This study investigates peach ripening using Fourier transform near-infrared spectroscopy and volatilome-based headspace gas chromatography coupled to ion mobility spectrometry and mass spectrometry. A total of 344 samples from two varieties at different ripening stages were analyzed to identify optimal harvest timing.Principal component analysis revealed distinct clustering of samples by ripening stage. Using these groups, partial least-squares discriminant analysis models successfully differentiated both ripening stages and varieties. The results demonstrate strong potential for volatilome profiling with HS-GC–IMS and HS-GC–MS as reliable tools for monitoring peach quality.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jafc.5c01524">Fingerprinting of Peach During the Ripening Process Using an Analytical Platform with Spectrometric and Volatilome-Based Chromatographic Techniques</a></h4>Claudia Giménez-Campillo, Natalia Arroyo-Manzanares, Marta Pastor-Belda, Natalia Campillo, Lukas Bodenbender, Philipp Weller, and Pilar Viñas*J. Agric. Food Chem. 2025, 73, 26, 16636–16647<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.jafc.5c01524">https://doi.org/10.1021/acs.jafc.5c01524</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.Peach, along with nectarine, plum, apricot, cherry, and almond, belongs to the genus Prunus (genus described by Charles Linnaeus in 1753). <a href="javascript:void(0);">(1)</a> Originally from China, it is the second most important fruit in Europe after apple, <a href="javascript:void(0);">(2)</a> is of great economic importance, and is consumed fresh, canned and in juice. It is rich in vitamins, minerals, and antioxidants. Currently, in addition to China, there are other world leaders in the production of this fruit, including Spain, Italy, Turkey, and Greece. <a href="javascript:void(0);">(3)</a>In recent decades, consumer complaints about poor fruit quality have often been the result of premature harvesting for commercial reasons. This practice, while facilitating transport and distribution by extending the fruit’s presence in the market, has a negative impact on its flavor, texture, and nutritional value. Consequently, ripening monitoring and control have become an important part of fruit management. <a href="javascript:void(0);">(4)</a> Despite extensive research on the physicochemical properties of peaches during the ripening stage to obtain adequate maturity indices, objective indicators of consumer acceptance are still lacking. <a href="javascript:void(0);">(5)</a> Ripening time has been shown to be directly related to flavor characteristics, which are influenced by sugar accumulation, acidity, and the production of low-molecular-weight volatile organic compounds (VOCs), <a href="javascript:void(0);">(6)</a> also called volatilomes. Therefore, VOCs can be a key indicator in peach quality assessment. Understanding exactly which VOCs are responsible for aroma at different ripening stages can help to develop rational and objective methods to ensure high-quality fruit, both in consumption and processing. <a href="javascript:void(0);">(7)</a> This approach would not only be beneficial for consumers, who would receive products with optimal flavor, texture, and nutritional value, but also would support producers.A literature review reveals the existence of several studies on the characteristic volatile profile of peaches. <a href="javascript:void(0);">(1,8−14)</a> However, it is important to note that there is a significant lack of research on the variation of these compounds during ripening. <a href="javascript:void(0);">(15)</a> Most of the works on VOC determination used gas chromatography coupled to mass spectrometry (GC–MS) <a href="javascript:void(0);">(1,6,8−19)</a> and solid-phase microextraction as the sample preparation technique. <a href="javascript:void(0);">(6,9−12,15−17,19)</a> In recent years, ion mobility spectrometry (IMS) has been used as an alternative technique for the determination of VOCs in peach. <a href="javascript:void(0);">(11,12,18,20,21)</a> The aim of these studies was to investigate the volatile profile, <a href="javascript:void(0);">(11)</a> the differentiation between varieties, <a href="javascript:void(0);">(18)</a> and the influence of packaging and storage of this fruit. <a href="javascript:void(0);">(20,21)</a> Therefore, the current state-of-the art techniques reveal that GC–MS and GC–IMS techniques have not been applied to predict peach ripening. It has been shown that it is possible to predict optimal ripening by near-infrared spectrometry (NIR) in relation to the measurement of different parameters, such as soluble solid content, <a href="javascript:void(0);">(22−28)</a> flesh firmness, <a href="javascript:void(0);">(23,26,28,29)</a> pectin content, <a href="javascript:void(0);">(30)</a> dry matter, <a href="javascript:void(0);">(23−25)</a> absorbance difference index, <a href="javascript:void(0);">(25,26)</a> and acidity. <a href="javascript:void(0);">(27)</a>The objective of this work was the development of methodologies for the prediction of the ripening stage of peaches and, therefore, of their quality for the consumer by means of a simple analysis based on their VOC content, applying headspace GC (HS-GC)–MS and HS-GC–IMS techniques and on their Fourier transform near-infrared spectroscopy (FT-NIR) spectral fingerprint. The novelty of the study lies in the fact that correlations have not been established with other chemical parameters measured in peaches, but rather chemometric models based on an analytical platform have been applied to obtain relevant information on the ripening of this fruit. The studies were conducted on two different peach varieties (yellow-fleshed and red-fleshed) grown in one geographical origin of the agricultural sector (Jumilla, Region of Murcia, Spain), which have not been previously analyzed, and chemometric models were also applied to discriminate between these two varieties.