Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS–NIR spectroscopy

The use of visible–near infrared (VIS–NIR) and mid infrared (MIR) spectroscopies for rapid characterisation of 15 traditional and stabilised retail soft cheeses, manufactured with different cheese making procedures was described. A fiber-type, VIS–NIR spectrophotometer (Zeiss Corona 45 VIS–NIR) in a measurement range of 315–1700 nm and a Fourier transform spectrometer (IFS 66V/S, Bruker, Belgium) in a measurement range between 3000 and 900 cm⁻¹ were used to scan spectra in reflectance mode at the external (E) and central (C) zones of the investigated cheeses. The principal component analysis (PCA) applied to the normalised spectral data set (VIS–NIR and MIR) did not provide a good discrimination of cheeses. Therefore, the factorial discriminant analysis (FDA) was applied separately to the first 5 principal components (PCs) of the PCA performed on the VIS–NIR and MIR data sets. Regarding the MIR spectra, the percentage of samples correctly classified into six groups (three for the E and three for the C zones) by the FDA was 64.8% and 33.3% for the calibration and validation samples, respectively. Better classification was obtained from the VIS–NIR spectra since the percentage of samples correctly classified was 85.2% and 63.2% for the calibration and validation samples, respectively. Finally, a concatenation technique was applied on the first 5 PCs of the PCA performed on the VIS–NIR and MIR data sets. This technique allowed a quite satisfactory classification of the investigated cheeses according to their manufacturing process and their sampling zone. In this case, correct classifications (CC) of 90.7% and 80.6% were obtained for the calibration and the validation samples, respectively.     

基于脂肪酸和甘三酯组成的茶油掺伪检测参数的确定

本实验研究了测定脂肪酸和甘三酯在检测茶油中掺伪4种其他植物油时的有效性。在本研究中,与脂肪酸和甘三酯指纹图谱向量夹角余弦相似度、亚麻酸相对含量、三亚油酸甘油酯(LLL)相对含量、碳当量数(ECN)42甘三酯的实际与理论含量绝对差(ΔECN42)值、(LLL/ECN42)×100值和ECN46/LLL值相比,(ECN44+ECN46)/LLL值是茶油中掺伪大豆油、葵花籽油和玉米油的最有效检测参数,分别可以检测茶油中掺伪3.35%的大豆油、1.73%的葵花籽油和3.01%的玉米油,亚麻酸相对含量是茶油中掺伪芥花籽油的最有效检测参数,可以检测茶油中掺伪7.55%的芥花籽油。     

近红外光谱结合偏最小二乘法用于纯茶油中掺杂菜籽油和大豆油的定量分析

采用偏最小二乘法(PLS)建立了油茶籽油中掺杂菜籽油和大豆油的近红外光谱定量检测模型。配制不同比例(0～100%)的油茶籽油和菜籽油、油茶籽油和大豆油混合样品共256个,采集样品在10000～4000cm-1范围内的近红外透反射光谱,模型采用交互验证和外部检验来考察所建立模型的可靠性,不需进行任何光谱预处理,所建立的PLS模型相关系数为0.9997,训练集的交叉验证均方根误差(RMSECV)为0.504,预测集的预测均方根误差(RMSEP)为0.66。应用建立的模型对未知样品进行预测,并对预测值和真实值进行比较,在掺杂油含量为2.5%～100%之间范围内准确可靠,研究结果表明,采用近红外光谱技术可以实现纯茶油中菜籽油和大豆油掺杂量检测。     

基于傅里叶变换红外光谱的大豆原油掺伪定量 分析模型研究

目的应用傅里叶变换红外光谱(FTIR)结合最小偏二乘法(PLS)建立大豆原油-棕榈油二元掺伪体系的定量分析模型。方法以42个大豆原油、21个精炼油、88个掺伪油的FIIR谱图为模型样本,预处理方法选用标准正态变量(SNV),在此基础上应用主成分分析(PCA)提取特征变量,随机选取60个掺伪油样组成校正集,28个掺伪油样组成验证集,以PLS方法建立大豆原油的掺伪定量模型。结果 PCA可将大豆原油及精炼油分成独立的2类。经PCA分析,大豆原油中掺入棕榈油的掺伪检测限为5%。PLS校正模型的判定系数R2为0.9926,校正误差均方根RMSEC为1.8121。预测模型的R2为0.9823,交叉验证误差均方根RMSECV为2.8189。同时得到的预测结果的偏差在1.3909%~3.1019%之间,差异不显著,说明此模型可行。结论 FTIR-PLS模型能够实现大豆原油的掺伪定量分析,分析速度快,能够满足大豆原油入库要求,是一种可行的大豆原油掺伪分析方法。     

近红外透射法快速测定大豆油中的含磷量

采用近红外光谱仪,通过光学处理、数据处理和改进偏最小二乘法(MPLS)建立了快速测定高含磷量(321～632 mg/kg)和低含磷量(0～297 mg/kg)大豆油的近红外(NIR)模型。结果表明:高含磷量和低含磷量大豆油定标方程的交互定标决定系数(1-VR)分别为0.988和0.974,定标决定系数(R2)分别为0.992和0.980,定标标准误差(SEC)分别为2.420和2.512,交互定标标准误差(SECV)分别为2.538和2.678;现有数据预测标准偏差(SEP)分别为2.602和2.683;该近红外法在生产加工过程中可快速准确检测大豆油中含磷量。     

