A novel technique for detection of vanaspati ( hydrogenated fat ) in cow ghee ( clarified butter fat ) using flash gas chromatography electronic nose combined with chemometrics

Abstract

This study aimed to detect various proportions of vanaspati mixed with cow ghee using an electronic nose (e‐nose) based on fast gas chromatography. The e‐nose apparatus was equipped with a non‐polar and medium polar column used to generate two chromatograms simultaneously along with the volatile compound information. Further investigation on the adulteration detection in ghee was carried out using chemometric analysis such as principal component analysis (PCA), discriminant function analysis (DFA), and soft‐independent modeling of class analogy (SIMCA). An outstanding differentiation among all the samples was obtained using PCA and DFA with 99.85% and 99.83% classification accuracy in the e‐nose dataset. SIMCA presented a validation score of 76, indicating SIMCA could also be a potential method to detect higher levels of adulteration in ghee. The validation study shows good agreement between FGCEN and GC–MS methods. Practical applications Cow ghee is frequently adulterated with vanaspati, due to its high market demand. The existing methods for adulteration detection are time‐consuming, and require tedious sample preparation and expertise in these fields. The e‐nose based on fast GC combined with chemometric analysis turned out to be a reliable and promising technique for various proportions of adulteration detection in pure cow ghee. The technique was efficient enough to get the volatile compound information from the samples in a fast‐performing way. The chemometrics used has shown to be effective to confirm adulterations in ghee.

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges

“…In this paper [10], the authors present an IoT-based system for detecting the presence of formalin in fruits and vegetables using machine learning approaches. The study [11] demonstrated the potential of using an e-nose equipped with eight different MOS-type gas sensors, in combination with chemometrics techniques such as PCA and DFA. The goal of this study [12] was to the development of cost-effective electronic nose technology was accomplished by employing colorimetric sensors.…”

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

“…Sesame oil has a distinctive fatty acid composition, characterized by the presence of specific fatty acids, such as oleic acid, linoleic acid, and palmitic acid [ 14 ]. Gas chromatography (GC) and high-performance liquid chromatography (HPLC) are widely employed to separate and identify fatty acids in oil samples [ 15 , 16 ]. By comparing the fatty acid profiles of suspected samples with known authentic sesame oil profiles, it is possible to identify adulteration or oil substitutions [ 17 ].…”

“…Continued research and advancements in this field will contribute to more robust and reliable methods for combating fraud in the sesame oil industry, ensuring consumer trust and product integrity. However, it is important to note that while e-nose technologies show promise, there are still significant challenges because this is still an emerging field with ongoing research and development [ 16 ]. Further studies are needed to optimize e-nose sensor arrays, improve detection limits, and enhance the specificity and selectivity of the electronic nose for sesame oil fraud detection.…”