应用近红外光谱对老陈醋进行定性定量的检测研究

利用近红外光谱结合DA和PLS算法在不同光谱预处理方法下,对山西老陈醋醋龄进行定性判别分析,并建立陈醋可溶性固形物(SSC)及pH值的定量模型.结果表明:原始光谱、5点平滑以及SNV校正建立的DA模型性能良好,校正集判别正确率为100％,预测集判别正确率为88.89％;原始光谱建立的可溶性固形物的PLS定量模型最优,校正集和预测集的相关系数r分别为0.99988和0.99960,RMSEC,RMSEP和RMSECV分别为0.0421,0.0911和0.0777;5点平滑建立的pH值的PLS定量模型最优,校正集和预测集的相关系数r分别为0.99733和0.97411,RMSEC,RMSEP和RMSECV分别为0.0151,0.0386和0.0468.     

拉曼光谱法快速检测猪肉脯中的掺伪鸡肉

目的 肉脯掺假制假事件层出不穷,严重损害消费者权益,为了快速无损的检测肉脯掺假的质量安全问题,建立拉曼光谱技术快速、准确、无损的检测猪肉脯样品中是否掺假鸡肉。方法 试验制备33份猪肉中掺入不同比例鸡肉的肉脯样品,采集拉曼光谱数据,分别采用标准正态变换、多元散射校正、卷积平滑、归一化、一阶导数等5种不同预处理方法,对原始光谱数据进行预处理,采用连续投影算法、竞争性自适应重加权算法及随机蛙跳算法对光谱数据进行特征波长筛选,建立偏最小二乘法(partial least squares,PLS)模型对猪肉脯进行定性定量判别。结果 拉曼光谱数据经过多元散射校正处理的效果最佳,竞争性自适应重加权算法竞筛选效果更佳,构建猪肉脯中猪肉含量(%)的PLS定量模型,其预测集相关系数(Rp₂⁾和预测均方根误差分别为0.9762、7.2998。建立的PLS判别模型的校正集和预测集总判别正确率分别为100%和98.33%。拉曼光谱分析技术可有效鉴别猪肉脯是否掺伪及其猪肉含量的比例,为肉脯掺假的快速准确地无损检测提供技术支持。结论 拉曼光谱分析技术可有效用于定性鉴别猪肉脯是否掺伪及定量分析猪肉肉脯中掺入鸡肉的比例,为肉脯掺假的快速无破坏性检测的应用提供支持。     

基于近红外光谱的大鲵肉粉掺伪鉴别及纯度检测

利用近红外光谱技术进行大鲵肉粉的掺伪鉴别及纯度检测。分别采集大鲵纯肉粉、掺入江团鱼肉粉、草鱼肉粉和土豆淀粉的掺伪大鲵肉粉（各40 个样本,4 类共160 个样本）的近红外光谱图。原始光谱经光谱预处理后,利用偏最小二乘-判别分析（partial least square-discriminant analysis,PLS-DA）法分别建立2分类（纯样和掺伪样）和4分类（纯样、掺江团鱼样、掺草鱼样和掺淀粉样）的定性判别模型,利用偏最小二乘回归（partial least squares regression,PLSR）分析法分别建立3 类掺伪大鲵肉粉的纯度定量校正模型。结果表明,PLS-DA定性模型中,经一阶导数＋多元散射校正光谱预处理后,所建2分类和4分类模型性能均为最佳,校正集和预测集的预测准确率均为100%;PLSR定量模型中,大鲵肉粉掺江团鱼肉粉、大鲵肉粉掺草鱼肉粉和大鲵肉粉掺土豆淀粉模型的校正集相关系数（Rc2）分别为0.990 6、0.986 4和0.993 3,校正集的均方根误差分别为1.14%、1.39%和0.88%;测试集的相关系数（Rp2）分别为0.994 4、0.992 4和0.990 8,测试集的均方根误差分别为0.83%、0.89%和1.22%。运用近红外光谱技术结合化学计量学方法能够对大鲵肉粉进行掺伪鉴别及纯度检测。     

