Methods of multivariate analysis of NIR reflectance spectra for classification of yerba mate

Marcelo, Marcelo Caetano Alexandre; Martins, Camila Alves; Pozebon, Dirce; Ferrão, Marco Flôres

doi:10.1039/c4ay01350f

Cited by 30 publications

(5 citation statements)

References 38 publications

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“…To quantitatively evaluate the predictive capability of our SERS taster, we constructed confusion matrices using SVM-DA (Figure E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavor molecules by examining their SERS superprofiles with a high degree of flexibility and robustness. − In the first model, we introduced superprofiles used in the PCA earlier. In the second model, we introduced SERS spectra derived by exposing flavor molecules only to bare Ag.…”

Section: Resultsmentioning

confidence: 99%

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

et al. 2021

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Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven “SERS taster” to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor–flavor SERS spectra, we construct comprehensive “SERS superprofiles” for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

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Section: Resultsmentioning

confidence: 99%

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

et al. 2021

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“…Additionally, IR spectroscopy combined with multivariate statistical methods has proven to be an effective approach for distinguishing similar paper products, modeling systematic data variances, and presenting data in a concise manner (Marcelo et al 2014). Multivariate statistical methods offer several classification models, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and K-nearest neighbor (KNN) (Agarwal et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Lee,

Kim

2023

BioRes

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The evaluation and classification of chemical properties in different copy-paper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery.

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“…Near-infrared spectroscopy (NIR) combined with multivariate statistical methods can solve these detection problems. The NIR spectrum essentially provides a "fingerprint" for a given material, and raw materials can be classified through multivariate analysis of the NIR spectrum (Alexandre Marcelo et al 2014). NIR is a widely used technique for non-destructively analyzing organic materials because it produces no toxic residues and is rapid, cheap, and allows for the simultaneous analysis and characterization of various components.…”

Section: Introductionmentioning

confidence: 99%

Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods

Jang

Heo

Jeong

2020

BioRes

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Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.

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Methods of multivariate analysis of NIR reflectance spectra for classification of yerba mate

Abstract: A methodology based on multivariate analysis of the NIR reflectance spectra was developed for yerba mate classification. All brands of yerba mate analyzed were correctly classified according to the country of origin.

Cited by 30 publications

References 38 publications

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods

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