Signal Enhancement Evaluation of Laser-Induced Breakdown Spectroscopy of Extracted Animal Fats Using Principal Component Analysis Approach

Hanasil, Nur Syaida; Ibrahim, Roslina; Duralim, Maisarah; Sapingi, Husni Hani Jameela; Mahdi, Mohd Adzir

doi:10.1177/0003702820915532

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…The cluster of extracted chicken fat, lamb fat and lard were clearly distinguished in 3D compared to 2D score plot. In previous study, the discrimination of extracted animal fats has been achieved using freezing methods coupled with the ungated LIBS system and PCA approach [19]. A good discrimination of extracted chicken fat, beef fat, lamb fat and lard was obtained using 3D PCA score plot.…”

Section: Resultsmentioning

confidence: 99%

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Discrimination of Extracted Animal Fats Using Laser-Induced Breakdown Spectroscopy Assisted With Principal Component Analysis

et al. 2020

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This study focuses on the discrimination of extracted animal fats in liquid form using laser induced breakdown spectroscopy (LIBS) technique assisted with principal component analysis (PCA). The interaction of laser and liquid sample produces liquid splashing due to strong shock wave effect and subsequently generates lower intensity of LIBS signals. LIBS difficulties in liquid are resolved using paper substrate to enhance LIBS emission intensity. Laser pulse from Q-switched Nd:YAG laser with energy of 220 mJ and frequency of 1 Hz was used to ablate extracted animal fats. The obtained LIBS spectra of extracted animal fats were further evaluated using PCA. LIBS spectra are compressed and visualised as data points in the score plot of PCA. PCA results demonstrated that data points from different extracted animal fats were clustered separately in the score plot with variance greater than 90%. The findings show LIBS system assisted with PCA was capable to differentiate various extracted animal fats.

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Section: Resultsmentioning

confidence: 99%

“…Extracted animal fats were prepared according to previous study [19]. Raw chicken fat, lamb fat and lard were cleaned from meat tissues and blood.…”

Section: Methodology 21 Samples Preparationmentioning

confidence: 99%

Discrimination of Extracted Animal Fats Using Laser-Induced Breakdown Spectroscopy Assisted With Principal Component Analysis

et al. 2020

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“…7 As one of the many LIBS applications, metal sorting 8 has recently attracted significant attention because of its commercial value. [9][10][11] For a reliable identification model, LIBS can be combined with multivariate chemometric analysis to distinguish similar types of materials using machine learning (ML) techniques, such as principal component analysis (PCA), 12,13 partial least square-discriminant analysis, [14][15][16] and artificial neural network. 17 To obtain a desirable classification accuracy, a robust ML model should be trained with extensive LIBS training data under the basic assumption that training and test data follow the same distribution.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials

et al. 2023

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In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been actively studied for industrial applications such as scrap metal recycling. However, in machine learning models, a training set of the used samples may not cover the diversity of the scrap metal encountered in field measurements. Moreover, differences in experimental configuration, where laboratory standards and real samples are analyzed in situ, may lead to a wider gap in the distribution of training and test sets, dramatically reducing the performance of the LIBS-based fast classification system for real samples. To address these challenges, we propose a two-step Aug2Tran model. First, we augment the SRM dataset by synthesizing spectra of unobserved types through attenuation of dominant peaks corresponding to sample composition and generating spectra depending on the target sample using a generative adversarial network. Second, we used the augmented SRM dataset to build a robust real-time classification model with a convolutional neural network, which is further customized for the target scrap metal with limited measurements through transfer learning. For evaluation, SRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass, are measured with a typical setup to form the SRM dataset. For testing, scrap metal from actual industrial fields is experimented with three different configurations, resulting in eight different test datasets. The experimental results show that the proposed scheme produces an average classification accuracy of 98.25% for the three experimental conditions, as high as the results of the conventional scheme with three separately trained and executed models. Additionally, the proposed model improves the classification accuracy of arbitrarily shaped static or moving samples with various surface contaminations and compositions, and even for differing ranges of charted intensities and wavelengths. Therefore, the proposed Aug2Tran model can be used as a systematic model for scrap metal classification with generalizability and ease of implementation.

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An Improved Classification of Pork Adulteration in Beef Based on Electronic Nose Using Modified Deep Extreme Learning with Principal Component Analysis as Feature Learning

et al. 2022

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Signal Enhancement Evaluation of Laser-Induced Breakdown Spectroscopy of Extracted Animal Fats Using Principal Component Analysis Approach

Cited by 6 publications

References 26 publications

Discrimination of Extracted Animal Fats Using Laser-Induced Breakdown Spectroscopy Assisted With Principal Component Analysis

Discrimination of Extracted Animal Fats Using Laser-Induced Breakdown Spectroscopy Assisted With Principal Component Analysis

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials

An Improved Classification of Pork Adulteration in Beef Based on Electronic Nose Using Modified Deep Extreme Learning with Principal Component Analysis as Feature Learning

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