Direct, reagent-free determination of free fatty acid content in olive oil and olives by Fourier transform Raman spectrometry

Fourier transform (FT) Raman spectrometry in combination with partial least squares (PLS) regression was used for direct, reagent-free determination of free fatty acid (FFA) content in olive oils and olives. Oils were directly investigated in a simple flow cell. Milled olives were measured in a dedicated sample cup, which was rotated eccentrically to the horizontal laser beam during spectrum acquisition in order to compensate sample heterogeneity. Both external and internal (leave-one-out) validation were used to assess the predictive ability of the PLS calibration models for FFA content (in terms of oleic acid) in oil and olives in the range 0.20-6.14 and 0.15-3.79%, respectively. The root mean square error of prediction (RMSEP) was 0.29% for oil and 0.28% for olives. The predicted FFA contents were used to classify oils and olives in different categories according to the European Union regulations. Ninety percent of the oil samples and 80% of the olives were correctly classified. These results demonstrate that the proposed procedures can be used for screening of good quality olives before processing, as well as, for the on-line control of the produced oil.     

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.     

A new fluorescent derivatization reagent and its application to free fatty acid analysis in pomegranate samples using HPLC with fluorescence detection

A new fluorescent labeling reagent has been developed for the determination of fatty acids (FAs) by HPLC with fluorescence detection. The derivatization conditions including the amount of derivatization reagent, temperature, and type of catalyst were investigated, the results indicated that the reaction proceeded within 30 min at 90°C in the presence of K₂CO₃ catalyst. The maximal yield was obtained with a four‐ to fivefold molar reagent excess. The derivatives exhibited strong fluorescence with an excitation maximum at λ_ex = 245 nm and an emission maximum at λ_em = 410 nm. Twenty‐five FA derivatives were well separated by RP‐HPLC on a Hypersil BDS C₈ column in combination with gradient elution. All FAs were found to give excellent linear responses with correlation coefficients >0.9992. The method gave a low LOQ of 0.85–5.5 ng/mL (S/N of 10). The developed method was employed to analyze free FAs (FFAs) composition in pomegranate samples without any purification. FFAs in samples were doubly identified by HPLC retention time and protonated molecular ion corresponding to m/z [M+H]⁺. This newly developed method allows a highly sensitive determination of trace FFAs from pomegranate and other foodstuffs.     

Predicting percent composition of blends of biodiesel and conventional diesel using gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography-mass spectrometry, and partial least squares analysis

The percent composition of blends of biodiesel and conventional diesel from a variety of retail sources were modeled and predicted using partial least squares (PLS) analysis applied to gas chromatography-total-ion-current mass spectrometry (GC-TIC), gas chromatography-mass spectrometry (GC-MS), comprehensive two-dimensional gas chromatography-total-ion-current mass spectrometry (GCxGC-TIC) and comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS) separations of the blends. In all four cases, the PLS predictions for a test set of chromatograms were plotted versus the actual blend percent composition. The GC-TIC plot produced a best-fit line with slope = 0.773 and y-intercept = 2.89, and the average percent error of prediction was 12.0%. The GC-MS plot produced a best-fit line with slope = 0.864 and y-intercept = 1.72, and the average percent error of prediction was improved to 6.89%. The GCxGC-TIC plot produced a best-fit line with slope = 0.983 and y-intercept = 0.680, and the average percent error was slightly improved to 6.16%. The GCxGC-MS plot produced a best-fit line with slope = 0.980 and y-intercept = 0.620, and the average percent error was 6.12%. The GCxGC models performed best presumably due to the multidimensional advantage of higher dimensional instrumentation providing more chemical selectivity. All the PLS models used 3 latent variables. The chemical components that differentiate the blend percent compositions are reported.     

Determination of lecithin and soybean oil in dietary supplements using partial least squares-Fourier transform infrared spectroscopy

Lecithin and soybean oil in dietary supplements were determined by Fourier transform infrared spectrometry transmission measurements in dichloromethane in combination with a partial least squares (PLS) regression. Two different PLS models were developed, using 16 synthetic mixtures of analytes in dichloromethane, making measurements in the spectral range from 931.8 to 1252.3 cm⁻¹ for lecithin and from 911.4 to 1246.9 cm⁻¹ and 1695.3 to 1774.5 cm⁻¹ for soybean oil. Seven products from the Spanish market with lecithin concentrations between 21.1% and 99.1% and soybean oil concentrations between 0% and 37.2% were analyzed by the proposed method and the data was compared to a chromatographic reference procedure obtaining accurate results. For samples spiked with amounts between 50 and 250 mg of lecithin and soybean oil recovery percentages between 98.0% and 102.1% and between 93.6% and 102.0% with an average precision of 0.35% and 0.41% were achieved for lecithin and soybean oil, respectively. This method can be applied for the quality control of dietary supplements.     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

