A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.     

Density estimates on the unit simplex and calculation of the mode of a sample

This paper addresses reliable and efficient calculation of the mode of a multivariate sample, which is a classical fusion function. In particular, we focus on the inputs given on the unit simplex, when aggregating elements of Atanassov intuitionistic fuzzy sets, interval-valued fuzzy sets and their extensions, as well as compositional data. We outline the use of a specially designed 2-additive fuzzy measures and the Choquet integral for the purposes of reducing computational complexity in higher dimensions. We present computational analysis and benchmark four different methods of density-based mode estimation.     

On auto- and cross-interdependence in interval field finite element analysis

This paper discusses the concepts of auto- and cross-interdependence in interval field finite element analysis. In classic interval analysis, independent intervals are used to construct hyper-rectangular input spaces that correspond to the bounded uncertainty that is present on some model parameters. This is a direct result from the inability of modeling interdependence. Such assumption of complete independence might prove in some cases to be highly over-conservative. A first example is the modeling of spatial uncertainty, where the interdependence is governed by allowable spatial gradients of field realizations. Secondly, interdependence can also occur in case uncertainty in several structural quantities has the same root cause (eg, the manufacturing process). Recent work by the authors introduced concepts for modeling dependence between intervals in a spatial and multivariate context. However, it is unclear how an analyst has to deal with multiple quantities that have a spatial uncertainty component and are furthermore interdependent. This paper presents an approach to link multiple interval fields using recently introduced convex hull pair constructions and inverse distance weighting interpolation. Two case studies to illustrate the new methodology are included, proving the flexibility of the methodology in the modeling of auto- and cross-interdependence between multiple interval scalars and/or interval fields.     

Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.     

基于改进的最大似然法识别风力发电机转子的载荷参数

风机不对中故障是多载荷参数耦合而成,工程师难以凭经验了解载荷参数的大致范围。针对因转子结构参数和测试条件等不确定性参数的存在及载荷参数先验信息未知,最大似然法识别转子不对中故障的载荷参数时采用的搜索计算面临计算量大,且迭代过程中灵敏度会带来一些数值问题。引入搜索区间进退法到敏感矩阵法与最大似然法中,对最大似然法进行改进。改进后的最大似然法中的迭代适于处理复杂工程优化问题,避免传统迭代数值法对搜索空间的苛刻要求。以输入尺寸和输出测试响应具有随机性测量误差的风力发电机转子系统为例,在三种测量误差下的识别结果表明,改进的最大似然法提高了最大似然法的辨识效果,可以减少不确定性因素对识别结果的影响,鲁棒性好。     

In situ monitoring of the biocatalysed partial hydrolysis of cocoa butter and palm oil fraction

Partial glycerides are widely used ingredients in confectionery products that can be produced from natural fats. In a biocatalytic partial hydrolysis of cocoa butter and palm oil fractions, a product mixture containing 1.5% monoglycerides and 5.5% diglycerides intended for the use in confectionery products was created. This study is a proof of principle that shows the feasibility of monitoring the biocatalytic partial hydrolysis of these two natural fats in situ with ATR-FTIR spectroscopy. An economic approach was utilised for calibration since partial glyceride calibration standards are costly and poorly available. The released compounds were quantified by means of chemometric modelling, and the model was validated with gas chromatography. Resulting root mean square errors were in the low per cent range. Additionally, the results indicate that distinction of the released free fatty acids is possible with ATR-FTIR spectroscopy.     

Effects of isothermal treatment parameters on the microstructure of semisolid alloys

Cold-rolled 6061 aluminium alloys are used to prepare semisolid billets by recrystallisation and partial melting. The effects of isothermal treatment parameters on the microstructures were investigated. The results indicated that the high isothermal holding temperature increases the experimental liquid fraction, average grain size and shape factor. A long isothermal holding time also increases the experimental liquid fraction and improves the spheroidisation degree of solid grains, but the average grain size also increases. Moreover, when the isothermal holding time at high temperature is increased, the size of intragranular liquid droplets increases, but their quantity decreases. The optimal isothermal holding temperature and time during semisolid isothermal treatment were 635°C and 5?–?10?min, respectively.     

Identification of factors that accelerate hydrogen production by Clostridium butyricum RAK25832 using casamino acids as a nitrogen source

The ability of Clostridium butyricum RAK25832 to use casamino acids as a nitrogen source was investigated. Strain RAK25832 showed the capacity to utilize different types of carbon sources. With glucose as a carbon source (10 g/L), the preferred final concentration of casamino acids was 26.67 g/L, with a cumulative hydrogen production, production rate, and yield of 2505 mL H₂/L, 160 mL/h, and 1.81 mol H₂/mol glucose, respectively. Eighteen metal elements were screened to identify the most important metals for biohydrogen production, and four elements were optimized. The optimal medium composition was MgCl₂·6H₂O (0.1 g/L), K₂HPO₄·3H₂O (6.67 g/L), NaHCO₃ (2.6 g/L), and FeCl₂·4H₂O (0.002 g/L). Vitamin supplementation of the medium showed no significant effect on hydrogen production. Under the optimized conditions, cumulative hydrogen production reached 3074 mL H₂/L. This is the first study to demonstrate the use of casamino acids as a nitrogen source by C. butyricum.     

A Modular Solder System with Hierarchical Morphology and Backward Compatibility

A modular solder system with hierarchical morphology and micro/nanofeatures in which solder nanoparticles are distributed on the surface of template micropowders is reported. A core–shell structure of subsidiary nanostructures, which improved the intended properties of the modular solder is also presented. In addition, polymer additives can be used not only as an adhesive (like epoxy resin) but also to impart other functions. By combining all of these, it is determined that the modular solder system is able to increase reflowability on a heat‐sensitive plastic substrate, oxidation resistance, and electrical conductivity. In this respect, the system could be readily modified by changing the structure and composition of each constituent and adopting backward compatibility with which the knowledge and information attained from a previously designed solder can offer feedback toward further improving the properties of a newly designed one. In practice, In–Sn–Bi nanoparticles engineered on the surface of Sn–Zn micropowders result in pronounced reflowing on a flexible Au‐coated polyethylene terephthalate (PET) substrate even at the low temperature of 110 °C. Depending on their respective concentrations, the incorporation of CuO@CeO₂ nanostructures and poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) polymers increases oxidation resistance and electrical conductivity of the modular solder.