近红外光谱技术鉴别核桃油中掺入菜籽油、大豆油及玉米油的研究

采用近红外光谱技术与偏最小二乘法(PLS)相结合的方式对核桃油中菜籽油、大豆油的现象进行定量分析。结果显示,当核桃油中菜籽油时,采用最小-最大归一化进行光谱预处理,菜籽油定量模型校正相关系数RC及校正标准误差RMSEC和交叉验证模型的相关系数RCV和交叉均方根RMSECV分别为99.97%,0.532%和99.96%,0.562%;当核桃油中大豆油时,采用一阶导数+矢量归一化进行光谱预处理,大豆油定量模型RC及RMSEC和RCV及RMSECV分别为99.99%,0.336%和99.99%,0.352%;当核桃油中2种油(菜籽油和大豆油)时,采用二阶导数进行光谱预处理,菜籽油定量模型的RC及RMSEC和RCV和RMSECV为99.99%,0.220%和99.98%,0.313%,大豆油定量模型的RC及RMSEC和RCV和RMSECV为99.99%,0.210%和99.97%,0.386%;当核桃油中菜籽油、大豆油和玉米油时,分别以菜籽油、大豆油和玉米油为研究对象,同时分别用一阶导数+多元散射校正、二阶导数、一阶导数方法进行光谱预处理,可以得到菜籽油定量模型的RC及RMSEC和RCV和RMSECV为99.93%,0.347%和99.90%,0.401%,大豆油定量模型的RC及RMSEC和RCV和RMSECV为99.87%,0.524%和99.80%,0.615%,玉米油定量模型的RC及RMSEC和RCV和RMSECV为99.94%,0.371%和99.89%,0.451%。经过重复性试验和外部试验验证可知,近红外光谱分析技术可以快速准确地检测核桃油中掺入1种油、2种油和3种油的含量。     

分子荧光光谱结合归一化法快速判定大豆原油掺伪

本试验通过大豆原油与成品油的荧光谱图的共同差异,结合谱峰归一化建立了无损、简单、快速、低成本的大豆原油掺伪判别方法。该方法采用荧光光度计,固定激发波长360 nm,设定激发狭缝与发射狭缝均为5 nm,收集不同来源大豆原油、成品油及其不同比例掺混油在370~800 nm的发射荧光谱图。由于大豆原油在388、525和676 nm处均有谱峰,而各成品油仅在417 nm处有强峰,使得它们的掺混油在388nm和525 nm的谱峰强度发生明显变化。利用这一特性,本试验设525 nm处峰强为1,定义388 nm与525 nm的峰强比值乘以100为掺伪指数(Ia)。试验表明:大豆原油中掺入了成品油的Ia均超过20。该方法的最低检出限为10%。     

应用近红外光谱技术快速测定葵花籽油的理化指标

应用傅里叶变换近红外光谱技术结合不同的定量分析方法建立葵花籽油4 种不同理化指标的快速预测模型。在用化学方法获得不同氧化程度葵花籽油的共轭二烯、共轭三烯、p-茴香胺值、总极性化合物的参照数据的基础上,比较不同光谱预处理方法及建模方法对预测效果的影响。结果表明,葵花籽油原始光谱不经处理、经Norris平滑和一阶导数处理或Norris平滑和二阶导数处理后应用偏最小二乘法可分别建立共轭二烯、共轭三烯、p-茴香胺值及总极性化合物的预测模型,建模决定系数均在0.98以上,交互验证均方根误差分别为1.37、0.38%、0.51、0.46,相对预测均方根误差分别为1.24、0.18%、0.40、0.16。说明利用近红外光谱技术结合化学计量法可实现不同氧化程度葵花籽油理化指标的快速、准确检测。     

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.     

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%).     

A Feasibility Study of the Rapid Evaluation of Oil Oxidation Using Synchronous Fluorescence Spectroscopy

Synchronous fluorescence spectroscopy coupled with nonlinear regression analysis was developed and used to discriminate the degree of oil oxidation based on the acid and peroxide values. It is easy to discriminate the degree of oil oxidation after treatments with different heat treatment times and styles for both synchronous fluorescence spectroscopy and low-field nuclear magnetic resonance (NMR). The low-field NMR results proved that oil oxidation probably proceeds via more than one pathway for different treatment styles. To decompose the three-dimensional matrix into two-dimensional data, a parallel factor analysis (PARAFAC) algorithm was used to select an optimized Δλ of 120 nm. Then, an artificial neural network (ANN) was used to build a regression model for both synchronous fluorescence and low-field NMR to evaluate the degree of oil oxidation. Compared with other regression results, synchronous fluorescence coupled with the ANN model achieved the best results, with 1.00 in the training set and 1.00 in the acid value test set at room temperature. The overall results suggest that synchronous fluorescence spectroscopy coupled with the ANN regression algorithm is useful for rapidly evaluating oil quality and perhaps foods with high oil contents.     

