Elsholtziazhongyangii (Lamiaceae), a new species from Sichuan, China.

Elsholtziazhongyangii (Lamiaceae), a new species from Sichuan Province, China, is described and illustrated. The new species is morphologically similar to E.feddeif.feddei, but it can be easily distinguished from E.feddeif.feddei by smaller corolla (3.2-3.5 mm vs. 4.5-5.3 mm), bract indumentum (glabrous, except margin ciliate vs. villous, especially on veins abaxially, glabrous adaxially) and bract stalked (ca. 1.2 mm vs. sessile). Phylogenetic analyses, based on two nuclear ribosomal (ETS, ITS) and five plastid (rbcL, matK, trnL-F, ycf1, ycf1-rps15) regions, confirmed that the new species formed a monophyletic clade with robust support. The new species is currently known from western Sichuan.

Multi-mycotoxins analysis in liquid milk by UHPLC-Q-Exactive HRMS after magnetic solid-phase extraction based on PEGylated multi-walled carbon nanotubes.

A simple and rapid magnetic solid-phase extraction (MSPE) method using PEGylated multi-walled carbon nanotubes magnetic nanoparticles (PEG-MWCNTs-MNP) as absorbents is proposed for isolation and enrichment of aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1), aflatoxin G2 (AFG2), aflatoxin M1 (AFM1), aflatoxin M2 (AFM2), ochratoxin A (OTA), zearalenone (ZEA), zearalanone (ZAN), α-zeralanol (α-ZAL), ß-zeralanol (ß-ZAL), α-zeralenol (α-ZOL), and ß-zeralenol (ß-ZOL) from liquid milk. Combined with ultra-high performance liquid chromatography Q-Exactive high resolution mass spectrometry, simultaneous qualification of these mycotoxins was achieved with sensitivity and specificity. The proposed method showed a good linearity (R2 ≥ 0.995), high sensitivity (limit of detection in the range of 0.005-0.050 µg/kg and limit of quantification in the range of 0.015-0.150 µg/kg), adequate recovery (81.8-106.4%), and good repeatability (intra-day precision in the range of 2.1-8.5% and inter-day precision in the range of 3.9-11.7%). It has been successfully applied to the determination of 13 mycotoxins in real liquid milk samples.

Ligand fishing of anti-neurodegenerative components from Lonicera japonica using magnetic nanoparticles immobilised with monoamine oxidase B.

In this work, monoamine oxidase B was immobilised onto magnetic nanoparticles to prepare a new type of affinity solid-phase extraction adsorbent, which was used to extract the possible anti-neurodegenerative components from the Lonicera japonica flower extracts. Coupled with high-performance liquid chromatography with mass spectrometry, two monoamine oxidase B ligands were fished-out and identified as isochlorogenic acid A and isochlorogenic acid C, which were found to be inhibitors of the enzyme for the first time, with similar half maximal inhibitory concentration values of 29.05 ± 0.49 and 29.77 ± 1.03 µM, respectively. Furthermore, equilibrium-dialysis dissociation assay of enzyme-inhibitor complex showed that both compounds have reversible binding patterns to monoamine oxidase B, and kinetic analysis demonstrated that they were mixed-type inhibitors for monoamine oxidase B, with Ki and Kis values of 9.55 and 37.24 µM for isochlorogenic acid A, 9.53 and 35.50 µM for isochlorogenic acid C, respectively. The results indicated that isochlorogenic acid A and isochlorogenic acid C were the major active components responsible for the anti-degenerative activity of the flowers of L. japonica, while magnetic nanoparticles immobilised monoamine oxidase B could serve as an efficient solid-phase extraction adsorbent to specifically extract monoamine oxidase B inhibitors from complex herbal extracts.

[Growth status of children under 7 years in Wuzhong City, Ningxia Hui Autonomous Region, China and its influential factors].

OBJECTIVE: To investigate the growth status of children under 7 years in Wuzhong City, Ningxia Hui Autonomous Region, China and its influential factors, and to provide a basis for related intervention measures. METHODS: Children under 7 years were selected from two county-level districts in Wuzhong by stratified cluster sampling, and their growth status were evaluated by the Z score method. RESULTS: The prevalence rates of growth retardation, underweight, and wasting were 12.58%, 5.71%, and 5.55% respectively. The height-for-age Z score, weight-for-age Z score, and weight-for-height Z scores were -0.26±2.50, 0.29±4.54. and 0.65±3.02 respectively. There were significant differences in the prevalence rate of wasting among children of different ethnic groups (P<0.05); also, there were significant differences in the prevalence rates of growth retardation and underweight among children from different regions and with different age (P<0.05). The main influential factors for growth retardation were region (OR=0.369, P<0.001), ethnic groups (OR=1.694, P=0.027), and age (OR=1.143, P=0.002). The main influential factors for underweight were region (OR=0.453, P=0.001) and age (OR=1.204，P=0.002). The main influential factor for wasting was nation (OR=1.735, P=0.024). CONCLUSIONS: In Wuzhong, children under 7 years have poor growth status, which are related to ethnic groups, region, and age.

[Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique].

The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1 x PC2 x PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles, and implementing rationalization production and transaction of textile materials and its production.

[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

[Discrimination of varieties of dry red wines based on independent component analysis and BP neural network].

