The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans.

Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy.

Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.

TRPA1 Agonist Cinnamaldehyde Decreases Adipogenesis in 3T3-L1 Cells More Potently than the Non-agonist Structural Analog Cinnamyl Isobutyrate.

The cinnamon-derived bioactive aroma compound cinnamaldehyde (CAL) has been identified as a promising antiobesity agent, inhibiting adipogenesis and decreasing lipid accumulation in vitro as well as in animal models. Here, we investigated the antiadipogenic effect of cinnamyl isobutyrate (CIB), another cinnamon-derived aroma compound, in comparison to CAL in 3T3-L1 adipocyte cells. In a concentration of 30 µM, CIB reduced triglyceride (TG) and phospholipid (PL) accumulation in 3T3-L1 pre-adipocytes by 21.4 ± 2.56 and 20.7 ± 2.05%, respectively. CAL (30 µM), in comparison, decreased TG accumulation by 37.5 ± 1.81% and PL accumulation by 28.7 ± 1.83%, revealing the aldehyde to be the more potent antiadipogenic compound. The CIB- and CAL-mediated inhibition of lipid accumulation was accompanied by downregulation of essential adipogenic transcription factors PPARγ, C/EBPα, and C/EBPß on gene and protein levels, pointing to a compound-modulated effect on adipogenic signaling cascades. Coincubation experiments applying the TRPA-1 inhibitor AP-18 demonstrated TRPA1 dependency of the CAL, but not the CIB-induced antiadipogenic effect.