<h4>2. Materials and Methods</h4><h5>2.2. Instruments, Analytical Procedures, and Data Processing</h5><h5>2.2.1. FT-NIR Analysis</h5>FT-NIR analysis was performed using a FT-NIR multipurpose analyzer spectrophotometer, equipped with a solid sample compartment, from Bruker Optik GmbH (Ettlingen, Germany).A weight of 0.5 g of homogenized peach was placed in a vial and measured by FT-NIR in the reflectance mode. Before each analysis, a background scan was performed, and the measurement was repeated five times. Spectral data covered the wavenumber range from 12,500 to 3600 cm–1 with a resolution of 8 cm–1 and are expressed in units of absorbance. Each spectrum was obtained from 32 scans, and the average of the five measurements for each peach sample was calculated to obtain a representative spectrum.The FT-NIR software used to process the data was OPUS software, version 8.5. In addition, the data set obtained was subjected to three transformations using Unscrambler X software. Multiplicative scattering correction was applied to correct for the effects of particle-size-induced light scattering effects. Next, Savitzky–Golay smoothing was applied (window = 9, polyorder 2, first derivative) to reduce the inherent instrumental noise and to correct for the additive and multiplicative effects present in the spectrum. The full spectrum (2224 features) was used as a variable in the chemometric models constructed from the FT-NIR data. This approach was adopted in order to make use of all available spectral information given the observed variations between the samples across the entire range studied.<h5>2.2.2. HS-GC–IMS Analysis</h5>For GC-IMS analysis, a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=6890N">6890N gas chromatograph</a> (version N.05.05) from Agilent Technologies (Waldbronn, Germany) was coupled to a G.A.S. (Gesellschaft für Analytische Sensorsysteme) IMS module (Dortmund, Germany). A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=2&amp;search=multipurpose+sampler">multipurpose sampler</a> (MPS) from Gerstel GmbH (Mühlheim, Germany) was used, configured for headspace mode and injecting the sample in the gas phase, using a 2.5 mL syringe. The carrier gas was nitrogen with a purity of 99.99%, operated at a constant flow rate of 1 mL min–1, and supplied by Air Liquide (Madrid, Spain). An Agilent HP-5MS UI capillary column (0.25 mm × 30 m with a film thickness of 0.25 μm), consisting of 5% diphenyl and 95% dimethyl polysiloxane, was used for the analysis.<h5>2.2.3. HS-GC–MS Analysis</h5>GC–MS analysis was carried out on an Agilent Technologies (Santa Clara, CA, USA) model <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890+gas+chromatograph">8890 gas chromatograph</a> and an Agilent <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977B">5977B quadrupole mass spectrometer</a> equipped with an inert ion source. A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=2&amp;search=multipurpose+sampler">Gerstel MPS</a> operating in headspace mode was used for sample injection.<h4>3. Results and Discussion</h4><h5>3.2. Spectral Peach Fingerprint Using FT-NIR</h5>The spectral fingerprints of the peaches analyzed showed very similar profiles, with differences in the absorbance values. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jafc.5c01524#fig1">Figure 1</a> shows the FT-NIR spectra of the average raw data obtained from all peach samples collected at different stages of ripeness, recorded in the range 12,500–3600 cm–1. It is observed that as the ripening stage progresses, the absorbance increases in the range 11,800–3600 cm–1.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/J_Agric_Food_Chem_2025_73_26_16636_16647_Figure_1_Raw_FT_NIR_spectra_of_the_average_of_all_peach_samples_at_different_stages_of_ripening_T_b6e18989f1.jpg" alt="J. Agric. Food Chem. 2025, 73, 26, 16636–16647: Figure 1. Raw FT-NIR spectra of the average of all peach samples at different stages of ripening. The figure shows the FT-NIR spectra corresponding to the average of peach samples at different stages of ripeness: the blue spectrum represents unripe peaches, the red spectrum also represents unripe peaches but in a more advance ripeness process, and the green spectrum represents ripe peaches ready for consumption. Also, the most representative absorption bands of the peach observed in the spectra are indicated." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_J_Agric_Food_Chem_2025_73_26_16636_16647_Figure_1_Raw_FT_NIR_spectra_of_the_average_of_all_peach_samples_at_different_stages_of_ripening_T_b6e18989f1.jpg 245w," sizes="100vw" width="1600" height="857">In the spectrum of the different peach samples, four different regions can be identified according to the NIR bands: 12,500–9400 cm–1, 9400–7700 cm–1, 7700–5800 cm–1, and 5800–3600 cm–1. Peach samples contain mainly water (85–95%) in their composition, and therefore characteristic water bands were found from right to left, including bands with different absorbances. In the first region, from 5800 to 3600 cm–1, there is a broad absorption band with a maximum at around 5160 cm–1, which corresponds to the first combination of asymmetric stretching and bending of the water molecule. Also, in this region, there is another narrow and weaker combination band at 5620 cm–1, associated with the bending, asymmetric stretching, and intramolecular modes of the water molecule. In the region between 7700 and 5800 cm–1, a broad band is observed at 6880 cm–1 involving the symmetric and asymmetric stretching modes of the water molecule, known as the first overtone of OH stretching. Between 9400 and 7700 cm–1, there is a second weak combination band involving the symmetric stretching, asymmetric stretching, and bending modes of the water molecule. Finally, in the region from 12,500 to 9400 cm–1, an absorption band related to the asymmetric and symmetric stretching modes of the water molecule is observed, called the second overtone of OH stretching. <a href="javascript:void(0);">(35)</a> The spectra show that the maximum absorbance value of each band increases as the ripening stage progresses. This correlation suggests that the ripening process in peaches is associated with an increase in water content, which affects the texture, weight, and possibly the flavor of the fruit.