Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut sample characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products).

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, and particularly in classification and authentication issues, very few applications have been reported. In this study, differential mobility spectrometry coupled to mass spectrometry (DMS-MS) is presented for the first time as an alternative and high-throughput 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 analyzed by DMS-MS to address their classification -using partial least squares regression-discriminant analysis (PLS-DA)- and authentication -through soft independent modeling 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.

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.

Glucosinolates and Polyphenols of Colored Cauliflower as Chemical Discriminants Based on Cooking Procedures.

The impact of mild oven treatments (steaming or sous-vide) and boiling for 10 min, 25 min, or 40 min on health-promoting phytochemicals in orange and violet cauliflower (Brassica oleracea L. var. botrytis) was investigated. For this purpose, targeted ultra-high performance liquid chromatography-high-resolution mass spectrometry analysis of phenolics and glycosylates, combined with chemometrics, was employed. Regardless of cooking time, clear differentiation of cooked samples obtained using different procedures was achieved, thus demonstrating the distinct impact of cooking approaches on sample phytochemical profile (both, compound distribution and content). The main responsible components for the observed discrimination were derivatives of hydroxycinnamic acid and kaempferol, organic acids, indolic, and aromatic glucosinolates, with glucosativin that was found, for the first time, as a discriminant chemical descriptor in colored cauliflower submitted to steaming and sous-vide. The obtained findings also highlighted a strict relationship between the impact of the cooking technique used and the type of cauliflower. The boiling process significantly affected the phytochemicals in violet cauliflower whereas orange cauliflower boiled samples were grouped between raw and either steamed or sous-vide-cooked samples. Finally, the results confirm that the proposed methodology is capable of discriminating cauliflower samples based on their phytochemical profiles and identifying the cooking procedure able to preserve bioactive constituents.

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.

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 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, such as 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 the 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 Phenolic Compounds in Paprika by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry: Application to Product Designation of Origin Authentication by Chemometrics.

An ultrahigh performance liquid chromatography-electrospray-tandem mass spectrometry method was developed for the determination of 36 phenolic compounds in paprika. The proposed method showed good method performance with limits of quantitation between 0.03 and 50 µg/L for 16 compounds and between 50 µg/L and 1 mg/L for 12 compounds. Good linearity (R2 > 0.995), run-to-run and day-to-day precisions (%RSD values < 12.3 and < 19.2%, respectively), and trueness (relative errors < 15.0%) were obtained. The proposed method was applied to the analysis of 111 paprika samples from different production regions: Spain (La Vera PDO and Murcia PDO) and Czech Republic, each one including different flavor varieties (sweet, bittersweet, and spicy). Phenolic profiles and concentration levels showed to be good chemical descriptors to achieve paprika classification and authentication according to the production region by principal component analysis and partial least squares regression-discriminant analysis. In addition, perfect classification among flavor varieties for Murcia PDO and Czech Republic samples was also obtained.

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

Hen eggs are classified into four 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.