Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)

Abstract

In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.

Oxidative Stress and Autophagy Are Important Processes in Post Ripeness and Brown Film Formation in Mycelium of Lentinula edodes

“…Variables with <30% relative standard deviation of the QC were retained for further multivariate data analysis. Unsupervised principal components analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) ( Barbosa et al, 2020 ) clustering methods were run on GC-MS data in Simca-P software v14.0 (Umetrics AB, Umeå, Sweden) 6 . Unit variance scaling was used in PCA and orthogonal projections latent structures discriminant analysis (OPLS-DA).…”

Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics

“…The proposed UHPLC-HRMS-based strategy achieved the goal, reaching a classification rate of 80.9%. A similar work was conducted by Barbosa et al [10], who coupled UHPLC-HRMS with PLS-DA in order to discriminate the same classes of paprika. The metabolomic fingerprint, coupled with PLS-DA, resulted in a suitable strategy, leading to a correct classification rate of 100%.…”

“…Unlike detection of adulterants (illegal artificial colorants or bulking agents), false origin labelling or contamination of origin-certified spices with low-quality products cultivated elsewhere cannot be unveiled by conventional targeted analytical methods due to the lack of specific markers directly related to the product origin [6]. Various fingerprinting or profiling methods, based on vibrational spectroscopies [7,8], high-or ultrahigh-performance liquid-chromatography coupled to different detector systems [9][10][11][12], and energy dispersive X-ray fluorescence [13], have been proposed to identify the origin of bell pepper spices. In this context, it has been found that the profiles of phenolic acids, polyphenolic compounds and capsacinoids are promising indicators for geographical traceability purposes.…”

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

“…As an established method, PCA has recently been used as an exploratory approach, for example, in LC-MS-based authenticity studies to distinguish tissue origin of bovine gelatin [ 164 ], for the differentiation of the geographical origin of peppers [ 165 ] and pork [ 166 ]. However, PCA was always used together with another supervised method to identify clear differences.…”