Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

Hyperspectral images contain both spectral and spatial image information and were investigated to characterize the freshness of fish. However, most studies of this application have focused on spectral signals rather than image features. The goal of this work was to investigate the ability of spectral and image textural variables for predicting the chemical and physical qualities of fish, respectively, and to optimize the variables for the specific quality determination. The chemical (total volatile basic nitrogen, TVB-N) and physical (texture profile analysis, TPA) properties were investigated. Partial least square (PLS) was applied to develop fish quality prediction models with the spectral and textural variables from the hyperspectral images. The results showed that the TVB-N content of fish fillets was accurately predicted using the spectra. Meanwhile, the TPA parameters were determined through the image textural features with high accuracy, which indicated image textural features were highly related with the TPA parameters. Moreover, spectral and textural features were also extracted from fish eyes and gills and were further used to predict the intact fish quality, taking advantage of the freshness sensitivity of the eyes and gills. The results illustrate that spectra from fish eyes and gills are a potential tool to predict the TVB-N content and TPA parameters for intact fish.     

Determination of the textural characteristics of carbon samples using scanning electronic microscopy images: comparison with mercury porosimetry data

In this paper, scanning electron microscopy (SEM) images of carbon samples were analysed, and some textural characteristics were obtained and compared with those determined using mercury porosimetry data. Fractal dimensions were calculated from both mercury porosimetry and SEM images as methods for characterising the porous distribution (heterogeneity) of the samples. Lacunarity is easily determined from SEM images as a measure of the degree of heterogeneity of a porous surface. A relationship between the lacunarity and the fractal dimensions calculated using both methods is shown. Pore-size distributions were also determined from the analysis of SEM images. We show that the analysis of SEM images is a valuable complement to mercury porosimetry measurements and a useful tool for the characterisation of porous surfaces. This method offers the possibility of evaluating the features of porous materials and comparing the results to those obtained using mercury intrusion analysis.     

Principal component multinomial regression and spectrometry to predict soil texture

The mineral particles are classified in different textural classes according to their size. Reflectance spectrometry and spectra can be valid instruments to classify the soils according to their texture. This is possible using different statistical methods, for example, discriminant analysis. However, other multivariate methods, like multinomial logistic regression, can be used, but the presence of multicollinearity among explicative variables could affect the estimation of the parameters. The solution proposed to remedy this problem is an alternative way to apply the multinomial logit model. To evaluate its performances, we compare the results with both classical multinomial logit and discriminant analysis ones. Copyright © 2015 John Wiley & Sons, Ltd.     

Silica supported sulfated zirconia prepared by a sol-gel process: Effect of solvent evacuation procedure on the structural, textural and catalytic properties

Sol-gel process was used to prepare silica supported sulfated zirconia catalysts. The main parameter studied in this work was the gel drying method through four different ways of solvent evacuation. Textural, structural as well as the acidic properties of the four samples were studied using N₂ physisorption, X-ray diffraction, mass spectrometry, sulfur chemical analysis and adsorption-desorption of pyridine. The isomerization of n-hexane was used as a catalytic test. The surface areas and the pore distributions are highly affected by the drying mode. One of the four drying methods leads to a solid having improved textural properties and presenting both the crystalline ZrO₂ tetragonal phase and a particular type of sulfate mode bond. It seems that these three conditions are necessary for achieving high catalytic activity.     

Tea Classification by Near Infrared Spectroscopy with Projection Discriminant Analysis and Gene Expression Programming

Traditional gene expression programming for classification is designed for binary decisions. Herein, projection discriminant analysis for direct multiclass categorization using gene expression programming is described. Gene expression programming was first employed to examine new synthetic variables that were built as nonlinear combinations of the original features. The data were projected on planes spanned by these new synthetic variables and the nearest centroid was employed to classify new samples. A new objective function was formulated to determine optimum synthetic variables. Direct multiclass categorization using a gene expression programming algorithm was used to classify six tea varieties analyzed by near infrared spectroscopy. Compared with traditional gene expression programming, principal component analysis, and linear discriminant analysis, direct multiclass categorization with gene expression programming algorithm was more efficient. Visual inspection of high dimensional data by this approach also facilitated classification and comprehension of data.     

