Class Modeling Techniques in Chemometrics: Theory and Applications

Luca, Silvia De; Bucci, Remo; Magrì, Andrea; Marini, Federico

doi:10.1002/9780470027318.a9578

Cited by 10 publications

(20 citation statements)

References 66 publications

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“…Contrarily to discriminant techniques, modelling approaches are capable of capturing similarities among samples belonging to the same category [97,133]. They basically define a multivariate boundary for each considered class, which delimits a specific region of the multidimensional space of the original descriptors where objects proceeding from it are likely to be found.…”

Section: Modelling Techniquesmentioning

confidence: 99%

Chemometric Strategies for Spectroscopy-Based Food Authentication

et al. 2020

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In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

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Section: Modelling Techniquesmentioning

confidence: 99%

Chemometric Strategies for Spectroscopy-Based Food Authentication

et al. 2020

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“…Several techniques fall under the family of classification methods. A general and relevant distinction of these methods is between discriminant and modeling approaches (Cocchi, Biancolillo, & Marini, 2018; De Luca, Bucci, Magrì, & Marini, 2018; Oliveri, & Downey, 2012).…”

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

“…The characteristics of DC methods are—(a) a sample is always assigned to a class; (b) it requires at least two classes; (c) very rarely it produces ambiguous assignations. A possible drawback of these techniques is the very fact that a sample always has to be assigned to a class, even if it does not belong to any of the classes (Berrueta et al., 2007; Cocchi et al., 2018; De Luca et al., 2018; Oliveri, & Downey, 2012). Examples of DC techniques used in beer science are partial least squares regression discriminant analysis (PLS‐DA), K‐nearest neighbors ( k NN), ANN, and LDA (Table 2).…”

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

“…Class modeling, or one‐class classifiers (OCC), on the other hand, are a group of classification methods that exploits the similarities among samples belonging to a particular category (Cocchi et al., 2018; De Luca et al., 2018). OCC techniques work by defining a boundary based on similarities among samples for the same class independent of external samples.…”

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

“…OCC techniques work by defining a boundary based on similarities among samples for the same class independent of external samples. Moreover, as the technique works by defining a single category at a time, different classes may overlap (Berrueta et al., 2007; Cocchi et al., 2018; De Luca et al., 2018; Oliveri, & Downey, 2012). However, this technique allows to solve problems in which only one known category is involved, contrary to DC techniques (Rodionova, Titova, & Pomerantsev, 2016).…”

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

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Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Mutz

Rosário

Conté-Júnior

2020

Comp Rev Food Sci Food Safe

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Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

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Aberrant crosstalk between insulin signaling and mTOR in young Down syndrome individuals revealed by neuronal‐derived extracellular vesicles

Perluigi

Picca

Montanari

et al. 2021

Alzheimer's & Dementia

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Introduction Intellectual disability, accelerated aging, and early‐onset Alzheimer‐like neurodegeneration are key brain pathological features of Down syndrome (DS). Although growing research aims at the identification of molecular pathways underlying the aging trajectory of DS population, data on infants and adolescents with DS are missing. Methods Neuronal‐derived extracellular vesicles (nEVs) were isolated form healthy donors (HDs, n = 17) and DS children (n = 18) from 2 to 17 years of age and nEV content was interrogated for markers of insulin/mTOR pathways. Results nEVs isolated from DS children were characterized by a significant increase in pIRS1Ser636, a marker of insulin resistance, and the hyperactivation of the Akt/mTOR/p70S6K axis downstream from IRS1, likely driven by the higher inhibition of Phosphatase and tensin homolog (PTEN). High levels of pGSK3βSer9 were also found. Conclusions The alteration of the insulin‐signaling/mTOR pathways represents an early event in DS brain and likely contributes to the cerebral dysfunction and intellectual disability observed in this unique population.

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Class Modeling Techniques in Chemometrics: Theory and Applications

Cited by 10 publications

References 66 publications

Chemometric Strategies for Spectroscopy-Based Food Authentication

Chemometric Strategies for Spectroscopy-Based Food Authentication

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Aberrant crosstalk between insulin signaling and mTOR in young Down syndrome individuals revealed by neuronal‐derived extracellular vesicles

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