An improved method for the measurement of added vegetable fats in chocolate

A method for identifying refined vegetable fats added to chocolate (cocoa butter equivalents, CBEs) was combined with established quantitative methods for determining the level of vegetable fat added to cocoa butter with the aim of providing improved precision. The identification of fats was based on the analysis of sterol and triterpene alcohol degradation products formed during the processing of the fat. The procedure was able to successfully discriminate between 95% of pairs of fats from a set (33) of CBE-type vegetable fats. Subsequent analysis of 80 mixtures of four CBEs with chocolate successfully identified, on cross-validation, 94% of the samples. Combining the qualitative procedure with established quantitative methodology, based on the analysis of triacylglycerols, improved the method precision from ± 2.1% to ± 0.3% (5% addition of CBE at 95% confidence). Identifying the fat analytically permits the use of quantitative methods for determining the level of added fat in chocolate that have improved precision in comparison with the measurement of an unidentified fat. This may obviate the need to use factory inspection as a means to identify the ingredients of a product and monitor compliance with proposed legislation.     

Review of cocoa butter and alternative fats for use in chocolate—Part B. Analytical approaches for identification and determination

This work reviews the literature on analytical methods suitable for the detection, identification and determination of foreign fats in cocoa butter and chocolate. Most methods are based on chromatography, analysing either the fatty acids, triglyceride or the fractions of the unsaponifiables (sterols, triterpenes, etc.). None of the methods reviewed here allows the unequivocal quantification, or just the detection, of foreign fats in chocolate without any exemption for the kind of foreign fat added. The type of foreign fats which are not detectable depends strongly on the analytical techniques applied. It is proposed to combine a number of techniques with a chemometric approach to develop unique patterns allowing the unequivocal detection and quantification of foreign fats, without having prior knowledge regarding the kind of fat added. This approach is based on the use of a complex pattern of the constituents of cocoa butter and any deviation from this pattern is attributed to the existence of foreign fat.     

不同预处理方法对烟草近红外光谱预测模型的影响

采用基线校正、去卷枳、一阶微分、二阶微分、主成分回归(PCR)和偏最小二乘(PLS)法对198个烟叶样品的近红外光谱和总糖、还原糖、总烟碱含量数据进行了处理,建立了相应的总糖、还原糖和总烟碱校正模型,并将这些模型的回归参数作了比较。结果表明:①二阶微分处理光谱建立的模型的相关系数比基线校正、去卷枳、一阶微分预处理法建立的模型的高,而其相对偏差比基线校正、去卷枳、一阶微分法的低;②PLS算法建立的定量分析模型比PCR算法的好。     

Comparison of classical and alternative extraction methods for the quantitative extraction of fat from plain chocolate and the subsequent application to the detection of added foreign fats to plain chocolate formulations

Three methods were compared for the quantitative extraction of fat from plain chocolate and cocoa products ranging from simple intermediary products such as cocoa butter, cocoa masses or liquors to market purchased chocolate samples. The classical method of extraction using an acid digestion followed by Soxhlet extraction was compared to two potentially alternative methods: microwave extraction and supercritical fluid extraction (SFE). The extracted intact triacylglycerols were then analysed directly by high temperature capillary gas chromatography (HT-GC). The results of extractions were compared by plotting the percentage areas of the three major triacylglycerols present one vs. the other. The results showed that the microwave extraction technique gave similar results to the more time-consuming classical extraction method. SFE gave results with higher standard deviations and a tendency to lower yields taking the acid digestion–Soxhlet method as the reference method. The minimal variations due to the methods did not impact the detection of foreign fats added to chocolate. However, the quantification of the foreign fats was impaired when using the SFE method compared to the other techniques due to the larger variations of replicate analyses of intact triacylglycerol profiles.     

灵武长枣蔗糖含量的高光谱无损检测

利用高效液相色谱法检测蔗糖含量,同时运用高光谱成像技术结合化学计量方法建立蔗糖预测模型;通过竞争性自适应加权（competitive adaptive reweighted sampling,CARS）算法、连续投影算法（successive projection algorithm,SPA）和无信息消除变量（uninformative variable elimination,UVE）降维处理,建立特征波段和全波段的主成分回归（principal component regression,PCR）、偏最小二乘回归（partial least squares regression,PLSR）和多元线性回归（multivariable linear regression,MLR）模型。结果表明,采用蒙特卡洛方法剔除异常样本后,相关系数由0.611增大到0.846;正交信号校正法预处理效果最佳,RC和RP分别为0.853和0.794;利用SPA、UVE、CARS、CARS＋SPA和CARS＋UVE五种方法提取了5、21、17、10、18 个特征变量,其中CARS-PCR模型最好,校正集、预测集的相关系数为0.861、0.843,校正集、预测集的均方根误差为0.013 mg/g和0.014 mg/g。综上,高光谱成像技术可以实现长枣蔗糖含量的预测,为更深一步探讨枣的内部品质提供参考。     

