川味五香香肠的常规理化和微生物特性及挥发性风味物质分析

以成都地区香肠为代表分析川味五香香肠的常规理化和微生物特性及挥发性风味特征,揭示川味五香香肠的品质特性。结果表明,川味五香香肠的水分含量为11.73%±1.64%、pH为5.68±0.06、硫代巴比妥酸值(thiobarbituric acid reactive substances,TBARS)为1.47±0.48 mg/kg、亚硝酸盐含量为1.31±0.12 mg/kg,亮度值(L*)、红度值(a*)、黄度值(b*)分别为48.12±1.46、16.08±0.91、18.31±0.73,菌落总数、疑似葡萄球菌数、疑似乳酸菌数和肠杆菌数分别为7.09±0.14、5.74±0.23、7.64±0.14、4.27±0.15 lg CFU/g。从川味五香香肠样品中共检测出97种挥发性风味物质,气味活度值OAV大于1的共有挥发性物质有22种,是川味五香香肠的主要挥发性风味物质。经主成分载荷图分析可知,(+)-柠檬烯、芳樟醇、草蒿脑对香肠的典型风味贡献最大,赋予了川味五香香肠柑橘、薄荷、花香、薰衣草、甘草、茴香的气味特征。研究结果揭示了川味五香香肠主要挥发性物质及典型风味特征,为进一步研究川味五香香肠的风味特征奠定了基础。

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

Fault detection method with PCA and LDA and its application to induction motor

A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

A nonlinear PCA algorithm based on RBF neural networks

Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.

Analyzing inspection results of Port State Control by using PCA

The paper applied Principal Components Analysis Method to analyze the PSC inspection results in the area of T-MOU and P-MOU. Set up the assessment of ship detention, the ships＇ main deficiencies of detentions were found out by the standardization of data processing and correlation matrix calculating. Provide the basis for shipping company to master the safety management focus and pass the PSC inspection.

Locally linear embedding-based seismic attribute extraction and applications

How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.

FUZZY WITHIN-CLASS MATRIX PRINCIPAL COMPONENT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION

Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.

Real-Time Face Tracking and Recognition in Video Sequence

A framework of real time face tracking and recognition is presented, which integrates skin color based tracking and PCA/BPNN (principle component analysis/back propagation neural network) hybrid recognition techniques. The algorithm is able to track the human face against a complex background and also works well when temporary occlusion occurs. We also obtain a very high recognition rate by averaging a number of samples over a long image sequence. The proposed approach has been successfully tested by many experiments, and can operate at 20 frames/s on an 800 MHz PC.

Wide-Band Multi-spectral Space for Color Representation

This paper develops a wide-band multi-spectral space for color representationwith Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matchingmethod aims at improving the accuracy of color representation as well as reducing costs forprocessing and storing multi-spectral images. The results show that the space can present ourexperimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with highefficiency, and that the spanning space with three eigenvectors can present the original space atmore than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole o-riginal spaces.

Water bodies extraction from TM images

Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modified normalized difference water index （MNDWI） which was improved by XU Han-qiu to construct a false color composite image that could separate water from others easily. This method can realize the water extraction based on TM image by analyzing the spectral characteristics of water in this false color image and establishing a water extraction model. This paper also compares the effi- ciency of this method with MNDWI, （TM2 ＋ TM3） - （TM4 ＋ TM5） and new water index （NWI）, which were applied in the city and mountain of Taiyuan, respectively. The results show that the proposed method can extract water body from TM im- age more rapidly and efficiently and its accuracy is up to 94.03 %. In addition, this method does not require a manual selec- tion threshold, which meets the research reuuirement of high automaticm.

Morphological Study of Ficus deltoidea Jack in Malaysia

Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis （PCA） and cluster analysis （CA） using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component （PC） with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base （BL）, shape of leaf apex （SA）, ratio of lamina width to lamina length （R）, dots color at the lower midrib （DLM）, color of young syconium （CYS）, color of mature syconium （CMS） and the number of syconium on trees （DST）. This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised.

A Novel Model for Context Conflict Resolution Basec on Set Pair Analysis

Context-aware plays a pivotal role in the wisdom network of information processing. Due to the limited resources, context conflict is inevitable in context-aware. User satisfaction is a good reflection for the wisdom degree of the network. In this paper, considering the user satisfaction, we propose a novel context-aware conflict solution model based on set pair Analysis （SPA）. The model obtains the best server mode by the maximum satisfaction connection degree to solve the conflict in context-aware. The simulation analysis shows that the proposed method is effective and can solve many users competition for the same scene of conflict.

Visual tracking based on the sparse representation of the PCA subspace

We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Accurate blood pressure （BP） measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis （PCA） is fused with forward stepwise regression （SWR）, artificial neural network （ANN）, adaptive neuro-fuzzy inference system （ANFIS）, and the least squares support vector machine （LS-SVM） model for the prediction of BP reactivity to an unsupported back in norrnotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ＇principal components＇ （PCs）, from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM （PCA-LS-SVM） model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.