<h5>3.3. Identification and Quantification of Volatile Compounds Using GC–MS and GC–IMS</h5>The volatile fingerprint of the peach samples was established by using GC coupled to MS and IMS detectors. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jafc.5c01524#fig2">Figures 2</a> and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jafc.5c01524#fig3">3</a> show a topographical map using IMS and a chromatogram obtained by the MS detector for a peach sample, respectively. In both cases, identification of the VOCs was carried out.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/J_Agric_Food_Chem_2025_73_26_16636_16647_Figure_2_Topographic_map_of_a_peach_sample_264e0b037c.jpg" alt="J. Agric. Food Chem. 2025, 73, 26, 16636–16647: Figure 2. Topographic map of a peach sample. The figure shows a topographical map obtained by HS-GC–IMS, indicating the VOCs identified and verified in peach samples." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_J_Agric_Food_Chem_2025_73_26_16636_16647_Figure_2_Topographic_map_of_a_peach_sample_264e0b037c.jpg 214w," sizes="100vw" width="996" height="726"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/JAGRIC_3_28c1356481.jpg" alt="J. Agric. Food Chem. 2025, 73, 26, 16636–16647: Figure 3. Chromatogram of a peach sample. The figure shows a chromatogram obtained by HS-GC–MS, indicating the compounds identified and verified in peach samples." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_JAGRIC_3_28c1356481.jpg 212w," sizes="100vw" width="1600" height="1182">Untargeted studies were performed by processing the raw data from the HS-GC–MS analysis of the samples using MS-DIAL software under the conditions described in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.jafc.5c01524#sec2_2_3">Section 2.2.3</a>. A total of 427 features were extracted from the peach samples, taking into account the different varieties and the whole ripening process.HS-GC–IMS was used to obtain the topographical maps of all samples. The GC–MS metabolomic MSP spectral library and the GAS IMS library allowed putative identification of some of the features detected.<h4>Conclusion</h4>The results obtained allow us to conclude that both the quality and the specific variety of the peach can be verified by a single analysis of the volatilome or the NIR spectral fingerprint. However, better results are obtained in both cases when working with the volatile profile, since HS-GC–MS and HS-GC–IMS techniques are more powerful and specific tools. Although HS-GC–MS stands out for its high resolution and sensitivity, its high cost, the need for vacuum conditions, and the large size of the equipment limit its applicability in sectors with limited resources such as many fruit quality control companies. In contrast, the HS-GC–IMS technique offers greater simplicity, accessibility, and cost-effectiveness, as it requires less energy and resources, making it a more efficient and sustainable option. <a href="javascript:void(0);">(40)</a> Furthermore, its ability to provide results comparable to those of HS-GC–MS, as demonstrated in this work, underscores its potential as an ideal alternative for many studies seeking to reduce resource consumption without compromising analytical quality. <a href="javascript:void(0);">(41)</a>In summary, multiple factors must be considered to determine the best analytical tool for carrying out routine controls aimed at verifying the authenticity of labeling and predicting the optimal stage of ripening in order to prevent food fraud and minimize economic losses. These factors include economic cost, analysis time, complexity of sample preparation, and discrimination capability of the generated models. Although the FT-NIR technique allows for fast and relatively low-cost analysis, its analytical performance is significantly low. In some cases, the classification and validation percentages are close to 70%. HS-GC–MS, on the other hand, has shown very good discrimination capacity (with values above 87%), but it is limited by its high operating cost and greater instrumental complexity. Considering all these aspects, the technique that offers the most balanced approach for implementation in routine controls is HS-GC–IMS, as it combines excellent discrimination capability (with classification and validation percentages above 90% in all cases studied) with moderate analysis times, very simple sample preparation, and not very high operating costs. These features make HS-GC–IMS a particularly suitable tool for practical applications requiring a high degree of reliability, efficiency, and sustainability.Although the results obtained were very promising, particularly regarding the volatile profiles analyzed by HS-GC–IMS and HS-GC–MS, the study has some limitations. First, the study focused on two peach varieties grown in a specific geographical region, so the applicability of the developed models to other varieties or geographical origin requires additional validation. Furthermore, while the chromatographic techniques used were successful, they call for specialized equipment, which might restrict their use in some situations. Future research should consider expanding the number of varieties and geographical origins.

