Assajos bioanalítics quimioluminescents per biomarcadors clínics

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2017, Tutors: Maria Sarret Pons, Aldo RodaUn camp d’investigació en creixement en anàlisi química-biològica, dirigit a millorar i simplificar els procediments analítics, és el desenvolupament dels dispositius “Lab-On-Chip”. Permetent l’anàlisi directa en el punt de necessitat, l’ús d’aquests sistemes portàtils miniaturitzats ofereix un ampli rang d’aplicacions que van des d’anàlisis mediambientals fins al diagnòstic complementari per la medicina personalitzada. A més, degut a les seves nombroses avantatges respecte les tècniques clàssiques de detecció d’emissió de fotons, la quimioluminescència ha demostrat ser una tècnica convenient per a aquest tipus de dispositius. Aquest estudi s’ha basat en el desenvolupament de dos assajos bioanalítics quimioluminescents per biomarcadors clínics. En particular, l’optimització dels assajos bioanalítics ha requerit l’estudi de diferents materials i procediments per a l’obtenció de resultats òptims, amb especial atenció en el desenvolupament dels procediments d’immobilització adequats i en la selecció apropiada dels reactius, materials i de la detecció quimioluminescent. Per una banda, un immunoassaig quimioluminescent per a la detecció d’ATP ha estat optimitzat. D’entre tres formats d’immunoassaig, un immunoassaig competitiu indirecte basat en la immobilització d’ATP conjugat a ovoalbúmina va ser triat com el més adequat. Després de la seva optimització en microplaca com a plataforma, es va obtenir una corba de calibratge. D’altra banda, un dispositiu analític basat en paper, que empra una reacció enzimàtica per a la quantificació de glucosa en una mostra de sèrum, s'ha adaptat d'una càmera CCD ultrasensible a un sensor CMOS, més compacte i portàtil, com a detector. A partir dels resultats obtinguts, es va demostrar que l'adaptació es va aconseguir de forma exitosa sense una pèrdua significativa en la detectabilitat

Liquid chromatography coupled to high-resolution mass spectrometry for nut classification and marker identification

Fraud in nut and seed products poses an economic deception and a threat to human health because of their allergens. This study comprehensively evaluated the metabolomic diversity of ten different nut types through non-targeted liquid chromatography coupled to high-resolution mass spectrometry (LC−HRMS). First, LC−HRMS fingerprints were subjected to partial least squares regression-discriminant analysis (PLS-DA), and the developed multi-class model reached a classification accuracy of 100% after external validation. Then, variable importance in projection (VIP) scores obtained from two-input class PLS-DA models (i.e., a specific nut type against all the other samples) allowed the selection of 136 discriminant compounds that were tentatively annotated/identified through HRMS data. Finally, as a case of study, successful detection and quantitation of almond-based products adulteration (with hazelnut or peanut) was achieved through a targeted LC−HRMS study, using some of the found markers and partial least squares (PLS) regression. In this context, new profiling approaches could be further implemented based on the reported markers using cheaper techniques

FIA−HRMS fingerprinting subjected to chemometrics as a valuable tool to address food classification and authentication: application to red wine, paprika, and vegetable oil samples

The rise of food fraud practices, affecting a wide variety of goods and their specific characteristics (e.g., quality or geographical origin), demands rapid high-throughput analytical approaches to ensure consumers protection. In this context, this study assesses flow injection analysis coupled to high-resolution mass spectrometry (FIA−HRMS), using a fingerprinting approach and combined with chemometrics, to address four food authentication issues: (i) the geographical origin of three Spanish red wines, (ii) the geographical origin of three European paprikas, (iii) the distinction of olive oil from other vegetable oils and (iv) the assessment of its quality category. In each case, negative and positive ionisation FIA−HRMS fingerprints, and two different data fusion strategies, were evaluated. After external validation, excellent classification accuracies were reached. Moreover, high-resolution mass spectrometry (HRMS) allowed sample matrices characterisation by the putative identification of the most common ions

Berry-based products classification by FIA−HRMS fingerprinting and chemometric analysis

