Variety‐based discrimination of apple juices by an electronic nose and gas chromatography–mass spectrometry

The variety of raw material plays a crucial role in the quality and authenticity of fruit juices and juice products. To characterise and classify apple juices according to variety on the basis of their volatile compounds, electronic nose (EN) and gas chromatography–mass spectrometry (GC‐MS) were applied to detect the apple juices prepared by eight different varieties. The EN was used to analyse the mixture of volatile compounds as a whole and enabled rapid classification of juice samples when coupled with linear discriminant analysis (LDA). LDA showed a perfect discrimination of apple juices based on varieties. GC‐MS was utilised to illustrate the differences of volatile compounds among juice samples. Identification of volatile compositions and their contents provides useful access to differentiate juices from different varieties.     

Discrimination of oolong tea (Camellia sinensis) varieties based on feature extraction and selection from aromatic profiles analysed by HS-SPME/GC–MS

This study aimed to develop an objective and accurate analytical method to discriminate oolong tea varieties that easily causing adulteration by potential volatile compounds. A total of 75 oolong tea samples of five similar varieties (Tieguanyin, Benshan, Maoxie, Huangjingui and Jinguanyin) were analysed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS). The relative content of 26 major volatile compounds varied significantly according to variety, combined with the results of hierarchical cluster analysis (HCA), indicating that the varietal differences of aromatic profile remain significant for tea cultivars with very close origin. Principal component analysis (PCA) of the aromatic profiles showed that the feature of variety dominated over the other features (like producing region and quality). By stepwise linear discriminant analysis (S-LDA), 18 volatiles with the best discriminating capacity were selected, and 4 discriminant functions (DFs) enabled simultaneously discrimination of the five oolong varieties with 100% correct rate.     

Feature Selection and Recognition from Nonspecific Volatile Profiles for Discrimination of Apple Juices According to Variety and Geographical Origin

Abstract: Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid‐phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography‐mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. Practical Application: This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS‐SPME‐GC‐MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product.     

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.     

Differentiation of raw spirits of rye, corn and potato using chromatographic profiles of volatile compounds

BACKGROUND: The origin of the raw spirits influences the sensory quality of rectified spirits that are subsequently used for the production of vodka. The aim of this research was to evaluate the effectiveness of two methods based on the comparison of profiles of volatile compounds [solid phase microextraction–gas chromatography–mass spectrometry (SPME‐GC‐MS) and gas chromatography with flame ionisation detection (GC‐FID)] for the determination of origin of raw spirits obtained from rye, corn (maize) and potato. RESULTS: Profiles obtained for the volatile compounds by using these methods were different and were influenced by the sample introduction method. The main groups of volatiles obtained using SPME‐GC‐MS method were fatty acid ethyl esters, while in the case of GC‐FID fusel alcohols and ethyl acetate dominated. Data obtained from these methods were treated using principal component analysis and linear discriminant analysis to test the possibility of sample differentiation and classification. It was relatively easy to differentiate potato spirits from the remainder; however, it was not possible to fully distinguish the corn samples from rye samples. The classification ability of the SPME‐GC‐MS method was 95% but the prediction ability was 97.4%. For the GC‐FID method the classification ability was 90.1%, whereas the prediction ability was 94.27%. CONCLUSIONS: The methods presented can be used for reliable differentiation of potato spirits from corn and rye spirits. Copyright © 2012 Society of Chemical Industry     

LC–MS/MS analysis of phenols for classification of red wine according to geographic origin,grape variety and vintage

A rapid LC–MS/MS method for quantification of phenols and polyphenols in authentic wine samples with unrivalled sensitivity was developed. Excellent limits of detection in the low μg L⁻¹ range were achieved. Reversed phase HPLC employing sub-2 μm particles as stationary phase allowed high-throughput analysis with analysis times of 10 min for 11 compounds. The phenolic pattern was assessed in 97 authentic wine samples (stored under identical conditions without further treatment or modification) comprising eleven geographical Austrian regions, six grape varieties and five vintages. Canonical discriminant analysis was applied to the data set showing the suitability of the (poly)phenolic spectrum for classification of the wine samples. The method allowed a geographic discrimination of several grape varieties and a grape variety based discrimination of four regions. As a novel finding excellent (poly)phenol-based differentiation of the five investigated vintages (2003–2007) was achieved.     

