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。     

基于近红外光谱技术的赣南茶油掺假快速鉴别北大核心CSCD

为了探索基于近红外光谱技术快速无损鉴别掺假油茶籽油的可行性,以赣南茶油为研究对象,通过掺入不同植物油如玉米油、花生油、菜籽油、葵花籽油和大豆油等制备掺假油茶籽油,应用近红外光谱技术采集其光谱特征信息,对比不同预处理方法和主成分数,并结合线性和非线性建模方法建立油茶籽油掺假鉴别模型,以识别准确率(纯油茶籽油样品和掺假油茶籽油样品被正确判别的比例)、灵敏度(纯油茶籽油样品被正确判别为纯油茶籽油的比例)、特异性(掺假油茶籽油样品被正确判别为掺假油茶籽油的比例)作为模型的评价指标,优选出最佳模型。结果表明:二阶微分联合线性判别分析(SD-LDA)模型为最优线性模型,标准正态变量变换联合人工神经网络(SNV-ANN)模型为最优非线性模型,两个模型的识别准确率、灵敏度、特异性分别为97.58%、100%、97.33%和98.99%、100%、98.88%。SNV-ANN模型鉴别效果优于SD-LDA模型,说明非线性模型更适于油茶籽油掺假判别,该模型能更准确地鉴别油茶籽油是否掺假。     

近红外光谱技术应用于云芝蛋白含量的定量分析

应用近红外光谱结合偏最小二乘法(PLS)建立测定真菌云芝中蛋白含量定量分析模型.所建立的模型经过i选择最有效的光谱预处理方法,光谱区域和最适主因子数使模型最优化.实验结果表明:采用傅里叶变换在1330 nm～725 nm光谱区域,主因子数为6,建立的模型最优.模型校正集的交互验证均方根误差(RMSECV)为0.010,交互验证所得校正集样品中的蛋白含量的预测值与真实值间相关系数(Rv)为0.968;应用此模型对预测集样品中的蛋白质含量进行预测,得到预测均方根误差(RMSEP)为0.009,预测集的相关系数(Rp)为0.990.     

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。     

基于近红外光谱技术的茶籽油掺伪定性鉴别和定量分析

茶籽油是我国特有的高级食用油。近年来茶籽油掺伪现象层出不穷，本研究利用傅里叶近红外(FT-NIR)光谱与化学计量学相结合，获得一种快速对不同掺假类型（低芥酸菜籽油、大豆油、二元混合油）和不同掺假度（0%-100%）的茶籽油进行定性和定量检测的方法。基于近红外差异光谱进行判别分析（discriminant analysis，DA），DA成功识别了不同掺假度的二元与三元混合茶籽油。通过对手动与自动筛选的不同波段结合不同预处理方法建立偏最小二乘法（Partial least squares，PLS）定量分析模型，最佳模型对掺假水平具有良好的预测性能，决定系数(R2)均大于0.91。校正均方根误差(RMSEC)和预测均方根误差(RMSEP)均接近于0。通过交叉验证，最佳模型的交叉验证相关系数均大于0.98，交叉验证均方根误差均小于0.05，表明筛选的最佳模型均具有良好的稳定性。通过外部验证，最佳模型对不同掺假类型的中、高掺假度（≥10%）的样品识别率高达100%。研究表明，利用近红外光谱技术结合化学计量学能够实现茶籽油掺假的快速、无损鉴定。     

近红外透射光谱分析油茶籽油掺入豆油的研究

采用傅里叶近红外透射光谱技术,应用于不同比例(0%～50%)的豆油和油茶籽油的二元体系样品,通过各种光谱预处理方法和回归方法的优化,建立了检测油茶籽油中掺杂豆油含量的近红外光谱的定量模型。以"二阶导数+Norris derivative filter"的最佳预处理方法和PLS的回归方法所建模型最佳,其校正相关系数(Rc)和校正标准误差(RMSEC)分别为0.999 99、0.057 70;交叉检验相关系数(Rcv)和交叉检验校准误差(RMSECV)分别为0.999 99、0.071 9;最优波段为5 037.16～4 728.60 cm-1、7 852.72～7 089.04 cm-1、8 577.82～8 323.26 cm-1;最佳主因子数为6。另外,经外部验证15份随机样品的化学值和NITS预测值的相关系数(R2)为0.998。表明近红外透射光谱法能够快速、准确地定量分析掺假油茶籽油中豆油的含量(范围为0%～50%)。     

