Combined effect of thermosonication and high hydrostatic pressure on bioactive compounds, microbial load, and enzyme activities of blueberry juice.

The current study is aimed to evaluate the combined effect of thermosonication (TS) and high hydrostatic pressure (HHP) on enzyme activities (polyphenolase and peroxidase), microbial load and phenolic compounds (phenols, flavonoids, and anthocyanins) of blueberry juice. Blueberry juice has been treated with TS (40 kHz and 240 W) at different temperatures (25 °C and 45 °C) for 15 mins with subsequent different HHP (200, 400 and 600 MPa) for 5 mins at room temperature. The results revealed that a combined use of HHP of 400 MPa and 600 MPa with TS at 45 °C not only reduced microorganisms below 1 logCFU/mL, but also significantly inactivated enzymes. The treatments also increased the phenolic compounds, peroxyl radical scavenging capacity (PSC), and DPPH free radical scavenging activity to a higher level without causing any changes in soluble solids and pH. Therefore, the combination of HHP and TS can be used as a novel alternative nonthermal technology to improve the nutritional qualities of blueberry juice, which produces a desirable, healthy juice for consumers.

A "Green" Homogenate Extraction Coupled with UHPLC-MS for the Rapid Determination of Diterpenoids in Croton Crassifolius.

Clerodane diterpenoids are the main bioactive constituents of Croton crassifolius and are proved to have multiple biological activities. However, quality control (QC) research on the constituents are rare. Thus, the major research purpose of the current study was to establish an efficient homogenate extraction (HGE) process combined with a sensitive and specific ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC⁻MS) technique together for the rapid extraction and determination of clerodane diterpenoids in C. crassifolius. All calibration curves showed good linearity (r > 0.9943) within the test ranges and the intra- and inter-day precisions and repeatability were all within required limits. This modified HGE⁻UHPLC⁻MS method only took 5 min to extract nine clerodane diterpenoids in C. crassifolius and another 12 min to quantify these components. The results indicated that the quantitative analysis based on UHPLC⁻MS was a feasible method for QC of clerodane diterpenoids in C. crassifolius, and the findings outlined in the current study also inferred the potential of the method in the QC of clerodane diterpenoids in other complex species of plants.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance.