Rapid Detection of Melamine in Milk Using Immunological Separation and Surface Enhanced Raman Spectroscopy

We integrated immunological separation and surface‐enhanced Raman spectroscopy (SERS) to detect melamine in milk. Antimelamine was produced by New Zealand white rabbits following the injection with melamine hapten‐ovalbumin immunogen. Melamine was separated from milk by binding to the converted protein G‐antimelamine complex. After releasing antimelamine and melamine from the complex, the eluents were deposited directly onto the silver dendrite SERS‐active substrate for spectral collection. Multivariate statistical analysis including unsupervised principal component analysis and supervised soft independent modeling of class analogy validated the feasibility of applying this method to detect trace levels of melamine in milk. The limit of detection can be as low as 0.79×10^–3 mmol/L. The overall analysis can be completed in 20 min, thus, it is a high‐throughput technique to screen for melamine in milk samples.     

同步荧光光谱法快速鉴别食用油

利用同步荧光光谱法鉴别沙棘果油、沙棘籽油、核桃油、菜籽油、芝麻油、亚麻仁油。结果显示：不同食 用油荧光光谱具有明显的差异,在激发波长250～720 nm范围内,沙棘果油、沙棘籽油、核桃油、亚麻仁油、芝麻 油和菜籽油的最大激发波长范围分别为315～450、520～650、315～490、300～500、300～550、300～490 nm。在 激发光与发射光波长差为90 nm、激发波长250～720 nm的条件下,对6 种不同食用油进行同步荧光扫描,利用主成 分分析得分图可以直观、快速地区分鉴别各种食用油。     

基于近红外光谱技术检测红曲米中的红曲色素

采用近红外光谱技术结合化学计量学方法构建红曲米中红曲橙色素、红曲红色素、红曲黄色素的预测模型。分别采用多元线性回归(SMLR)、偏最小二乘回归(PLS)、主成分回归(PCR)构建所有色素组分的数学模型,以相关系数(R)、校正均方根误差(RMSEC)、预测均方根误差(RMSEP)、预测相对分析偏差(RPD)值来评价模型的综合性能。结果显示,MSC、SNV方法能够消除红曲米粉颗粒不均对光谱的散射影响;导数处理消除了基线漂移;对于红曲橙色素、红曲黄色素、红曲红色素三种模型均具有良好的稳定性;利用三种模型对未知红曲样品预测时,预测结果具有较高的线性,预测性能较好(RPD=2.86~5.39),可用于准确定量预测。结果表明近红外光谱技术可用于红曲色素的快速无损测定,为红曲米质量的智能化控制提供了新的途径。     

一种锌金属有机骨架(Zn-MOF)材料的荧光光谱法在食品Fe~(3+)检测中的应用

以2,5-呋喃二甲酸和六水硝酸锌为原料,N,N-二甲基甲酰胺为溶剂合成一种锌金属有机骨架材料[(Me_2NH_2)_2Zn_3(fdc)_4]_n·DMA(Zn-MOF)。用荧光光谱法对该材料的荧光特性进行研究,发现该材料在波长379 nm和398 nm处有很强的荧光发射峰。考察食品中可能存在的16种不同金属离子对该材料荧光强度的影响,研究表明,Fe~(3+)能选择性地令该材料产生荧光淬灭现象。此外,考察Fe~(3+)浓度、浸泡时间和2种混合金属离子(Fe~(3+)-M~(+/2+/3+))对荧光淬灭敏感度的影响。结果表明,锌金属有机骨架材料对Fe~(3+)具有高度选择性传感功能,可作为荧光探针应用到食品Fe~(3+)的检测中,并可以高效地检测出Fe~(3+)。     

Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis

ABSTRACT: Fourier transform infrared spectroscopy with an attenuated total reflection sampling accessory was combined with multivariate analysis to determine the level (1% to 25%, wt/wt) of invert cane sugar adulteration in honey. On the basis of the spectral data compression by principal component analysis and partial least squares, linear discriminant analysis (LDA), and canonical variate analysis (CVA), models were developed and validated. Two types of artificial neural networks were applied: a quick back propagation network (BPN) and a radial basis function network (RBFN). The prediction success rates were better with LDA (93.75% for validation set) and BPN (93.75%) than with CVA (87.50%) and RBFN (81.25%).     

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。     

Determination of Tert‐Butylhydroquinone in Vegetable Oils Using Surface‐Enhanced Raman Spectroscopy

Monitoring the level of tert‐butylhydroquinone (TBHQ), a permissible antioxidant additive in edible vegetable oils in many countries, is important to ensure that oils and products that contain them comply with the relevant import regulations. Surface‐enhanced Raman spectroscopy (SERS) technology coupled with chemometric methods including partial least squares (PLS) and support vector machine (SVM) regression was applied to determine levels of TBHQ in spiked corn oils (0 to 500 mg/kg, n = 40) and commercial vegetable oils (0 to 99.7 mg/kg, n = 25). The lowest detectable concentration was 5 mg/L for TBHQ in standard solutions and 10 mg/kg of TBHQ in vegetable oils from various plant sources. The TBHQ levels predicted by the PLS or SVM model had a high correlation with actual TBHQ levels in commercial oil samples (SVM: R² = 0.972, ratio of performance to deviation [RPD] = 5.55, root mean square error [RMSE] = 5.73 mg/kg; PLS: R² = 0.976, RPD = 6.43, RMSE = 4.94 mg/kg), indicating great potential of SERS methods for detection and quantification of TBHQ in oils from a variety of sources.     

X-射线荧光光谱法快速测定谷物碾磨加工品中的重金属

目的 本研究利用X-射线荧光光谱法原理建立了一种谷物碾磨加工品中铅、总砷、镉、铬、总汞的快速检测方法。方法 选择谷物碾磨加工品中小麦粉典型基质,制备成含有一定量5种重金属标准样品,利用标准样品对检测时样品的粒径、装样质量、装样压力等检测条件进行优化,并对检测方法线性范围、定量限、精密度、重复性、稳定性等方法学进行考察。结果 检测方法中铅、总砷、镉定量限为0.1mg/kg,铬定量限为0.5mg/kg,总汞定量限为0.05mg/kg;线性范围铅、总砷、镉约为0.1mg/kg~2mg/kg,铬约为0.5mg/kg~9mg/kg,总汞约为0.05mg/kg~2mg/kg,线性相关系数(R2)均大于0.99;方法精密度、重复性、稳定性相对偏差均小于15%,对X-射线荧光光谱法与国检测结果进行比较,相对误差小于15%。结论 本研究建立的方法简便、快捷、准确性高,适宜对谷物碾磨加工品中5种重金属进行快速检测。     

Fluorescence Spectroscopy for Characterization and Differentiation of Beers

Total luminescence and synchronous scanning fluorescence spectroscopic techniques were applied for characterization of the intrinsic fluorescence of eight different beers. Spectra were measured using different geometries to reveal the presence of similar fluorescent components. The total luminescence and synchronous fluorescence spectra exhibit a relatively intense short‐wavelength emission ascribed to aromatic amino acids and less intense emission in the long‐wavelength region, which may originate from B vitamins. Classification of beers based on their synchronous fluorescence spectra was performed using non‐parametrical k nearest neighbours method and linear discriminant analysis. Very good discrimination was obtained in both methods with a low classification error. The results demonstrate the potential of fluorescence techniques to characterize and differentiate beers.     

柑橘近红外光谱无损检测技术研究进展

柑橘是世界第一大水果,中国是柑橘生产和销售大国.现阶段我国柑橘产业存在各环节分离、加工技术粗糙、采后品质分级落后等问题,导致我国柑橘在国际市场上缺乏竞争力.对柑橘产品进行检测与分级是提高竞争力的有效手段,然而传统的柑橘品质检测手段如肉眼识别法、图像识别法、化学滴定法等存在费时费力、精度不高等缺陷,且化学滴定法等对样品具...     

