Recent advances in the determination of biogenic amines in food samples by (U)HPLC

The determination of biogenic amines (BAs) in food products stirs up an increasing interest because of the implications in toxicological and food quality issues. Apart from these aspects, in the last years, the relevance of BAs because of some organoleptic and descriptive concerns has been pointed out by several researchers. This overview aims at revising recent advances in the determination of BAs in food samples based on liquid chromatography. In particular, papers published in the last five years have been commented. Special attention has been paid in the great possibilities of ultra-high performance liquid chromatography and high-resolution mass spectrometry. Regarding applications, apart from the determination of BAs in a wide range of food matrices, novel lines of research focused on the characterization, classification and authentication of food products based on chemometrics have also been discussed

Targeted HPLC-UV Polyphenolic profiling to detect and quantify adulterated tea samples by chemometrics

Tea can be found among the most widely consumed beverages, but also highly susceptible of fraudulent practices by adulteration, with other plants such as chicory, to obtain an illicit economic gain. The development of simple, feasible and cheap analytical methodologies to assess tea authentication is therefore required. In this work, a targeted high-performance liquid chromatography with ultraviolet-visible detection (HPLC-UV) method for polyphenolic profiling, monitoring 17 polyphenolic and phenolic acids typically described in tea, was proposed for the classification and authentication of tea samples versus chicory. For that purpose, the obtained HPLC-UV polyphenolic profiles (based on the peak areas at three different acquisition wavelengths) were employed as sample chemical descriptors for principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) studies. Overall, PLS-DA showed a good sample grouping and discrimination of chicory against any tea variety, but also among the five different tea varieties under study (black, green, red, oolong, and white teas), with classification errors below 8% and 10.5% for calibration and cross-validation, respectively. In addition, the potential use of polyphenolic profiles as chemical descriptors for the detection and quantitation of frauds was evaluated by studying the adulteration of each tea variety with chicory, as well as the adulteration of red tea extracts with oolong tea extracts. Very satisfactory results were obtained in all cases, with calibration, cross-validation, and prediction errors below 2.0%, 4.2%, and 3.9%, respectively, when using chicory as an adulterant, clearly improving previously reported results when using non-targeted HPLC-UV fingerprinting methodologies

Off-line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey

The feasibility of non-targeted off-line SPE LC-LRMS polyphenolic fingerprints to address the classification and authentication of Spanish honey samples based on both botanical origin (blossom- and honeydew-honeys) and geographical production region was evaluated. With this aim, 136 honey samples belonging to different botanical varieties (multifloral and monofloral) obtained from different Spanish geographical regions with specific climatic conditions were analyzed. Polyphenolic compounds were extracted by off-line solid phase extraction (SPE) using HLB (3 mL, 60 mg) cartridges. The obtained extracts were then analyzed by C18 reversed-phase LC coupled to low-resolution mass spectrometry in a hybrid quadrupole-linear ion trap mass analyzer and using electrospray in negative ionization mode. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the pattern recognition capabilities of the obtained fingerprints to address honey classification and authentication. In general, a good sample discrimination was accomplished by PLS-DA, being able to differentiate both blossom-honey and honeydew-honey samples according to botanical varieties. Multiclass predictions by cross-validation for the set of blossom-honey samples showed sensitivity, specificity, and classification ratios higher than 60%, 85%, and 87%, respectively. Better results were obtained for the set of honeydew-honey samples, exhibiting 100% sensitivity, specificity, and classification ratio values. The proposed fingerprints demonstrated also to be good honey chemical descriptors to deal with climatic and geographical issues. Characteristic polyphenols of each botanical variety were tentatively identified by LC-MS/MS in multiple reaction monitoring mode to propose possible honey markers for future experiments (i.e., naringin for orange/lemon blossom honeys, syringic acid in thyme honeys, or galangin in rosemary honeys

Honey fraud detection based on sugar syrup adulterations by HPLC-UV fingerprinting and chemometrics

