低场核磁共振结合化学计量学方法快速检测掺假核桃油

以掺假核桃油样品为低场核磁共振检测对象,利用主成分分析法(PCA)和偏最小二乘回归法(PLSR)分析处理Carr-Purcell-Meiboom-Gill(CPMG)序列的核磁共振弛豫数据,旨在探求一种能快速检测核桃油品质的新方法。对几种常见掺假形式(掺入大豆油、玉米油、葵花油)的核桃油样品和纯核桃油样品进行检测和评价。实验结果表明:纯核桃油和掺入不同种类食用油的掺假核桃油在主成分得分图上可以得到很好的区分,且掺假样品随掺假比例在图中呈规律性分布;采用PLSR法对CPMG数据和实际掺假率进行回归,可实现对核桃油掺假水平的准确定量测定。方法快速、无损、准确,在食用油制品的品质控制及评价方面具有很大的应用潜力。     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

低场核磁结合偏最小二乘回归预测含油污泥水和油含率

快速准确分析处理过程中含油污泥的含水率和含油率有助于现场评价其原油回收效率和优化处理工艺参数。以Dean-Stark装置测定的含油污泥样品的含水率和含油率作为参考值,利用低场核磁共振结合偏最小二乘回归法建立了样品含水率和含油率校正集模型,考察了回波衰减曲线和横向弛豫时间T2曲线对校正集模型性能的影响。结果表明,采用前者建立的校正集模型性能优于后者;在此基础上,建立了31个样品的含水率和含油率通用校正集模型,其含水率和含油率模型的决定系数(R2)分别为0.965 7和0.978 5,校正标准差(RMSECV)分别为2.73%和2.22%。利用3个不同批次采集的HZ-OS样品对该模型进行验证,对于含水率和含油率模型,其验证集R2分别为0.914 1和0.924 7,预测标准差(RMSEP)分别为1.85%和2.04%,与RMSECV值比较接近,说明该模型的稳定性较好,可用于准确分析样品的含水率和含油率。     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

固相萃取-高效液相色谱法同时测定大豆和大米中的磺酰脲类和二苯醚类除草剂残留

建立了大豆和大米中磺酰脲类和二苯醚类除草剂多残留同时检测的高效液相色谱分析方法。样品经乙腈提取,正己烷液-液分配,C18固相萃取小柱净化后,采用高效液相色谱方法分离,以乙腈-三乙胺盐酸溶液作流动相,梯度洗脱,紫外检测器检测。对样品前处理和色谱分析条件进行了优化,8种除草剂(甲磺隆、氯磺隆、苄嘧磺隆、吡嘧磺隆、三氟羧草醚、精恶唑禾草灵、乙氧氟草醚、乙羧氟草醚)在0.05~2.0 mg/L范围内线性关系良好。方法的定量限(S/N=10)为0.01~0.02 mg/kg,能达到国家有关上述除草剂残留限量的要求。大豆和大米样品的平均加标回收率分别为91.6%~116.1%和76.6%~110.8%,相对标准偏差(RSD)为1.0%~12.2%。所建立的方法在30 min内可完成一次检测,具有简便快速、灵敏可靠的特点,适用于大豆和大米中除草剂多残留的测定。     

Multi-way partial least squares modeling of water quality data

A 10 years surface water quality data set pertaining to a polluted river was analyzed using partial least squares (PLS) regression models. Both the unfold-PLS and N-PLS (tri-PLS and quadri-PLS) models were calibrated through leave-one out cross-validation method. These were applied to the multivariate, multi-way data array with a view to assess and compare their predictive capabilities for biochemical oxygen demand (BOD) of river water in terms of their relative mean squares error of cross-validation, prediction and variance captured. The sum of squares of residuals and leverages were computed and analyzed to identify the sites, variables, years and months which may have influence on the constructed model. Both the tri- and quadri-PLS models yielded relatively low validation error as compared to unfold-PLS and captured high variance in model. Moreover, both of these methods produced acceptable model precision and accuracy. In case of tri-PLS the root mean squares errors were 1.65 and 2.17 for calibration and prediction, respectively; whereas these were 2.58 and 1.09 for quadri-PLS. At a preliminary level it seems that BOD can be predicted but a different data arrangement is needed. Moreover, analysis of the scores and loadings plots of the N-PLS models could provide information on time evolution of the river water quality.     