近红外光谱法结合化学计量学测定油茶籽油中脂肪酸组成

选择97个标称纯油茶籽油样品经过皂化、甲酯化后,先经气相色谱分析得到脂肪酸组成相对含量,然后利用透反射模式采集所有样品的近红外光谱,采用偏最小二乘法(partial least squares analysis,PLS)建立油茶籽油的饱和脂肪酸(C16:0+C18:0)、油酸(C18:1)和亚油酸(C18:2)相对含量的校正模型,并将模型用于预测,并对光谱预处理方法进行优化。结果表明:C16:0+C18:0、C18:1和C18:2的交叉验证均方根误差(root mean square error in cross-vali-dation of prediction,REMSECV)分别为0.143、0.448、0.392,预测均方根误差值(root-mean-square error value,RMSEP)分别为0.180、0.598和0.269,上述3种成分预测集相关系数(Rp2)依次分别为0.996、0.999和0.999。近红外光谱法可作为一种快速、无损和准确的方法用于测定油茶籽油的脂肪酸组成,从而鉴别纯油茶籽油的真伪。     

近红外光谱技术快速测定鹅肉嫩度

目的：应用近红外光谱技术快速检测鹅肉的嫩度值。方法：采集完整鹅肉的近红外光谱（950～ 1 650 nm）,光谱经多种校正预处理后,再分别采用主成分回归和偏最小二乘法建立鹅肉嫩度的定量预测数学模 型。结果：采用5点移动窗口平滑处理结合偏最小二乘法所建立模型的预测效果最好,嫩度定量校正数学模型的模 型决定系数为0.908 0,内部交互验证均方根误差为113.618 6。用此模型对预测集20 个样品进行预测,预测值与实 测值的相关系数达到0.971 1,预测值平均偏差为21.673 g,预测值和实测值之间没有显著性差异（P＞0.05）。结 论：近红外光谱作为一种无损快速的检测方法,可用于评价鹅肉的嫩度。     

基于GC-IMS和化学计量学分析海鲈鱼肉蒸制过程中风味物质的变化

以海鲈鱼为对象,采用气相色谱-离子迁移谱（gas chromatography-ion mobility spectroscopy,GC-IMS）技术分析鱼肉蒸制过程中挥发性风味物质的变化情况。结果表明,采用GC-IMS法从海鲈鱼肉中共鉴定出43 种挥发性物质,包括醛类、酮类、醇类、酸类和酯类化合物等。不同蒸制时间下海鲈鱼肉的风味物质组成存在明显差异,其中新鲜鱼肉中3-甲基丁醇、二甲基二硫和丙酸等物质的含量较高,经蒸制后鱼肉中的辛醛、壬醛、3-甲基丁醛、(E,E)-2,4-辛二烯醛、苯甲醛、2-庚酮、2-丁酮、1-辛烯-3-醇、1-庚醇和1-丁醇等物质的含量显著增加。采用正交偏最小二乘法判别分析筛选出11 种特征标志物（变量重要性投影值大于1）,通过热图聚类分析可以区分不同蒸制时间下海鲈鱼肉样品的挥发性风味差异,其中3-甲基丁醇、1-庚醇、糠醇和(E)-2-庚烯醛可作为生熟海鲈鱼肉的特征挥发性标志物。本研究结果表明GC-IMS可有效区分鱼肉的熟化过程。     

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。     

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。     

Rapid Quantitative Determination of Walnut Oil Adulteration with Sunflower Oil Using Fluorescence Spectroscopy

Adulteration of walnut oil with sunflower oil is a major issue for the walnut oil industry. In this paper, the potential use of total synchronous fluorescence (TSyF) spectra to differentiate walnut oil from sunflower oil and synchronous fluorescence spectra combined with multivariate analysis to assess the adulteration of walnut oil is demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 250–700 nm and the wavelength interval (Δλ) in the region from 10 to 100 nm. TSyF contour plots for walnut oil, in contrast to sunflower oil, show an extra fluorescence region in the excitation wavelength lower than 280 nm. Fifty-one oil mixtures were prepared by adulterating walnut oil with sunflower oil at varying levels (0–100 %). The partial least-squares regression model was used for the quantification of adulteration using wavelength intervals of 20, 40, 60 and 80 nm. This technique is useful for the detection of sunflower oil in walnut oil at levels down to 0.3 % (v/v) in just 2.5 min using an 80-nm wavelength interval.     

