Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples.According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.     

A statistical measure of association and a series expansion of chain conformations

A simple, easily calculated, nonparametric statistic is described that can detect the presence of a functional relationship in bivariate data. Given a sample of data points (x,y), the statistic's value is nearly 1 if y is a linear function of x with little noise; it is greater than 1 if y is a nonlinear function of x; and it is close to 2 if x and y are uniformly and independently distributed. The statistic can be used to rapidly screen through large data sets to identify the most functionally related variable pairs. As an illustration, the statistic is used to detect relations between polypeptide conformational energy and functions of a series expansion for chain conformations.     

Multivariate probit models for conditional claim-types

This paper considers statistical modeling of the types of claim in a portfolio of insurance policies. For some classes of insurance contracts, in a particular period, it is possible to have a record of whether or not there is a claim on the policy, the types of claims made on the policy, and the amount of claims arising from each of the types. A typical example is automobile insurance where in the event of a claim, we are able to observe the amounts that arise from say injury to oneself, damage to one’s own property, damage to a third party’s property, and injury to a third party. Modeling the frequency and the severity components of the claims can be handled using traditional actuarial procedures. However, modeling the claim-type component is less known and in this paper, we recommend analyzing the distribution of these claim-types using multivariate probit models, which can be viewed as latent variable threshold models for the analysis of multivariate binary data. A recent article by Valdez and Frees [Valdez, E.A., Frees, E.W., Longitudinal modeling of Singapore motor insurance. University of New South Wales and the University of Wisconsin-Madison. Working Paper. Dated 28 December 2005, available from: http://wwwdocs.fce.unsw.edu.au/actuarial/research/papers/2006/Valdez-Frees-2005.pdf] considered this decomposition to extend the traditional model by including the conditional claim-type component, and proposed the multinomial logit model to empirically estimate this component. However, it is well known in the literature that this type of model assumes independence across the different outcomes. We investigate the appropriateness of fitting a multivariate probit model to the conditional claim-type component in which the outcomes may in fact be correlated, with possible inclusion of important covariates. Our estimation results show that when the outcomes are correlated, the multinomial logit model produces substantially different predictions relative to the true predictions; and second, through a simulation analysis, we find that even in ideal conditions under which the outcomes are independent, multinomial logit is still a poor approximation to the true underlying outcome probabilities relative to the multivariate probit model. The results of this paper serve to highlight the trade-off between tractability and flexibility when choosing the appropriate model.     

Correlation Inequalities and Applications to Vector-Valued Gaussian Random Variables and Fractional Brownian Motion

In this paper we extend certain correlation inequalities for vector-valued Gaussian random variables due to Kolmogorov and Rozanov. The inequalities are applied to sequences of Gaussian random variables and Gaussian processes. For sequences of Gaussian random variables satisfying a correlation assumption, we prove a Borel-Cantelli lemma, maximal inequalities and several laws of large numbers. This extends results of Be?ka and Ciesielski and of Hytönen and the author. In the second part of the paper we consider a certain class of vector-valued Gaussian processes which are α-Hölder continuous in p-th moment. For these processes we obtain Besov regularity of the paths of order α. We also obtain estimates for the moments in the Besov norm. In particular, the results are applied to vector-valued fractional Brownian motions. These results extend earlier work of Ciesielski, Kerkyacharian and Roynette and of Hytönen and the author.     

Reflection and transmission at normal incidence onto air-saturated porous materials and direct measurements based on parametric demodulated ultrasonic waves

The present work is related to the characterization of air-saturated porous media by using parametric demodulated ultrasonic waves. One uses two different powerful ultrasonic emitters working either at 47 kHz or at 162 kHz which are electronically amplitude modulated over the 200 Hz-4 kHz or 2 kHz-40 kHz bandwidths respectively. The demodulation process takes place in air, due to its nonlinearity enabling to generate audio range acoustical waves or alternatively low frequency ultrasonic waves which can be used to characterize porous materials in the reflection configuration at normal incidence. Some appropriate theoretical calculations are introduced for three configurations of interest, i.e. a porous slab, a porous layer mounted onto a rigid plate, and a porous half space, in the case of the equivalent-fluid model. Comparisons between theoretical modeling and experimental data are provided and prospective industrial applications are discussed.     