Fingerprinting of Peach During the Ripening Process Using an Analytical Platform with Spectrometric and Volatilome-Based Chromatographic Techniques

Synthesis and Identification of 3-Oxazolines in Cocoa

Comprehensive GC×GC-TOFMS is widely used to characterize petroleum, but traditional targeted approaches can be slow and limited for large, complex data sets. A pixel-based preprocessing workflow enables rapid, untargeted chemometric exploration of two-dimensional chromatograms, allowing researchers to visualize subtle compositional differences in complex crude oils.Using this strategy, fifty Brazilian pre-salt crude oils from the Búzios field were analyzed to investigate variations in light hydrocarbons. After alignment, normalization, and multivariate analysis of total ion chromatogram matrices, PCA revealed minor yet meaningful differences among samples, while OPLS-DA successfully distinguished oils from the Barra Velha and Itapema formations—attributing lighter hydrocarbons to Barra Velha. These results demonstrate that pixel-level chemometrics is a powerful, high-throughput alternative for reservoir characterization, offering faster, more detailed insight than traditional GC×GC-TOFMS workflows.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243">Pixel-Based Chemometric Analysis of Pre-Salt Crude Oils: Advancing GC×GC-TOFMS for Reservoir Characterization</a></h4>Mônica C. Santos*, Dayane M. Coutinho, Clarisse L. Torres, Thamara A. Barra, Victor G. K. Cardoso, Raquel V. S. Silva, Daniel S. Dubois, Joelma P. Lopes, Francisco R. Aquino Neto, and Débora A. Azevedo*Energy Fuels 2025, 39, 15, 7204–7213<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.energyfuels.5c00243">https://doi.org/10.1021/acs.energyfuels.5c00243</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.Our research group has employed advanced chromatographic techniques to analyze complex matrices over the past two decades, mainly focusing on Brazilian crude oil. <a href="javascript:void(0);">(21−24)</a> GC×GC-TOFMS analysis has emerged as a pivotal tool in these studies. Vale et al. <a href="javascript:void(0);">(25)</a> demonstrated that significant improvements in crude oil sample characterization can be gained by integrating GC×GC-TOFMS with high-order chemometric methods, especially N-way partial least-squares analysis (N-PLS). Their innovative approach supports the precise calibration and prediction of properties such as American Petroleum Institute gravity (API gravity) and wax appearance temperature (WAT), achieving high accuracy and low error rates. In another work, <a href="javascript:void(0);">(26)</a> the authors used GC×GC-TOFMS with PCA analysis to assess the source- and maturity-related geochemical parameters of 11 Brazilian crude oil samples, yielding maturation indicators. These studies highlight the potential of GC×GC-TOFMS combined with chemometrics in advancing crude oil characterization; enhancing our understanding of molecular composition, biodegradation processes, source information, and maturity through enhanced data resolution and reduced data set dimensionality.As part of the ongoing stream of research on advancing analytical methods for crude oil characterization, <a href="javascript:void(0);">(7,27)</a> this work explores a novel and rapid untargeted approach to GC×GC-TOFMS data treatment with high sensitivity to detect slight differences between complex samples. This method integrates GC×GC-TOFMS analysis with pixel-based data processing and multivariate statistical analysis to differentiate the light hydrocarbon compositions (C5–C15) of 50 presalt crude oil samples from the Búzios field in Santos Basin, Brazil. This promising analytical tool can be used to support reservoir geochemistry in oil–oil correlation studies.<h3>2. Materials and Methods</h3><h4>2.4. GC-FID</h4>GC-FID analyses were performed using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=6890N">6890N Network Gas Chromatograph </a>equipped with a fused silica capillary column and a 5%-phenyl-95%-methyl-siloxane HP5-MS film (Agilent Technologies; 30 m × 0.25 mm di; 0.25 μm df). Previous studies have described the experimental conditions we adopted. <a href="javascript:void(0);">(33,34)</a> The samples were prepared by dissolving 10 mg of crude oil in 500 μL of a mixture containing deuterated internal standards (n-hexadecane-D34 and n-tetracosane-D50) at a final concentration of 100 μg mL–1. Whole oil chromatogram profiles were used to assess contamination by drilling fluids.<h4>2.5. GC×GC-TOFMS</h4>The analyses were carried out using comprehensive two-dimensional gas chromatography (GC×GC) coupled with a time-of-flight mass spectrometry (TOFMS) system (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=Pegasus+4D">Pegasus 4D</a>, Leco, St. Joseph, MI). The experimental parameters were optimized in accordance with previous research. <a href="javascript:void(0);">(7)</a> The column set consisted of a DB-5 ms (30 m, 0.25 mm ID, 0.25 μm) in the first dimension and a DB-17ht (1.0 m, 0.25 mm ID, 0.15 μm) in the second dimension (Agilent Technologies) to enhance the separation and detection of light hydrocarbons, particularly in the C5–C15 range. <a href="javascript:void(0);">(35)</a>The GC oven temperature was programmed from 35 °C (hold 3 min) to 40 °C with a heating rate of 1.0 °C min–1. The oven was then heated to 160 °C at 3.0 °C min–1. Finally, a heating rate of 20 °C min–1 was employed up to 330 °C. Helium was the carrier gas with a constant flow rate of 1.0 mL min–1. The transfer line temperature was set to 280 °C, with the ion source temperature at 230 °C, electron energy at 70 eV, mass scanning range from m/z 25 to 600 Da, and