Abstract-Terahertz time-domain spectroscopy of edible oils

Alex Dinovitser1, Dimitar G. Valchev1,2 and Derek Abbott1 

http://rsos.royalsocietypublishing.org/content/royopensci/4/6/170275.full.pdf

Chemical degradation of edible oils has been studied using conventional spectroscopic methods spanning the spectrum from ultraviolet to mid-IR. However, the possibility of morphological changes of oil molecules that can be detected at terahertz frequencies is beginning to receive some attention. Furthermore, the rapidly decreasing cost of this technology and its capability for convenient, in situ measurement of material properties, raises the possibility of monitoring oil during cooking and processing at production facilities, and more generally within the food industry. In this paper, we test the hypothesis that oil undergoes chemical and physical changes when heated above the smoke point, which can be detected in the 0.05–2 THz spectral range, measured using the conventional terahertz time-domain spectroscopy technique. The measurements demonstrate a null result in that there is no significant change in the spectra of terahertz optical parameters after heating above the smoke point for 5 min. 

Abstract-Identification of edible oils using terahertz spectrum combined with genetic algorithm and partial least squares discriminant analysis

Ming Yin,   Shoufeng Tang and   Minming Tong  

Anal. Methods, 2016, Accepted Manuscript

DOI: 10.1039/C6AY00259E
http://pubs.rsc.org/en/Content/ArticleLanding/2016/AY/C6AY00259E?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+rss%2FAY+(RSC+-+Anal.+Methods+latest+articles)#!divAbstract

The authentication and identification of different edible oils have become a focus of attention in food safety field. In this work, we propose a method for distinction of edible oils by using terahertz (THz) spectrum combined with genetic algorithm (GA) and partial least squares discriminant analysis (PLS-DA). To evaluate the robustness of the model, we also employ full spectra PLS (fsPLS), interval PLS (iPLS), and backward interval (biPLS) algorithms to verify the classification performance through a variable selection. The results demonstrate that the GA-PLS-DA model has a smaller root mean square error of prediction (RESEP), a larger correlation coefficient of prediction (Rp), and higher classification accuracy than other models. In conclusion, the THz spectrum coupled with chemometrics is an effective method for differentiating various types of edible oil.