近红外技术对不同动物来源肉掺假的检测

采用近红外光谱结合主成分分析法(PCA)、判别分析法,分别建立了牛肉和羊肉中掺杂其它动物肉的定性鉴别模型,根据鉴别准确率评价模型的预测性能。采用近红外光谱结合PCA、偏最小二乘法(PLS),建立了掺假物的定量检测模型,根据模型对预测集样品的预测均方差(RMSEP)以及预测值与实测值间的相关系数(r)验证模型的预测能力。结果,牛肉掺猪肉模型对训练集和预测集的鉴别准确率分别为97.86%和91.23%,羊肉掺猪肉模型对训练集和预测集的鉴别准确率分别为98.28%和92.98%,羊肉掺鸭肉模型对训练集和预测集的鉴别准确率分别为99.59%和93.97%,羊肉掺假模型对训练集和预测集的鉴别准确率分别为97.57%和90.76%。牛肉掺假定量模型对训练集的交互验证均方差(RMSECV)和预测集的RMSEP分别为3.87%和4.13%,r分别为0.9505和0.9134;羊肉掺假定量模型对训练集的RMSECV和预测集的RMSEP分别为4.48%和4.86%,r分别为0.9306和0.9082。表明近红外技术结合一定的化学计量学方法可实现不同动物来源肉掺假的鉴别,且能够对掺假物进行定量检测。     

近红外光谱法高通量检测罗布麻纤维胶质含量

研究了近红外光谱法高通量测定罗布麻纤维中的胶质含量。采用近红外光谱漫反射模式采集罗布麻纤维的近红外光谱,结合常规化学法所测参考值,运用偏最小二乘法(PLS)建立罗布麻纤维胶质含量的定量模型,并用未知样品对模型进行验证。结果表明,所建定量模型校正集相关系数(Rc)和校正集均方根误差(RMSEC)分别为0.994 0和1.676 1;经外部验证的验证集相关系数(Rp)和验证集均方根误差(RMSEP)分别为0.956 1和4.843 7。该方法操作简单,快速,可用于罗布麻纤维胶质含量的高通量检测。     

基于红外光谱技术的银耳产地识别研究

采用傅里叶红外光谱数据分析技术结合多元统计分析对银耳产地进行识别研究。采集福建古田、四川通江和河南的41个银耳样品的红外光谱信息,分析银耳的红外光谱特征;运用基线校正、多元散射校正(Multiplicative signal correction,MSC)、一阶导数(First derivative,FD)、二阶导数(Second derivative,SD)、Norris平滑(ND)、Savitzky-Golay平滑(SG)等方法对其进行预处理,选择最佳组合方法预处理后的光谱数据,建立最小偏二乘判别(PLS-DA)模型,并进行验证。结果表明,MSC+SD+SG(13,3)组合方法的预处理光谱效果最佳,与原始光谱相比,样品正确识别率提高了17.41%,达到100%。训练集和预测集的相关系数分别为0.9766、0.9559,校正标准差和预测标准差分别为0.1182、0.1740,模型对训练集和预测集样品的识别率均为100%。因此,红外光谱通过PLS-DA分析可以对不同产地的银耳进行快速、有效、准确的识别。     

烟草中淀粉近红外光谱变量的筛选及校正模型的建立

为提高校正模型的预测精度,以烟草中淀粉近红外光谱(NIR)校正模型为研究对象,分别利用全光谱波段(FS)、方差光谱(VS)筛选光谱变量和遗传算法(GA)筛选光谱波长,结合偏最小二乘法建立校正模型(FS+PLS、VS+PLS和GA+PLS),并对100个初烤烟叶样品进行了预测。结果显示:①FS+PLS(变量数1557个)、VS+PLS(变量数781个)和GA+PLS(变量数72个)3种校正模型的决定系数R_c2、交互验证均方根误差(RMSECV)分别为0.9764、0.433,0.9871、0.332和0.9885、0.314。②与FS+PLS和VS+PLS模型相比,GA+PLS模型的光谱变量数分别减少为FS和VS变量数的4.62%和9.22%,主因子数由15降至12,R_c2明显提高,RMSECV显著降低。③FS+PLS、VS+PLS和GA+PLS模型对100个初烤烟叶样品的预测结果显示,R_p2、预测均方根误差(RMSEP)分别为0.9652、0.780,0.9843、0.501和0.9853、0.496,预测值与其对应的化学检测值之间通过配对T检验,显著性Sig.值、T值和平均相对误差(%)分别为0.271、1.107、17.48%,0.973、0.034、13.13%和0.722、0.357、13.12%,3种方法所建立校正模型的预测值与检测值之间均无显著性差异,模型预测精度(RSD)分别为10.34%、6.98%和4.76%。基于逐步优化光谱信息法建立的GA+PLS校正模型的预测精度优于FS+PLS和VS+PLS模型,该方法对于提高复杂化学体系模型的精度有参考意义。      