Application of principal component analysis to the prediction of lucerne forage protein content and in vitro dry matter digestibility by NIR spectroscopy

Application of principal component regression (PCR) was proposed for the development of a prediction equation of forage composition by near infra-red spectroscopy. PCR involves two steps: (a) the creation of new synthetic variables by principal component analysis (PCA) of spectral data, and (b) multiple linear regression with these new variables. Results obtained by this procedure have been compared with those generated by the conventional application of multiple linear regression (MLR) on spectral data. The comparison used the determination of protein content and in vitro dry matter digestibility (IVDMD) in 345 samples of lucerne forages. For protein determination, results of both procedures were quite similar (correlation coefficients: 0.978 and 0.980; standard errors of calibration: 0.86 and 0.84% DM; standard errors of prediction: 0.81 and 0.80% DM respectively for MLR and PCR prediction equations). The same was observed for IVDMD determination (correlation coefficients: 0.942 and 0.951; standard errors of calibration: 1.89 and 1.71% DM; standard errors of prediction: 2.22 and 2.22% DM, respectively). A large number of PCA variables were necessary for an accurate prediction of both constituents. The influence of the number of regression terms introduced in the PCR equation has been studied. The criterion for stopping the introduction of new terms in PCR did not seem as critical as in MLR.     

大米蛋白质、脂肪、总糖、水分近红外检测模型研究

采用近红外光谱技术对大米蛋白质、脂肪、总糖、含水量进行检测。运用经典Kennard-Stone法选取校正集及预测集样本,运用分段小波消噪对光谱进行预处理,通过竞争性自适应重加权采样筛选出与样本性质相关的特征波长,比较偏最小二乘法和BP神经网络法所建立的大米蛋白质、脂肪、总糖、含水量的检测模型。对于大米蛋白质、总糖和水分的检测,2种方法所建立模型的决定系数均大于0.9,相对标准差均小于2.6%,具有良好的精度和稳定性。对于大米脂肪的检测,偏最小二乘模型的性能相对稍好,其决定系数为0.949 5,相对标准差为13.69%。     

Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy

Meatball is one of the favorite foods in Indonesia. The adulteration of pork in beef meatball is frequently occurring. This study was aimed to develop a fast and non destructive technique for the detection and quantification of pork in beef meatball using Fourier transform infrared (FTIR) spectroscopy and partial least square (PLS) calibration. The spectral bands associated with pork fat (PF), beef fat (BF), and their mixtures in meatball formulation were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure PF and BF. For quantitative analysis, PLS regression was used to develop a calibration model at the selected fingerprint regions of 1200-1000 cm(-1). The equation obtained for the relationship between actual PF value and FTIR predicted values in PLS calibration model was y = 0.999x + 0.004, with coefficient of determination (R(2)) and root mean square error of calibration are 0.999 and 0.442, respectively. The PLS calibration model was subsequently used for the prediction of independent samples using laboratory made meatball samples containing the mixtures of BF and PF. Using 4 principal components, root mean square error of prediction is 0.742. The results showed that FTIR spectroscopy can be used for the detection and quantification of pork in beef meatball formulation for Halal verification purposes.     

近红外漫反射光谱检测损伤猕猴桃内部品质

研究贮藏期间损伤猕猴桃内部品质与其近红外漫反射光谱之间的关系。利用近红外光谱(12000～4000cm-1)技术和多元线性回归(multiple linear regression,MLR)、主成分回归(principal component regression,PCR)和偏最小二乘法(partial least squares,PLS)3种校正方法分别对损伤华优猕猴桃在2℃条件下贮藏4周期间的可溶性固形物含量、pH值和硬度进行定量分析;并对比吸光度原始光谱、一阶微分和二阶微分3种不同预处理方法的PLS模型校正结果。结果表明：一阶微分预处理方法时,应用PLS建立的可溶性固形物含量、pH值和硬度校正模型的效果最佳;预测集样品预测值与测量值之间的相关系数分别为0.812、0.703、0.919,预测均方根误差分别为0.749、0.153、1.700。说明应用近红外漫反射技术检测贮藏期间损伤猕猴桃的内部品质是可行的。     