激光近红外结合SVM的花生油掺伪定性定量分析

利用激光近红外技术结合支持向量机(support vectormachines,SVM)对花生油掺伪进行定性和定量分析。使用激光近红外光谱仪采集188个掺入餐饮废弃油、大豆油、玉米油以及菜籽油的花生油样品光谱图。结果表明,建立的SVC分类模型均能实现100%的预测准确率,但经提取波长后的模型的变量变少,由全波段的451个波长数减少为136个。建立的SVR回归模型也能准确预测花生油中掺伪油的含量,其中非全波段模型参与建模变量变少,由451个降低到66个,预测精度也更高,校正集和测试集相关系数分别达到99.88%、99.90%,均方根误差都低于6.99E-4。由此可知,特征波长提取方法不仅可以减少建模变量,提高建模效率,也能够提高模型的预测能力。结果表明,运用激光近红外结合SVM可以实现花生油掺伪油脂的定性和定量分析。     

Honey Adulteration Detection Using Raman Spectroscopy

In this study, the Raman spectroscopy was used to detect honey adulterated with fructose (F), glucose (G), inverted sugar (IS), hydrolyzed inulin syrup (IN), and malt must (M). Thus, 56 samples of authentic honeys (acacia, sunflower, tilia, polyfloral, and honeydew) and 900 adulterated samples (with 5, 10, 20, 30, 40, and 50% fructose, glucose, inverted sugar, malt must, and hydrolyzed inulin syrup) were analyzed. The classification of honey authenticity has been made using the partial least square linear discriminant analysis (PLS-LDA), and a total accuracy of 96.54% (authentic honey vs. adulterated honey) was observed, while in the case of adulterated honey, a total accuracy of 90.00% was observed, respectively. The determination of the adulterant agent concentration has been made using partial least squares regression (PLSR) and principal component regression (PCR) methods. The proposed method can be considered easy and rapid for honey adulteration detection to provide continuous in-line information.     

油脂同步荧光光谱检测及橄榄油掺假鉴别

采用同步荧光光谱仪,在激发波长250~720 nm,波长间隔Δλ=15 nm时,采集20种食用植物油和掺杂的特级初榨橄榄油的荧光光谱图,分析比较了各种植物油脂的同步荧光光谱图。结果表明,同步荧光光谱法能够将特级初榨橄榄油与其他17种植物油明显地区分开来。在橄榄油掺杂鉴别中,其中14种植物油掺兑量在1%的情况下,同步荧光光谱图与特级初榨橄榄油有着明显的差异。同步荧光光谱法对橄榄油掺假鉴别,无需复杂的样品前处理,本方法简便、快速、灵敏,适合快速筛查。     

Rapid Detection of Adulteration in Extra-Virgin Olive Oil using Three-Dimensional Fluorescence Spectra Technology with Selected Multivariate Calibrations

To rapidly and efficiently detect the presence of adulterants in extra-virgin olive oil, 3D fluorescence spectra technology was employed with the help of multivariate calibration. Parallel factor analysis and characteristic parameters method were comparatively employed to compress and extract the data of 3D fluorescence spectra. Then, three different non-linear and linear classification tools (i.e., back-propagation artificial neural network, least-square support vector machine and k-nearest neighbor) were systemically studied and compared in developing the model. The number of principle components and parameters of models were optimized by cross-validation. Compared with parallel factor analysis, characteristic parameters method, in this article, has its own superiority. Experimental results also showed that the performance of least-square support vector machine model is the best among the three models. The optimal least-square support vector machine model was achieved when seven principle components were used, with the discrimination rate of 98.96% in calibration set and 96.88% in prediction set, respectively. The misclassified samples are adulterated extra-virgin olive oil, and their adulterated concentrations were lower than 2.5% (wt/wt). The overall results sufficiently demonstrated that 3D fluorescence spectroscopy technology coupled with characteristic parameters method and least-square support vector machine classification tool has the potential to detect adulterated extra-virgin olive oil products when their adulterant concentrations are more than 2.5% (wt/wt).     