In recent years, honey-producing sector has faced the increasing presence of adulterated honeys, implying greateconomic losses and questioning the quality of this highly appreciated product by the society. Due to the highsugar content of honey, sugar syrups are among its most common adulterants, being also the most difficult todetect even with isotope ratio techniques depending on the origin of the sugar syrup plant source. In this work, ahoney authentication method based on HPLC-UV fingerprinting was developed, exhibiting a 100% classificationrate of honey samples against a great variety of sugar syrups (agave, corn, fiber, maple, rice, sugar cane andglucose) by partial least squares-discriminant analysis (PLS-DA). In addition, the detection and level quantitationof adulteration using syrups as adulterants (down to 15%) was accomplished by partial least squares (PLS)regression with low prediction errors by both internal and external validation (values below 12.8% and 19.7%,respectively

Characterization and Classification of Spanish Honey by Non-targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods

A non-targeted LC-HRMS fingerprinting methodology using an Orbitrap mass analyzer, and based on C18 reversed-phase mode under universal gradient elution, was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dis-solution with water and a 1:1 dilution with methanol was proposed. 136 honey samples belonging to different blossom- and honeydew-honeys from different botanical varieties and produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished, with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1%, and classification errors below 10.5%

Targeted HPLC-UV-FLD Polyphenolics to Assess Paprika Geographical Origin

Paprika is a red powder seasoning with a characteristic flavour obtained from the drying and grinding of red pepper fruits of the genus Capsicum (Solanaceae family). In Europe, seven paprika products are distinguished with the protected designation of origin (PDO) label, which ensures a high-quality product through strict requirements, leading to higher retail prices than unlabelled paprika and making them susceptible to fraudulent practices. Contents of polyphenol and phenolic compounds depend on several factors, such as the environmental conditions of the production area. Thus, in the present study, a simple and feasible high-performance liquid chromatography with ultraviolet and fluorescent detection (HPLC-UV-FLD) method was developed to determine 17 polyphenols in paprika samples, aiming to authenticate them through chemometrics. Reversed-phase chromatographic separation was optimised, using a C18 column and 0.1% formic acid aqueous solution and acetonitrile as the mobile phase components. The proposed methodology exhibited limits of detection below 0.9 mg L−1, as well as good linearity (R2 ≥ 0.984), precision (RSD day-to-day values below 24%), and trueness (relative errors below 14%). Moreover, compound confirmation was carried out via high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS). The proposed methodology was applied to 109 paprika samples, including samples from Spain (La Vera PDO, Murcia PDO, and Mallorca PDO), Hungary, and the Czech Republic. The obtained HPLC-UV-FLD polyphenolic profiles were employed as sample chemical descriptors to authenticate paprika geographical origin using a classification decision tree constructed via partial least squares regression-discriminant analysis (PLS-DA) models. As a result, a sample classification rate of 87.8% was reached after external validation. Moreover, two different paprika geographical origin blend scenarios (La Vera vs. Murcia and the Czech Republic vs. Murcia) were evaluated through partial least squares (PLS) regression, allowing blend percentage prediction with errors below 10.8% after external validation

Differential mobility spectrometry coupled to mass spectrometry (DMS−MS) for the classification of Spanish PDO paprika

Ion mobility spectrometry (IMS) has proved its huge potential in many research areas, especially when hyphenated with chromatographic techniques or mass spectrometry (MS). However, focusing on food analysis, very few applications have been reported following a fingerprinting approach. Therefore, in this study, differential mobility spectrometry coupled to mass spectrometry (DMS−MS) is presented for the first time as an alternative technique for food classification and authentication purposes using a fingerprinting strategy. As a study case, 70 Spanish paprika samples (from La Vera , Murcia , and Mallorca ) were analysed by DMS−MS to address their classification ¿using partial least squares regression-discriminant analysis (PLS-DA)¿ and authentication ¿through soft independent modelling of class analogy (SIMCA)¿. As a result, after external validation, complete sample classification according to their geographical origin and excellent La Vera and Mallorca sample authentication were reached

Green extraction of antioxidant compounds from olive tree leaves based on naturaldeep eutectic solvents

