Chemical Composition, Antimicrobial Activity, and Mode of Action of Essential Oils against Paenibacillus larvae, Etiological Agent of American Foulbrood on Apis mellifera.

This study aimed to characterize the chemical composition of Aloysia polystachia, Acantholippia seriphioides, Schinus molle, Solidago chilensis, Lippia turbinata, Minthostachys mollis, Buddleja globosa, and Baccharis latifolia essential oils (EOs), and to evaluate their antibacterial activities and their capacity to provoke membrane disruption in Paenibacillus larvae, the bacteria that causes the American Foulbrood (AFB) disease on honey bee larvae. The relationship between the composition of the EOs and these activities on P. larvae was also analyzed. Monoterpenes were the most abundant compounds in all EOs. All EOs showed antimicrobial activity against P. larvae and disrupted the cell wall and cytoplasmic membrane of P. larvae provoking the leakage of cytoplasmic constituents (with the exception of B. latifolia EO). While, the EOs' antimicrobial activity was correlated most strongly to the content of pulegone, carvone, (Z)-ß-ocimene, δ-cadinene, camphene, terpinen-4-ol, elemol, ß-pinene, ß-elemene, γ-cadinene, α-terpineol, and bornyl acetate; the volatiles that better explained the membrane disruption were carvone, limonene, cis-carvone oxide, pentadecane, trans-carvyl acetate, trans-carvone oxide, trans-limonene oxide, artemisia ketone, trans-carveol, thymol, and γ-terpinene (positively correlated) and biciclogermacrene, δ-2-carene, verbenol, α-pinene, and α-thujene (negatively correlated). The studied EOs are proposed as natural alternative means of control for the AFB disease.

Untargeted Metabolomic Liquid Chromatography High-Resolution Mass Spectrometry Fingerprinting of Apple Cultivars for the Identification of Biomarkers Related to Resistance to Rosy Apple Aphid.

Liquid chromatography high-resolution mass spectrometry fingerprinting together with pattern recognition techniques was used to determine the metabolites involved in the susceptibility of apple cultivars to rosy apple aphid (RAA). Preprocessing of ultra-high-performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry raw data of resistant and susceptible apple cultivars was carried out with XCMS and CAMERA packages. Univariate statistical tools and multivariate data analysis highlighted significant different profiles of the apple metabolomes according to their tolerance to RAA. Optimized and cross-validated Partial least squares discriminant analysis and orthogonal projections to latent structures discriminant analysis models confirmed trans-4-caffeoylquinic acid and 4-p-coumaroylquinic acid as biomarkers for the identification of resistant and susceptible apple cultivars to RAA and disclosed that only hydroxycinnamic acids are involved in the disease susceptibility of cultivars. In this sense, the final steps of the biosynthesis of caffeoylquinic acid (CQA) and p-coumaroylquinic acid (p-CoQA) become decisive because the isomerization of 5-CQA to 4-CQA is favored in resistant cultivars, whereas the isomerization of 5-p-CoQA to 4-p-CoQA is favored in susceptible cultivars.

Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation.

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a "year-by-year" validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.

Polyphenolic profile of butterhead lettuce cultivar by ultrahigh performance liquid chromatography coupled online to UV-visible spectrophotometry and quadrupole time-of-flight mass spectrometry.

In the present study, the butterhead lettuce cultivar was analyzed by ultrahigh performance liquid chromatography (UHPLC) coupled online to diode array detection (DAD), electrospray ionization (ESI) and quadrupole time-of-flight mass spectrometry (QToF/MS) in the positive and negative ion mode in order to characterize its polyphenolic profile for the first time. The instrument acquisition mode MSE was used to collect automatic and simultaneous information of exact mass at high and low collision energies of precursor ions as well as other ions produced as a result of their fragmentation. One hundred eleven phenolic compounds were identified in the acidified hydromethanolic extract of freeze-dried leaves of butterhead lettuce cultivar: 40 hydroxycinnamic acid derivatives, 21 hydroxybenzoic acid derivatives, 2 hydroxyphenylacetic acid derivatives, 18 flavonols, 9 flavones, one flavanone, 7 coumarins, one hydrolysable tannin and 12 lignans. Forty-seven of these compounds have been tentatively identified for the first time in lettuce.

Relationship between hydroxycinnamic acids and the resistance of apple cultivars to rosy apple aphid.

