Interactive effects of gallic/ferulic/caffeic acids and anthocyanins on pigment thermal stabilities.

The data presented in this article are related to the research article entitled "The effects of gallic/ferulic/caffeic acids on colour intensification and anthocyanin stability" (Qian et al., 2017) [1]. This paper described preparation and isolation of anthocyanins from purple sweet potatoes (PSP) and the time-course of anthocyanin profiles treated with gallic, ferulic, or caffeic acids at 95 °C. The color appearance of PSPanthocyanins alone, or with gallic, ferulic, or caffeic acids was described after the 15 h of thermal treatment. The high resolution mass spectrographs of PSP anthocyanins were determined using UPLC-ESI-HRMS. The spatial interaction of peonidin 3-O-(2-O-ß-D-glucopyranocyl-ß-D-glucopyranoide)-5-O-ß-D-glucopyranoside and gallic/ferulic/caffeic acids was illustrated by molecular dynamic simulation.

The effects of gallic/ferulic/caffeic acids on colour intensification and anthocyanin stability.

The mechanism by which copigments stabilize colour, by protecting anthocyanin chromophores from nucleophilic attack, seems well accepted. This study was to determine effects of gallic/ferulic/caffeic acids on colour intensification and anthocyanin stability. Molecular dynamics simulations were applied to explore molecular interactions. Phenolic acids intensified the colour by 19%∼27%. Colour fading during heating followed first-order reactions with half-lives of 3.66, 9.64, 3.50, and 3.39h, whereas anthocyanin degradation, determined by the pH differential method (or HPLC-PDA), followed second-order reactions with half-lives of 3.29 (3.40), 3.43 (3.39), 2.29 (0.39), and 2.72 (0.32)h alone or with gallic/ferulic/caffeic acids, respectively, suggesting that anthocyanin degradation was faster than the colour fading. The strongest protection of gallic acids might be attributed to the shortest distance (4.37Å) of its aromatic ring to the anthocyanin (AC) panel. Hyperchromic effects induced by phenolic acids were pronounced and they obscured the accelerated anthocyanin degradation due to self-association interruption.

Updated incidence rates and risk factors of esophageal cancer in Nan'ao Island, a coastal high-risk area in southern China.

Esophageal cancer (EC) is one of the most common cancers in China. The purpose of this study was to investigate the updated incidence rates and risk factors of EC in Nan'ao Island, where the EC incidence rate was chronically the highest in southern China. To calculate the annual incidence rate, data on 338 EC cases from Nan'ao Cancer Registry system diagnosed during 2005-2011 were collected. A case-control study was conducted to explore the EC risk factors. One hundred twenty-five alive EC patients diagnosed during 2005-2011 and 250 controls were enrolled into the case-control study. A pre-test questionnaire on demography, dietary factors, drinking water treatment, and behavioral factors was applied to collect information of all participants. The average EC incidence rates during 2005-2011 were 66.09/105, 94.62/105, 36.83/105 for both genders, males and females, respectively, in Nan'ao Island. The EC incidence rate in males was 2.40- to 4.55-fold higher than that in females in the period from 2006 to 2011 (P < 0.05). Considering the onset age, males tend to be much younger than females and reached peak incidence rate at a younger age (P < 0.05). Drinking water treatment by filter (odds ratio [OR] = 0.28, 95% confidence interval [95% CI] = 0.13-0.58) and fruit consumption (OR = 0.55, 95% CI = 0.32-0.94) reduced the risk for EC. On the contrary, the pickled vegetables consumption (OR = 2.64, 95% CI = 1.46-4.76) and liquor drinking (OR = 2.32, 95% CI = 1.21-4.44) increased the risk for EC. These results may be of importance for future research on EC etiology and prevention strategies.

Using FTIR spectra and pattern recognition for discrimination of tea varieties.

In order to classify typical Chinese tea varieties, Fourier transform infrared spectroscopy (FTIR) of tea polysaccharides (TPS) was used as an accurate and economical method. Partial least squares (PLS) modeling method along with a self-organizing map (SOM) neural network method was utilized due to the diversity and heterozygosis between teas. FTIR spectra results of tea extracts after spectra preprocessing were used as input data for PLS and SOM multivariate statistical analyses respectively. The predicted correlation coefficient of optimization PLS model was 0.9994, and root mean square error of calibration and cross-validation (RMSECV) was 0.03285. The features of PLS can be visualized in principal component (PC) space, contributing to discover correlation between different classes of spectra samples. After that, a data matrix consisted of the scores on the selected 3PCs computed by principle component analysis (PCA) and the characteristic spectrum data was used as inputs for training of SOM neural network. Compared with the PLS linear technique's recognition rate of 67% only, the correct recognition rate of the PLS-SOM as a non-linear classification algorithm to differentiate types of tea reaches up to 100%. And the models become reliable and provide a reasonable clustering of tea varieties.