Multiplicative scatter correction and principal component analysis of UV-Vis absorption spectra during acid hemolysis of erythrocyte suspension

2021 ◽  
Author(s):  
Miroslav Karabaliev ◽  
Boyana Paarvanova ◽  
Bilyana Tacheva ◽  
Mitko Mitev ◽  
Radoslav Ginin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectra ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Erythrocyte Suspension ◽  
Multiplicative Scatter Correction

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis

2020 ◽  
Vol 16 (8) ◽  
Author(s):  
Haoran Li ◽  
Tianhong Pan ◽  
Yuqiang Li ◽  
Shan Chen ◽  
Guoquan Li
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Functional Principal Component Analysis ◽  
Support Vector ◽  
Edible Fungi ◽  
Functional Principal Component

AbstractTricholoma matsutakeis (TM) is the most expensive edible fungi in China. Given its price and exclusivity, some dishonest merchants will sell adulterated TM by combining it with cheaper fungi in an attempt to earn more profits. This fraudulent behavior has broken food laws and violated consumer trust. Therefore, there is an urgent need to develop a rapid, accurate, and nondestructive tool to discriminate TM from other edible fungi. In this work, a novel detection algorithm combined with near-infrared spectroscopy (NIR) and functional principal component analysis (FPCA) is proposed. Firstly, the raw NIR data were pretreated by locally weighted scatterplot smoothing (LOWESS) and multiplication scatter correction (MSC). Then, FPCA was used to extract valuable information from the preprocessed NIR data. Then, a classifier was designed by using the least-squares support-vector machine (LS-SVM) to distinguish categories of edible fungi. Furthermore, the one-versus-one (OVO) strategy was included and the binary LS-SVM was extended to a multi-class classifier. The 166 samples of four varieties of fungi were used to validate the proposed method. The results show that the proposed method has great capability in near infrared spectra classification, and the average accurate of FPCA-LSSVM is 97.3% which is greater than that of PCA-LSSVM (93.5%).

Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

2015 ◽  
Author(s):  
Yu. V. Kistenev ◽  
A. V. Shapovalov ◽  
A. V. Borisov ◽  
D. A. Vrazhnov ◽  
V. V. Nikolaev ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectra ◽  
Principal Component ◽  
Component Analysis

Applications of principal component analysis to breath air absorption spectra profiles classification

2015 ◽  
Author(s):  
Yu. V. Kistenev ◽  
A. V. Shapovalov ◽  
A. V. Borisov ◽  
D. A. Vrazhnov ◽  
V. V. Nikolaev ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectra ◽  
Principal Component ◽  
Component Analysis

scholarly journals XAS studies of Se speciation in selenite-fed rats

Metallomics ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 2193-2203 ◽  
Author(s):  
Claire M. Weekley ◽  
Jade B. Aitken ◽  
Paul K. Witting ◽  
Hugh H. Harris
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Selenium Speciation ◽  
X Ray ◽  
X Ray Absorption ◽  
Se Speciation

An investigation of selenium speciation in the tissues of selenite-fed rats by principal component analysis of X-ray absorption spectra.

scholarly journals Aplikasi NIRS dan Principal Component Analysis (PCA) untuk Mendeteksi Daerah Asal Biji Kopi Arabika (Coffea arabica)

2016 ◽  
Vol 1 (1) ◽  
pp. 954-960
Author(s):  
Syahrul Ramadhan ◽  
Agus Arip Munawar ◽  
Diswandi Nurba
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Coffee Beans

Abstrak. Kopi merupakan spesies tanaman berbentuk pohon yang termasuk dalam famili Rubiaceae dan genus Coffea, tumbuh tegak, bercabang dan bila dibiarkan dapat tumbuh mencapai tinggi 12 meter. Pendeteksian mutu pangan yang cepat dan efisien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Sebanyak 54 sampel biji kopi diambil dari 6 Provinsi yang berbeda, yaitu: Aceh, Bali, Bengkulu, Nusa Tenggara Barat, Jawa Barat dan Jawa Timur. Pengamatan meliputi Principal Component Analysis (PCA) sebagai metode klasifikasi dan Pretreatment Multiplicative Scatter Correction (MSC) sebagai metode koreksi spektrum. Hasil pengujian menunjukkan bahwa PCA hanya mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Jawa Timur, sedangkan dengan penambahan Pretreatment MSC mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Bali dengan tingkat keberhasilan 100%.Abstract. Coffee is belong to family Rubiaceae and the genus Coffea, grow upright, branched, and can grow up to 12 meters high. The detection of food quality quickly and efficiently can be realized through the development of Near Infrared Reflectance Spectroscopy (NIRS) technology. A total of 54 Coffee bean samples were taken from 6 different province, namely: Aceh, Bali, Bengkulu, West Nusa Tenggara, West Java and East Java. Data analysis included Principal Component Analysis (PCA) were used to classify coffee based on geographic origin. Multiplicative Scatter Correction (MSC) method was used as spectra correction. The results shows that PCA is able to classify coffee beans from the Aceh and East Java province, while the addition of MSC Pretreatment able to classify the coffee beans from the province of Aceh and Bali province with 100% success rate.

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