Aroma Characterization of Virgin Olive Oil from Two Turkish Olive Varieties by SPME/GC/MS

Virgin olive oil samples of Ayvalik and Memecik olive varieties from the Aegean region of Turkey were evaluated for their acidity, peroxide, and color values, and the volatile compounds were investigated by using solid phasemicro extraction/gas chromatography/mass spectroscopy (SPME/GC/MS). The acidity of all the samples were found to be between 0.35–1.0% oleic acid and peroxide values of the oil samples ranged between 14–52 meq/O₂ kg of oil. The Y, R, and B values of the samples were between 70–73.60, 1.30–4.70, and 0–3.60, respectively. The SPME/GC/MS results have shown that hexanal (5.66–15.48%), 3 hexen 1 ol (3.12–12.1%), cis 3 hexenol (6.15–13.49%), and 9 octadecenoic acid (5.99-16.01%) were observed as the characteristic volatiles for Ayvalik variety, while trans 2 hexanal (8.65–38.09%) and 3 hexen 1 ol acetate (0.97–7.43%) were observed as the characteristic volatiles for Memecik variety.     

Multiresidue Analysis of 113 Pesticides in Different Maturity Levels of Mangoes Using an Optimized QuEChERS Method with GC-MS/MS and UHPLC-MS/MS

In this study, a simple and high-throughput multiresidue pesticide analysis method based on the quick, easy, cheap, effective, rugged, safe (QuEChERS) extraction combined with gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) and ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) is presented for the determination of 113 pesticides in green and ripe mangoes. Different buffer systems and purification sorbents were optimized to get better recovery. Extracts from mangoes after acid acetonitrile and ammonium acetate extraction were directly injected to UHPLC-MS/MS analysis, whereas other GC-amenable compounds were treated with cleanup approaches with a mixture of sorbents [400 mg primary-secondary amine (PSA) and 400 mg graphitized carbon black (GCB)] for GC-MS/MS analysis. Recoveries for the majority of pesticides at spike levels of 10, 50, and 100 μg/kg were ranged between 70 and 120% with RSD values below 20%. Limits of detection (LODs) were below 4 μg/kg, whereas limits of quantification (LOQs) were below 10 μg/kg. Response linearity was good in the range between 5 and 500 μg/kg. The matrix effect for 56 pesticides by GC-MS/MS analysis exhibited a non-significant matrix effect (≤?25%) in green and ripe mangoes. For 57 pesticides used for UHPLC-MS/MS analysis, the optimized method in green mango provided no significant matrix effect (≤?25%) for 63% of the compounds, whereas only for 16 and 21% compounds, the matrix effect was medium (25–40%) and strong (>?40%); the optimized method in ripe mango provided no significant matrix effect (≤?25%) for 61% of the compounds, whereas only for 26 and 12% compounds, the matrix effect was medium (25–40%) and strong (>?40%). Following the application of a “top-down” approach, the expanded measurement uncertainty was both 21% on average (coverage factor k =?2, confidence level 95%) in green and ripe mangoes. The application of GC-MS/MS and UHPLC-MS/MS coupled with the modified QuEChERS extraction procedures was proved to adequately quantitate these pesticides in different maturity levels of mangoes.     

Simultaneous determination of bisphenol A,octylphenol, and nonylphenol by pressurised liquid extraction and liquid chromatography–tandem mass spectrometry in powdered milk and infant formulas

A new analytical method, using pressurised liquid extraction (PLE) and liquid chromatography–tandem mass spectrometry (LC–MS/MS), was developed for the simultaneous determination of bisphenol A (BPA), octylphenol (OP) and nonylphenol (NP) in powdered infant formulas (IF) and powdered skimmed milk (PM). The analytes were extracted by PLE, using this optimised conditions: ethyl acetate as solvent, 70 °C of temperature, reversed-phase silica C₁₈ as dispersing agent and three cycles of extraction. The extracts were then injected in LC–MS/MS using a Gemini C₁₈ column and a mixture of 5% water and 95% methanol/acetonitrile, both with 0.1% ammonia, as a mobile phase. Recoveries at different fortification levels (0.5 and 0.05 mg kg⁻¹), were between 89% and 92% for BPA, 84 and 98% for OP, and 93% and 101% for NP. The method was applied to the analysis of samples of PM and IF, bought in Italian and Spanish markets. In positive samples, phenols concentration ranged from 0.07 to 1.29 mg kg⁻¹ for BPA, from 0.028 to 1.55 mg kg⁻¹ for OP and from 0.026 to 1.47 mg kg⁻¹ for NP.     

