Chemometric and sensometric techniques in enological data analysis

Mafata, Mpho; Brand, Jeanne; Medvedovici, Andrei; Buica, Astrid

doi:10.1080/10408398.2022.2089624

Cited by 13 publications

(6 citation statements)

References 110 publications

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“…Finally, the authors acknowledge that the data and its analysis can also be approached in different ways. In a previous work, the authors already showed how specific data analysis strategies can be tailored depending on the complexity of your matrix (Garrido‐Bañuelos et al, 2020; Mafata et al, 2022). For example, attribute intensity could have been analyzed similar to rate‐all‐that‐apply.…”

Section: Discussionmentioning

confidence: 99%

Sensory discrimination of single hop beers by using sorting combined with profiling and intensity rating

Garrido-Bañuelos

Buica

2023

Journal of Sensory Studies

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Sensory methodologies are continuously being optimized based on industry needs. Sorting is a widely used sensory method; however, it provides limited information about the uniqueness of individual products. The present work was performed on nine single‐hop beers using a combination of sorting with profiling and intensity rating (SPIR) rating to highlight the intra‐group variability and individual characteristics. Results have shown that SPIR can effectively highlight the intra‐group differences. It has also shown that a higher frequency of citation did not always correspond to a higher attribute intensity. For the considered application, the results highlighted the role of hop varieties on the sensory profile of beers. Overall sensory similarities were found between Mosaic (MO), Citra (CI), and Celeia (CE) hops. However, the use of SPIR highlighted differences between CE and the other hops. Amarillo (AM) hops showed the most distinctive profile in all cases. Practical applications The use of sorting in combination with profiling and intensity rating (SPIR) rating can be a valuable tool in new product development. It is not only important to understand the overall sensory similarities and differences between groups of samples, but also unique aspects compared to similar products. The present work has shown that adding intensity to sorting and profiling can highlight the inner characteristics of individual products in a group of samples with an overall similar sensory profile.

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Section: Discussionmentioning

confidence: 99%

Sensory discrimination of single hop beers by using sorting combined with profiling and intensity rating

Garrido-Bañuelos

Buica

2023

Journal of Sensory Studies

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“…These types of methodologies are usually employed to display the similarities and heterogeneities of the dataset. The most well-known and applied unsupervised chemometrics models are reported to be principal component analysis (PCA), hierarchical cluster analysis (HCA), cluster analysis (CA) and multiple correspondence analysis (MCA) [9].…”

Section: Unsupervised Pattern Recognitionmentioning

confidence: 99%

“…Supervised pattern recognition models have reported to require a definite training set, which is further used to predict the identity of unknown samples [9]. In other words, in supervised methods, spectral information is assigned to a definite class and statistical treatment is performed to display the relationship between the data and the class [10].…”

Section: Supervised Pattern Recognitionmentioning

confidence: 99%

“…Supervised models can be used for classification, discrimination or prediction on the basis of analytical data produced in the laboratory. While classification models search for the similarities in the sample group on the basis of experimental information, discrimination models evaluate the divergence between the regressions of each class [9]. Both of these techniques can be used in the classification of agricultural products on the basis of their origin, growing conditions and maturity.…”

Section: Supervised Pattern Recognitionmentioning

confidence: 99%

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Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

Cebi,

Bekiroglu,

Erarslan

2023

Molecules

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In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

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“…A good omics-based strategy should logically partner the chemical and data analyses accordingly. 24 For example, an exploratory study of unknown product space should combine untargeted analyses with an unsupervised data strategy, and a predictive study of well-characterised products would use targeted analytical approaches with a supervised data strategy. These maintain an alignment of purpose and execution.…”

Section: Introductionmentioning

confidence: 99%

Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study

Mafata

Stander

Masike

et al. 2023

Eur J Mass Spectrom (Chichester)

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Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography–high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences.

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Chemometric and sensometric techniques in enological data analysis

Cited by 13 publications

References 110 publications

Sensory discrimination of single hop beers by using sorting combined with profiling and intensity rating

Sensory discrimination of single hop beers by using sorting combined with profiling and intensity rating

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study

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