Dataset Information

Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis.

ABSTRACT: The development of a simple HPLC-UV method towards the evaluation of Spanish paprika's phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

SUBMITTER: Ceto X

PROVIDER: S-EPMC6308838 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Determination of HPLC-UV Fingerprints of Spanish Paprika (<i>Capsicum annuum</i> L.) for Its Classification by Linear Discriminant Analysis.

Cetó Xavier X Serrano Núria N Aragó Miriam M Gámez Alejandro A Esteban Miquel M Díaz-Cruz José Manuel JM Núñez Oscar O

Sensors (Basel, Switzerland) 20181218 12

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika's phenolic profile and their discrimination based on the former is reported herein. The approach is based on C<sub>18</sub> reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identifie ...[more]

PMID: 30567367

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Project description:Background:Danmu preparations (Danmu Capsule and Danmu Syrup), which are made from Nauclea officinalis stem extracts, have good clinical efficacy in acute tonsillitis, acute pharyngitis and upper respiratory tract infection. However, there is currently no reliable and systematic method to control the quality of these two Danmu preparations. Methods:Using high-performance liquid chromatography (HPLC) coupled with diode array detection (DAD), the fingerprints of the Danmu preparations were established at 250 nm to comprehensively investigate the stability of preparation process. The chemical constituents in the Danmu preparations were separated and identified by HPLC coupled with quadrupole-time-of-flight high-definition mass spectrometry (HPLC-Q-TOF-MS). And seven major components were simultaneously determined at dual wavelengths (250 nm, 326 nm). Results:The results of HPLC fingerprint similarity evaluation showed that the similarity values of 25 batches of Danmu preparations were more than 0.993. Twenty-three compounds, including 10 alkaloids, 6 phenolic acids, 2 iridoids, and 5 unknown compounds, were identified or tentatively characterized according to the retention times and MS/MS fragment patterns of compounds. The developed assay method of seven components was validated with acceptable linearity, precision, repeatability, stability and recovery. The contents of strictosamide belonging to alkaloids as the most abundant constituent in Danmu Capsule and Danmu Syrup were 43,681.20-99,652.49 ?g/g and 1567.83-2427.25 ?g/mL respectively. The contents of protocatechuic acid which were the highest in measured phenolic acids were 2633.01-7739.78 ?g/g in Danmu Capsule and 192.05-448.71 ?g/mL in Danmu Syrup, respectively. As an iridoid, the contents of sweroside in Danmu Capsule and Danmu Syrup were 1573.82-2789.81 ?g/g and 70.32-182.81 ?g/mL, respectively. Conclusion:The established qualitative analysis method of fingerprint can be used to attain standardization, uniformity and stability of the preparation process. Meanwhile, the quantitative analysis in this study can be used as an accurate assay method for preparations.

Project description:Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation.

Dataset Information

Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis.

Publications

Determination of HPLC-UV Fingerprints of Spanish Paprika (<i>Capsicum annuum</i> L.) for Its Classification by Linear Discriminant Analysis.

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