Identification of liquid crystalline phases in 7O.O9 compound based on structural similarity index measure

Textural analysis is done in the case of the thermotropic liquid crystal (LC), 4-heptyloxybenzylidene-4′-nonyloxyaniline, 7O.O9, using a polarising microscope attached with a hot stage with a high-resolution camera. Natural images are highly structured: their pixels exhibit strong dependencies and carry important information about the structure of the objects. In this article, we consider the structural similarity index measure parameter computed as a function of the temperature. The results exhibit abrupt changes with temperature showing different liquid crystalline phases. This statistical image analysis is compared with the differential scanning calorimeter data and good agreement was found. The proposed methodology is very sensitive and reliable technique to identify the LC phases.     

A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise

The fuzzy C‐means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to the presence of noise and intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that using a single fuzzy membership function the FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a modified FCM (mFCM) algorithm by incorporating scale control spatial information for segmentation of MRI images in the presence of high levels of noise and intensity inhomogeneity. The algorithm utilizes scale controlled spatial information from the neighbourhood of each pixel under consideration in the form of a probability function. Using this probability function, a local membership function is introduced for each pixel. Finally, new clustering centre and weighted joint membership functions are introduced based on the local membership and global membership functions. The resulting mFCM algorithm is robust to the noise and intensity inhomogeneity in MRI image data and thereby improves the segmentation results. The experimental results on a synthetic image, four volumes of simulated and one volume of real‐patient MRI brain images show that the mFCM algorithm outperforms k‐means, FCM and some other recently proposed FCM‐based algorithms for image segmentation in terms of qualitative and quantitative studies such as cluster validity functions, segmentation accuracy and tissue segmentation accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Use of Legendre moments for the fast comparison of two-dimensional polyacrylamide gel electrophoresis maps images

In this paper, Legendre moments are calculated to extract the global information from a set of two-dimensional polyacrylamide gel electrophoresis map images. The dataset contains 18 samples belonging to two different cell lines (PACA44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of this work was to obtain the correct classification of the 18 samples, using the Legendre moments as discriminant variables. For each image the Legendre moments up to a maximum order of 100 were computed. The stepwise linear discriminant analysis (LDA) was performed in order to select the moments with the highest discriminating power. The results demonstrate that the Legendre moments can be successfully applied for fast classification purposes and similarity analysis.     

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.     

新的聚类方法综合评价血脂指标

本文结合中心聚类和模糊聚类提出了一个新的聚类方法。该法避免了在迭代过程中出现局部最小问题,并可给出每个样本归入每一类的成分矩阵,因而提供更多的聚类程度的信息。应用这种方法对正常人和冠心病患者的血脂指标进行了综合评价,结果满意。     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

Preparation and characteristics of electrospun activated carbon materials having meso- and macropores

Mesoporous activated carbon samples were prepared from electrospun PAN-based carbon fibers using physical activation with silica. Textural characterization was performed using nitrogen adsorption at 77 K. The BET specific surface area and pore size distribution of silica activated carbon materials were investigated. According to the increment of silica, BET specific surface area was increased about thirty times and it was found that silica activated carbon materials were highly mesoporous by studying pore surface distribution and pore volume distribution. Surface morphology of silica activated carbon materials were observed by SEM images. The spherical typed carbon materials were investigated. The diameter of spherical typed carbon materials was increased in proportional of the increment of silica.     

Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy

Native fluorescence characteristics of blood plasma were studied in the visible spectral region, at two different excitation wavelengths, 405 and 420 nm, to discriminate patients with different stages of oral malignancy from healthy subjects. The fluorescence spectra of blood plasma of oral malignant subjects exhibit characteristic spectral differences with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values at those emission wavelengths that give characteristic spectral features of each group of experimental subjects studied. These fluorescence intensity ratios were used as input variables for a multiple linear discriminant analysis across different groups. Leave-one out cross-validation was used to check the reliability of each discriminant analysis performed. The discriminant analysis performed across normal and oral cancerous subjects classified 94.7% of the original grouped cases and 93.7% of the cross-validated grouped cases. A classification algorithm was developed on the basis of the score of the discriminant functions (discriminant score) resulted in the analyses. The diagnostic potentiality of the present technique was also estimated in the discrimination of malignant subjects from normal and nonmalignant diseased subjects such as liver diseases. In the discriminant analysis performed across the three groups, normal, oral malignancy (including early and advanced stages) and liver diseases, 99% of the original grouped cases and 95.9% of the cross-validated grouped cases were correctly classified. Similar analysis performed across normal, early stage of oral malignancy, advanced oral malignancy and liver diseases correctly classified 94.9% of the original grouped cases and 91.8% of the cross-validated grouped cases.     