Application of synchronous fluorescence spectroscopy with multivariate data analysis for determination of butter adulteration

The potential of fluorescence spectroscopy for detection of butter adulteration with palm and coconut oils was investigated. Synchronous fluorescence spectra were collected in the range of 240–700 nm with wavelength intervals (?λ) of 10, 30, 60 and 80 nm. The applied technique was used to detect the addition of palm and coconut oils to butter, and the lowest limit of detection for adulteration (LOD – 5.5%) was observed after applying the wavelength interval of 60 nm. The multiple linear regression (MLR) models were used to calculate the level of adulteration with the lowest root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV) of 3.8 and 3.9%, respectively, for the measurements acquired at the wavelength interval of 60 nm.     

可见/近红外光谱技术无损检测新鲜鸡蛋蛋白质含量的研究

鸡蛋是一种重要的食品,蛋白质是鸡蛋的主要营养成分。本研究利用可见近红外反射光谱技术无损检测新鲜鸡蛋的蛋白质含量。使用光谱仪获取新鲜鸡蛋在400~1100 nm波段范围内的漫反射光谱;分别使用多元散射校正(MSC)法和一阶导数法(1-D)对反射光谱进行预处理;对反射光谱、MSC处理光谱和1-D光谱,使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果表明,可见/近红外反射光谱经过多元散射校正后,确定的10个最优波长(400、403.16、407.9、714.6、715、715.58、970.4、970.75、973和974.45 nm)组合建立模型的校正和验证结果最好:选定模型的校正结果为R=0.92,SEC=0.42%;验证结果为Rcv=0.89,SECV=0.47%。研究表明可见/近红外反射光谱技术可以较好的预测新鲜鸡蛋的蛋白质含量,本研究可为可见近红外光谱技术在鸡蛋营养成分的快速检测提供一定的理论基础。     

近红外结合线性回归算法快速预测小麦籽粒干物质和重量

为了实现小麦品质(干物质、重量)的快速无损检测,对35个小麦品种样品进行了近红外系统扫描,获取光谱信息,并进行高斯滤波平滑(GFS)、归一化(N)和基线校正(BC)预处理.采用偏最小二乘(PLS)算法分别建立光谱信息与干物质和重量参考值之间的定量关系.采用回归系数法(RC)和连续投影算法(SPA)两种方法在900～17...     

Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

The frying qualities of palm and soybean oils are determined using infrared spectroscopy and multivariate calibration. Compare to soybean oil, palm oil is more resistive to the chemical and physical changes and this is attributed to the high degree of unsaturation of soybean oil (61.9%) compare to palm oil (13.8%). After 48 h in service, the oil samples were effectively clustered into two groups using principal component analysis which indicated that both oils still maintain their chemical identities. Partial least squares regression (PLS1 and PLS2) a long with mid-FTIR data are used for predicting free fatty acid, viscosity, and total polar compounds of the used oils without running expensive standard procedures. PLS1 and PLS2 outperformed PCR and MLR for predicting the quality indicators of the frying oils. For palm oil and at the optimum calibration conditions, the obtained accuracies (SD) are 105.6% (0.05), 99.8% (1.10), and 103.9% (0.16) for free fatty acid, viscosity, and total polar compounds, respectively. The proposed method is simple, less-expensive, and has comparable accuracy/precision with standard procedures that often used for monitoring frying oils.     

Multiproduct,Multicomponent and Multivariate Calibration: a Case Study by Using Vis-NIR Spectroscopy

Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view.     

Cholesterol content and lipid composition of low fat dairy products

The lipid composition of different products obtained from the same raw milk was investigated. The cholesterol content of skim milk (0.05% total fat) and butter milk (0.57% total fat) lipids was 4.20% and 1.31%, respectively, while raw milk lipids and butter fat contained 0.34% or 0.30%, respectively. As the cholesterol content of these low fat milk products increases with decreasing fat content, equations were derived to estimate the fat-related cholesterol content from the value analysed in butter fat. In contrast to the fat-related cholesterol content, the product-related content is still relatively small in skim and butter milk. Their fat-related cholesterol content was found to be influenced by drying. The high content of phospholipids in skim and butter milk led to considerable changes in the fatty acid composition and affected the detection of foreign fat by butyric acid as well as by triglycerides. Skim and butter milk lipids contained only 2.41% or 2.33% butyric acid, respectively, instead of 3.36% found in raw milk lipids. Further, the overlap of phospholipids as well as cholesterol with the gas chromatographic triglyceride pattern resulted in calculated foreign fat contents of more than 20%.     