超高效合相色谱-质谱法快速分析食用植物油中常见脂肪酸

建立了超高效合相色谱-质谱(UPC²-MS)快速分析6种食用植物油(玉米油、葵花籽油、大豆油、茶油、菜籽油、花生油)中棕榈酸、硬脂酸、油酸、亚油酸、亚麻酸等5种常见脂肪酸的方法,并比较了这6种食用油中上述5种脂肪酸的含量差异。采用皂化反应对植物油进行前处理,以ACQUITY UPC² BEH 2-EP色谱柱(100 mm×2.1 mm, 1.7 μm)为分析柱,以超临界CO₂-甲醇/乙腈(1:1, v/v)为流动相进行梯度洗脱,流速为0.8 mL/min。在电喷雾负离子模式下进行检测,外标法定量。结果表明:5种脂肪酸标准物质在0.5~100 mg/L范围内呈现良好的线性关系,相关系数为0.9985~0.9998,定量限(S/N≥10)为0.15~0.50 mg/L;在3个添加水平下,样品的加标回收率为89.61%~108.50%;方法重复性的相对标准偏差(RSD)为0.69%~3.01%。该方法简单、快速、分离效果好,无需对脂肪酸样品进行衍生化,已成功地用于玉米油、葵花籽油、橄榄油、茶油、大豆油和花生油等6种食用油中常见脂肪酸含量的测定。     

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.     

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Non-invasive determination of ethanol, propylene glycol and water in a multi-component pharmaceutical oral liquid by direct measurement through amber plastic bottles using Fourier transform near-infrared spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy was used to quantify rapidly the ethanol (34-49% v/v), propylene glycol (20-35% v/v) and water (11-20% m/m) contents within a multi-component pharmaceutical oral liquid by measurement directly through the amber plastic bottle packaging. Spectra were collected in the range 7302-12,000 cm-1 and calibration models set-up using partial least-squares regression (PLSR) and multiple linear regression. Reference values for the three components were measured using capillary gas chromatography (ethanol and propylene glycol) and Karl Fischer (water) assay procedures. The calibration and test sets consisted of production as well as laboratory batches that were made to extend the concentration ranges beyond the natural production variation. The PLSR models developed gave standard errors of prediction (SEP) of 1.1% v/v for ethanol, 0.9% v/v for propylene glycol and 0.3% m/m for water. For each component the calibration model was validated in terms of: linearity, repeatability, intermediate precision and robustness. All the methods produced statistically favourable outcomes. Ten production batches independent of the calibration and test sets were also challenged against the PLSR models, giving SEP values of 1.3% v/v (ethanol), 1.0% v/v (propylene glycol) and 0.2% m/m (water). NIR transmission spectroscopy allowed all three liquid constituents to be non-invasively measured in under 1 min.     

超高效液相色谱-电喷雾串联质谱法测定生活饮用水中49种药物及5种代谢物

建立了超高效液相色谱-电喷雾串联质谱(UPLC-ESI MS/MS)分析生活饮用水中54种药物的方法。采用HLB固相萃取柱对水样中的目标化合物进行富集净化,以5 mL甲醇洗脱;洗脱液用氮气吹至近干,用0.4 mL 0.1%甲酸水溶液定容,上机分析;ACQUITY UPLC^TMBEH C₁₈柱用作色谱分离,以0.1%甲酸水溶液-甲醇为流动相进行梯度洗脱;多反应监测(MRM)模式进行检测;目标药物使用基质外标法定量。54种药物在自备井水、市政末梢水和地表水中的加标回收率分别为58.7%~104.4%、53.1%~109.5%和50.7%~118.8%,相对标准偏差(n=6)分别为0.3%~12.8%、1.0%~15.5%和0.4%~19.3%;方法定量限为0.002~5.000 ng/L。将建立的方法应用于北京部分自备井水、市政末梢水和地表水样品的分析,结果在自备井水样中检出26种药物。     