山茶油多元掺假近红外模型的建立与研究

目的建立基于近红外光谱结合偏最小二乘法的山茶油、花生油和玉米油多元掺假模型。方法通过对比不同建模光谱波段、预处理方法对模型进行优化研究,确定最优的山茶油、花生油和玉米油多元掺假模型。结果所建模型性能指数均大于0.99,校正集和预测集的均方差在0.6以内。经未知掺假样品的外部验证,预测值与实测值之间有较好的相关性,二元掺假模型预测值的相对误差在1%以内,三元掺假在6%以内,验证结果良好。结论近红外光谱结合偏最小二乘法的检测技术快速、有效、环保,可用于定量检测山茶油的掺假。     

Evaluation of freshness decay of minced beef stored in high-oxygen modified atmosphere packaged at different temperatures using NIR and MIR spectroscopy

Meat freshness has been monitored by various microbiological, chemical and sensorial indices. However, these methods are slow and not suited to automation. Infrared spectroscopy is one of the most convenient analytical tools which could be used to monitor the evolution of food quality. The aim of this work was to investigate the ability of both NIR (Near Infrared) and MIR (Mid Infrared) spectroscopy to follow meat freshness decay. The minced beef was packaged in high-oxygen modified atmosphere (30% CO₂ and 70% O₂) and stored at three temperatures. Spectra were collected by Fourier-Transformation (FT)-NIR and FT-IR instruments. PCA, applied to the data, was able to discriminate samples on the basis of storage time and temperature. The modelling of PC scores versus time allowed the setting of the time of initial freshness decay for the samples (6–7 days at 4.3 °C, 2–3 days at 8.1 °C and less than 1 day at 15.5 °C).     

气相色谱法鉴别掺假山茶油定性及定量研究

用气相色谱法分析测定山茶油、棕榈油、菜籽油、大豆油、棉籽油脂肪酸组成及含量,获得其脂肪酸组成和含量的正常值。测定模拟掺入棕榈油、菜籽油、大豆油、棉籽油的山茶油脂肪酸组成和含量,根据其特征脂肪酸组成和含量变化,对山茶油中掺入棕榈油、菜籽油、大豆油、棉籽油可作定性及定量分析。     

Detection of olive oil adulteration with some plant oils by GLC analysis of sterols using polar column

A new method was developed to determine the presence of some refined vegetable oils in olive oil based on the sum of campesterol and stigmasterol percentages. Model systems of corn, soybean, sunflower and cotton seed oils in olive oil at levels of 5%, 10% and 20% were prepared. The unsaponifiables of these model systems were analysed by GLC using polar column with high thermal stability. An olive oil authenticity factor based on the summation of campesterol and stigmasterol percentages was established as an indicator of olive oil adulteration with vegetable oils. The results indicate the possibility to detect the presence as little as 5% of these plant oils in olive oil.     

基于脂肪酸和生育酚组成的油茶籽油掺假判别可行性分析

从脂肪酸和生育酚的组成分析以及所得色谱数据的系统聚类分析(HCA)和主成分分析(PCA)方面考察了10种植物油的异同点。气相色谱分析10种植物油的脂肪酸组成,鉴别得到12种脂肪酸。对脂肪酸组成数据的HCA分析结果表明,油茶籽油与橄榄油和菜籽油具有相似的脂肪酸组成,而与其他植物油的脂肪酸组成差异显著;而PCA分析结果可以进一步区分油茶籽油与橄榄油和菜籽油。高效液相色谱分析10种植物油的生育酚组成,测得4种异构体的相对含量。对生育酚组成数据的HCA、PCA分析结果表明,油茶籽油和菜籽油的生育酚组成差异显著。因此,基于脂肪酸和生育酚组成分析可以作为油茶籽油掺假判别的依据。     

花生油中掺杂棉籽油、大豆油的鉴定方法概述

花生油中掺杂棉籽油、大豆油的现象比较普遍。主要以掺入这两种油的花生油为样品,通过伯利哀试验和气润色谱法测定不同掺入量的混浊温度和脂肪酸组成,从而对花生油的掺杂进行鉴定。     

基于差示扫描量热法的大豆原油掺伪 鉴别方法的建立

目的应用差示扫描量热仪(DSC)和油脂氧化稳定性测试仪(OSI)测试掺伪大豆原油的氧化稳定性(以诱导氧化时间表示),从而建立大豆原油掺伪的分析鉴别方法。方法设定OSI仪的氧化温度为110℃,氧气流量为20 L/h,DSC的氧化温度为110、120、130℃,氧气流量为50 m L/min,选择最佳氧化温度。记录各方法的诱导氧化时间。结果 DSC的诱导氧化时间随氧化温度提高不断缩短,确定130℃为DSC方法最佳氧化温度,不同比例掺伪大豆原油的OSI法的诱导氧化时间为320~495 min,DSC法诱导氧化时间为40~80 min。随着掺伪浓度增大,诱导氧化时间不断缩短。OSI法的掺伪检出限为掺伪浓度5%,DSC法的掺伪检出限为掺伪浓度10%。两种方法具有显著的正相关性:To(OSI110)=5.2480To(DSC130)+77.6799,r=0.9951。结论两种方法均可用于大豆原油掺伪鉴别,但DSC方法用量小,检测时间短,更适用快速鉴别。     

