Investigation of the Volatile Profile of Red Jujube by Using GC-IMS, Multivariate Data Analysis, and Descriptive Sensory Analysis.

The aroma characteristics of six red jujube cultivars (Jinchang-'JC', Junzao-'JZ', Huizao-'HZ', Qiyuexian-'QYX', Hetiandazao-'HTDZ', and Yuanzao-'YZ'), cultivated in Xinjiang Province, China, were studied by E-nose and GC-IMS. The presence of acetoin, E-2-hexanol, hexanal, acetic acid, and ethyl acetate played an important role in the classification results. JC, JZ, HZ, and YZ were different from others, while QYX and HTDZ were similar to each other. HZ had the most abundant specific VOCs, including linalool, nonanoic acid, methyl myristoleate, 2-acetylfuran, 1-octen-3-one, E-2-heptenal, 2-heptenone, 7-octenoic acid, and 2-pentanone. HZ had higher intensity in jujube ID, floral, sweet, and fruity attributes. Correlation analysis showed that jujube ID (identity) might be related to phenylacetaldehyde and isobutanoic acid that formed by the transamination or dehydrogenation of amino acids; meanwhile, the sweet attribute was correlated with amino acids, including threonine, glutamic acid, glycine, alanine, valine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine.

Glass transition and crystallization of solid model system of jujube slice as influenced by sugars and organic acids.

To understand the specific contributions of amorphous sugars and organic acids to the quality of food matrix, the solid model system of jujube slice skeleton (JSS) was firstly established. Effects of fructose (F), glucose (G), malic acid (M) and citric acid (C) on the glass transition temperature (Tg) and crystallization of JSS were studied. JSS-F/G/M/C blends were prepared by osmosis in the solution at a range of 0 ~ 32 g/100 g. Sugars reduced the Tg in the system, structure of JSS-G/M blends was changed from "amorphous glassy" to "amorphous rubbery" by increasing the osmotic solute concentration. Tg was decreased from 50.8 to 14.0 °C when JSS was osmosed in a 4 g/100 g fructose solution. Organic acids induced their crystallization in JSS. The crystallinity of JSS-M immersed in 32 g/100 g osmotic solution concentration was increased from 2% to 75%. Fructose presented greater influence on the adverse quality of jujube slices.

Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm.

Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer's high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R2, 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.

Calcium chloride and 1-methylcyclopropene treatments delay postharvest and reduce decay of New Queen melon.

In this study, newly harvested New Queen melons were treated with calcium chloride (CaCl2) and 1-methylcyclopropene (1-MCP) alone or in combination before storage. The results showed that the respiration rate, ethylene release, the activity and gene expression of pectinases such as polygalacturonase (PG), pectin methylesterase (PME) and pectate lyase (PL) in New Queen melons were dramatically decreased by treatments with 0.18 mol/L CaCl2 and/or 1 µL/L 1-MCP. Meanwhile, the climacteric behavior and flesh hardness reduction were inhibited. We also found that softer melon flesh was more conducive to the growth and reproduction of decay-causing microorganisms according to their growth curves in melons that were different in flesh hardness, suggesting inhibiting fruit softening can slow down the growth of microorganisms in fruit flesh, and thus reduce fruit decay rate. The combined use of CaCl2 and 1-MCP was more effective in suppressing respiration rate, ethylene release and protopectin hydrolysis, which could greatly delay the softening, reduce the decay rate, and extend the shelf life of New Queen melons.

A urinary metabolomics study of rats after the exposure to acrylamide by ultra performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry.

Acrylamide (ACR) is known to induce neurotoxicity in humans and occupational exposure to ACR has an effect on human health. Since some animal experiments indicate the metabolic change caused by the ACR based on the metabolomics, increasing concern is the change of metabolite profiles by the low-dose ACR. In the present study, a low-dose of ACR (18 mg kg(-1)) was administered to male Wistar rats for 40 days. Ultra performance liquid chromatography/time of flight mass spectrometry (UPLC-Q-TOF MS) was used to examine urine samples from ACR-dosed and control animals. Multiple statistical analyses with principal component analysis (PCA) were used to investigate metabolite profile changes in urine samples, and to screen for potential neurotoxicity biomarkers. PCA showed differences between the ACR-dosed and control groups 20 days after the start of dosing; a bigger separation between the two groups was seen after dosing for 40 days. Levels of 4-guanidinobutanoic acid and 2-oxoarginine were significantly higher in urine from the ACR-dosed group than in urine from the control group after 10 days (p < 0.05). Receiver operator characteristic (ROC) curve analysis suggested that 4-guanidinobutanoic acid and 2-oxoarginine were the major metabolites. Our results suggest that high levels of 4-guanidinobutanoic acid and 2-oxoarginine may be related to ACR neurotoxicity. These metabolites could, therefore, act as sensitive biomarkers for ACR exposure and be useful for investigating toxic mechanisms. They may also provide a scientific foundation for assessing the effects of chronic low-dose ACR exposure on human health.

Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer's disease.

Previous studies showed the relationship between fatty acids and the risk of developing Alzheimer's disease (AD). However, they did not address potential differences in free fatty acid (FFA) profiles that could be used to distinguish between AD patients and healthy controls. In the present study we used gas chromatography-mass spectrometry (GC-MS) technology coupled with multivariate statistical analysis to study profiles of FFA in AD. The results indicated 2 saturated fatty acids (C14:0 and C16:0; p < 0.001 and p < 0.05, respectively), 3 unsaturated fatty acids (C18:1, C18:3, and C22:6; p < 0.05, p < 0.05, and p < 0.001, respectively), where mean levels in serum from AD patients were significantly lower than controls. Partial least squares discriminant analysis (PLS-DA) models with unit variance (UV) scaling and orthogonal signal correction (OSC) data preprocessing methods were employed to refine intergroup differences between FFA profiles. The results of the analysis have highlighted docosahexaenoic acid (DHA) as the FFA with the greatest potential as a biomarker of AD, and this study has demonstrated that FFA biomarkers have considerable potential in diagnosing and monitoring AD.