acquisition rate at 100 spectra/second. The cold-jet–hot-jet ratio between the stages was set to 2:1. The modulation periods applied were variable. Detailed information on these modulation periods, time intervals, and variable intervals can be found in the <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>.The samples were prepared by solubilizing 10.0 mg of the crude oil in 200 μL of a carbon disulfide solution containing the internal standards (IS) 2,4,4-trimethyl-1-pentene, dodecane-D26, methylcyclohexane-D14, Bicyclo[4.4.0]decane-D18 (decalin-D18), toluene-D8, p-xylene-D10, and naphthalene-D8 at a concentration of 100 μg mL–1. The selected IS represent a variety of hydrocarbon classes, including n-alkanes, iso-alkanes, naphthenes, and aromatics, covering different molecular weights. Each crude oil was weighed and solubilized in a carbon disulfide solution containing the mixture of internal standards. Immediately after, the vial was capped and kept in the refrigerator at 4 °C until analysis to avoid the loss of most volatile compounds.<h3>3. Results and Discussion</h3><h4>3.2. Untargeted Pixel-Based GC×GC-TOFMS Approach</h4>GC×GC-TOFMS crude oil analysis provided information-rich chromatographic profiles of compounds in the range of C5–C15. Different hydrocarbon classes were separated in the two-dimensional chromatogram space according to their volatility and polarity properties: n-alkanes, iso-alkanes, aromatic, and naphthenic compounds. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig1">Figure 1</a> shows the characterization of light hydrocarbons by class for two samples: #12 (from the Barra Velha Formation) and #1 (from the Itapema Formation). Depending on the signal-to-noise ratio applied in the conventional processing method, around 1,000 peaks may be detected and need to be checked manually by an analyst, which takes considerable time. Additionally, most crude oil samples exhibit similar GC × GC-TOFMS chromatogram profiles, as illustrated by <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig1">Figure 1</a>.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_1_Total_ion_chromatograms_of_samples_12_Barra_Velha_Formation_and_1_Itapema_Formation_obtained_by_GC_GC_TOFMS_analysis_deba18bb6c.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 1. Total ion chromatograms of samples #12 (Barra Velha Formation) and #1 (Itapema Formation) obtained by GC×GC-TOFMS analysis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_1_Total_ion_chromatograms_of_samples_12_Barra_Velha_Formation_and_1_Itapema_Formation_obtained_by_GC_GC_TOFMS_analysis_deba18bb6c.jpg 178w," sizes="100vw" width="992" height="870">The high compositional homogeneity among the studied samples illustrates the complexity of identifying subtle differences in crude oil samples from the Búzios field. This shows how challenging grouping crude oil according to their molecular composition can be, especially when low-concentration compounds are relevant to the differentiation. In this regard, untargeted approaches followed by chemometric analyses have become promising tools for achieving deeper crude oil correlations without assessing peaks individually.<h5>3.2.1. PCA and OPLS-DA Analyses</h5>Unsupervised PCA analysis was used as an exploratory model for the 50 GC×GC-TOFMS crude oil profiles that employed ROI 1 (range: n-C6–n-C13 in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>). The PCA score plot (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig2">Figure 2</a>A) showed a slight tendency to separate samples by GC×GC-TOMFS chromatogram profile, roughly grouping the test samples according to their geological formation of origin. The supervised OPLS-DA method was then used for sample classification, with the Itapema and Barra Velha formations incorporated as labels in the analysis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig2">Figure 2</a>B). The main advantage of OPLS-DA is that class information can be used to maximize covariance with chromatographic profiles. <a href="javascript:void(0);">(45)</a> An orthogonalization procedure in which the variance is related to class discrimination in the first latent variable (LV 1) facilitates more rigorous model interpretation. <a href="javascript:void(0);">(46)</a><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_2_Score_plot_of_A_PCA_model_applied_to_distinguish_crude_oils_4154bb5c88.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 2. Score plot of (A) PCA model applied to distinguish crude oils showing the identification of the three highly contaminated samples (#29, #41, and #47). (B) OPLS-DA model applied for sample classification. Itapema formation is represented by orange squares and Barra Velha by blue circles." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_2_Score_plot_of_A_PCA_model_applied_to_distinguish_crude_oils_4154bb5c88.jpg 245w," sizes="100vw" width="1750" height="797">Therefore, when comparing OPLS-DA and PCA analysis, it was observed that the capacity to differentiate between the geological formations was improved using the supervised model. Nevertheless, based on the OPLS-DA loadings of the reconstructed chromatogram per pixel using MATLAB (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig3">Figure 3</a>), this separation was partly influenced by the presence of olefins, which are absent from crude oil but known to be a common constituent of drilling fluids. <a href="javascript:void(0);">(47)</a> By checking the PCA score plot, three crude oil samples from the Itapema formation with high PC1 values (#29, #41, and #47) were strongly contaminated with drilling fluid (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#tbl1">Table 1</a>). This result shows that interference related to drilling fluid (an external source of compounds) may prevent the identification of slight differences in light hydrocarbons. Therefore, samples #29, #41, and #47 were removed from the original set. Additionally, a new ROI (ROI 2, n-C6-n-C12 range in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>) was established in the GC×GC chromatogram to avoid any remaining contaminant peaks without missing relevant information.