The phenolic profiles of apple cultivars from the SERIDA Asturian cider apple breeding program, including parents and progenies, were determined by ultrahigh-performance liquid chromatography-diode array detector-electrospray ionization-quadrupole time of flight/mass spectrometer in order to study the relationship between phenols and the resistance of apple tree cultivars to rosy apple aphid (RAA). A pattern recognition technique named partial least square discriminant analysis (PLS-DA) was used to classify apple cultivars based on resistance to RAA, resistant and susceptible, reaching scores with accuracy higher than 97% and 91% respectively. Hydroxycinnamic acids, particularly 4-caffeoylquinic acid (4-CQA) and 4-p-coumaroylquinic acid (4-pCoQA), were identified as the major player in RAA resistance by the PLS-DA model. Indeed, the isomerisation 5-CQA → 4-CQA is favoured in resistant cultivars, whereas the isomerisation 5-pCoQA → 4-pCoQA is favoured in susceptible cultivars. As a result, resistant cultivars accumulate higher amounts of 4-CQA than susceptible ones, and the opposite occurs for 4-pCoQA. Also, minor isomerisations of 5-CQA to 1-CQA or 3-CQA show opposite behaviour for resistant and susceptible cultivars. Cultivar resistance to RAA is concluded to be related with the phenylpropanoid pathway, the isomerisation reactions being the key metabolic reaction for a cultivar to be resistant or susceptible to RAA.

Supervised pattern recognition in food analysis.

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.

Characterization of phenolic compounds in green and red oak-leaf lettuce cultivars by UHPLC-DAD-ESI-QToF/MS using MSE scan mode.

Lettuce (Lactuca sativa) is one of the most popular leafy vegetables in the world and constitutes a major dietary source of phenolic compounds with health-promoting properties. In particular, the demand for green and red oak-leaf lettuces has considerably increased in the last years but few data on their polyphenol composition are available. Moreover, the usage of analytical edge technology can provide new structural information and allow the identification of unknown polyphenols. In the present study, the phenolic profiles of green and red oak-leaf lettuce cultivars were exhaustively characterized by ultrahigh-performance liquid chromatography (UHPLC) coupled online to diode array detection (DAD), electrospray ionization (ESI), and quadrupole time-of-flight mass spectrometry (QToF/MS), using the MSE instrument acquisition mode for recording simultaneously exact masses of precursor and fragment ions. One hundred fifteen phenolic compounds were identified in the acidified hydromethanolic extract of freeze-dried lettuce leaves. Forty-eight of these compounds were tentatively identified for the first time in lettuce, and only 20 of them have been previously reported in oak-leaf lettuce cultivars in literature. Both oak-leaf lettuce cultivars presented similar phenolic composition, except for apigenin-glucuronide and dihydroxybenzoic acid, only detected in the green cultivar; and for luteolin-hydroxymalonylhexoside, an apigenin conjugate with molecular formula C40 H54 O19 (monoisotopic MW = 838.3259 u), cyanidin-3-O-glucoside, cyanidin-3-O-(3″-O-malonyl)glucoside, cyanidin-3-O-(6″-O-malonyl)glucoside, and cyanidin-3-O-(6″-O-acetyl)glucoside, only found in the red cultivar. The UHPLC-DAD-ESI-QToF/MSE approach demonstrated to be a useful tool for the characterization of phenolic compounds in complex plant matrices.

Validated analytical strategy for the determination of polycyclic aromatic compounds in marine sediments by liquid chromatography coupled with diode-array detection and mass spectrometry.

The aim of this work was to optimise and validate the experimental conditions for the analysis of 20 polycyclic aromatic compounds (PACs) [19 polycyclic aromatic hydrocarbons (PAHs) and dibenzothiophene as polycyclic aromatic sulphur heterocycle (PASH)] in marine sediments by reversed-phase high-performance liquid chromatography (LC) coupled to photodiode array detection (DAD) and to mass spectrometry (MS). The LC-MS interface used was atmospheric pressure chemical ionization (APCI) in the positive ion mode. The operational parameters of the APCI interface and MS detection, such as organic modifier, fragmentation voltage, gain, vaporizer temperature, corona current, capillary voltage, drying gas (N2) and nebulizer pressure, were studied. The sediments were subjected to microwave-assisted solvent extraction (MAE) and clean-up by solid-phase extraction (SPE). The relevance of the selected PACs lies in the fact that 16 PACs are classified by the US Environmental Protection Agency as priority pollutants; 17 PACs are detected in the Prestige oil spill; and 8 PACs are included in the priority substance list of the EU water policy. Recoveries from 47% to 102% were obtained for SRM 1944 certified reference sediment. The limits of quantitation were lower than 100 ngg(-1) dry weight for most PACs, and good precision was achieved.