蚁群和遗传算法优化花茶花青素近红外光谱预测模型的比较

以建立花茶花青素含量的最优近红外光谱模型为目标,对比研究了蚁群算法(Ant ColonyOptimization,ACO)和遗传算法(Genetic Algorithm,GA)优化近红外光谱谱区的效果。ACO-i PLS将全光谱划分为12个子区间时,优选出第1、9、10共3个子区间,所建的校正集和预测集相关系数分别为0.901 3和0.864 2;交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.160 0 mg/g和0.202 0 mg/g;GA-i PLS将全光谱划分为15个子区间时,优选出第1、5共2个子区间,所建模型的校正集和预测集相关系数分别为0.906 3和0.879 3,交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.156 0 mg/g和0.206 0 mg/g。研究结果表明:ACO-i PLS和GA-i PLS均可以有效选择近红外光谱特征波长,其中GA-i PLS模型的精度更高。     

哈姆林甜橙果实内在品质的可见-近红外光谱无损检测法

采用可见- 近红外漫反射光谱技术,结合偏最小二乘法,以不同时间采摘的哈姆林甜橙果实为样品建立其可溶性固形物、含酸量和VC 的无损检测数学模型,同时对不同光谱预处理方法和不同建模波段范围对模型的预测性能进行对比分析。结果表明：原始光谱在400～1000nm 波段的模型预测精度较高。经多元散射校正和5 点移动平均平滑预处理后,果实可溶性固形物含量的PLS 模型最好,校正集样品的相关系数为0.995RMSEC和RMSEP分别为0.026%、0.028%;预测集样品的相关系数为0.992。经多元散射校正和9 点移动平均平滑预处理后,果实含酸量的PLS 模型最好,校正集样品的相关系数为0.997,RMSEC 和RMSEP 分别为0.012%、0.013%;预测集样品的相关系数为0.997。经多元散射校正和9 点移动平均平滑预处理后,果实VC 含量的PLS 模型最好,校正集样品的相关系数为0.998,RMSEC 和RMSEP 分别为0.009%、0.009%;预测集样品的相关系数为0.999。可见由不同时间采摘的果实组成的样品集所建立的数学模型可以提高模型的预测精度,从而提高模型的适用范围。应用可见-近红外漫反射光谱检测哈姆林甜橙果实的内在品质可行。     

基于近红外漫反射光谱技术的小麦蛋白质含量检测

收集来自国内3个主要小麦生产省份的小麦样品共73份,应用近红外光谱漫反射技术结合化学计量学方法建立小麦蛋白质含量的近红外光谱定量模型,并对模型的稳定性和可靠性进行评价。结果表明,光谱预处理的最佳条件为:Savitzky-Golay平滑+一阶导数处理,使用偏最小二乘法(Partial least square,PLS)建立模型,校正集(Rc)和预测集相关系数(Rp)为0.936和0.925,校正集均方根误差(RMSEC)和预测集均方根误差(RMSEP)分别为0.23和0.28,表明模型准确可靠,可应用于小麦蛋白质含量的快速无损检测。     

基于中红外光谱结合化学计量法快速检测油炸食品用油品质

目的：保障食品工业化生产过程中最终油炸食品的安全。方法：以某大学食堂油炸食品用油为研究对象,以国标法为依据,测定油样酸价、过氧化值、羰基价,通过配备多点衰减全反射的傅里叶变换红外光谱仪获取其红外谱图,并采用偏最小二乘回归法分别建立控制油样、随机油样及所有油样的数学模型。结果：模型对油样酸价的测定均有很好的预测性,但对过氧化值的预测能力较弱。此外,控制油样模型比随机油样模型的预测性更强。结论：采用衰减全反射附件的傅里叶变换中红外光谱结合化学计量法有望实现快速、同时检测油炸食品工业化生产过程中油炸用油的酸价、过氧化值和羰基价。     

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.     