In recent years, nutraceuticals prepared with cranberry (Vaccinium macrocarpon) have gained special attention because of their beneficial effects on human health (e.g., antioxidant activity and antimicrobial activity against bacteria involved in a wide range of diseases), which are mainly attributed to the high content of specific polyphenols in cranberry. However, these products present a risk of fraud consisting of the total or partial substitution of cranberry extracts with cheaper and more abundant fruit extracts. Therefore, in this study, flow injection analysis coupled with high-resolution mass spectrometry (FIA−HRMS) fingerprinting was proposed as a rapid high-throughput analytical approach to address the classification of berry-based products through chemometrics, focusing on cranberry-based products authentication. Thus, several berry-based natural products (including 18 based on blueberry, 25 on grape, 12 on raspberry, and 28 on cranberry) and 21 cranberry-based nutraceuticals were analyzed. Sample treatment consisted of a simple solid-liquid extraction method, using acetone:water: hydrochloric acid (70:29.9:0.1, v/v/v) as the extracting mix. After both negative and positive electrospray ionization FIA−HRMS sample analysis, raw data were processed with mzMine 2.53 software to obtain the corresponding fingerprints. In this line, four different data matricesincluding negative, positive, low-level data fusion (LLDF), and mid-level data fusion (MLDF) FIA−HRMS fingerprints were then subjected to principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA) using Solo 8.6 chemometrics software. PCA results allowed the identification of specific sample groups and trends. Subsequently, the complete sample classification was segregated through a classification decision tree consecutive two-input class PLS-DA models leading to excellent assignment accuracies after external validation according to sample botanical origin (independently of the employed data matrix). The poster of this work is provided in the supplementary materials

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

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

Assessment of paprika geographical origin fraud by high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprinting

Paprika production under the protected designation of origin (PDO) standardized procedures leads to more quality products. However, it is also related to higher retail prices, making them susceptible to adulteration with low-quality paprika or its agricultural origin's mislabeling. Therefore, in this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints, strongly related to phenolic acid and polyphenolic compounds, were proposed as chemical markers to assess the classification of paprika from five European regions (three Spanish PDO, Hungary, and the Czech Republic), through a classification decision tree constructed by partial least squares-regression discriminant analysis (PLS-DA) models. After external validation, an excellent classification accuracy of 97.9% was achieved. Moreover, the chromatographic fingerprints were also proposed to detect and quantitate two different paprika geographical origin blend scenarios by partial least squares (PLS) regression. Low external validation and prediction errors ¿with values below 1.6 and 10.7%, respectively¿ were obtained

Classification of Hen Eggs by HPLC-UV Fingerprinting and Chemometric Methods

Hen eggs are classified into 4 groups according to their production method: organic, free-range, barn or caged. It is known that a fraudulent practice is the misrepresentation of a high quality egg with a lower one. In this work, high performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs non-organic hen eggs) and demonstrating the suitability of the proposed method

Determination of bioactive compounds in sequential extracts of chia leaf (Salvia hispanica L.) using UHPLC-HRMS (Q-Orbitrap) and a global evaluation of antioxidant in vitro capacity

Consumers' interest in foods that are nutritionally balanced and with health benefits has increased. The food industry is paying attention to the use of the ancestral seed Salvia hispanica L., commonly known as chia. At present, only the chia seeds, which are a natural source of omega-3 and omega-6, fiber, proteins, and natural antioxidants, are commercialized. Although some studies reveal the presence of several bioactive compounds like polyphenols (e.g., vitexin, orientin, and some hydroxycinnamic acids) in chia leaf methanolic extracts, the chia plant is commonly used as fertilizer or treated as waste after harvest. Therefore, it can represent a by-product that could be considered a great source of bioactive compounds with unexplored potential in medicine and food industry applications. In this work, UHPLC-HRMS (Q-Orbitrap) was employed to tentatively identify and determine bioactive compounds present in different leaf extracts of chia plants of black and white seed phenotype obtained with solvents of different polarity (ethanol, ethyl acetate, dichloromethane, and hexane) to address chia plant by-product revalorization. The chemical antioxidant capacity was also studied and correlated to the found bioactive compounds. In these experiments, black chia showed a higher antioxidant capacity than white chia in the ethanolic extracts. Moreover, experiments on cellular antioxidant activity were also performed with a predominance of the white chia extract. It is noted that the cellular antioxidant activity results make chia ethanolic extracts promising antioxidants

Determination of capsaicinoids and carotenoids for the characterization and geographical origin authentication of paprika by UHPLC-APCI- HRMS

The production area mislabeling of a food product is considered a fraudulent practice worldwide. In this work, a method that uses ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry using atmospheric pressure chemical ionization (UHPLC-APCI-HRMS) was used for the geographical origin authentication of paprika based on the determination of capsaicinoids and carotenoids. Satisfactory instrumental method performance was obtained, providing good linearity (R2 > 0.998), run-to-run and day-to-day precisions (%RSD < 15 and 10%, respectively), and trueness (relative errors < 10%), while method limits of quantification were between 0.21 and 51 mg·kg-1. Capsaicinoids and carotenoids were determined in 136 paprika samples, from different origins (La Vera, Murcia, Hungary, and the Czech Republic) and types (hot, sweet, and bittersweet). The composition of capsaicinoids and carotenoids was used as chemical descriptors to achieve paprika authentication through a classification decision tree built by partial least squares regression−discriminant analysis (PLS-DA) models and reaching a rate of 80.9%