The complete chloroplast genome of Hydrocotyle pseudoconferta Masamune 1932 (Araliaceae).

Hydrocotyle pseudoconferta was an important medicinal plant. The complete plastid genome of this species was reported for the first time. The full length of the complete chloroplast genome is 153,302 bp, with a typical quadripartite organization: a large single-copy (LSC) region of 84,417 bp, a small single-copy (SSC) region of 18,767 bp, and a pair inverted repeat regions (IRa and IRb) with 25,059 bp for each. The complete chloroplast genome of H. pseudoconferta encoded 133 genes, comprising 86 protein-coding genes, 37 tRNA genes, 8 rRNA genes, and 2 pseudogenes. The phylogenetic analysis suggested the closest relationship between H. pseudoconferta and Hydrocotyle nepalensis.

Microalgae carbon fixation integrated with organic matters recycling from soybean wastewater: Effect of pH on the performance of hybrid system.

Microalgae have been considered as promising alternative for CO2 fixation and wastewater purification. In our previous work, a hybrid microalgae CO2 fixation concept has been put forward, which initially used carbonate solution absorb CO2, and then provided obtained bicarbonate as nutrition for microalgae growth to avoid the challenge of low CO2 solubility and carbon fixation efficiency in the conventional process. In this work, the proposed hybrid system was further intensified via integrating soybean wastewater nutrition removal with bicarbonate-carbon (NH4HCO3 and KHCO3) conversion. The investigation results indicated that the maximum biomass productivity (0.74 g L-1) and carbon bioconversion efficiency (46.9%) were achieved in low-NH4HCO3 concentration system with pH adjusted to 7. pH adjustment of different bicarbonate systems also enhanced total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD) removal efficiency up to 87.5%, 99.5% and 77.6%, respectively. In addition, maximum neutral lipid (14.4 mg L-1·d-1) and polysaccharide (14.5 mg L-1·d-1) productivities could be obtained in the KHCO3 systems, while higher crude protein productivity (48.1 mg L-1·d-1) was yielded in the NH4HCO3 systems.

The complete chloroplast genome of Heteromorpha arborescens (Apiaceae).

Heteromorpha arborescens has long been recognized and cultivated as an important medicinal plant. We reported its complete plastid genome for the first time and reconstructed its phylogenetic position. The complete plastid genome was 157,172 bp in length with a typical quadripartite organization: a large single-copy (LSC) region of 86,436 bp, a small single-copy (SSC) region of 18,222 bp, and two inverted repeat regions (IRa and IRb), each of 26,257 bp. A total of 130 functional genes were recovered, consisting of 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The phylogenetic analysis suggested that H. arborescens is sister to other species except Cetella arborescens in Apiaceae with strong ultrafast support.

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

[Rapid quantitative analysis of hydrocarbon composition of furfural extract oils using attenuated total reflection infrared spectroscopy].

A set of rapid analysis system for hydrocarbon composition of heavy oils was designed using attenuated total reflection FTIR spectrometer and chemometrics to determine the hydrocarbon composition of furfural extract oils. Sixty two extract oil samples were collected and their saturates and aromatics content data were determined according to the standard NB/SH/T0509-2010, then the total contents of resins plus asphaltenes were calculated by the subtraction method in the percentage of weight. Based on the partial least squares (PLS), calibration models for saturates, aromatics, and resin+asphaltene contents were established using attenuated total reflection FTIR spectroscopy, with their SEC, 1.43%, 0.91% and 1.61%, SEP, 1.56%, 1.24% and 1.81%, respectively, meeting the accuracy and repeatability required for the standard. Compared to the present standard method, the efficiency of hydrocarbon composition analysis for furfural extract oils is significantly improved by the new method which is rapid and simple. The system could also be used for other heavy oil analysis, with excellent extension and application foreground.

[Study on building MLR model using orthogonal signal correction].

MLR and PLS models with or without OSC were studied through establishing quantitative calibration models for the peanut oil content in blending edible oils, and for the dimethylsulfoxide concentration in water solution. The cross validation results and the predication results of MLR models, were compared to evaluate the effectiveness of OSC for improving the performance of MLR model. The results show that the SEC or SEP of MLR models using OSC gets smaller. Selecting appropriate wavelengths combination by CARS method, prediction capacity of MLR model using OSC is better than PLS1 model using raw spectrum.