Rapid Determination of Bitterness in Beer Using Fluorescence Spectroscopy and Chemometrics

Two fluorescence spectroscopic methods with the aim to develop a fast quantitative determination of bitterness in beer were tested. The first method was based on autofluorescence of the diluted and degassed beer samples without any further processing. A total of 21 dark and light beer samples were analyzed and multivariate Partial Least Squares (PLS) regression models to bitterness in form of international bitter units (IBU) were performed. A prediction error in the form of Root Mean Square Error of Cross‐Validation (RMSECV) of 2.77 IBU was obtained using six PLS components. Focusing only on the light beer samples the RMSECV was reduced to 1.81 IBU. The second method developed was based on addition of europium to induce delayed fluorescence signals in the beer samples. PLS models yielded an RMSECV of 2.65 IBU for all beers, while a model on the light beer samples gave an RMSECV of 1.75 IBU. The obtained prediction errors were compared to the errors given in the literature for the traditional extraction method of determining IBU.     

Determination of Calcium Content in Powdered Milk Using Near and Mid-Infrared Spectroscopy with Variable Selection and Chemometrics

Near infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were evaluated to determine calcium content in powdered milk. A hybrid spectral variable selection algorithm combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA) selected 11 NIR and 15 MIR variables from full 2,756 NIR and 3,727 MIR variables, respectively. Predicted results of least-squares support vector machine models for the samples in the prediction set show that the 15 MIR variables obtained much better results (0.930 for coefficient of determination (r ²), 3.703 for residual predictive deviation (RPD), 30.162 for root mean square error of prediction set (RMSEP) and 5.22% for relative errors of prediction (RSEP)) than 11 NIR variables did (0.636 for r ², 1.587 for RPD, 78.815 for RMSEP, and 13.40% for RSEP). The overall results indicate that MIR spectroscopy could be applied as a precision and rapid method to determine calcium content in powdered milk. The good performance shows a potential application using UVE-SPA to select NIR and MIR effective variables.     

Characterization of Croatian Honeys by Right-Angle Fluorescence Spectroscopy and Chemometrics

The potential of right-angle fluorescence spectroscopy and selected chemical parameters for discrimination of botanical origin of Croatian honey types (n = 55) previously classified by physicochemical and melissopalynological analyses was evaluated. Systematic step-by-step fluorescence analysis included the measurement of complete excitation-emission matrix (EEM) in the range of excitation wavelengths from 260 to 400 nm, and emission wavelengths from 300 to 600 nm, followed by fluorescence intensity measurement at detected peaks of different excitation/emission wavelengths, and emission spectra recordings at selected specific excitation wavelengths of honey solutions in 50 mmol L^?1 phosphate buffer pH 7.0 and methanol. A total of five different sets of emission spectral data for buffer and two for methanolic honey solutions were considered for chemometric analysis of original and normalized emission spectra including principal component analysis and linear discriminant analysis. Additionally, chemical analysis of buffer and methanolic honey solutions included determination of protein, total polyphenol and reactive amino group content. Results showed that right-angle fluorescence spectroscopy of honey solutions has great potential for honey botanical origin discrimination, either by visual comparison of excitation-emission spectra landscapes, or even better, by normalized emission fluorescence spectra recordings at 260 and 280 nm of excitation. Moreover, increased honey discrimination was achieved in conjunction with chemometrics of fluorescence spectras. In addition, two rapid markers/indicators of honey authentication were found. Chestnut honey could be clearly discriminated from the other honey types by simple measurement of fluorescence intensity at 390/470 nm, while sage honeys by measurement of reactive amino group content.     

Combined Application of Fluorescence Spectroscopy and Chemometrics Analysis in Oxidative Deterioration of Edible Oils

Combined methods of fluorescence spectrometry with chemometrics were used to monitor oxidation deterioration of edible oil. Synchronous and three dimensional fluorescence spectroscopy techniques were proposed for monitoring palm oil, camellia oil, sunflower oil and perilla oil during oven accelerated oxidation. Principal component analysis plot of fluorescence intensity (λex = 320–700 nm) clearly showed oxidative evolution of oils over heating time. High saturated or monounsaturated oils exhibited high regression coefficients between peroxide values and fluorescence intensity (R ²  = 0.973 for 400 nm in palm oil; R ²  = 0.956 for 370 nm in camellia oil). High diunsaturated oil exhibited high regression coefficient between nonpolar carbonyl compounds and fluorescence intensity (R ²  = 0.970 for 370 nm in sunflower oil). High triunsaturated oil exhibited high regression coefficient between p-anisidine value and fluorescence intensity (R ²  = 0.938 for 665 nm in perilla oil). In conclusion, Fluorescence spectroscopy is a rapid and green nondestructive method for oxidation monitoring. Differences of fatty acid compositions played key rules in formation of oxidation products and evolution of fluorescence spectra.     

基于近红外光谱技术的腐竹脂肪定量分析

采用近红外光谱技术结合化学计量学方法,建立腐竹脂肪含量的快速分析方法。收集不同生产线、不同时间的腐竹样本180 份,利用积分球附件采集漫反射光谱（4 000～10 000 cm－1）。为消除颗粒散射影响和光谱基线漂移,二阶导数和卷积平滑用于光谱预处理。采用反向区间偏最小二乘法、组合区间偏最小二乘法、搜索组合移动窗口偏最小二乘法和遗传偏最小二乘法优化建模变量,最终构建了定量预测模型。结果显示,4 种方法均可有效地提取信息变量、降低模型维度、提高预测性能;遗传偏最小二乘法一次优选获得143 个变量,构建的模型性能最佳,其校正相关系数、校正均方根误差、预测相关系数、预测均方根误差分别为0.96、0.95、0.92和1.17。研究表明,经过信息变量提取后所构建的近红外模型简单、预测精度高,可用于腐竹脂肪含量的日常监测。     

Simultaneous Determination of Flumequine and Oxolinic Acid Residues in Aquatic Products Using Pressurized Capillary Electrochromatography

A rapid method for detection of flumequine (FQ) and oxolinic acid (OA) residues from fish tissues was established using pressurized capillary electrochromatography (pCEC). Residual FQ and OA were extracted from fish tissue samples with acidified acetonitrile and purified on an Oasis HLB C₁₈ solid-phase extraction column. The extract was then analyzed by pCEC with the mobile phase (pH?=?3) consisting of acetonitrile and 15 mM ammonium acetate (45: 55, v/v, respectively) at the flow rate of 0.07 mL/min, detection wavelength of 324 nm, and applied voltage of ?10 kV. Baseline separation of FQ and OA was achieved in 10 min; both drugs showed good linearity in the range of 0.2–5 μg/mL. The recoveries of FQ and OA were between 82 and 92 % (relative standard deviation?=?3.63–5.96). The limits of detection for FQ and OA were 0.1 and 0.08 mg/kg, respectively.     

A Novel Technique on Determining Water Content in Milk Using Radio-Frequency/Microwave Dielectric Spectroscopy and Chemometrics

Water is the major component of milk. More water means less total solids concentration. Therefore, routine analysis on water content is very important in milk factory. To offer a new method for determining water content of milk in situ and in-line quality monitoring, dielectric spectroscopy, an instrumental method used to obtain spectra describing dielectric properties of materials, was used to determine the water content of milk in this study. The dielectric spectra of 161 milk samples were obtained at 201 discrete frequencies on a logarithmic scale from 20 to 4500 MHz at 25 °C. Ten, 34, and 14 optimal dielectric variables (ODVs) were extracted from the full dielectric spectra (FDS) with 402 dielectric variables by using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and combination of CARS and SPA (CARS + SPA), respectively. Four models, including two linear models, i.e., multiple linear regression (MLR) and partial least squares regression (PLSR), and another two artificial neural network models, i.e., extreme learning machine (ELM) and least squares-support vector machine (LSSVM), were established. The artificial neural network models had better prediction performance than the linear models at the same ODV selection methods. Moreover, the ODVs extracted by CARS could give better prediction performance than SPA and CARS + SPA. Among all developed models, the FDS-LSSVM model had the lowest root mean squares error of prediction set (0.054%), followed by CARS-LSSVM with 0.094%. Few variables and high prediction precision of CARS-LSSVM have great potential in developing portable milk water detector used in situ and in-line inspection. This study indicated that dielectric spectroscopy is a promising approach for accurately determining the water content of milk.     