Direct determination of lycopene content in tomatoes (Lycopersicon esculentum) by attenuated total reflectance infrared spectroscopy and multivariate analysis

Lycopene is a potent antioxidant that has been shown to play critical roles in disease prevention. Efficient assays for detection and quantification of lycopene are desirable as alternatives to time- and labor-intensive methods. Attenuated total reflectance infrared (ATR-IR) spectroscopy was used for quantification of lycopene in tomato varieties. Calibration models were developed by partial least-squares regression (PLSR) using quantitative measures of lycopene concentration from liquid chromatography as reference method. IR spectra showed a distinct marker band at 957 cm(-1) for trans Carbon-Hydrogen (CH) deformation vibration of lycopene. PLSR models predicted the lycopene content accurately and reproducibly with a correlation coefficient (sigma) of 0.96 and standard error of cross-validation <0.80 mg/100 g. ATR-IR spectroscopy allowed for rapid, simple, and accurate determination of lycopene in tomatoes with minimal sample preparation. Results suggest that the ATR-IR method is applicable for high-throughput quantitative analysis and screening for lycopene in tomatoes.     

Near-infrared reflectance spectroscopy as a fast and non-destructive tool to predict foliar organic constituents of several woody species

Near-infrared reflectance spectroscopy (NIRS) was used to estimate N, neutral detergent fibre (NDF), acid detergent fibre (ADF), lignin and cellulose contents in leaves of a heterogeneous group of 17 woody species from the Central Western region of the Iberian Peninsula. The sample set consisted of 182 samples of leaves of deciduous and evergreen species, showing a wide range of concentrations determined by reference methods: 6.60–35.2 g kg⁻¹ (N), 15.5–66.0% (NDF), 10.2–57.3% (ADF), 3.45–27.4% (lignin) and 5.79–31.3% (cellulose). Reflectance spectra, obtained for samples of dried and ground leaves, were recorded as log1/R (R=reflectance) from 1,100 to 2,500 nm. NIRS calibrations were developed using multiple linear (MLR) and partial least-squares (PLSR) regressions, and tested by external validation. Spectral data were transformed to the first and second derivative (1D, 2D). The PLSR method and derivative transformations provided the best statistics and showed lower standard errors of calibration (SEC) and higher coefficients of multiple determination (R ²). In the external validation the standard errors of prediction (SEP) were 0.76 g kg⁻¹ (N), 2.11% (NDF), 1.47% (ADF), 0.85% (lignin) and 0.86% (cellulose). The results obtained show that NIRS is very effective for the estimation of these organic constituents in leaf tissue of woody species. This technique can be used in ecological or ecophysiological studies as an alternative to the more time-consuming standard methods.     

近红外光谱技术结合4种算法分析尾巨桉-马占相思制浆原料的混合程度与主化学成分

为缓解我国木浆供应压力,满足混合原料制浆的实际需求,该文进行了近红外光谱快速分析混合制浆原料的研究。采集145个人为控制尾巨桉含量的尾巨桉-马占相思混合样品的近红外光谱,用常规方法测定其综纤维素、聚戊糖、Klason木质素含量。对原始光谱进行一阶导数与标准正态变换预处理后,分别运用偏最小二乘法、支持向量机法、人工神经网络法和LASSO算法建立尾巨桉、综纤维素、聚戊糖、Klason木质素含量分析模型。其中LASSO法建立的尾巨桉和综纤维素含量分析模型最优,预测均方根误差(RMSEP)分别为1.80%、0.60%;绝对偏差(AD)分别为-3.03%~3.17%、-1.03%~0.98%,模型性能可满足较精确的快速分析。偏最小二乘法建立的聚戊糖含量分析模型最优,RMSEP为0.75%,AD为-1.26%~1.33%;支持向量机法建立的Klason木质素含量分析模型最优,RMSEP为0.48%,AD为-0.82%~0.86%,两个模型性能适用于非精确性的分析。该研究为混合制浆原料的快速分析提供了可能,同时也证实了LASSO算法的适用性。     