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_3_Loadings_of_the_OPLS_DA_model_comprising_the_most_important_variables_f897eb0c88.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 3. Loadings of the OPLS-DA model comprising the most important variables (compounds) that express the differences between Itapema and Barra Velha formations. Itapema formation is enriched in olefins related to contamination by drilling fluids." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_3_Loadings_of_the_OPLS_DA_model_comprising_the_most_important_variables_f897eb0c88.jpg 245w," sizes="100vw" width="992" height="474"><h3>4. Conclusions</h3>This analytical strategy enabled the identification of subtle differences in fluid composition that facilitated the differentiation of samples from the Barra Velha and Itapema formations. Supervised OPLS-DA analyses indicated the critical light hydrocarbons that enabled this discrimination.The results show that the Barra Velha formation is enriched with lighter hydrocarbons than the Itapema formation. Additional PCA analysis revealed potential differences between crude oil samples within each geological formation. In the Barra Velha Formation, three samples were highlighted as distinct from the main group (#43, #7, and #9), while in the Itapema Formation, only sample #4 was distinct. This information shows that even when samples are very similar, it is still possible to identify slight differences that provide a better understanding of the fluid distribution and its relationship with the reservoir characterization.Pixel-based chemometrics proves to be a rapid and innovative complement to crude oil characterization, representing a faster alternative to the current methodologies used to process and analyze GC×GC-TOFMS data. In sum, a complex oil–oil correlation case study encompassing 47 presalt crude oil samples from Búzios Field was addressed by identifying sample-distinguishing analytes, which can be further explored with targeted chemometric tools.

Pixel-Based Chemometric Analysis of Pre-Salt Crude Oils: Advancing GC×GC-TOFMS for Reservoir Characterization

Natural rose extracts are highly valued yet prone to adulteration, making robust analytical tools essential for verifying authenticity. In this study, GC-MS was used to characterize rose fragrances and quantify major rose alcohols, including phenyl ethyl alcohol, citronellol, and geraniol. Standard accords composed of six synthetic fragrance compounds were baseline-resolved on a nonpolar column, with calibration curves established at concentrations representative of commercial samples. Principal component analysis (PCA) further differentiated accords based on their chemical signatures.To capture the volatile bouquet, HS-SPME was applied to fragrance strips and commercial materials, including rose absolute, rose oil, a perfume, and a peony flower. Both liquid injection and HS-SPME approaches provided complementary qualitative and quantitative insights. Overall, the results demonstrate the applicability of GC-MS and HS-SPME for evaluating rose alcohol composition in complex fragrance matrices and for identifying potential adulteration.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112">Determination of Rose Alcohol Composition in Extracts and Flowers via Headspace Solid-Phase Microextraction and GC-MS</a></h4>Amber M. Hupp*ACS Meas. Sci. Au 2025<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acsmeasuresciau.5c00112">https://doi.org/10.1021/acsmeasuresciau.5c00112</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.With the availability of routine synthetic methods, some fragrance houses will opt to utilize entirely synthetic accords that replicate the desired olfactory profile of rose. These accords typically include synthetic versions of the three rose alcohols in varying proportions, along with other compounds found in natural rose (e.g., eugenol, linalool, rose oxide) or those that contribute specific aromatic facets (e.g., dimethyl acetaldehyde phenyl acetate (PADMA) or cis-3-hexenal for a green note). Such accords may also incorporate natural oils or isolated molecules (isolates) from natural oils. Many of these components have been studied for their olfactory contributions to rose accords. <a href="javascript:void(0);">(10)</a>The most commonly employed instrumental technique for determining the composition of volatile fragrance compounds is gas chromatography, typically coupled with Mass Spectrometry (GC-MS). In this technique, fragrance oils and absolutes are diluted in an organic solvent and analyzed for both qualitative identification of chemical composition and quantitative determination of concentration. GC-MS is widely used in the literature for analyzing rose oils and absolutes, and although proprietary methods are rarely published, GC-MS remains the preferred instrumental technique in major fragrance houses. <a href="javascript:void(0);">(4,5,8,10,11)</a>As with all volatile mixtures, the headspace vapor above a liquid exhibits a different component ratio than the liquid itself, in accordance with Raoult’s Law. Variations in the vapor pressure cause certain components to be either enriched or diminished in the