Authentication of pure camellia oil by using near infrared spectroscopy and pattern recognition techniques

Abstract: Total of 4 pattern recognition methods for the authentication of pure camellia oil applying near infrared (NIR) spectroscopy were evaluated in this study. Total of 115 samples were collected and their authenticities were confirmed by gas chromatography (GC) in according to China Natl. Standard (GB). A preliminary study of NIR spectral data was analyzed by unsupervised methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA). Total of 2 supervised classification techniques based on discriminant analysis (DA) and radical basis function neural network (RBFNN) were utilized to build calibration model and predict unknown samples. In the wavenumber range of 6000 to 5750 cm^?1, correct classification rate of both supervised and unsupervised solutions all can reach 98.3% when smoothing, first derivative, and autoscaling were used. The good performance showed that NIR spectroscopy with multivariate calibration models could be successfully used as a rapid, simple, and nondestructive method to discriminate pure camellia oil.     

Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat)

Cow ghee is very used in some regions of Iran, such as Kermanshah province. Cow ghee is a natural source that contains high-quality nutrients which are needed for the human body. Adulteration in dairy products is not only a serious threat to human health but also it causes economic losses. Diagnosis of foodstuff cheating and its estimation is one of the key concerns in recent years. The aim of this study was the detection of the adulteration in cow ghee by olfactory machine system. Therefore, an electronic nose system was used for the different levels of sunflower oil and cow body fat mixed with pure cow ghee (10%, 20%, 30%, 40%, and 50%). The principal components analysis (PCA) and artificial neural networks (ANNs) methods were used to achieve this goal. Based on the results, the accuracy of the principal components analysis of sunflower oil and cow body fat were 96% and 97% of the data variance, respectively. According to the results, artificial neural networks identified the adulteration with sunflower oil and cow body fat with an accuracy of 91.3% and 82.5%, respectively.     

The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000–650 cm⁻¹ on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R²) of 0.999.     

基于UVE-GA变量优选的山茶油可见/近红外光谱掺假鉴别

利用可见/近红外光谱结合无信息变量消除-遗传算法 （UVE-GA）变量选择方法对山茶油和掺杂低比例菜籽油（1%~10%）的山茶油进行鉴别分类,并应用线性判别分析 （LDA）方法建立分类模型。结果表明：UVE-GA是一种有效的波长变量选择方法,能简化分类模型和提高分类模型精度;UVE-GA-LDA分类模型适用于掺杂2%以上菜籽油的山茶油鉴别分类,其分类正确率为100%;对掺杂1%菜籽油的山茶油鉴别分类正确率有待提高,其分类正确率仅为50%。     

在油茶籽油国家标准中增设脂质伴随物营养声称指标的可行性研究

综述了油茶籽油中脂质伴随物含量尤其是加工过程中含量变化的现有研究成果,分析了现行茶籽油各类产品标准中脂质伴随物含量的指标,提出了在茶籽油国家标准中增设统一的脂质伴随物营养声称指标的可行性建议。     

分光光度法测定油茶籽油中总三萜含量的前处理方法研究

为准确定量油茶籽油中三萜类物质,对分光光度法测定油茶籽油中总三萜含量的样品前处理方法进行研究。通过单因素试验和正交试验考察了乙醇体积分数、提取温度、提取时间和提取次数对油茶籽油中总三萜含量的影响。结果表明：各因素对油茶籽油中总三萜提取的影响程度从大到小依次为乙醇体积分数>提取次数>提取温度=提取时间;油茶籽油中总三萜的最佳提取工艺条件为乙醇体积分数80%、提取温度80℃、提取时间40 min、提取次数4次,在此条件下提取测得油茶籽油中总三萜含量为(0.78±0.06)%(以齐墩果酸计)。综上,该方法操作简便、快速、灵敏度高,可用于油茶籽油中总三萜含量的测定。     

Detection of adulteration by caprine body fat and mixtures of caprine body fat and groundnut oil in bovine and buffalo ghee using differential scanning calorimetry

Differential scanning calorimetry was used to detect adulteration of pure ghee with caprine body fat when added singly (at 5, 10 and 15%) and in combination with groundnut oil (GNO) (at 5, 10 and 15%). Samples were analysed for transition behaviour in terms of crystallising and melting curves. When compared to pure ghee, adulterated ghee samples showed a shift in the midrange temperature of thermal curves, indicating the presence of foreign fats. The results revealed that the detection of adulteration was possible at the lowest level of the study (5%), irrespective of the nature of the adulterants.