基于高光谱的茄子外部缺陷检测

利用高光谱(900～1700nm)对完好、木栓化和烂果茄子进行识别研究。共采摘了252个茄子样本,包含完好茄子170个,木栓化茄子60个和烂果茄子22个,利用高光谱成像系统采集完好、木栓化和烂果3种区域一共252个样本的高光谱图像,然后提取合理的感兴趣区域(ROI)获得样本光谱数据。采用多种预处理方法进行光谱预处理,建立偏最小二乘(partial least squares method,PLS)判别分析模型,结果表明,经normalize预处理后模型的预测效果最好,因此选择normalize作为预处理方法。基于预处理后的光谱数据,采用连续投影法(SPA)、回归系数法(RC)和竞争性自适应重加权算法(CARS)提取特征波长,并分别建立偏最小二乘(PLS)和多元线性回归(MLR)判别模型进行研究。结果表明:CARS-MLR模型对3种类型样本鉴别效果最佳,其校正集决定系数Rc2为0.94,预测集决定系数Rp2为0.90,RMSEC和RMSEP分别为0.19和0.21,预测集判别准确率达到96.82%。本研究采用高光谱可以对完好、木栓化和烂果茄子进行有效鉴别,为茄子的缺陷无损检测提供了理论参考。     

Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n?=?120) and prediction (n?=?40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r ²), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.     

Consumer acceptability and purchase intent of probiotic yoghurt with added glucose oxidase using sensometrics,artificial neural networks and logistic regression

This research aimed to identify the drivers of acceptance and purchase intent of a probiotic (Bifidobacterium longum BL05) nonflavoured yoghurt supplemented with glucose oxidase, and to model the consumers’ acceptability using sensometrics and artificial neural networks (ANN). Consumers (n = 100) evaluated the degree of liking of yoghurt assays in respect of appearance, aroma, taste, texture and overall linking. Sensometric techniques – multiple linear regression (MLR), partial least squares regression (PLS), principal component regression (PCR) – and ANN were used to model the overall liking. Sensory drivers of global acceptance and purchase intent were also determined using logistic regression (LR). Hierarchical cluster analysis (HCA) identified three consumer segments that presented differences in all sensory attributes evaluated (P < 0.05). The ANN model showed the best performance to predict overall liking, followed by the MLR, PLS and PCR, indicating that taste and texture were the most significant attributes impacting the yoghurts overall liking. In accordance with the logistic models, overall acceptance and purchase intent could be predicted with 81.94 and 85.49% accuracy, respectively. The logistic regression indicated that taste was the attribute that contributed significantly (P < 0.0001) to higher scores for purchase intent and was considered the driver of acceptance.     

Approach to the quantification of milk mixtures by partial least-squares,principal component and multiple linear regression techniques

Four of the most widely employed multivariate calibration methods, partial least-squares regressions (PLS-1 and PLS-2), principal component regression (PCR) and multiple linear regression (MLR) were applied to predict the percentages of ternary mixtures of cow’s, ewe’s and goat’s milk based in the analysis of casein fraction by capillary electrophoresis. The prediction models were calculated by using three batches of 10 milk mixtures each prepared in three different seasons and were validated by applying them to the analysis of nine milk mixtures. All the models were good for the prediction of percentages of milk of each species. However, it was found that MLR led to more precise predictions than the other multivariate calibration methods with a root square error under 1.2%.     

On-line versus off-line NIRS analysis of intact olives

Visible/near-infrared calibrations were developed for the determination of the quality parameters (fat content, moisture and free acidity) of intact olive fruits. The reflectance spectra were acquired in two different instruments (diode-array versus grating monochromator based instruments). The grating monochromator based instrument was used at the laboratory (off-line analysis), whereas the portable diode-array based device was placed on top of a conveyor belt set to simulate measurements in an olive oil mill plant (on-line analysis). Partial least squares (PLS) regression and least squares support vector machine (LS-SVM) were used for the development of the calibration models. A total of 174 samples were prepared for the calibration (N = 122) and validation (N = 52) sets. The root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD) values were better using the diode-array instrument and applying the PLS regression method for the fat content parameter while for the free acidity and moisture content, the LS-SVM algorithm gave the best results. The results obtained seems to suggest the viability of the on-line system, instead of the off-line analysis, for the determination of physicochemical composition in intact olives.