

Bias %Percent TOT

Bias %Percent Partial



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Figure 4

A

Normalized 2D-Volumes Observations (axes F1 and F2: 31.19 %)

Absolute 2D-Volumes Observations (axes F1 and F2: 38.02 %)

B

10

7

Baza 2_ Set-up 1

Baza 4_ Set-up 1

Baza 2_ Set-up 2

Ben 3_ Set-up 1 Baza 3_ Set-up 2

-3

-20

-15

-10

0

Baza 2_ Set-up 2

Baza 3_ Set-up 1

Bena 2_ Set-up 2

Baza 4_ Set-up 1 Baza 3_ Set-up 2

Ben 4_ Set-up 1

-5

Baza 3_ Set-up 1

Bena 4_ Set-up 2

Bena 1_ Set-up 2

Baza 4_ Set-up 2 Bena 3_ Set-up 2

Ben 4_ Set-up 1

-8

Baza 2_ Set-up 1 Ben 3_ Set-up 1

Baza 1_ Set-up 2

Bena 3_ Set-up 2 Baza 4_ Set-up 2

Baza 1_ Set-up 2

Baza 1_ Set-up 1

Ben 2_ Set-up 1 Baza 1_Set-up 1

2

Ben 2_ Set-up 1

5

Ben 1_ Set-up 1

F2 (14.60 %)

F2 (13.18 %)

Bena 4_ Set-up 2

Bena 1_ Set-up 2

Ben 1_ Set-up 1

Bena 2_ Set-up 2

-5

-10

0

5

10

15

20

-20

-15

-10

-5

0

5

10

15

20

F1 (23.42 %)

F1 (18.01 %) SuppVarQ1-Stage 4

SuppVarQ1-Stage 3

SuppVarQ1-Stage 2

SuppVarQ1-Stage 1

Set-up 1 Setup-SETUP1

Set-up 2 2 Setup-SETUP


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