RESUMEN
The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.
RESUMEN
The Southern Region has reported a large number of contagious pandemic outbreaks. These epidemics brought threats to human health and resulted in serious economic losses. The COVID-19 is a global virus and has weakened the global financial markets with significant effect on stock returns and market volatilities. The study obtained a dataset about the financial market structure of South Asian Association for Regional Cooperation (SAARC) Countries. The purpose of the study is to determine the effect of 2019-nCov on stock market performance of SAARC member states. The study considered indexes of the National Stock Exchanges of each country and applied an event study approach for estimating the impact of Mad COVID-19 on the stock returns and market volatilities with an event window of 25 days of severe pandemic hits. The CAR approach proved the declining effect of Mad COVID-19 on the stock returns of SAARC countries. Asymmetric GJR-GARCH Model estimated the changeable volatility and proved the increase in volatility with COVID-19 as a negative shock. SAARC Region significantly reacts to Mad COVID-19 with falling markets and rising volatility.