Monitoring of Adulteration and Purity in Coconut Oil Using Raman Spectroscopy and Multivariate Curve Resolution

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2–30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle.     

Rapid Determination of Tetracyclines Hydrochloride Using ATR FT-MIR Spectroscopy

Antibiotic residues in animal-derived foods have brought serious threats to human health as well as economic losses to the food industry. Given that analytical methods are crucial but remain limited, a rapid, reliable, and cost-effective method is needed for detecting antibiotic residues. In this paper, a method using attenuated total reflection Fourier transform mid-infrared (ATR FT-MIR) spectroscopy combined with chemometrics was established for the detection of tetracyclines hydrochloride (TCsH). Firstly, TCsH powders were scanned using ATR FT-MIR spectroscopy, and the characteristic peaks of these samples were found in this region. Then, milk samples with different TCsH concentrations (1 to 160 ppb) were measured and were analyzed using principal component analysis (PCA) and partial least squares regression (PLSR) algorithms. The results showed that the kinds of TCsH in milk could not be classified. However, TCsH concentration ranging from 1 to 160 ppb in milk samples was successfully determined with high determination coefficient (R ²) values of 0.88–0.90, low root-mean-square errors values of 9.76–18.2, and high residual predictive deviation (RPD) values close to or greater than 3. These results indicated that ATR FT-MIR spectroscopy could be used to identify TCsH powders based on their unique spectral features, and was suitable for the rapid detection of TCsH concentration in milk.     

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。     

2种方法测定核桃油脂氧化稳定性相关性

为了比较2种不同方法测定核桃油氧化稳定性的效果,分别采用氧化酸败法(Rancimat法)和烘箱法评价核桃油脂氧化稳定性,并进行货架期预测,对测定结果进行相关性分析。结果表明,核桃油的氧化稳定指数(OSI)与其过氧化值、共轭烯烃值、羰基价呈极显著线性负相关(P0.01),相关系数分别为-0.963、-0.941和-0.952;与酸价呈显著线性负相关(P0.05),相关系数为-0.681。进一步研究表明,在核桃油氧化过程中,反应速率常数(k)与活化能(Ea)并非定值,因此通过Rancimat法和烘箱法推算得到的货架期(1 457.00 h和565.72 h)与真实货架期(733.21 h)之间存在显著差异(P0.01)。Rancimat法可以代替烘箱法快速、准确评价核桃油脂氧化稳定性,但利用此法预测核桃油脂货架期误差较大。     

近红外光谱协同BP神经网络的泰国茉莉香米掺伪含量快速鉴定

利用近红外光谱协同BP神经网络算法,对泰国茉莉香米及其掺伪样品的近红外光谱进行多元散射校正预处理,挑选出48个特征波长;以特征波长的吸光度为BP神经网络输入层神经元,以样品中泰国茉莉香米的含量为输出层神经元,获得BP神经网络算法的最优结构模型,即为单层隐含层、隐含层神经元数7、隐含层传递函数logsig、输出层传递函数...     

傅里叶红外光谱法油脂定量分析研究进展

论述了傅里叶红外光谱法油脂定量分析基本原理、优越性以及在油脂过氧化值、游离脂肪酸、反式脂肪酸、碘值、皂化值、顺式和反式脂肪酸和固体脂肪含量等方面应用研究进展.同时,对傅里叶光谱法分析模型的稳定性和传递性及其在自动化分析中应用进行探讨,以期为傅里叶光谱法油脂自动分析检测提供参考.     

葛粉掺假的近红外漫反射光谱快速检测

研究葛粉中掺假红薯淀粉和马铃薯淀粉的近红外漫反射光谱快速检测方法。采集样品的近红外漫反射光 谱,采用主成分回归和偏最小二乘法建立校正模型,并对比光谱预处理方法和光谱建模区间对模型的影响。结果表 明,采用偏最小二乘法建模,光谱采用标准正态变量变换预处理,光谱区间选择在962～1 389 nm时,模型预测效 果最佳,外部验证预测相关系数（RP 2）达0.994 5,均方根误差2.298 7%,相对分析误差13.56,平均回收率99.89% （n=9,RSD=2.96%）,这表明近红外漫反射技术能对葛粉中掺假红薯淀粉和马铃薯淀粉进行有效检测。