Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line Katrine Harder; Ersbøll, Bjarne Kjær

doi:10.1016/j.engappai.2017.07.004

Cited by 39 publications

(18 citation statements)

References 21 publications

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“…Electronic sensors generate a vast volume of data; therefore, it is necessary to apply methods of data analyses, which allows for data classification [ 33 , 34 , 45 , 46 , 47 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. Principal component analysis (PCA) is a dimension reduction technique, which creates a few new variables, called principal components (PCs), from the linear combinations of the original variables, allowing the distribution of samples and variables to be easily plotted and visually analyzed, using the Euclidean distance as a similarity metric [ 61 , 62 , 63 ]. In order to discriminate between the different honey varieties, a SIMCA (Soft Independent Modeling Class Analogy) method has been developed.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Rosa

Marino

Leone

et al. 2018

Sensors

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Honey is usually classified as “unifloral” or “multifloral”, depending on whether a dominating pollen grain, originating from only one particular plant, or no dominant pollen type in the sample is found. Unifloral honeys are usually more expensive and appreciated than multifloral honeys, which highlights the importance of honey authenticity. Melissopalynological analysis is used to identify the botanical origin of honey, counting down the number of pollens grains of a honey sample, and calculating the respective percentages of the nectariferous pollens. In addition, sensory properties are also very important for honey characterization, and electronic senses emerged as useful tools for honey authentication. In this work, a comparison of the results obtained from melissopalynological analysis with those provided by a potentiometric electronic tongue is given, resulting in a 100% match between the two techniques.

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Section: Methodsmentioning

confidence: 99%

Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Rosa

Marino

Leone

et al. 2018

Sensors

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“…PCA is an unsupervised machine-learning method that uses dimension reduction and data visualization [43,44]. This algorithm transforms the original data set into a new set of so-called Principal Components (PC).…”

Section: Data Analysis and Classificationmentioning

confidence: 99%

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Okur

Zhang

et al. 2021

Chemosensors

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Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.

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“…During the discrimination analysis of the scents, the first cycle of the loading/purg- PCA is an unsupervised machine learning method that uses dimension reduction, and data visualization [38,39]. This algorithm transforms the original dataset into a new set of so-called Principal Components (PC).…”

Section: Data Analysis and Classificationmentioning

confidence: 99%

Sniff Species - SURMOF Based Sensor Array Discriminate Aromatic Plants Beyond the Genus Level

Okur¹,

Li²,

Zhang³

et al. 2021

Preprint

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The Lamiaceae belong to the species-richest families of flowering plants and harbor many species used as herbs or for medicinal applications, such as Basils or Mints. Evolution of this group has been driven by chemical speciation, mainly of Volatile Organic Compounds (VOCs). The commercial use of these plants is characterized by a large extent of adulteration and surrogation. To authenticate and discern the species, is, thus, relevant for consumer safety, but usually requires cumbersome analytics, such as Gas Chromatography, often to be coupled with Mass Spectroscopy. We demon-strate here that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for a fast, automated discrimination of scents emitted from leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with Lemongrass as similarly scented, but non-related outgroup. In order to unambiguously differentiate the scents from the different plants, the output of the 6 different SURMOF/QCM sensors was analyzed using machine learning (ML) methods, together with a thorough statistical analysis. The exposure and purging datasets (4 cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed with Linear Discriminant Analysis (LDA) classification model. Prediction accuracies with repeating test measurements reached values of up to 90%. We show that it is not only possible to discern and identify plants on the genus level, but even to discriminate closely related sister clades within a genus (Basil), demonstrating that e-Noses are a powerful technology to safeguard consumer safety against the challenges of globalized trade.

show abstract

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

Cited by 39 publications

References 21 publications

Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Sniff Species - SURMOF Based Sensor Array Discriminate Aromatic Plants Beyond the Genus Level

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