Response surface methodology toward the optimization of high-energy carotenoid extraction from Aristeus antennatus shrimp

High-energy assisted extraction techniques, like ultrasound assisted extraction (UAE) and microwave assisted extraction (MAE), are widely applied over the last years for the recovery of bioactive compounds such as carotenoids, antioxidants and phenols from foods, animals and herbal natural sources. Especially for the case of xanthophylls, the main carotenoid group of crustaceans, they can be extracted in a rapid and quantitative way with the use of UAE and MAE.     

Effect of hybrid fillers on the mechanical behavior of polypropylene based hybrid composites

The present study investigates the effect of hybrid fillers such as graphene nanoplatelets (GnPs) and Titanium di-oxide (TiO₂) in polypropylene (PP) composites on the mechanical properties. The compatibilizing agent of Maleic anhydride grafted polypropylene (MAPP) are used in the polypropylene based composites to increase the interfacial adhesion between matrix and fillers. The experiments are designed according to L₁₆ orthogonal array (OA) based design of experiments (DOE). The parameters selected for this study are GnPs, TiO₂ and MAPP with four different levels are used.By using Orthogonal array and Taguchi based experimental design, the performance characteristics of tensile modulus, tensile strength, elongation at break and toughness can be analyzed with more objective through a small set of experiments.Taguchi based analysis are used to find out the optimal parameters to maximize the tensile properties for the GnPs and TiO₂ reinforced PP hybrid composites. Further, analysis of variance (ANOVA) is investigated to identify the most significant parameters which influence the mechanical properties.From the analysis it was found that the optimal parameters of 3 ?wt% GnPs, 2 ?wt% TiO₂ and 6 ?wt% MAPP for maximum tensile modulus and tensile strength. The most significant parameter for tensile modulus and tensile strength is GnPs followed by TiO₂ and MAPP according to ANOVA analysis.     

Evaluation of the influence of proline, hydroxyproline or pyrrolidine in the presence of sodium nitrite on N-nitrosamine formation when heating cured meat

N-nitrosamines are meant to be probable or possible carcinogenic components, possibly formed out of a reaction between nitrite and N-containing substances such as amino acids and secondary amines. Nitrite is often used for processing meat products because of its colouring and antimicrobial properties.During this experimental setup, the influence of proline, hydroxyproline or pyrrolidine on N-nitrosamine formation in meat samples was evaluated. The N-nitrosamines concentrations were measured with gas chromatography-thermal energy analyzer. Only the concentrations of N-nitrosodimethylamine and N-nitrosopyrrolidine were found above the limit of detection in a number of tested experimental conditions. The concentration of these two N-nitrosamines was modelled as a function of temperature and nitrite concentration for different situations (presence or absence of added natural N-containing meat components). It could be concluded that proline and pyrrolidine promoted the formation of N-nitrosopyrrolidine. It could also be confirmed that the higher the temperature of the meat processing procedure and the higher the sodium nitrite amounts added, the higher were the yields of the respective N-nitrosamines.     

Cluster resolution: a metric for automated, objective and optimized feature selection in chemometric modeling

A novel metric termed cluster resolution is presented. This metric compares the separation of clusters of data points while simultaneously considering the shapes of the clusters and their relative orientations. Using cluster resolution in conjunction with an objective variable ranking metric allows for fully automated feature selection for the construction of chemometric models. The metric is based upon considering the maximum size of confidence ellipses around clusters of points representing different classes of objects that can be constructed without any overlap of the ellipses. For demonstration purposes we utilized PCA to classify samples of gasoline based upon their octane rating. The entire GC-MS chromatogram of each sample comprising over 2 × 10⁶ variables was considered. As an example, automated ranking by ANOVA was applied followed by a forward selection approach to choose variables for inclusion. This approach can be generally applied to feature selection for a variety of applications and represents a significant step towards the development of fully automated, objective construction of chemometric models.     

素食人群尿液1H NMR代谢轮廓的多因素方差分析

结合方差分析(ANOVA)和偏最小二乘法判别分析(PLS-DA)两种分析技术,对素食和普食人群的尿液1H NMR谱进行分析.利用ANOVA方法将数据矩阵分解为几个独立因素矩阵,滤除干扰因素后,再利用PLS-DA对单因素数据进行建模分析.实验结果表明,ANOVA/PLS-DA方法可以有效地减少饮食因素和性别因素之间的相互...     

An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.     

Brand-dependent volatile fingerprinting of Italian wines from Valpolicella

Head-space solid-phase microextraction (HS-SPME) coupled to gas-chromatography-mass spectrometry was developed and applied to obtain the volatile aromatic fingerprints of three typical Italian wines, Valpolicella, Amarone and Recioto, all produced in the restricted geographical area of Valpolicella (Veneto, Italy) with the same grape cultivars within the regulations of a rigid disciplinary of production. Differences between the three typologies are mainly linked to the different withering times to which grapes are subjected before vinification, which strongly influences the concentration and the development of volatile aroma compounds. A total of 22 different wines (7 Valpolicella, 10 Amarone and 5 Recioto) were characterised in terms of aromatic volatile profile with the aim to distinguish the different products and to evaluate the possibility to differentiate the same product from different brands. For the chemometric evaluation of the data one-way analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were tested. All the chemometric tools employed allow to differentiate between the three products. More intriguing is the ability of the chemometric approach to differentiate between the same product (Amarone, Recioto) from different winery, thus showing the potential of this approach to characterize the brand-dependent typicality of wines, which is usually related to subtle technological differences which nevertheless have strong influences on the organoleptic characteristics of the products.     

方差分析模型中参数的经验Bayes估计及其优良性问题

本文考虑单向分类的方差分析模型，构造了Ｐ′α的线性Ｂａｙｅｓ估计和经验Ｂａｙｅｓ（ＥＢ）估计，此处αａ×１是效应参数向量，Ｐａ×ｋ是常数矩阵．在较一般的条件下，基于均方误差矩阵准则和ＰｉｔｍａｎＣｌｏｓｅｎｅｓｓ准则，我们分别证明了ＥＢ估计优于最小二乘估计     

On ANOVA expansions and strategies for choosing the anchor point

The classic Lebesgue ANOVA expansion offers an elegant way to represent functions that depend on a high-dimensional set of parameters and it often enables a substantial reduction in the evaluation cost of such functions once the ANOVA representation is constructed. Unfortunately, the construction of the expansion itself is expensive due to the need to evaluate high-dimensional integrals. A way around this is to consider an alternative formulation, known as the anchored ANOVA expansion. This formulation requires no integrals but has an accuracy that depends sensitively on the choice of a special parameter, known as the anchor point.We present a comparative study of several strategies for the choice of this anchor point and argue that the optimal choice of this anchor point is the center point of a sparse grid quadrature. This choice induces no additional cost and, as we shall show, results in a natural truncation of the ANOVA expansion. The efficiency and accuracy is illustrated through several standard benchmarks and this choice is shown to outperform the alternatives over a range of applications.