近红外光谱技术快速测定鹅肉新鲜度

目的：应用近红外光谱技术快速检测鹅肉的新鲜度,评价指标包括总挥发性盐基氮和pH值。方法：采集完整冷鲜鹅肉的近红外光谱（950～1 650 nm）,光谱经多种校正预处理后,采用偏最小二乘法建立鹅肉新鲜度的定量预测数学模型。结果：对于这2 种指标均采用标准常态变量结合偏最小二乘法所建立模型的预测效果最好,总挥发性盐基氮和pH值定量校正数学模型的模型决定系数分别为0.727、0.991,内部交互验证均方根误差分别为3.666、0.028。用此模型对预测集20 个样品进行预测,预测值与实测值的相关系数分别达到0.976、0.705,预测值平均偏差分别为－0.240、－0.024,预测值和实测值之间没有显著性差异（P＞0.05）。结论：近红外光谱作为一种无损快速的检测方法,可用于评价鹅肉新鲜度。     

Simultaneous Determination of Danofloxacin and Flumequine in Milk Based on Fluorescence Spectroscopy and Chemometrics Tools

The aim of this paper was to develop a rapid screening method to determine danofloxacin (DANO) and flumequine (FLU) in milk by fluorescence spectroscopy combined with three different chemometric tools. In this study, 2-D fluorescence data and multivariate calibration based on a partial least squares discriminant analysis (PLS-DA) regression were combined to simultaneously qualify and quantify DANO and FLU concentrations in commercial ultra-high-temperature (UHT) sterilized and pasteurized milk. Calibration sets based on the UHT whole milk from brand A were built and performed using a partial least squares (PLS) regression after deproteinization. Prediction sets based on 13 types of milk were analyzed using principal component analysis (PCA), principal PLS-DA, and PLS regression models. The multivariate calibration models were better able to determine the DANO and FLU concentrations than the univariate models, and these models could be applied to other types of milk. In contrast to the PLS-DA, which had good sensitivity and specificity, the PCA yielded less satisfactory results. In the quantitative analysis, the recoveries of the two analytes were reasonable and the root mean square error of prediction was within the acceptable range. The relative standard deviations of the predicted DANO and FLU concentrations on the various testing days were 9.2 and 6.2 %, respectively, demonstrating that the analytical method had a good reproducibility.     

Identity Confirmation of a Branded,Fermented Cereal Product by UV Spectroscopy: A Feasibility Study Involving a Trappist Beer

Brand protection is important for a food processor; trust in brand identity is essential for consumer confidence. In this study, the combination of UV spectroscopy and multivariate mathematics was investigated to confirm the identity of a processed cereal product—a Trappist beer. Samples (52) of Rochefort 8, other Rochefort and non‐Rochefort beers, diluted 1:100 with distilled water, were analysed by UV spectroscopy (220–400 nm). Partial least squares discriminant analysis (PLS‐DA) and soft independent modelling of class analogy (SIMCA) multivariate methods were applied separately to confirm the identity of Rochefort 8 beer. Spectral data were analysed in both raw and standard normal variate (SNV) pre‐treated forms. Using PLS‐DA, a two‐stage modelling procedure was applied involving initial classification as either Rochefort or non‐Rochefort followed by classification within the Rochefort class as Rochefort 8 or non‐Rochefort 8. Correct classification rates for these two steps were (a) 100 and 94.4% and (b) 90.9 and 100% respectively. Applying the 3 principal component SIMCA model to the 8 test Rochefort and all of the non‐Rochefort (n = 36) samples, 6 of the 8 Rochefort beers were correctly classified (75%; p = 0.05) but only two of the 36 non‐Rochefort beers were wrongly classified as Rochefort (correct classification rate of 94.4%).