Determination of Total Sugar Content in Soy-Based Drinks Using Infrared Spectroscopy and Chemometrics

Partial least square (PLS) regression models were developed and compared in order to determine the total sugar content in soy-based drinks using an infrared spectroscopy technique known as attenuated total reflectance Fourier transform infrared (ATR-FTIR). On a spectrophotometer set for analyzing on the middle infrared region, spectral band of 1900 to 900 cm^?1, commercial samples of soy beverage were analyzed, as well as samples with crescent water additions of 5, 10, and 20% v/v. Reference data for total sugars were obtained using the Lane-Eynon method. To construct regression models, algorithms of interval partial least square (iPLS) and synergy of interval partial least square (siPLS) were applied using iToolbox package on Matlab 8.1 environment. Kennard-Stone algorithm was used to the selection of calibration and prediction sets. Two models have been the best obtained: the first was an iPLS with seven latent variables, which selected the spectral band of 1399–900 cm^?1 and presented root mean square error of cross-validation (RMSECV)?=?0.1678% (w/w). The second best model was siPLS with six latent variables, which selected spectral bands of 1025–1150 and 1151–1476 cm^?1 and presented RMSECV?=?0.1963% (w/w). The proposed method presents advantages such as a small-required amount of sample for spectrum achievement, no sample destruction, and a high analytical frequency.     

偏最小二乘速差动力学分光光度法同时测定酪氨酸和色氨酸

本实验采用速差动力学分光光度法对酪氨酸和色氨酸的混合物进行不经分离的同时测定.在碱性介质下,KMnO4可以氧化酪氨酸和色氨酸,同时KMnO4被还原为浅绿色的MnO42-,测定反应过程中MnO2-的吸光度可推知其浓度的变化情况.测定所得的数据采用化学计量学.偏最小二乘方法解析,从而可对酪氨酸和色氨酸分别进行测定.     

水牛奶酪蛋白胶束结构的荧光光谱研究

利用内源荧光团色氨酸（Trp）和外源荧光探针8-苯胺基-1-萘磺酸（8-anilino-1-naphthalenesulfonic acid,ANS）的荧光特性对水牛奶酪蛋白胶束结构进行研究。结果表明：当蛋白质质量浓度较低时,酪蛋白胶束结构变化不明显,而较高的蛋白质质量浓度会破坏其胶束结构,使其疏水基团暴露;此外,在离子强度较大、pH值较低条件下,对酪蛋白胶束结构影响较大,使酪蛋白疏水基团暴露,酪蛋白微球先膨胀后聚集并形成酪蛋白胶束。     

基于PLS-模式识别近红外光谱技术快速检测鲜乳和掺假乳

本实验采用PLS-马氏距离法建立了鲜乳和掺假植物奶油牛乳的判别分析模型,用PLS法将原始数据压缩成3个主成分,在原始光谱的全波数段范围内,无需任何预处理方式,判别准确率达100％。同时对10个未知样品进行预测,预测准确率均为100％。其次建立了植物奶油掺假量的定量检测PLS模型,并采用交互校验和外部检验考察模型的可靠性,模型的校正相关系数为0．9963,均方估计残差（RMSEC）为0．110;交互校验均方残差（RMSECV）为0．142;应用所建PLS模型对样品中植物奶油添加量进行预测,并对预测值与真值进行配对t-检验,结果表明两者差异均不显著。     

应用高效液相色谱同时测定牛奶中三种四环素的方法研究

应用高效液相色谱法,优化了牛奶中3种四环素的检测方法且力求操作简单、快捷.本方法采用0.1 mol/LEDTA-McIlvaine缓冲液(pH 4.0)提取奶中的残留的三种四环素类抗生素,通过C18固相萃取柱进行提纯、净化,提高了方法的灵敏度.结果表明,该方法最低检出限达到国标的兽药残留最低限量要求,加标回收率103 ％～109％之间.     