Classification of Boal,Malvazia, Sercial and Verdelho wines based on terpenoid patterns

Thirty-six Madeira wine samples from Boal, Malvazia, Sercial and Verdelho white grape varieties were analyzed in order to estimate the free fraction of monoterpenols and C₁₃ norisoprenoids (terpenoid compounds) using dynamic headspace solid phase micro-extraction (HS-SPME) technique coupled with gas chromatography–mass spectrometry (GC–MS). The average values from three vintages (1998–2000) show that these wines have characteristic profiles of terpenoid compounds. Malvazia wines exhibits the highest values of total free monoterpenols, contrary to Verdelho wines which had the lowest levels of terpenoids but produced the highest concentration of farnesol. The use of multivariate analysis techniques allows establishing relations between the compounds and the varieties under investigation. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to the obtained matrix data. A good separation and classification power between the four groups as a function of their varietal origin was observed.     

Study of chloropropanols in soy sauce by gas chromatography-triple quadrupole mass spectrometry with coupled column separation without derivatisation

A qualitative and quantitative determination method for chloropropanols in soy sauce was developed by GC-MS/MS with coupled column separation without derivatisation. The target compounds were 1,3-dichloropropan-2-ol (1,3-DCP), 2,3-dichloropropan-1-ol (2,3-DCP), 3-monochloropropane-1,2-diol (3-MCPD) and 2-monochloropropane-1,3-diol (2-MCPD). 3-MCPD-d₅ was used as an isotope internal standard for MCPDs and 1,3-DCP-d₅ for DCPs. After spiking with internal standards and mixed with 1?g of Extrelut? NT, about 1?g of the sample was washed by 4?ml of hexane and the analytes were eluted with 15?ml of 95% (v/v) ethyl ether/hexane mixture. The concentrated extract was directly injected without derivatisation, separated by a coupled column – a 3?m Innowax (polyethylene glycol) combined with a 30?m DB-5?ms ((5%-phenyl)-methylpolysiloxane) by a quartz capillary column connector – and detected by GC-MS/MS. The limits of detection (LODs) in the sample matrix were 1.0, 2.5, 5.0 and 5.0?µg?kg^?1 for the above chloropropanols, respectively. The relative proportions of 2-MCPD/3-MCPD ranged from 0.19 to 3.74 in soy sauce samples. 2,3-DCP and 2-MCPD were more stable than 1,3-DCP and 3-MCPD under alkaline condition. The levels of chloropropanols can be decreased by an alkaline treatment process.     

Supercritical fluid extracts from the Brazilian cherry (Eugenia uniflora L.): Relationship between the extracted compounds and the characteristic flavour intensity of the fruit

Supercritical carbon dioxide (SC-CO₂) extractions of Brazilian cherry (Eugenia uniflora L.) were carried out under varied conditions of pressure and temperature, according to a central composite 2² experimental design, in order to produce flavour-rich extracts. The composition of the extracts was evaluated by gas chromatography coupled with mass spectrometry (GC/MS). The abundance of the extracted compounds was then related to sensory analysis results, assisted by principal component and factorial discriminant analysis (PCA and FDA, respectively). The identified sesquiterpenes and ketones were found to strongly contribute to the characteristic flavour of the Brazilian cherry. The extracts also contained a variety of other volatile compounds, and part of the fruit wax contained long-chain hydrocarbons that according to multivariate analysis, contributed to the yield of the extracts, but not the flavour. Volatile phenolic compounds, to which antioxidant properties are attributed, were also present in the extracts in high proportion, regardless of the extraction conditions.     

