Gelatin 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.

The issue of food authenticity has become a concern among religious adherents, particularly Muslims, due to the possible presence of nonhalal ingredients in foods as well as other commercial products. One of the nonhalal ingredients that commonly found in food and pharmaceutical products is gelatin which extracted from porcine source. Bovine and fish gelatin are also becoming the main commercial sources of gelatin. However, unclear information and labeling regarding the actual sources of gelatin in food and pharmaceutical products have become the main concern in halal authenticity issue since porcine consumption is prohibited for Muslims. Hence, numerous analytical methods involving chemical and chemometric analysis have been developed to identify the sources of gelatin. Chemical analysis techniques such as biochemical, chromatography, electrophoretic, and spectroscopic are usually combined with chemometric and mathematical methods such as principal component analysis, cluster, discriminant, and Fourier transform analysis for the gelatin classification. A sample result from Fourier transform infrared spectroscopy analysis, which combines Fourier transform and spectroscopic technique, is included in this paper. This paper presents an overview of chemical and chemometric methods involved in identification of different types of gelatin, which is important for halal authentication purposes.

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.

Counterfeiting, in particular, the forgery of banknotes is a serious crime problem and has become a great challenge to the global economies. The forensic science experts have been utilizing chemical technique such as infrared spectroscopy to analyze genuine and counterfeit banknotes. Nevertheless, chemometrics techniques are required to further discriminate the banknotes. In this paper, an advanced fuzzy graph chemometrics method is used to discriminate genuine and counterfeit fifty Ringgit Malaysian (RM50) banknotes. The development of the technique, namely chemometrics fuzzy autocatalytic set (c-FACS) is presented in this paper, together with the results and its comparison to principal component analysis (PCA) method. The results from the c-FACS analysis showed distinct patterns and features of the counterfeit banknotes in the c-FACS plot. Furthermore, the new method is faster than PCA in authentication analysis of counterfeit banknotes. Hence, the c-FACS provides better performance in terms of computing time as compared to PCA, and has the potential in assisting the investigation involving counterfeit banknotes.

Achieving the Sustainable Development Goals (SDG) presents distinct challenges across different income economies, necessitating a comprehensive analysis to identify critical factors influencing progress. This study systematically examines obstacles to SDG attainment across various income groups by analyzing data from 215 nations spanning 2012 to 2021. Principal Component Analysis (PCA) was employed to uncover patterns within the factors, while fuzzy graph modeling elucidated their dynamic influences. The analysis focused on nine key variables: poverty, unemployment, youth literacy, adult literacy, health (undernourishment), food security, access to electricity, carbon dioxide (CO₂) emissions, and other greenhouse gas emissions. Findings indicate that CO₂ emissions serve as the primary barrier to achieving SDG 13 (climate action) in high-income nations. Conversely, poverty and undernourishment emerge as significant challenges impeding progress in upper-middle-income, lower-middle-income, and low-income groups. The study provides a novel, integrated view of the multifaceted impacts and interactions between socio-economic and environmental factors in addressing SDG challenges. The results offer valuable insights for policymakers, highlighting the need for differentiated strategies tailored to income-specific contexts. It is recommended that governments in high-income countries extend financial support to lower-income groups to alleviate poverty and improve food security, while fostering collaboration in climate mitigation and adaptation to promote balanced and sustainable global development.