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。     

Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil

Fourier transform infrared (FTIR) spectroscopy has been developed for analysis of extra virgin olive oil (EVOO) adulterated with palm oil (PO). Measurements were made on pure EVOO and that adulterated with varying concentrations of PO (1.0–50.0% wt./wt. in EVOO). Two multivariate calibrations, namely partial least square (PLS) and principle component regression (PCR) were optimized for constructing the calibration models, either for normal spectra or its first and second derivatives. The discriminant analysis (DA) was used for classification analysis between EVOO and that adulterated with PO and the other vegetable oils (palm oil, corn oil, canola oil, and sunflower oil). Frequencies at fingerprint region, especially at 1500–1000 cm^?1, were exploited for both quantification and classification. Either PLS or PCR at first derivative spectra revealed the best calibration models for predicting the concentration of adulterated EVOO samples, with coefficient of determination (R²) of 0.999 and root mean standard error of cross validation (RMSECV) of 0.285 and 0.373, respectively. DA was able to classify pure and adulterated samples on the basis of their FTIR spectra with no misclassified group obtained. In addition, DA was also effective enough to classify EVOO samples as the distinct group from the evaluated other vegetable oils.     

Potential Use of FTIR-ATR Spectroscopic Method for Determination of Virgin Coconut Oil and Extra Virgin Olive Oil in Ternary Mixture Systems

The aim of this study was to investigate the feasibility of Fourier-transform infrared (FTIR) spectroscopy combined with multivariate calibrations of partial least square (PLS) and principle component regression (PCR) for analysis of virgin coconut oil (VCO) in the ternary mixture with palm oil (PO) and olive oil, and for analysis of extra virgin olive oil (EVOO) mixed with soybean oil (SO) and corn oil (CO). The spectra of individual oils and their blends with certain concentrations were scanned using horizontal attenuated total reflectance accessory at mid-infrared region of 4,000–650 cm⁻¹. The optimal frequency regions selected for calibration models were based on its ability to give the highest values of coefficient of determination (R ²) and the lowest values of root mean standard error of calibration (RMSEC). PLS was slightly better for quantitative analysis of VCO and EVOO compared with PCR. VCO in ternary mixtures is successfully determined at frequency region of 1,200–1,000 using second derivative FTIR spectra with R ² and RMSEC values of 0.999 and 0.200, respectively. Meanwhile, EVOO is best determined at 1,200–1,000 using first derivative FTIR spectra with R ² and RMSEC values of 0.999 and 0.975, respectively. The results showed that FTIR spectroscopy offers accurate and reliable technique for quantitative analysis of VCO and EVOO in ternary systems. In addition, the developed method can be used for the monitoring of VCO and EVOO adulteration with cheaper oils like PO in VCO as well as SO and CO in EVOO.     

基于红外光谱快速鉴别压榨油茶籽油与浸出油茶籽油的研究

为规范油茶籽油市场、维护消费者权益,建立了快速、准确鉴别压榨油茶籽油和浸出油茶籽油的方法。通过傅里叶变换红外光谱仪对大量压榨油茶籽油和浸出油茶籽油样品进行扫描,提取特征波段数据,运用Savitzky-Golay平滑(SG)、多元散射校正(MSC)、标准正态变量变换(SNV)、一阶导数(FD)和二阶导数(SD)方法进行预处理,然后结合偏最小二乘法(PLS)、支持向量机(SVM)和BP人工神经网络(BPANN)建立鉴别模型。结果表明,偏最小二乘法和BP人工神经网络建模时,SG平滑预处理方法最好,得到的SG-PLS和SG-BPANN两模型的验证集相关系数、验证集均方根误差、鉴别准确率分别为0. 767 9和0. 921 2、0. 322 6和0. 205 9、88. 46%和100%;支持向量机建模宜采用SNV预处理,建立的SNV-SVM模型验证集相关系数、验证集均方根误差和鉴别准确率分别为0. 761 4、0. 882 1、88. 46%。因此,红外光谱技术用于鉴别压榨油茶籽油和浸出油茶籽油是可行的。     