基于~1H NMR指纹图谱结合多变量分析的地沟油检测方法

地沟油事件是中国近期发生的严重食品安全事故,而针对地沟油的检测目前尚无非常有效的方法报道.本研究对60种市售植物油和地沟油样品分别进行了核磁H谱测定,建立了12个鉴别指标,采用聚类分析技术对样品进行聚类分析,并将样品分为8大类,建立了判别函数.将未知样品的1H NMR数据代入判别函数,可检测和判别未知油的来源和品质.将该方法应用于两次盲测共69个样品,结果显示,两次盲测的正确率分别为91.9%和93.8%,可作为食用油品质检测的重要参考.     

Simplex optimization of acoustic assay for plasminogen activators

This article discusses the optimization of a newly developed method for measuring the activity of plasminogen activators using a thickness-shear-mode acoustic sensor. A variable-size simplex algorithm was used for optimization. Preliminary tests were performed to design the first simplex. A desirability function was defined to translate each performance value to a membership value of 0 to 1. If there was more than one performance variable, their membership values were translated to an aggregated membership value using another function that considers their individual influence on sensor performance. Two rounds of optimization were carried out for streptokinase followed by a single optimization for tissue-type plasminogen activator. In the last optimization, ratios of control variables were used in order to reduce the number of parameters and to formulate easily adjustable assay conditions. The results showed the usefulness of the simplex method for optimizing this type of assay, and the importance of preliminary tests and prior knowledge in providing rapid convergence using fewer experiments. The optimized plasminogen activator assay can be considered a reference method for measurement of all members of this drug class.     

A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

Laser-induced breakdown spectroscopy has been used to obtain spectral fingerprints from live bacterial specimens from thirteen distinct taxonomic bacterial classes representative of five bacterial genera. By taking sums, ratios, and complex ratios of measured atomic emission line intensities three unique sets of independent variables (models) were constructed to determine which choice of independent variables provided optimal genus-level classification of unknown specimens utilizing a discriminant function analysis. A model composed of 80 independent variables constructed from simple and complex ratios of the measured emission line intensities was found to provide the greatest sensitivity and specificity. This model was then used in a partial least squares discriminant analysis to compare the performance of this multivariate technique with a discriminant function analysis. The partial least squares discriminant analysis possessed a higher true positive rate, possessed a higher false positive rate, and was more effective at distinguishing between highly similar spectra from closely related bacterial genera. This suggests it may be the preferred multivariate technique in future species-level or strain-level classifications.     

A new method of comparing 2D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools

The aim of this work was to obtain the correct classification of a set of two-dimensional polyacrylamide gel electrophoresis map images using the Zernike moments as discriminant variables. For each 2D-PAGE image, the Zernike moments were computed up to a maximum p order of 100. Partial least squares discriminant analysis with variable selection, based on a backward elimination algorithm, was applied to the moments calculated in order to select those that provided the lowest error in cross-validation. The new method was tested on four datasets: (1) samples belonging to neuroblastoma; (2) samples of human lymphoma; (3) samples from pancreatic cancer cells (two cell lines of control and drug-treated cancer cells); (4) samples from colon cancer cells (total lysates and nuclei treated or untreated with a histone deacetylase inhibitor). The results demonstrate that the Zernike moments can be successfully applied for fast classification purposes. The final aim is to build models that can be used to achieve rapid diagnosis of these illnesses.     

Metabolite profiling to discriminate different species and genus from thistles in Korea using liquid chromatography with quadrupole time‐of‐flight mass spectrometry

This study was designed to classify and identify closely related thistle species in the genus Cirsium, as well as Carduus and Cephalonoplos species, which are also thistles. The comprehensive and untargeted metabolite profiles of nine Korean thistles were determined using ultra high performance liquid chromatography combined with hybrid quadrupole time‐of‐flight mass spectrometry. The difference in metabolite profiles among species was explored using principal component analysis and hierarchical clustering analysis. The significantly different metabolites (Bonferroni‐corrected P‐value < 0.001) were used to construct a partial least squares discriminant analysis model to predict the species of thistle. Nine species were successfully classified using a partial least squares discriminant analysis model and confirmed using a cross‐validation method. Species with similar features were grouped based on unique patterns in variable clusters. The present study suggests that liquid chromatography with quadrupole time‐of‐flight mass spectrometry untargeted metabolomic profiling with chemometric analysis is an efficient and powerful tool for discriminating between different species of medicinal herbs.