scholarly journals Identification of bovine, porcine and fish gelatin signatures using chemometrics fuzzy graph method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nurfarhana Hassan ◽  
Tahir Ahmad ◽  
Norhidayu M. Zain ◽  
Siti Rahmah Awang
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Principal Component ◽  
The Novel ◽  
Fish Gelatin ◽  
Fuzzy Graph ◽  
Alternative Source ◽  
Linear Discriminant ◽  
Novel Method ◽  
Pharmaceutical Industries

AbstractGelatin is a protein substance that is widely used in food and pharmaceutical industries. Gelatin is mainly derived from bovine and porcine sources. Fish gelatin is becoming alternative source of gelatin due to concern on health issue and religious constraints. Numerous studies for identification of gelatin sources have been reported. In this study, Fourier transform infrared (FTIR) spectroscopy was used in combination with chemometrics fuzzy autocatalytic set (c-FACS) to distinguish between bovine, porcine and fish gelatins. The gelatin spectra at Amide and 1600–1000 cm−1 regions were analyzed using c-FACS and the results were compared to principal component analysis (PCA) and linear discriminant analysis (LDA). The results obtained from c-FACS method showed that each bovine, porcine and fish gelatin possessed dominant wavenumbers at 1470–1475 cm−1, 1444–1450 cm−1 and 1496–1500 cm−1 respectively, which represent their unique signatures. Furthermore, a clear distinction for porcine gelatin was observed in coordinated FACS. The c-FACS method is rigor and faster than PCA and LDA in differentiating the gelatin sources. The novel method promises at least another chemometrics method for FTIR related analysis and the possibilities for other applications are endless.

scholarly journals A Fuzzy Graph Based Chemometrics Method for Gelatin Authentication

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1969
Author(s):  
Nurfarhana Hassan ◽  
Tahir Ahmad ◽  
Norhidayu M. Zain ◽  
Siti Rahmah Awang
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Graph Theory ◽  
Network Analysis ◽  
Principal Component ◽  
Religious Communities ◽  
Fuzzy Graph ◽  
Facs Analysis ◽  
Sensitive Issue ◽  
Distinct Features

Graph theory is a well-established concept that is widely used in numerous applications such as in biology, chemistry and network analysis. The advancement in the theory of graph has led to the development of a new concept called fuzzy autocatalytic set. In this paper, a fuzzy graph-based chemometrics method, namely, chemometrics fuzzy autocatalytic set (c-FACS) is developed and applied for gelatin authentication. The issue on authenticity of gelatin has become a sensitive issue among some religious communities. Due to the matter, Fourier transform infrared (FTIR) spectra of bovine, porcine and fish gelatins are analyzed using c-FACS to identify their signatures and differences and presented in this paper. The results from the c-FACS analysis showed distinct features of each gelatin, particularly porcine. Furthermore, the new method is faster than principal component analysis (PCA) in identifying the gelatin sources.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

Chemical Structure-Odor Correlation in a Series of Synthetic n-Nonen-1-ols

1995 ◽  
Vol 50 (11-12) ◽  
pp. 757-765 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Tadahiko Kajiwara ◽  
Akikazu Hatanaka
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Spatial Structure ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Acetylenic Compound ◽  
Analysis Method ◽  
Entire Series ◽  
Novel Method

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

Sign in / Sign up

Export Citation Format

Share Document