Evaluation of the quality of deep frying oils with Fourier transform near-infrared and mid-infrared spectroscopy

Deep frying oils are subjected to high temperature and prolonged heating that may lead to a series of quality and safety problems for fried foods. This study evaluated the quality of deep frying oils collected from a local college canteen (n = 132) with Fourier transform mid-infrared (FT-IR) and Fourier transform near-infrared (FT-NIR) spectroscopy. Partial least squares (PLS) regression was used to correlate spectral data with free fatty acids (FFA) and peroxide (PO) values of frying oils. The coefficient of determination (R(2)), standard error of prediction (SEP), and the RPD (ratio of the standard deviation of data set to the SEP) were used as indicators for the predictability of the PLS models. The FT-IR and FT-NIR methods exhibited similar predictability for the FFA values (FT-IR: R(2) = 0.954, SEP = 0.14, RPD = 4.48; FT-NIR: R(2) = 0.948, SEP = 0.14, RPD = 4.38). Although the predictability of the FT-IR method for the PO values was not as satisfactory as that of the FT-NIR method (FT-IR: R(2) = 0.893, SEP = 6.17, RPD = 2.93; FT-NIR: R(2) = 0.953, SEP = 4.15, RPD = 4.36), both FT-IR and FT-NIR methods could be used as simple and rapid approaches to determining the quality of deep frying oils.     

近红外光谱法快速测定晒青毛茶三种儿茶素组分含量

为了实现晒青毛茶儿茶素含量的快速检测,该研究利用高效液相色谱技术(High Performance Liquid Chromatography,HPLC)测定了50份晒青毛茶样品中的表没食子茶素没食子酸酯(Epigallocatechin Gallate,EGCG)、表没食子儿茶素(Epigallocatechin,EGC)、没食子酸(Gallic Acid,GA)三种儿茶素单体含量,结合样品的近红外光谱,分别建立了晒青毛茶三种儿茶素单体含量的偏最小二乘法(Partial Least Squares,PLS)模型,并对模型进行验证。实验结果表明,基于EGCG、EGC、GA含量所建立近红外模型的决定系数(Coefficient of Determination,R²)分别为99.99%、99.99%、99.92%;校正标准差(Root Mean Square Error of Calibration,RMSEC)分别为0.17、0.15、0.10;相对标准差(Relative Standard Deviation,RSD)分别为0.19%、0.27%、0.56%;外...     

基于近红外光谱技术的信阳毛尖品质判别研究

近红外光谱(Near-infrared spectroscopy,NIRS)技术作为一种快速无损检测方法,在许多领域广泛运用,尤其是在食品领域中的应用更加广泛。信阳毛尖茶是我国十大名茶之一,品质优异。本文通过采集三种不同等级信阳毛尖茶800~2500 nm处近红外光谱信息,对三个不同等级信阳毛尖茶的所有波长的响应进行单因素方差分析,选择了数据分析的波长范围;通过小波变换滤噪,对原始光谱进行预处理,采用正交偏最小二乘判别分析(OPLS-DA)和偏最小二乘法(PLS)对信阳毛尖茶品质进行判别。结果表明茶样品800 nm~1800 nm波长范围的近红外光谱数据可用于预测信阳毛尖茶品质;OPLS-DA分析表明三个不同等级信阳毛尖茶可以有效区分;所建立的PLS预测模型,理论值和预测值之间具有良好的相关性,相关系数为0.994,预测准确率为100%,交叉验证均方根误差(RMSECV)为0.090,表明模型预测准确、可靠。本研究建立的NIR光谱结合PLS分析方法可以用于快速无损检测河南信阳毛尖茶的等级品质。     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.