On-line characterisation of apple polyphenols by liquid chromatography coupled with mass spectrometry and ultraviolet absorbance detection.

Apple polyphenols were characterised by means of hyphenated techniques such as HPLC coupled to UV photodiode array detection (LC-DAD) and to mass spectrometry (LC-MS). LC-MS using atmospheric pressure ionisation (APCI) in the positive ion mode provided the molecular weight, the number of hydroxyl groups, the number of sugars and an idea about the substitution pattern of the polyphenols. LC-DAD with postcolumn addition of UV shift reagents afforded precise structural information about the position of the free hydroxyl groups in the polyphenolic nucleus. Five isorhamnetin glycosides, two hydroxyphloretin glycosides and quercetin were reported in apple peel for the first time. Postcolumn addition of UV shift reagents in LC-DAD analysis confirmed the presence of isorhamnetin glycosides and not the isomeric glycosides of rhamnetin. Moreover, isorhamnetin-3-O-rhamnoglucoside was identified unambiguously by comparison with a standard. These results are relevant not only from a chemotaxonomic point of view, but also in the control of authenticity of fruit derived products in order to detect fraudulent admixtures.

Polyphenolic profiles of Basque cider apple cultivars and their technological properties.

The polyphenolic compositions of 31 Basque cider apple cultivars were determined in pulp, peel, and juice by high-performance liquid chromatography with diode array detection analysis of crude extracts and after thiolysis. Total polyphenols are distributed in a wide concentration range depending on the cultivar. Procyanidins are the class of polyphenols that present major concentrations in apple. Their average degrees of polymerization range from 4 to 8 depending on the cultivar. Apple cultivars were technologically classified into bitter and nonbitter categories using different classification systems obtained by applying several pattern recognition techniques, such as principal component analysis, K-nearest neighbors, soft independent modeling of class analogy, partial least-squares, and multilayer feed-forward-artificial neural networks, to apple pulp, peel, or juice data (individual polyphenol concentrations, total procyanidin content, and the average degree of polymerization of procyanidins). Bitter apple cultivars present higher contents of flavan-3-ols and/or dihydrochalcones than nonbitter cultivars. Detailed knowledge of the polyphenolic profile of each apple cultivar affords information about their susceptibility to oxidation, their sensory properties (bitterness, astringency), and their possible influence on the characteristics and quality of the final product (juice, cider) when apples are processed.

Technological classification of basque cider apple cultivars according to their polyphenolic profiles by pattern recognition analysis.

The polyphenolic compositions of 31 Basque cider apple cultivars were determined in pulp, peel, and juice by high-performance liquid chromatography--diode array detection analysis of crude extracts and after thiolysis. Data sets, consisting of individual polyphenol concentrations, total procyanidin content, and the average degree of polymerization of procyanidins, were evaluated by multivariate chemometric techniques, to develop decision rules for classifying apple cultivars technologically into bitter and nonbitter categories. A preliminary study of the data structure was performed by cluster analysis and principal component analysis in each apple material. Bitter apple varieties presented higher contents of flavan-3-ols and/or dihydrochalcones than nonbitter cultivars. Different classification systems for the two categories on the basis of the chemical data were obtained applying several supervised pattern recognition procedures, such as linear discriminant analysis, K-nearest neighbors, soft independent modeling of class analogy, partial least-squares, and multilayer feed forward artificial neural networks. Excellent performance in terms of recognition and prediction abilities for both categories (100% of hits) was achieved in every case (pulp, peel, or juice). Polyphenolic profiles of apple pulp, peel, or juice provide enough information to develop classification criteria for establishing the technological group of apple cultivars (bitter or nonbitter).

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition.

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.

Virgin olive oil authentication by multivariate analyses of 1H NMR fingerprints and delta13C and delta2H data.

