scholarly journals Fast Classification of Geographical Origins of Honey Based on Laser-Induced Breakdown Spectroscopy and Multivariate Analysis

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1878 ◽  
Author(s):  
Zhangfeng Zhao ◽  
Lun Chen ◽  
Fei Liu ◽  
Fei Zhou ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Linear Discrimination ◽  
Acacia Honey ◽  
Laser Induced Breakdown ◽  
Fast Classification ◽  
Better Than

Traceability of honey is highly required by consumers and food administration with the consideration of food safety and quality. In this study, a technique named laser-induced breakdown spectroscopy (LIBS) was used to fast trace geographical origins of acacia honey and multi-floral honey. LIBS emissions from elements of Mg, Ca, Na, and K had significant differences among different geographical origins. The clusters of honey from different geographical origins were visualized with principal component analysis. In addition, support vector machine (SVM) and linear discrimination analysis (LDA) were used to quantitively classify the origins. The results indicated that SVM performed better than LDA, and the discriminant results of multi-floral honey were better than acacia honey. The accuracy and mean average precision for multi-floral honey were 99.7% and 99.7%, respectively. This study provided a fast approach for geographical origin classification, and might be helpful for food traceability.

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Classification Performance ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

Fast classification of tobacco based on laser-induced breakdown spectroscopy

2019 ◽  
Vol 12 (5) ◽  
pp. 1139-1146 ◽  
Author(s):  
李昂泽 LI Ang-ze ◽  
王宪双 WANG Xian-shuang ◽  
徐向君 XU Xiang-jun ◽  
何雅格 HE Ya-ge ◽  
郭 帅 GUO Shuai ◽  
...  
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Fast Classification

scholarly journals Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

2019 ◽  
Vol 74 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Daniel Diaz ◽  
Alejandro Molina ◽  
David W. Hahn
Keyword(s):  
Principal Component Analysis ◽  
Limit Of Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Lines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

scholarly journals Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1393 ◽  
Author(s):  
Yanwei Yang ◽  
Xiaojian Hao ◽  
Lili Zhang ◽  
Long Ren
Keyword(s):  
Machine Learning ◽  
Iron Ore ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Iron Ores ◽  
K Nearest Neighbor ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.

Fast Classification of Tea Varieties Based on Laser-Induced Breakdown Spectroscopy

2019 ◽  
Vol 46 (3) ◽  
pp. 0311003
Author(s):  
徐向君 Xu Xiangjun ◽  
王宪双 Wang Xianshuang ◽  
李昂泽 Li Angze ◽  
何雅格 He Yage ◽  
柳宇飞 Liu Yufei ◽  
...  
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Fast Classification

scholarly journals An Approach of Laser-Induced Breakdown Spectroscopy to Detect Toxic Metals in Crushed Ice Ball

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Rahul Agrawal ◽  
Ashok Kumar Pathak ◽  
Awadhesh Kumar Rai ◽  
Gyanendra Kumar Rai
Keyword(s):  
Toxic Metals ◽  
Principal Component ◽  
Analytical Tool ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Minimal Sample ◽  
Laser Induced Breakdown ◽  
Crushed Ice

This paper deals the application of laser-induced breakdown spectroscopy (LIBS) to toxic metals used as pigment in crushed ice-ball samples. The present work highlights the advantages of LIBS as in situ, real-time analytical tool for rapid detection of toxic or heavy metals like lead (Pb) and chromium (Cr) and non toxic elements like carbon (C), nitrogen (N), magnesium (Mg), calcium (Ca), sodium (Na), and potassium (K) in crushed ice-ball of different colors (red, green, yellow, pale yellow, and orange) collected from five different areas, with minimal sample preparation. For rapid surveillance of toxic metals we have used multivariate analysis, that is, principal component analysis (PCA) with the LIBS spectral data of ice-ball samples. This study suggests that LIBS coupled with PCA may be an instant diagnostic tool for identification and classification of adulterated and nonadulterated samples.

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