Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.     

Determination of phenolic compounds and authentication of PDO Lambrusco wines by HPLC-DAD and chemometric techniques

This work proposes a fast and simple method for detection and quantification of phenolic compounds in PDO Lambrusco wines using HPLC-DAD and chemometric techniques. Samples belonging to three different varieties of Lambrusco (Grasparossa, Salamino and Sorbara) were analyzed to provide a methodology appropriate for routine analysis. Given the high complexity of the sample and the coelution among chromatographic peaks, the use of chemometric techniques to extract the information of the individual eluting compounds was needed. Multivariate curve resolution-alternating least squares (MCR-ALS) allowed the resolution of the chromatographic peaks obtained and the use of this information for the quantification of the phenolic analytes in the presence of interferences. Use of multiset analysis and local rank/selectivity information was proven to be crucial for the correct resolution and quantification of compounds. The quantitative data provided by MCR-ALS about the phenolic targets and additional compounds present in the samples analyzed provided wine composition profiles, which were afterwards used to distinguish among wine varieties. Principal component analysis applied to the wine profiles allowed characterizing the wines according to their varieties.     

基于显微共聚焦拉曼技术对三种食源性致病菌的快速鉴别研究

采用显微共聚焦拉曼技术,建立了对三种常见食源性致病菌快速鉴别的检测方法。使用XploRA PLUS共聚焦拉曼光谱仪,在激光功率为5 mW、积分时间为30s、积分次数为1次的条件下,对德尔卑沙门氏菌、副溶血性弧菌和金黄色葡萄球菌进行了拉曼光谱数据的采集。对拉曼光谱采用多项式平滑算法和荧光背底扣除后,采用主成分分析法(PCA)对预处理后的数据进行降维,提取出前三个主成分的累计方差贡献率达到了95.4％,样本明显的聚为了3类。同时结合Fisher判别分析法(FLD)构建分类模型,对三种样本进行交叉验证,分类准确率达到了100％。结果表明,采用显微共聚焦拉曼技术与PCA-FLD方法结合可实现对三种食源性致病菌的快速准确鉴别且模型检测精度高,方法具有一定的实用性及参考价值。     

Analysis of plant genomic DNAs and the genetic relationship among plants by using surface-enhanced Raman spectroscopy

The study aimed to distinguish genomic DNAs from nine species of plants belonging to six families and analyze their genetic relationship by using surface-enhanced Raman scattering (SERS). The silver nano-colloid and excitation wavelength of 785 nm used in this study yielded excellent quality of the SERS spectra. Raman signals were remarkably enhanced. Although the spectra for the nine species of plants appeared very similar, there were significant differences according to the analysis of variance analysis. There were three strong characteristic peaks. The peak at 625 cm⁻¹ was due to the vibration overlap of C3′-endo/anti deoxyribose, cytosine, and guanine; the one at 715 cm⁻¹ was due to the scissoring vibrations of C2N1C6 of adenine; and that at 1011 cm⁻¹ was due to the stretching vibration of the CO bond of deoxyribose and vibrations of cytosine. The SERS data were smoothed and standardized and evaluated using second derivative analysis, principal component analysis, and hierarchical cluster analysis. A model was established using the data from hierarchical cluster analysis and principal components of the second derivative. The clustering result of this model was highly consistent with the traditional classification of plants; all plant species investigated were correctly clustered into classes according to the cluster distance coefficient among them; the accuracy of clustering was 100%. Chinese cabbage (Brassica pekinensis Rupr.) and green cabbage (Brassica chinensis L.) belonging to Cruciferae, maize (Zea mays L.) and bamboo (Sinocalamus affinis McClure) belonging to Gramineae, and magnolia (Magnolia delavayi Franch.) and champaca (Michelia alba DC.) belonging to Magnoliaceae were clustered into three separate classes, and fern (Nephrolepis auriculata L., Nephrolepidaceae), garlic (Allium sativum L., Amaryllidaceae), and ginkgo (Ginkgo biloba L., Ginkgoaceae) were each clustered into separate classes. These findings suggest that the SERS spectra of plant genomic DNAs can be used to classify species and analyze their genetic relationship. It is an effective and perfect supplement to traditional classification and can form the basis for genetic analysis.     

Theoretical analysis of magnetic sensor output voltage

The output voltage is an important parameter to determine the stress state in magnetic stress measurement, the relationship between the output voltage and the difference in the principal stresses was investigated by a comprehensive application of magnetic circuit theory, magnetization theory, stress analysis as well as the law of electromagnetic induction, and a corresponding quantitative equation was derived. It is drawn that the output voltage is proportional to the difference in the principal stresses, and related to the angle between the principal stress and the direction of the sensor. This investigation provides a theoretical basis for the principle stresses measurement by output voltage.     

基于主要成分分析的肝硬化数据处理与分析

本文利用主成分分析方法，针对66例肝硬化建立主成分分析模型，利用spss16.0求解模型。所得的结果简化了问题研究的复杂度，有助于医生的诊断。     

Principal component analysis of the main factors of line intensity enhancements observed in oscillating direct current plasma

Enhancement of emission line intensities by induced oscillations of direct current (DC) arc plasma with continuous aerosol sample supply was investigated using multivariate statistics. Principal component analysis (PCA) was employed to evaluate enhancements of 34 atomic spectral lines belonging to 33 elements and 35 ionic spectral lines belonging to 23 elements. Correlation and classification of the elements were done not only by a single property such as the first ionization energy, but also by considering other relevant parameters. Special attention was paid to the influence of the oxide bond strength in an attempt to clarify/predict the enhancement effect. Energies of vaporization, atomization, and excitation were also considered in the analysis. In the case of atomic lines, the best correlation between the enhancements and first ionization energies was obtained as a negative correlation, with weak consistency in grouping of elements in score plots. Conversely, in the case of ionic lines, the best correlation of the enhancements with the sum of the first ionization energies and oxide bond energies was obtained as a positive correlation, with four distinctive groups of elements. The role of the gas-phase atom-oxide bond energy in the entire enhancement effect is underlined.