New demodulation filter in digital phase rotation beamforming

In this paper, we present a new quadrature demodulation filter to reduce hardware complexity in digital phase rotation beamforming. Due to its low sensitivity to phase delay errors, digital quadrature demodulation is commonly used in ultrasound machines. However, since it requires two lowpass filters for each channel to remove harmonics, the direct use of conventional finite impulse response (FIR) filters in ultrasound machines is computationally expensive and burdensome. In our new method, an efficient multi-stage uniform coefficient (MSUC) filter is utilized to remove harmonic components in phase rotation beamforming. In comparison with the directly implemented FIR (DI-FIR) and the previously-proposed signed-power-of-two FIR (SPOT-FIR) lowpass filters, the proposed MSUC filter reduces the necessary hardware resources by 93.9% and 83.9%, respectively. In simulation, the MSUC filter shows a negligible degradation in image quality. The proposed method resulted in comparable spatial and contrast resolution to the DI-FIR approach in the phantom study. These preliminary results indicate that the proposed quadrature demodulation filtering method could significantly reduce the hardware complexity in phase rotation beamforming while maintaining comparable image quality.     

Variational model for one-dimensional quantum magnets

A new variational technique for investigation of the ground state and correlation functions in 1D quantum magnets is proposed. A spin Hamiltonian is reduced to a fermionic representation by the Jordan–Wigner transformation. The ground state is described by a new non-local trial wave function, and the total energy is calculated in an analytic form as a function of two variational parameters. This approach is demonstrated with an example of the XXZ-chain of spin-1/2 under a staggered magnetic field. Generalizations and applications of the variational technique for low-dimensional magnetic systems are discussed.     

Self-regulation in infinite populations with fission-death dynamics

The evolution of an infinite population of interacting point entities placed in ${\mathbb{R}}^d$ is studied. The elementary evolutionary acts are death of an entity with rate that includes a competition term and independent fission into two entities. The population states are probability measures on the corresponding configuration space and the result is the construction of the evolution of states in the class of sub-Poissonian measures, that corresponds to the lack of clusters in such states. This is considered as a self-regulation in the population due to competition.     

激光诱导击穿光谱的自组织特征映射结合相关判别对天然地质样品分类方法研究

激光诱导击穿光谱技术具有微损、原位、快速分析的特点,在样品分类识别、成分分析等领域有广阔的应用前景。为探索该技术在天然地质样品识别应用的可行性,提出了一种自组织特征映射神经网络结合相关判别对天然地质样品LIBS光谱分类识别的方法。为减小全谱中背景噪声等不相关数据干扰、降低计算量,在元素谱线归属的基础上进行了特征谱线提取,实现了高维光谱数据的降维。以特征谱数据为输入建立网络训练模型,得到具有输入样本特征的权向量,通过权向量与待测样本进行相关分析可以实现样品分类。对16种天然地质样品的分类算法实验证明,在全谱、主成分降维和特征谱段三种数据处理方法中,特征谱的降维和提取LIBS数据主特征效果最优。改进的SOM网络结合相关判别算法比支持向量机方法和直接应用SOM网络方法的分类准确度更高,初步证实了该方法的有效性。     

一种基于级联长周期光纤光栅的力传感装置

利用光纤布拉格光栅(FBG)具有谱线窄、反射率高的特性和级联长周期光纤光栅(级联LPFG)的边沿滤波特性, 设计了一种基于级联LPFG的力传感装置, 将拉力变化转变为FBG反射谱功率的变化. 在理论上分析了实验的可行性, 用光纤光栅刻写系统制作了符合要求的级联LPFG和FBG, 用光谱分析仪对FBG的反射谱进行了监测. 用光纤功率计对FBG的反射功率进行了测量, 并对测得的数据进行了线性拟合, 其线性相关系数为0.9924, 装置的灵敏度为1.074μW?N-1. 在拉力为0~0.6N的范围内功率与拉力有较好的线性关系. 测量范围和灵敏度取决于级联LPFG透射谱边沿的斜率. 该装置结构简单, 操作方便.