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。     

近红外光谱法测定面粉的水分、脂肪、碳水化合物和蛋白质含量

应用近红外光谱技术结合不同的定量分析方法建立面粉4种组分的快速定量模型。国标法测定68种面粉样品的水分、脂肪、碳水化合物和蛋白质的含量,并采集其近红外漫反射光谱图。选取58个校正集和10个验证集样品,通过马氏距离法剔除异常样品后,对比17种光谱预处理方式所建立的基于全光谱的偏最小二乘法(partial least squares,PLS)定量模型效果,在最佳预处理方法的基础上,采用向后区间偏最小二乘法(Backward interval PLS,BiPLS)筛选特征光谱,进一步得到最佳定量模型。结果表明,所建立的模型校正集相关系数Rcv均大于0.9650,内部交叉验证均方根误差均小于0.328;验证集相关系数均大于0.9926,预测均方根误差均低于0.383。因此,模型具有较好的准确性和稳定性,能应用于面粉的多指标快速检测。     

红外光谱快速测定油茶籽油中主要功能活性成分含量的模型建立与评价

以从企业采集的50个油茶籽油样品为试验材料,通过扫描获取红外光谱并筛选特征波段,利用偏最小二乘法(PLS)建立油茶籽油中甾醇、维生素E和类胡萝卜素含量的预测模型,并通过系列参数对模型进行评价。结果表明:在400~1 850 cm~(-1)波数范围内,甾醇、维生素E和类胡萝卜素校正集相关系数(R_C)分别为0.978 9、0.980 1和0.949 9,交叉验证均方根误差(RMSECV)分别为42.38、25.64、0.84 mg/kg,经对模型进行验证,上述3种成分预测集相关系数(R_P)分别为0.993 4、0.997 4和0.959 0,预测均方根误差(RMSEP)分别为13.31、6.24、0.18 mg/kg,RPD分别为7.742、12.696和2.889。可见,模型的预测效果较好,说明红外光谱法可应用于油茶籽油中甾醇、维生素E和类胡萝卜素等功能活性成分含量的快速检测。     

基于近红外高光谱成像快速无损检测注胶肉研究

采用近红外高光谱成像技术结合化学计量学方法建立注胶肉的快速无损检测模型。首先通过近红外高光谱成像系统获取含有不同浓度梯度卡拉胶的猪里脊肉高光谱图像,然后提取图像中的光谱数据,使用偏最小二乘法(Partial least square,PLS)探究光谱信息与不同掺假比例卡拉胶之间的定量关系。结果表明全波段光谱(900~1700 nm)所构建的PLS校正集模型均方根误差(Root mean square error,RMSE)为1.74%,预测模型RMSE为3.16%。表明基于全波段所建立的PLS模型具有较优的预测性能。利用连续投影算法(Successive projection algorithm,SPA)筛选获得11个特征波长,并优化全波长PLS模型,将预测集样品带入,以验证模型的预测效果,结果表明SPA算法结合PLS建模方法所建立的模型预测效果更优,预测集相关系数(R_P)为0.93,均方根误差(Root mean square error of prediction,RMSEP)为3.51%,预测偏差(Residual predictive deviation,RPD)为2.66。试验表明利用高光谱成像技术可实现对注胶猪肉的快速无损检测。     