vapor phase. This phenomenon has prompted interest in analyzing the headspace of fragrance materials. A widely adopted method for such analysis is solid-phase microextraction (HS-SPME). <a href="javascript:void(0);">(12)</a> In this approach, a specialized fiber is exposed to the vapor phase above a liquid or solid sample, where volatile compounds adsorb onto the fiber’s surface. The fiber is then inserted into the GC-MS injection port, where the volatiles are thermally desorbed and introduced into the chromatographic column. HS-SPME has proven effective for profiling the aroma of rose alcohols in flowers and other fragrant materials. <a href="javascript:void(0);">(13−15)</a> Moreover, HS-SPME is valuable for analyzing solid samples that are otherwise difficult to assess via conventional GC-MS. Examples include fresh roses on the bush, freshly cut flowers, and commercial products such as lotions and candles. <a href="javascript:void(0);">(3,9)</a> Most published HS-SPME studies examined samples in isolation, either the oil or the headspace, the flower or the oil, or the oil versus the absolute, rather than analyzing both simultaneously. Consequently, methodologies vary across research groups. Furthermore, no published data currently address how the composition of rose alcohols may differ from the liquid oil or absolute to its corresponding headspace of the oil or absolute or between the flower and the resulting extract.This study presents a method for analyzing both liquid and headspace rose samples applicable throughout the production process of commercial rose materials. The objectives of this experiment are (1) to develop a method for quantifying rose alcohols in liquid products (naturals and synthetics); (2) to establish a technique for identifying volatile components via headspace sampling of liquids (oils, absolutes, perfumes) and solid materials (scent strips, flowers); and (3) to investigate the influence of extraction time on the analysis of rose alcohols in the vapor phase. A unified method applicable across all material types, from the rose flower to the natural oil product to the final fragrance product represents a significant advancement in detecting adulteration and assessing rose alcohol content in situ and has not been previously documented.<h3>Materials and Methods</h3><h4>Instrumentation</h4>Separations were performed by using an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=6890">Agilent 6890 gas chromatograph</a> coupled with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5973">Agilent 5973 mass spectrometer</a> (Agilent Technologies, Santa Clara, TX). The GC was equipped with a nonpolar ZB-5 column (5% phenyl polydimethylsiloxane, Phenomenex, 30 m × 0.25 mm × 0.25 μm i.d.). The oven temperature was optimized for separation of the six components in the rose accord as follows: 100 to 200 °C at 10 °C/min, yielding a total run time of 10.0 min. High purity helium was used as a carrier gas at a flow rate of 0.8 L/min. Each liquid sample was manually injected (1 μL from 10 μL syringe, Hamilton Company) with a split ratio of 200:1. Each headspace sample was desorbed from the SPME fiber by using a split ratio of 50:1. The inlet and transfer line temperatures were held at 250 and 280 °C, respectively. An electron-impact ionization source was utilized with a quadrupole mass analyzer operated in full-scan mode (m/z 40 – 600) with a sampling rate of 2.6 scans/s. The mass spectrometer source and quadrupole were held at 230 and 150 °C, respectively.Peak identification was performed using a NIST database (NIST14, Gaithersburg, MD) as well as retention time and mass spectral comparisons to external standards. The area of each peak was identified via integration using a common threshold (Enhanced Chemstation D.03.00.611, Agilent). For chemometric analysis, peak areas were normalized using the total area under the chromatogram in Microsoft Excel 2016. Normalized peak areas were mean-centered in Pirouette 4.5 (Infometrix, Bothell, WA) prior to subsequent chemometric analysis. All figures were created using the plotting feature in Microsoft Excel.<h4>Part 2. SPME Headspace Analysis</h4>Sampling the headspace above a natural extract or perfume is advantageous, as only the volatile components in the sample are analyzed and materials can be sampled in situ. Using SPME, the headspace above each standard and commercial sample was analyzed for percent composition and compared to direct liquid injection results. A time-based extraction study was conducted, with two time points illustrated in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112#fig5">Figure 5</a> and <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112#tbl4">Table 4</a>. Within 20 min, the more volatile PEA and citronellol were readily extracted by the SPME fiber, while the least volatile components produced only small peaks. Over time, the remaining six components become more prominent. By the 2 h mark, the peak ratios more closely resembled those observed in the liquid samples (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112#fig1">Figure 1</a>). This shift indicates that the ratio of PEA to the other components changes over time, as equilibrium is not yet established at the 20 min time point.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Meas_Sci_Au_2025_Figure_5_Chromatography_of_standard_rose_A_using_SPME_for_20_min_and_2_h_107eebedb5.jpg" alt="ACS Meas. Sci. Au 2025: Figure 5. Chromatography of standard rose A using SPME for 20 min and 2 h." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Meas_Sci_Au_2025_Figure_5_Chromatography_of_standard_rose_A_using_SPME_for_20_min_and_2_h_107eebedb5.jpg 222w," sizes="100vw" width="1500" height="1055">Several factors contribute to the low extraction efficiency of the least volatile components. First, differences in volatility play a key role. Larger molecules with lower vapor pressures require more time to enter the vapor phase. Second, in this study, the least volatile components were also present at low concentrations. According to Raoult’s law, components with both low concentration and low vapor pressure will be underrepresented in the headspace relative to the liquid. Third, only one type of SPME fiber was used; alternative fiber chemistries may be better suited for capturing the lower volatility compounds. Lastly, the samples were neither agitated nor heated during extraction, both known factors to accelerate equilibrium. To improve extraction of less volatile components, it is hypothesized that extended extraction time, agitation, or heating would be beneficial, assuming concentration and fiber chemistry remain constant. However, the objective of this study was not to reach equilibrium, but rather to assess whether the percent composition of rose alcohols could be determined using a rapid extraction method comparable in duration to direct liquid injection. Since rose alcohols are the most volatile of the six components in the standard, a shorter extraction time point was appropriate for this analysis.Headspace analysis of the commercial samples was conducted using the same 20 min SPME extraction method. Chromatograms for the rose absolute and rose oil are presented in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112#fig7">Figure 7</a>, while for the chromatogram for the rose perfume is shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00112#fig8">Figure 8</a>. As observed previously, the rose absolute consists predominately of the three rose alcohols, whereas the rose oil contains a broader array of volatile constituents. Peak intensities and areas obtained via headspace SPME differ from those observed in liquid injection, with early eluting components exhibiting enhanced signals and later eluting components being underrepresented. Nonetheless, the detection of later eluting peaks demonstrates potential for qualitative analysis of less volatile analytes, even under short nonequilibrium extraction conditions. The rose perfume sample yielded a complex chromatographic profile with numerous peaks and relatively low concentrations of the three rose alcohols, consistent with results from liquid-phase analysis. Notably, the application of HS-SPME to a fragrance strip sprayed with perfume represents a novel analytical approach, as it has not been documented in existing literature.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Meas_Sci_Au_2025_Figure_7_Chromatograms_of_headspace_analysis_by_SPME_of_rose_absolute_and_rose_oil_16013a9dbb.jpg" alt="ACS Meas. Sci. Au 2025: Figure 7. Chromatograms of headspace analysis by SPME of rose absolute and rose oil." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Meas_Sci_Au_2025_Figure_7_Chromatograms_of_headspace_analysis_by_SPME_of_rose_absolute_and_rose_oil_16013a9dbb.jpg 218w," sizes="100vw" width="1500" height="1073"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/ACS_Meas_Sci_Au_2025_Figure_8_Chromatogram_of_headspace_analysis_by_SPME_of_peony_flower_db0b14322a.jpg" alt="ACS Meas. Sci. Au 2025: Figure 8. Chromatogram of headspace analysis by SPME of peony flower and rose parfum. The rose alcohols are identified as 1-PEA, 2-citronellol, and 3-geraniol." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_ACS_Meas_Sci_Au_2025_Figure_8_Chromatogram_of_headspace_analysis_by_SPME_of_peony_flower_db0b14322a.jpg 234w," sizes="100vw" width="1488" height="992"><h3>Conclusions</h3><div>This study presents a robust analytical method for the quantitation of three rose alcohols, phenyl ethyl alcohol, citronellol, and geraniol, in natural rose extracts, commercial perfumes, and botanical samples. Liquid injection provided the most accurate representation of the original liquid formulations, serving as a benchmark for comparison. PCA effectively clustered rose accord standards based on the percent composition of rose alcohols and minor chemical constituents, demonstrating its utility in distinguishing sample categories and chemical profiles.Calibration of the percent area for each rose alcohol was successfully achieved across both natural and synthetic materials without the use of an internal standard. In cases where direct liquid sampling is impractical or the sample is nonliquid, headspace analysis via HS-SPME offers a viable alternative. HS-SPME enabled rapid and effective detection of the target rose alcohols in various commercial products along with additional volatile components. While calibration accuracy decreased with increasing sample complexity, particularly in matrices containing numerous volatile compounds, the method remained capable of estimating rose alcohol percentages and identifying supplementary peaks. Notably, headspace analysis of a fresh peony flower confirmed the presence of all three rose alcohols, underscoring the potential of HS-SPME for analyzing solid botanical samples. Overall, this approach enables the determination of rose alcohol concentrations in both liquid and headspace of diverse sample types. The method offers a promising tool for monitoring compositional changes throughout the production process of rose-derived materials and may aid in the detection of adulteration in essential oils and absolutes.</div>

Determination of Rose Alcohol Composition in Extracts and Flowers via Headspace Solid-Phase Microextraction and GC-MS

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