(1)H NMR fingerprints of virgin olive oils (VOOs) from the Mediterranean basin (three harvests) were analyzed by principal component analysis, linear discriminant analysis (LDA), and partial least-squares discriminant analysis (PLS-DA) to determine their geographical origin at the national, regional, or PDO level. Further delta(13)C and delta(2)H measurements were performed by isotope ratio mass spectrometry (IRMS). LDA and PLS-DA achieved consistent results for the characterization of PDO Riviera Ligure VOOs. PLS-DA afforded the best model: for the Liguria class, 92% of the oils were correctly classified in the modeling step, and 88% of the oils were properly predicted in the external validation; for the non-Liguria class, 90 and 86% of hits were obtained, respectively. A stable and robust PLS-DA model was obtained to authenticate VOOs from Sicily: the recognition abilities were 98% for Sicilian oils and 89% for non-Sicilian ones, and the prediction abilities were 93 and 86%, respectively. More than 85% of the oils of both categories were properly predicted in the external validation. Greek and non-Greek VOOs were properly classified by PLS-DA: >90% of the samples were correctly predicted in the cross-validation and external validation. Stable isotopes provided complementary geographical information to the (1)H NMR fingerprints of the VOOs.

Botanical and geographical characterization of green coffee (Coffea arabica and Coffea canephora): chemometric evaluation of phenolic and methylxanthine contents.

Green coffee beans of the two main commercial coffee varieties, Coffea arabica (Arabica) and Coffea canephora (Robusta), from the major growing regions of America, Africa, Asia, and Oceania were studied. The contents of chlorogenic acids, cinnamoyl amides, cinnamoyl glycosides, free phenolic acids, and methylxanthines of green coffee beans were analyzed by liquid chromatography coupled with UV spectrophotometry to determine their botanical and geographical origins. The analysis of caffeic acid, 3-feruloylquinic acid, 5-feruloylquinic acid, 4-feruloylquinic acid, 3,4-dicaffeoylquinic acid, 3-caffeoyl-5-feruloylquinic acid, 3-caffeoyl-4-feruloylquinic acid, 3-p-coumaroyl-4-caffeoylquinic acid, 3-caffeoyl-4-dimethoxycinnamoylquinic acid, 3-caffeoyl-5-dimethoxycinnamoylquinic acid, p-coumaroyl-N-tryptophan, feruloyl-N-tryptophan, caffeoyl-N-tryptophan, and caffeine enabled the unequivocal botanical characterization of green coffee beans. Moreover, some free phenolic acids and cinnamate conjugates of green coffee beans showed great potential as means for the geographical characterization of coffee. Thus, p-coumaroyl-N-tyrosine, caffeoyl-N-phenylalanine, caffeoyl-N-tyrosine, 3-dimethoxycinnamoyl-5-feruloylquinic acid, and dimethoxycinnamic acid were found to be characteristic markers for Ugandan Robusta green coffee beans. Multivariate data analysis of the phenolic and methylxanthine profiles provided preliminary results that allowed showing their potential for the determination of the geographical origin of green coffees. Linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA) provided classification models that correctly identified all authentic Robusta green coffee beans from Cameroon and Vietnam and 94% of those from Indonesia. Moreover, PLS-DA afforded independent models for Robusta samples from these three countries with sensitivities and specificities of classifications close to 100% and for Arabica samples from America and Africa with sensitivities of 86 and 70% and specificities to the other class of 90 and 97%, respectively.

A validated solid-liquid extraction method for the HPLC determination of polyphenols in apple tissues Comparison with pressurised liquid extraction.

A solid-liquid extraction procedure followed by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled with a photodiode array detector (DAD) for the determination of polyphenols in freeze-dried apple peel and pulp is reported. The extraction step consists in sonicating 0.5g of freeze-dried apple tissue with 30mL of methanol-water-acetic acid (30:69:1, v/v/v) containing 2g of ascorbic acid/L, for 10min in an ultrasonic bath. The whole method was validated, concluding that it is a robust method that presents high extraction efficiencies (peel: >91%, pulp: >95%) and appropriate precisions (within day: R.S.D. (n = 5) <5%, and between days: R.S.D. (n = 5) <7%) at the different concentration levels of polyphenols that can be found in apple samples. The method was compared with one previously published, consisting in a pressurized liquid extraction (PLE) followed by RP-HPLC-DAD determination. The advantages and disadvantages of both methods are discussed.