茶油掺伪的数学回归模型研究

以纯茶油中掺伪不同比例大豆油、花生油和菜籽油中任意两种植物油脂来建立茶油掺伪模型,通过气质联用技术测得各样品脂肪酸的含量,并通过相关性、显著性分析来筛选出合适的标志性脂肪酸建立茶油掺伪的多元回归方程模型。结果表明:某些脂肪酸含量在茶油掺伪模型中变化显著,且与茶油掺伪模型间具有高的相关性,选取该部分脂肪酸建立的相应回归模型方程的相关系数(R~2值)均较高,说明本实验获得的回归方程模型真实、可靠。研究结果可以为茶油的产品质量监控和掺假检测提供借鉴。     

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。     

Identification and Detection of Adulterated Camellia Oleifera Abel. Oils by Near Infrared Transmittance Spectroscopy

Under the serious circumstances of Camellia oleifera adulteration, the accurate examination for quality trait of C. oleifera oil is extremely urgent. The use of near infrared transmittance spectroscopy as a rapid and cost-efficient classification technique for the authentication of Camellia oil was investigated. At the same time, the feasibility of near infrared transmittance spectroscopy for the rapid determination of soybean oil and maize oil adulterated in binary and ternary system Camellia oils was explored. The results showed that identifications was made based on the slight difference in raw near infrared transmittance spectra in Camellia oils, soybean oils, maize oils, and those adulterated with soybean and maize oil with discriminant equations techniques. Furthermore, the performance of near infrared transmittance spectroscopy models for binary and ternary system adulterated Camellia oils was satisfactory. Moreover, the near infrared transmittance spectroscopy calibration model of soybean oil (0–50%) in binary system adulterated Camellia oils was the best, and correlation coefficients of the cross-validation (R_cv) was 0.99999. For the near infrared transmittance spectroscopy calibration model of maize oil in binary system (0–50%) and ternary system (0–40%) adulterated Camellia oils, the R_cv were 0.99996 and 0.99961, respectively. In addition, the coefficients of external validation for three models were obtained (0.9998, 0.9999, and 0.9967, respectively). In all, near infrared transmittance spectroscopy could be conducted to identify Camellia oils and detect soybean oil and maize oil adulterated in binary and ternay system Camellia oils from the methodology.     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

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。     

基于特征性脂肪酸和甘油三酯指标的油茶籽油掺伪定性鉴别模型对比分析

为解决油茶籽油掺伪其他植物油的定性鉴别问题,在油茶籽油中分别掺入大豆油、花生油、葵花籽油、棉籽油、葡萄籽油、菜籽油、棕榈油和米糠油,设置高和低两种不同掺伪梯度,基于14个特征性脂肪酸和甘油三酯指标,运用Python语言构建并对比分析了二分类决策树模型、多分类决策树模型和多层感知机人工神经网络(MLP-ANN)模型用于油茶籽油掺伪定性鉴别的效果。结果表明：高和低掺伪梯度下,二分类决策树模型对油茶籽油掺伪其他植物油的定性鉴别的准确率均达到0.95以上;多分类决策树模型的精确率和准确率在高掺伪梯度下均达到了0.95,但在低掺伪梯度下仅为0.90;在高和低掺伪梯度下,MLP-ANN模型对油茶籽油掺伪定性鉴别的平均精确率均达到0.98,准确率分别达到0.97和0.98。相比于决策树模型,MLP-ANN模型能很好地实现油茶籽油掺伪定性鉴别。     

茶油品质鉴别的透射和透反射模式分析比较

采用近红外光谱透射模式与透反射模式结合化学计量学方法对茶油进行品质鉴别。对收集的139个样品,分别用透射和透反射模式扫描,采用全光谱10 000～4 000cm-1波段,对光谱不做任何预处理,采用判别分析(discriminant a-nalysis,DA)建立油茶籽油(包括原茶油)和其它油的分类模型,真假茶油分类模型及原茶油和精炼茶油分类模型。透反射和透射模式都能够得到满意的结果,两种光谱采集方法的分类准确率均达到92%以上,透反射采集模式要优于透射采集模式。结果表明,近红外光谱可作为一种简单、快速、无损、可靠的方法用于鉴别茶油品质。     

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.