X-ray Fluorescence Determination of Element Contents in Milk and Dairy Products

The concentrations of minerals (Na, Mg, P, S, Cl, K, and Ca) and trace elements (Mn, Fe, Ni, Cu, Zn, Rb, Sr, and Br) in different types of milk, dairy products, and infant formulas have been determined using wavelength-dispersive X-ray fluorescence analysis (WDXRF). Freeze-dried samples pressed as tablets of 4 g have been analyzed. Calibrations have been established using both plant and milk standard reference materials. The matrix correction method based on the power function of Compton scattered intensity was applied. The paper provides calibration data, detection limits for each element, and testing the accuracy of the proposed technique. The elemental compositions of the samples obtained by WDXRF were compared with the previously reported data from different countries.     

应用红外光谱结合化学计量学方法检测阿拉伯胶产地和蛋白质含量

研究利用傅里叶红外光谱结合化学计量学方法来实现对苏丹阿拉伯胶的产地和蛋白质含量的快速无损检测的可行性。采集自6?个不同的产地,每个产地12?个,总计72?个阿拉伯胶样本,作为研究对象,运用线性判别分析（linear discriminant analysis,LDA）和反向区间偏最小二乘（backward interval partial least squares,Bi-PLS）法分别实现对苏丹阿拉伯胶的产地区分和蛋白质含量检测。结果表明,当主成分数为6时,LDA对样本的训练集（48?个样本）和预测集（24?个样本）的识别率都为100%。Bi-PLS法回归联合20?个光谱子区间中的4?个子区间得到最佳的蛋白质预测模型,其预测集相关系数为0.937?3,均方根误差为0.173%。因此,利用傅里叶红外光谱结合化学计量学方法可实现对苏丹阿拉伯胶的产地以及蛋白质的含量的快速无损检测。     

Determination of Spiramycin and Josamycin in Milk By HPLC and Fluorescence Detection

ABSTRACT: A reversed-phase high-performance liquid chromatographic (HPLC) method is described for the simultaneous determination of spiramycin and josamycin in milk. The extract obtained from milk, using liquid-liquid extraction, was treated with cyclohexa-1,3-dione in ammonium acetate buffer (pH 7.0) for 60 min at 90°C. The derivative was chromatographed on an octadecylsilane (ODS) column at 45°C. Separation was performed by using a mobile phase consisting of an acetonitrile-methanol-phosphate buffer (pH 6.0) mixture. Fluorescence detection was achieved at 375 nm for the excitation wavelength and 450 nm for the emission wavelength. Linearity was demonstrated between 50 and 500 μg/L. Recoveries were about 90% and the detection limits were 8 and 13 μg/L for spiramycin and josamycin, respectively.     

甘薯水分和还原糖协同向量NIR快速检测方法

利用近红外光谱技术建立新鲜甘薯水分和还原糖含量的预测模型,实现快速检测与分析,为甘薯品质分析和种质资源筛选提供便捷。选用不同品系甘薯样品146份,109份作为校正样品,37份作为验证样品。运用不同光谱预处理方法、协同区间偏最小二乘最优波长选择法以及主成分回归和偏最小二乘法建立甘薯水分和还原糖模型。结果显示,所建甘薯水分(还原糖)最优模型的决定系数、预测均方根误差和标准差比率分别为0.974(0.885),1.154(0.270)和6.334(3.148)。表明2种模型具有较好的检测性能,近红外光谱模型的预测值与其相应的化学值有较好的相关性,适用于大批量甘薯选育时水分和还原糖含量的快速检测。