Agri-food industries generate a large amount of waste that offers great revalorization opportunities within the circular economy framework. In recent years, new methodologies for the extraction of compounds with more eco-friendly solvents have been developed, such as the case of natural deep eutectic solvents (NADES). In this study, a methodology for extracting phenolic compounds from olive tree leaves using NADES has been optimized. The conditions established as the optimal rely on a solvent composed of choline chloride and glycerol at a molar ratio of 1:5 with 30% water. The extraction was carried out at 80 °C for 2 h with constant agitation. The extracts obtained have been analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS) in MRM mode. The comparison with conventional ethanol/water extraction has shown that NADES, a more environmentally friendly alternative, has improved extraction efficiency. The main polyphenols identified in the NADES extract were Luteolin-7-O-glucoside, Oleuropein, 3-Hydroxytyrosol, Rutin, and Luteolin at the concentrations of 262, 173, 129, 34, and 29 mg kg−1 fresh weight, respectively

Non-targeted ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) fingerprints for the chemometric characterization and classification of turmeric and curry samples

In this work, non-targeted UHPLC-HRMS fingerprints obtained by C18 reversed-phase chromatography were proposed as sample chemical descriptors for the characterization and classification of turmeric and curry samples. 21 turmeric and 9 curry commercially available samples were analyzed in triplicate after extraction with DMSO. The results demonstrated the feasibility of non-targeted HPLC-HRMS fingerprints for sample classification, showing very good classification capabilities by partial least squares regression-discriminant analysis (PLS-DA). 100% classification rates were obtained by PLS-DA when randomly selected samples were processed as 'unknown' ones. Besides, turmeric curcuma species (curcuma longa vs. curcuma zedoaria) and turmeric curcuma longa varieties (Madras, Erodes, and Alleppey) discrimination was also observed by PLS-DA when using the proposed fingerprints as chemical descriptors. As a conclusion, non-targeted UHPLC-HRMS fingerprinting is a suitable methodology for the characterization, classification and authentication of turmeric and curry samples, without the requirement of using commercially available standards for quantification nor the necessity of metabolite identification

Characterization, Classification and Authentication of Honey by Non-Targeted UHPLC-HRMS Chromatographic Fingerprints and Chemometric Methods

Honey is a natural substance produced by bees of the genus Apis. Depending on the raw material used for its production, honey can be classified into two large groups: blossom honey, which results from the metabolization of nectar extracted from flowers; and honeydew honey, in which bees use plant or insect secretions for its production. The physicochemical characteristics are different between these two types of honey. For example, honeydew honey is darker and is characterized by a high content of phenolic acids. On the contrary, blossom honey stands out for its abundance of flavonoids. Blossom honey can be also classified based on the pollen origin. Thus, honey with more than 45% of the pollen coming from the same species can be considered monofloral; otherwise, it is considered multifloral. Honey is one of the food products with the highest level of fraudulent practices. Most of the adulterations consist of ingredient dilution, adding sweet substances, such as syrups, sugar cane, or corn syrup, among others. In the market, this was reflected in the dubious drop in prices for this product. In the last few years, several instance of honey fraud have come to light. This work aimed to develop a non-targeted ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting method to address the characterization, classification, and authentication of Spanish honey samples considering their botanical and geographical origin. A total of 136 kinds of honey from different Spanish production regions belonging to different botanical varieties were analyzed, including: blossom honey (orange blossom, rosemary, thyme, eucalyptus, and heather) and honeydew honey (holm oak, forest, and mountain). A simple sample treatment was carried out, consisting of dissolving 1 g of honey in 10 mL of water, followed by a 1:1 dilution with methanol. The chromatographic separation of the obtained extracts was performed using a Kinetex® C-18 core-shell column (100 × 4.6 mm I.D., 2.6 μm), working under gradient elution, using an aqueous solution of 0.1% formic acid and acetonitrile as the mobile phase components. HRMS acquisition was performed using electrospray in negative ionization mode (−2500 V) in an LTQ-Orbitrap working in full scan MS (m/z 100-1000) at a resolution of 50,000 full-width at half maximum (FWHM). The obtained non-targeted UHPLC-HRMS fingerprints (peak signals as a function of retention time and m/z) were considered as chemical descriptors of the analyzed honey samples for principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). PLS-DA revealed good discrimination between blossom and honeydew honey. Furthermore, the obtained chemometric models allowed the achievement of very good classification among the different botanical varieties under study for both blossom and honeydew honey. The discrimination of honey regarding the different Spanish climate production regions was more limited, although some trends were observed. Thus, the non-targeted UHPLC-HRMS fingerprinting approach proved to be an appropriate methodology to address honey characterization, classification, and authentication based on their different botanical origin