In order to achieve the rapid discrimination of the varieties of red wines, the authors selected 5 kinds of dry red wine for study with Vis/NIR spectroscopy. Firstly, Characteristics of the pattern were analyzed by independent component analysis (ICA). Through comparing the results of modeling performance by different number of independent components, 20 principal components presenting important information of spectra were confirmed as the best number of principal components. The 20 independent components (ICs) extracted by ICA were employed as the inputs of the BP neural networks, and then a three layers of BP neural network was built, category analysis was performed, and the work of building mathematics model and optimizing the algorithm was completed. Five samples from each variety and a total of 25 samples were selected randomly as the prediction sets. The remaining 150 samples were used as the training sets to build the training model, which was validated by the samples of the prediction sets. The recognition rate was 100%. In addition, based on the independent component analysis, the authors selected two characteristic wave bands in reference to vector loading map of mixed matrix. So the pattern recognition methods developed in this paper not only played a good role in the classification and discrimination, but also had the capability to extract the finger feature of red wine, and offered a new way for detecting and developing red wines.

[Application of wavelet threshold denoising model to infrared spectral signal processing].

Aimed at noise interference of infrared spectra, an example of using infrared spectra to detect fat content value on the surface of cashmere was applied to evaluate the effect of wavelet threshold denoising. The denoising capabilities of three wavelet threshold denoising models (penalty threshold denoising model, Brige-Massart threshold denoising model and default threshold denoising model) were compared and analyzed. Denoised spectra and measured cashmere fat content values were used for calibration and validation with multivariate analysis (partial least squares combined with support vector machine). The authors analyzed and evaluated denoising effects of these three wavelet threshold denoising models by comparing parameters (R2, RMSEC and RMSEP) obtained through calibration and validation of denoised spectra with these three wavelet threshold denoising models respectively. The results show that the three wavelet threshold denoising models all can denoise the infrared spectral signal, increase signal to noise ratio and improve precision of prediction model to some extent; Among these three wavelet threshold denoising models, the denoising effect of Brige-Massart threshold denoising model and default threshold denoising model were significantly better than that of default threshold denoising model; Compared with the prediction precision (R2 = 0.793, RMSEC = 0.233, RMSEP = 0.225) of multivariate analysis model established with original spectra, the prediction precision (R2 = 0.882, RMSEC = 0.144, RMSEP = 0.136) of multivariate analysis model established with spectra denoised by Brige-Massart threshold denoising model and the prediction precision (R2 = 0.876, RMSEC = 0.151, RMSEP = 0.142) both had much more improvements. All the above illustrates that wavelet threshold denoising models can denoise infrared spectral signal effectively, make multivariate analysis model of spectral data and measured cashmere fat values more representative and robust, and so it can improve detection precision of infrared spectral technique.

[Research on the sugar content measurement of grape and berries by using Vis/NIR spectroscopy technique].

Aiming at the nonlinear correlation characteristic of Vis/NIR spectra and the corresponding sugar content of grape and berries, the Vis/NIR spectra of grape and berries were obtained by diffusion reflectance. A mixed algorithm was presented to predict sugar content of grape and berries. The original spectral data were processed using partial least squares (PLS), and three best principal factors were selected based on the reliabilities. The scores of these 3 principal factors would be taken as the input of the three-layer back-propagation artificial neural network (BP-ANN). Trained with the samples in calibration collection, the BP-ANN predicted the samples in prediction collection. The values of decision coefficient (r2), the root mean squared error of prediction (RMSEP), and bias were used to estimate the mixed model. The observed results using PLS-ANN (r2 = 0.908, RMSEP = 0.112 and Bias = 0.013) were better than those obtained by PLS (r2 = 0.863, RMSEP = 0.171, Bias = 0.024). The result indicted that the detection of internal quality of grape and berries such as sugar content by nondestructive determination method was very feasible and laid a solid foundation for setting up the sugar content forecasting model for grape and berries.

[Identification of fine wool and cashmere by using Vis/NIR spectroscopy technology].

As a rapid and non-destructive methodology, near infrared spectroscopy technique has been attracting much attention recently. The present study applied Vis/NIR spectra to the identification of cashmere and fine wool fiber. Cashmere and fine wool are resemble in superficies, but they differs in diameter, height, thickness, angle of inclination, and marginal morphology of surface scale. Although researchers both at home and abroad did a lot researches and experiments to distinguish fine wool from cashmere, the resolution of cashmere and fine wool is still not satisfactory, and it is always a challenging task to differentiate and recognize fine wool and cashmere. This paper presents an automatic recognition scheme for the fine wool fiber and cashmere fiber by Vis/NIR spectroscopy technique, aiming at the characteristics of Vis/NIR spectra of cashmere and fine wool. One mixed algorithm was presented to discriminate cashmere and fine wool with principal component analysis (PCA) and artificial neural network (ANN). Preliminary qualitative analysis model has been built: Vis/NIRS spectroscopy diffuse techniques were used to collect the spectral data of cashmere and fine wool, and two kinds of data pretreatment methods were applied: the standard normal variate (SNV) was used for scatter correction. Savitzky-Golay with the segment size 3 was used as the smoothing way to decrease the noise processed. Following the pretreatment, spectral data were processed using principal component analysis, 6 principal components (PCs) were selected based on the reliabilities of PCs of 99.8%, and the scores of these 6 PCs would be taken as the input of the three-layer back-propagation (BP) artificial neural network (BP-ANN). The BP-ANN was trained with samples in calibration collection and predicted the samples in prediction collection were predicted. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems. The result indicated that a model had been built to discriminate cashmere from fine wool using Vis/NIR spectra method combined with PCA-BP technology.