Aromatic Herbs Classification by using Discriminant Analysis Techniques

Radzi, N. F. M.; Soh, Azura Che; Aris, Ishak; Hasan, Mohammad Khatim; Yusof, Umi Kalsom Mohamad

doi:10.11591/ijeecs.v5.i3.pp530-535

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…PCA is an effective unsupervised multivariate analysis that can be applied to transform data with high-dimension (many variables) into lowdimensional spaces without losing data characteristics. PCA commonly be used in various analyses, such as for data analysis on an e-nose device [29] and also be used in the same research on image processing [30]. An illustration of the PCA analysis when applied on an e-nose is presented in [20].…”

Section: Discussionmentioning

confidence: 99%

“…E-nose produces a specific response when a sample of aroma compound is exposed on the sensor array headspace. These responses are then converted into a specific pattern that can be recognized by pattern recognition methods such as principal component analysis (PCA) [17], [20], [28], common dimension analysis (ComDim) [24], linear discriminant analysis (LDA) [24] and multiple discriminant analysis (MDA) [29] or other pattern recognition methods.…”

Section: Introductionmentioning

confidence: 99%

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Freshness assessment of tilapia fish in traditional market based on an electronic nose

et al. 2021

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This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Freshness assessment of tilapia fish in traditional market based on an electronic nose

et al. 2021

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Implementation of Multiple Discriminant Analysis (MDA) for Clustering Smart Village in West Java Based Podes (Potensi Desa) Database

Tosida

Solihin

Jayawinangun

et al. 2022

2022 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

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The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs

Dankowska

Majsnerowicz²,

Kowalewski

et al. 2022

Sustainability

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The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling.

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Aromatic Herbs Classification by using Discriminant Analysis Techniques

Abstract: Abstract

Cited by 3 publications

References 7 publications

Freshness assessment of tilapia fish in traditional market based on an electronic nose

Freshness assessment of tilapia fish in traditional market based on an electronic nose

Implementation of Multiple Discriminant Analysis (MDA) for Clustering Smart Village in West Java Based Podes (Potensi Desa) Database

The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs

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