Chemometric Methods for Classification and Feature Selection

Cocchi, Marina; Biancolillo, Alessandra; Marini, Federico

doi:10.1016/bs.coac.2018.08.006

Cited by 78 publications

(53 citation statements)

References 79 publications

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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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Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

Section: Managing Beer Aging With Multivariate Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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“…In order to deeply investigate the system under study, Variable Importance in Projection (VIP) [24] was used to define which spectral variables provide the greater contribution to the discrimination. Customarily, variables exhibiting VIP indices higher than 1 are considered relevant for the solution of the investigated problem [25]. A graphical representation of the outcome of the VIP analysis is reported in Figure 3.…”

Section: Detection Of Adulterated Egg Pasta Samplesmentioning

confidence: 99%

Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

et al. 2020

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“Egg pasta” is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation.

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Understanding the flavor signature of the rice grown in different regions of China via metabolite profiling

et al. 2021

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BACKGROUND Rice is the staple food of most people in China. The fragrance of rice varies from region to region, and high‐quality rice always has a pleasant aroma. To protect consumers from misleading information and fraud, and to serve the interests of high‐quality rice producers, and to develop a rice regional protection system in China, the phenotype of rice grown in different Chinese regions needs to be known. Thus the flavor phenotype of the rice cultivated in China is studied. RESULTS The volatile organic compounds of rice samples in China have good classification potential and the 37 rice products investigated herein may be divided into three main categories: north‐eastern rice, central and southern rice, and Shanghai rice. Orthogonal projection to latent structure‐discriminant analysis (OPLS‐DA) model exhibited a good discrimination for rice samples in China. Based on selected distinctive biomarker compounds, data‐driven soft independent modeling of class analogy was successfully applied to identifying the origin of samples. Moreover, the differential volatile compounds identified in this study endow the rice samples with distinctive flavor characteristics. CONCLUSION The results of this study are valuable in understanding the difference of flavor characteristics of rice grown in different regions of China, and in the identification of geographical origins to develop China's geographic protection product industry. © 2021 Society of Chemical Industry.

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Chemometric Methods for Classification and Feature Selection

Cited by 78 publications

References 79 publications

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

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

Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

Understanding the flavor signature of the rice grown in different regions of China via metabolite profiling

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