Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data

The potential of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for their ability to differentiate between apple juice samples on the basis of apple variety and applied heat-treatment was evaluated. The heat-treatment involved exposure of juice samples (15 ml) for 30 s in a 900 W microwave oven and the apple varieties used to produce the juice samples were Bramley, Elstar, Golden Delicious and Jonagold. The chemometric procedures applied to the MIR and NIR data were partial least squares regression (PLS1 for differentiation on the basis of heat-treatment, PLS2 for varietal differentiation) and linear discriminant analysis (LDA) applied to principal component (PC) scores. PLS1 and PLS2 gave the highest level of correct classification of the apple juice samples according to heat-treatment (77.2% for both MIR and NIR data) and variety (78.3–100% for MIR data; 82.4–100% for NIR data), respectively.     

微机模拟处理pH络合滴定法研究——Ⅱ.终点确定方法和最佳实验条件模拟估计程序

本文利用简便的滴定过程模型,确立了微机模拟处理实验数据,方便、准确地求解络合滴定等当点的方法。设计了最佳实验条件模拟估计程序。该法测定Zn~(2+),Pb~(2+),Cu~(2+),Fe~(3+)结果的相对误差Δ<1％。pH=6-7时,测定Ca~(2+)的相对误差为-1.61％;σ<0.001mmol,(N=5)。     

基于并行PLS算法的化学计量学软件研究

现有化学计量学软件普遍采用的偏最小二乘(PLS)算法均以单线程方式计算,建模速度缓慢,给应用带来较大的不便。随着多核处理器的普及,采用多线程并行计算技术可显著提高算法执行速度。本文将多线程并行计算技术引入化学计量学软件开发,提出并实现了PLS算法的并行化计算。利用标准数据集进行了性能对比实验,结果表明在四核计算机中多线程并行计算比单线程计算有大约3.1倍的速度提升。     

Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.     

神经网络用于热固性酚醛树脂的研究

运用人工神经网络研究了热固性酚醛树脂的合成反应空间与产物空间的映射关系，建立了可用于定量预测树脂固化产物热性能的模型，并进行了实验验证，神经网络方法对于复杂高分子体系的合成－性能定量关系的研究显示出良好的应用前景。     

Environmentally Friendly Determination of Quality Parameters of Biodiesel/Diesel Blends Using Fourier Transform Infrared Spectra

Multivariate calibration models based on data from mid‐infrared spectroscopy of biodiesel/diesel blends were obtained. The blends were prepared from diesel oil and esters of soybean oil, waste cooking oil, and hydrogenated vegetable oil in proportions ranging from 0 to 100 % biodiesel. The results showed that the multivariate regression models with interval partial least squares (iPLS), backward interval partial least squares (biPLS), and synergy interval partial least squares (siPLS) were able to determine the fractions of the infrared spectrum that contain the relevant information for estimating the values of physicochemical properties, flash point, specific gravity, and cetane number, which are used in quality control of the blends. In the best models, the values of determination coefficients were greater than 0.9500, proving their efficiency as an alternative to traditional analytical methods.     

Portable Infrared Spectrometer to Characterize and Differentiate Between Organic and Conventional Bovine Butter

The performance of a portable infrared system combined with pattern recognition to discriminate between organically and conventionally produced bovine butter samples as well as to predict the levels of conjugated linoleic acid (CLA) were evaluated. Sixty butter (27 organic and 33 conventional) samples were used in this study. Bovine butter–fat were applied onto an attenuated total reflectance infrared (ATR‐IR) accessory equipped with a five‐bounce ZnSe crystal set at 65 °C for spectral collection. In addition, ATR‐IR spectra of bovine butter were directly collected at room temperature to avoid phase separation. The fatty acid profile and the levels of CLA were determined using reference FAME‐GC‐FID analysis. SIMCA models showed well separated clusters that discriminated between organic and conventional bovine butters due to C=C trans bending out of the plane vibration modes band at 967 cm^?1. Additionally, strong PLSR models were developed to predict CLA levels using butter–fat and bovine butter spectra with SEP of 0.05 % and RPD of 4.7, indicating that the models are suitable for quality control applications. Portable IR technology offers the ability for “in situ” analysis of butters that is much less time consuming than current analytical practices for authentication and quality control efforts by the industry.     

Incorporation of Alpha‐Linolenic Acid and Enhancement of n‐3 Fatty Acids in Nile Tilapia: a Factorial Design

A 2² factorial design (two factors at two levels, in triplicate) was performed to investigate the influence of factors A (time of treatment, 15 and 30 days) and B (chia oil content in a supplemented diet, at 2.1 and 4.2 %) in three responses of interest referring to: (a) the incorporation of alpha‐linolenic acid (LNA) in lipids of Nile tilapia fillet; (b) the enhancement of n‐3 fatty acids; and (c) the decrease in the omega‐6/omega3 (n‐6/n‐3) ratio in fish. Factors A and B were significant in the three regression models obtained and the interaction AB was a significant contributor to the LNA and n‐6/n‐3 ratio. Analysis of variance suggested three significant and predictive mathematical models. Response surfaces analyses from designs indicated higher LNA and n‐3 contents and a lower n‐6/n‐3 ratio using both factors A and B in the higher levels (30 days of treatment and 4.2 % of chia oil in the diet for fish) chosen for this study.     

Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils

The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose?) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares‐discriminant analysis (PLS‐DA) and hierarchical cluster analysis (HCA). Based on the zNose? analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS‐DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS‐DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS‐DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose? in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.