Evaluating the potential of phenolic profiles as discriminant features among extra virgin olive oils from Moroccan controlled designations of origin

Herewith, the potential of an approach based on the combination of the determination of phenolic compounds and the use of chemometric treatments has been evaluated to establish, for the first time, promising models to authenticate the provenance of Moroccan monovarietal olive oils produced under different geographical origin indication systems. To achieve this purpose, 136 commercial extra virgin olive oil samples from three diverse production areas (Meknès territory; the Protected Geographical Indication Ouazzane; and the Protected Designation of Origin Tyout-Chiadma) were collected over two consecutive crop seasons (2012/2013 and 2013/2014). Their phenolic fraction composition was investigated by using high performance liquid chromatography coupled to mass spectrometry (HPLC-ESI-IT MS). The results showed that geographical provenance and harvest season had a marked influence on the content of identified phenolic compounds. Principal components analysis (PCA) and linear discriminant analysis (LDA) were applied to test the potential of the determined compounds as geographical discriminant features, achieving a noticeable discrimination among the three evaluated regions. The contribution of each analyte to the statistic model has been evaluated in depth.     

采用气相色谱-离子迁移谱技术检测黄酒风味物质

为实现黄酒中挥发性风味物质的快速无损检测,本研究采用气相色谱-离子迁移谱(Gas chromatography-ion mobility spectrometry,GC-IMS)联用技术结合化学计量学方法对不同酒龄黄酒中的挥发性风味物质进行了研究。黄酒样本的GC-IMS图片库显示,不同酒龄黄酒的挥发性风味物质浓度存在显著差异。采用差谱法从谱图中筛选出33个特征峰,利用其中13个数据库可查询到对应物质的特征峰进行黄酒的风味成分分析。以33个特征峰峰高作为变量,通过主成分分析(principal component analysis,PCA)进行降维,前6个主成分累计贡献率为95%,可以有效区分各组样本。分别采用线性判别(Linear Discriminant Analysis,LDA)、K-最近邻分类算法(K-Nearest Neighbor,KNN)建立酒龄判别模型。结果显示,LDA方法得到的训练集和预测集识别率分别为95%和90%,KNN的判别效果较好,训练集和预测集的识别率均达到100%。这说明,GC-IMS可以有效应用于黄酒挥发性风味物质的检测,在食品风味物质分析等领域具有广阔的应用前景。     

A Comparative Study of the Ionization Modes in GC–MS Multi-residue Method for the Determination of Organochlorine Pesticides and Polychlorinated Biphenyls in Crayfish

A gas chromatography–mass spectrometry (GC–MS) multi-residue method with different ionization modes has been developed for the determination of 23 organochlorine pesticides and ten polychlorinated biphenyls (PCBs) in crayfish. The approach was based on an extraction with acetonitrile saturated by hexane (containing 0.1 % acetic acid), followed by a dispersed solid-phase extraction cleanup with primary secondary amine and octadecylsilane, and finally analyzed by GC–MS/MS operated in electron impact (EI) and positive chemical ionization (PCI), and GC–MS in negative chemical ionization (NCI), respectively. An isotope internal standard, DDT-D₈, was used for quantification. These three GC–MS procedures were validated and compared using crayfish samples fortified at three levels (5, 10 and 20 ng/g). Good linearity, accuracy, precision and sensitivity were achieved by all three ionization modes with recoveries in the range of 70 to 112 %, relative standard deviations (RSDs) below 10 % and limits of detection (LODs) in the range of 0.002 to 8 ng/g. Extremely high selectivity was obtained by both EI- and PCI-MS/MS, while PCI was more sensitive to compounds containing nitro and carbonyl groups, and EI was a better choice for dieldrin, endosulfan, and its metabolites. NCI-MS had the highest sensitivity but lower anti-interference ability and was not suitable for the determination of DDTs and PCBs. These three ionization modes can be used as complementary and alternative in routine GC–MS analysis of organochlorine pesticides and polychlorinated biphenyls in food.     

