scholarly journals Detection of Edible Bird’s Nest Using Fourier Transform Infrared Spectroscopy (FTIR) Combined with Principle Component Analysis (PCA)

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Noor Atikah S. ◽  
Nur Azira Tukiran ◽  
Nurul Azarima M. A. ◽  
Haizatul Hadirah G. ◽  
Siti Nur Syahirah Z.
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Component Analysis ◽  
Simple Approach ◽  
Pca Analysis ◽  
Edible Bird’S Nest ◽  
Limited Supply

Edible bird’s nest (EBN) is rich in nutrients and health benefits; making it one of the Chinese delicacies over the centuries. However, due to the overpriced and limited supply of EBN, it is being adulterated with other cheaper versions. Therefore, the aim of this study is to establish a method of detecting adulterants in EBN using Fourier transform infrared spectroscopy (FTIR) as the spectrum fingerprinting analysis together with principal component analysis (PCA). Spiked samples have been developed for Tremella fungus and porcine gelatine at the concentrations of 1%, 5%, 10%, 20% and 30% (w/w). The FTIR method combined with PCA analysis was able to detect the adulteration of porcine gelatine and Tremella fungus in the sample of adulterated EBNs at low concentration of 1% (w/w). The simple approach employing FTIR combined with PCA may provide a useful tool for EBN detection.

Rapid Differentiation and Identification of Shigella sonnei and Escherichia coli O157: H7 by Fourier Transform Infrared Spectroscopy and Multivariate Statistical Analysis

2014 ◽  
Vol 926-930 ◽  
pp. 1116-1119 ◽  
Author(s):  
Li Jun Yang ◽  
Jing Wang ◽  
Zhao Jie Li ◽  
Xiao Hua Song ◽  
Yu Min Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Statistical Analysis ◽  
Fourier Transform Infrared Spectroscopy ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Shigella Sonnei ◽  
Multivariate Statistical ◽  
Simple Method

Fourier transform infrared spectroscopy (FTIR) combined with multivariate statistical analysis was applied to differentiate and identify Shigella sonnei and Escherichiacoli O157: H7. FTIR absorption spectra from 4000-600 cm-1 were collected from sampling 10 μL of bacterial suspention. The spectra between 1800 and 900 cm-1 highlighted the most distinctive variations and were the most useful for characterizing the selected microorganisms. Spectra of the two bacteria were noticeably segregated with distinct clustering by principal component analysis (PCA). Further more, another cluster model of hierarchical cluster analysis (HCA) was established and could also gave a good separation between the two bacteria. These results demonstrate that FTIR technology has considerable potential as a rapid, accurate and simple method for differentiating and identifying bacteria.

scholarly journals Efficient Discrimination of Some Moss Species by Fourier Transform Infrared Spectroscopy and Chemometrics

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Zhen Cao ◽  
Yongying Liu ◽  
Jiancheng Zhao
Keyword(s):  
Cluster Analysis ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Ftir Spectra ◽  
Chemical Components ◽  
Discrete Wavelet ◽  
Moss Species

Fourier transform infrared spectroscopy (FTIR) technique was used to classify 16 species from three moss families (Mielichhoferiaceae, Bryaceae, and Mniaceae). The FTIR spectra ranging from 4000 cm−1to 400 cm−1of the 16 species were obtained. To group the spectra according to their spectral similarity in a dendrogram, cluster analysis and principal component analysis (PCA) were performed. Cluster analysis combined with PCA was used to give a rough result of classification among the moss samples. However, some species belonging to the same genus exhibited very similar chemical components and similar FTIR spectra. Fourier self-deconvolution (FSD) was used to enhance the differences of the spectra. Discrete wavelet transform (DWT) was used to decompose the FTIR spectra ofMnium laevinerveandM. spinosum. Three scales were selected as the feature extracting space in the DWT domain. Results showed that FTIR spectroscopy combined with DWT was suitable for distinguishing different species of the same genus.

Differentiating Between Malignant Mesothelioma and Other Pleural Lesions Using Fourier Transform Infrared Spectroscopy

2020 ◽  
Vol 74 (7) ◽  
pp. 808-818
Author(s):  
Fatlinda Sadiku-Zehri ◽  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Ardita Qerimi-Krasniqi ◽  
Filip Sedlić ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Correct Diagnosis ◽  
Principal Component ◽  
High Sensitivity ◽  
Fourier Transform Infrared ◽  
Diagnostic Tools ◽  
Discrete Groups ◽  
Student’S T

Histopathology, despite being the gold standard as a diagnostic tool, does not always provide a correct diagnosis for different pleural lesions. Although great progress was made in this field, the problem to differentiate between reactive and malignant pleural lesions still stimulates the search for additional diagnostic tools. Our research using vibrational spectroscopy and principal component analysis (PCA) statistical modeling represents a potentially useful tool to approach the problem. The objective method this paper explores is based on the correlation between different types of pleural lesions and their vibrational spectra. Obtained tissue spectra recorded by infrared spectroscopy allowed us to categorize spectra in different groups using a created PCA statistical model. The PCA model was built using tissues of known pathology as the model group. The validation samples were then used to confirm the functionality of our PCA model. Student’s t-test was also used for comparing samples in paired groups. The PCA model was able to clearly differentiate the spectra of mesothelioma, metastasis and reactive changes (inflammation), and place them in discrete groups. Thus, we showed that Fourier transform infrared spectroscopy combined with PCA can differentiate pleural lesions with high sensitivity and specificity. This new approach could contribute in objectively differentiating specific pleural lesions, thus helping pathologists to better diagnose difficult pleural samples but also could shed additional light into the biology of malignant pleural mesothelioma.

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