超高效亲水作用色谱-串联质谱法检测水中的苦味酸及苦氨酸

建立了一种超高效亲水作用色谱-串联质谱检测水中苦味酸及其降解产物苦氨酸的方法。采用Acquity UPLC BEH HILIC亲水作用色谱柱（100 mm×2.1 mm,1.7 μm,Waters）分离,用电喷雾电离串联质谱检测。地表水样品经过0.2 μm滤膜过滤之后即可直接进样,加标回收率达89%~107%;废水样品通过固相萃取（SPE）净化后进样分析,加标回收率达72%~101%。方法重复性的相对标准偏差为4.9%~14.7%。本方法对苦味酸和苦氨酸的检出限分别为0.1 μg/L和0.3 μg/L。此方法快速、准确,特异性强,灵敏度高,样品前处理方法简便易行,适用于地表水、废水样品的检测。     

高效液相色谱法检测多种食品基体中残留的喹氧灵

建立了采用液相色谱检测大豆、花椰菜、樱桃、木耳、葡萄酒、茶叶、蜂蜜、猪肝、鸡肉、鳗鱼等多种食品基体中喹氧灵残留的方法。利用乙酸乙酯提取样品中残留的喹氧灵,用氨基固相萃取小柱净化;对于脂肪含量较高的样品,在进行固相萃取前采用凝胶渗透色谱净化技术去脂。方法的准确度与精密度较好,在添加浓度为0.010～5.0 mg/kg时,平均回收率及相对标准偏差分别为82%～96%及3.2%～11.8%;在0.050～50.0 mg/L范围内有良好的线性关系,检测限达0.010 mg/kg。该方法适用性广,能消除复杂基质带来的干扰,可用于各类食品中喹氧灵残留的分析。     

近红外(NIRS)技术分析小样品油菜籽芥酸和含油量的应用研究

以完整油菜籽为样品,采用旋转杯和安培瓶两种样品杯、每种样品分为4×2种不同样品量并通过不同光谱预处理来优化油菜籽芥酸和含油量的近红外分析模型。结果表明:油菜籽各小样品含油量模型的决定系数(R2)从93.93%到96.93%不等,均方差(RMSECV)从0.56到0.79不等;油菜籽各小样品芥酸模型的决定系数(R2)从96.91%到98.42%不等,均方差(RMSECV)从1.73到2.43不等。随着样品量的逐渐增加,油菜籽芥酸和含油量不同样品杯模型各参数逐渐有所优化;同一样品厚度时,油菜小样品芥酸和含油量的旋转样品杯模型各参数均略优于安培瓶样品模型;不同样品量的NIRS模型,W3和W4差异不大,依次优于W2和W1。最小样品量AW1为0.3g。优化油菜小样品模型时,应该选择全部的预处理方法,根据优化结果选择最佳模型。外部检验结果表明:不同重量小样品(W1/0.3g、W2/1.0g、W3/2.0g和W4/4.0g)模型之间及其与标准化学值之间在0.01水平上差异不显著,说明W1和W2小样品模型同样可应用于油菜品质育种材料的分析选择。     

悬浮固化分散液液微萃取-高效液相色谱法测定水体中邻苯二甲酸酯

建立了悬浮固化分散液液微萃取（SFO-DLLME）结合高效液相色谱（HPLC）快速测定水样中6种邻苯二甲酸酯（PAEs）的分析方法。通过对影响萃取效率因素的优化,确定了最佳萃取条件：十二烷醇萃取剂20 μL、萃取温度60℃、离子强度20 g/L、萃取时间1 min。6种PAEs在2~2000 μg/L范围内呈良好的线性关系,相关系数（r）为0.9995~0.9999,检出限（S/N=3）为0.3~0.6 μg/L。对自来水、湖水、江水、污水、海水、市售塑料瓶装纯净水和矿泉水进行测定,能检测到部分PAEs。对加标水样进行回收率试验（10、100和1000 μg/L）,6种PAEs的回收率为84.9%~94.5%,相对标准偏差为4.1%~6.8%（n=5）。该法环保、简单,可用于实际水样中6种PAEs的检测分析。