Prediction of the genetic variety of Spanish extra virgin olive oils using fatty acid and phenolic compound profiles established by direct infusion mass spectrometry

The genetic varieties of Spanish extra virgin olive oils (Arbequina, Hojiblanca and Picual) were predicted by direct infusion of the samples in the electrospray ionization source of a mass spectrometer, followed by linear discriminant analysis of the spectral data. The samples were 1:50 diluted (v/v) with an 85:15 propanol/methanol (v/v) mixture containing 40 mM KOH and infused. The abundances of the [M–H]⁻ peaks of the free fatty acids (7 peaks) and 28 phenolic compounds (20 peaks) were measured. Ratios of pairs of peak abundances were used as predictors in the construction of the linear discriminant analysis models. An excellent resolution between the three genetic varieties was achieved.     

Classification of extra virgin olive oils according to their genetic variety using linear discriminant analysis of sterol profiles established by ultra-performance liquid chromatography with mass spectrometry detection

A method to classify extra virgin olive oils (EVOOs) according to their genetic variety using sterol profiles obtained by ultra-performance liquid chromatography (UPLC) with atmospheric pressure chemical ionization mass spectrometry (APCI-MS) detection has been developed. The optimal separation conditions were obtained using a gradient acetonitrile/water (0.01% acetic acid) at a flow rate of 0.8 mL min^− 1 and 10 °C. Linear discriminant analysis (LDA) models were constructed with the 11 UPLC-APCI-MS sterol peaks taken from the selective ion recording mode chromatograms. Ratios of the peak areas selected by pairs were used as predictors. With the sequential application of two LDA models and using a 95% probability, the EVOO samples belonging to seven genetic varieties mainly produced at La Comunitat Valenciana, Spain, were correctly classified with a prediction capability higher than 97%.     

Validation of a liquid chromatography–mass spectrometry method for determining the migration of primary aromatic amines from cooking utensils and its application to actual samples

Many cooking utensils are made of nylon, a material that may incorporate azodyes and where primary aromatic amines (PAAs) are the starting substances. Moreover, aromatic amines may also be present as technical impurities. Another source of PAAs could be aromatic isocyanates used as monomers in the production of polyurethanes. The aim of this work was to validate a simple LC–MS/MS method for the determination of eight primary aromatic amines (m-phenylenediamine, 2,6- and 2,4-toluenediamine, 1,5-diaminonaphthalene, aniline, 4,4′-diaminonaphenylether, 4,4′-methylenedianiline and 3,3′-dimethylbenzidine) in the aqueous food simulant 3% acetic acid (w/v). The detection limits calculated were adequate with respect to present legislation. The method was validated at four concentration levels (2, 5 10 and 20 µg kg^?1). Global internal reproducibility was in the range 5.6–21.4% (RSD_R) depending on the compound and concentration. Mean recoveries for all levels varied between 89 and 100%, depending on the amine. A total of 39 samples of cooking utensils were analyzed using the described method and the results obtained after the third migration test were not compliant in approximately half of the samples.     

Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries

Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n = 50) were scanned in the VIS and NIR regions (400–2500 nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required.     

LC-DAD-ESI/MS analysis of flavonoids and abscisic acid with chemometric approach for the classification of Slovenian honey

The flavonoid profiles of seven types of Slovenian honey were analysed. The flavonoids are phenolic compounds that have health-related properties, and their presence in honey is attributed to the plants visited by the bees. The flavonoids were extracted from honey samples by solid-phase extraction and analysed by liquid chromatography, with diode array detection and electrospray ionisation mass spectrometry (LC-DAD-ESI/MS). The honey samples had similar, but quantitatively different, flavonoid profiles. The flavonoids myricetin, luteolin, quercetin, naringenin, apigenin, kaempferol, pinocembrin, chrysin and galangin were identified using reference standards, while pinobanksin was tentatively identified through its retention time, m/z, UV spectrum and MS/MS data. Two non-phenolic compounds were also identified; cis–trans-abscisic acid and, tentatively, trans–trans-abscisic acid. The analysis did not show any specific compounds to use as markers for determination of the botanical origins of these different types of Slovenian honey. Further analyses of more honey samples with additional standards are needed to identify such potential markers, although linear discriminant analysis correctly classified 85% of these honey samples according to their botanical origins.