偏最小二乘法及主组分回归法用于药物组分的测定

本文研究了多元校准方法——偏最小二乘法(PLS)和主组份回归法(PCR)在药物多组份光度分析中的应用,获得了较满意的结果。而且在系列校准样品的实验设计、交叉证实法确定最佳因子数以及空缺组份体系的分析等方面进行了探讨。     

Determination of polar impurities in biodiesels using solid-phase extraction and gas chromatography-mass spectrometry

This paper reports on a method for development and validation for simultaneous characterization and determination of oxygenated polar impurities--free fatty carboxylic acids (FFAs), partial glycerides (monoacylglycerides, MGs), residual glycerol and free sterols--in various biodiesels based on the combination of solid-phase extraction (SPE), silylation and GC/MS technologies. The effects of various SPE and silylation conditions on the method recoveries were evaluated. Using this integrated SPE-GC/MS method, 38 target polar compounds (13 FFAs, 17 glycerides and 8 sterols) in 9 biodiesels derived from 4 different feedstocks were successfully separated and quantified. It was found that the carbon chain length of FFAs was ranged from C(6) to C(24), with C(16) and C(18) being the most abundant in all biodiesels. The total FFAs concentration was consistent with the acid values (AVs) measured by standard method ASTM D974-04. MG congeners with carbon number of 18 (mono-C18) were most abundant in the biodiesel samples, followed by mono-C(16) and free glycerol. β-Sitosterol and campesterol were found to be the prevailing phytosterols in all pure vegetable oil-based biodiesels, while brassicasterol and stigmasterol was only significant in the biodiesel from canola oil and soybean oil, respectively, and abundant cholesterol was only detected in animal fat-based biodiesels.     

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.     

Determination of captopril in pharmaceutical preparation and biological fluids using two- and three-way chemometrics methods

Spectrophotometric method has been developed for the direct quantitative determination of captopril in pharmaceuticalpreparation and biological fluids(human plasma and urine)samples.The method was accomplished based on parallel factoranalysis(PARAFAC)and partial least squares(PLS).The study was carried out in the pH range from 2.0 to 12.8 and with aconcentration from 0.70 to 61.50 μg mL~(-1)of captopril.Multivariate calibration models such as PLS at various pH and PARAFACwere elaborated from ultraviolet spectra deconvolution and captopril determination.The best models for this system were obtainedwith PARAFAC and PLS at pH 2.0.The applications of the method for determination of real samples were evaluated by analysis ofcaptopril in pharmaceutical preparations and biological fluids with satisfactory results.The accuracy of the method,evaluatedthrough the RMSEP,was 0.5801 for captopril with best calibration curve by PARAFAC and 0.6168 for captopril with PLS at pH 2.0model.     

Improved gas chromatographic method for the determination of the individual free fatty acids in cheese using a capillary column and a PTV injector

Summary A gas chromatographic method with a capillary column and a programmed temperature vaporizer injector has been used to analyze the individual free fatty acids in cheese. The lipids were extracted from an acidified cheese slurry with diethyl ether and treated with tetramethylamonium hydroxide (TMAH) to convert the free fatty acids to tetramethylammonium soaps (TMA-soaps), which were subsequently pyrolyzed to methyl esters in the injector. Carrying out injection at the initial column temperature resulted in lower dispersion of the results, but the solvent front prevented quantitative determination of butyric and caproic acids, and an injector temperature of 300°C was therefore employed. Under the conditions tested, trimethylamine (tma) flash-off did not affect the determinations. The accuracy of the method improved at higher free fatty acid contents (coefficient of variation of 0.53% for a total free fatty acid content of 9000 mg/kg as opposed to 7.0% for a total free fatty acid content of 1400 mg/kg). The recovery rate for individual free fatty acids ranged between 91 and 103%.     

Separation of triacylglycerols and free fatty acids in microalgal lipids by solid-phase extraction for separate fatty acid profiling analysis by gas chromatography

Microalgal lipids were separated into two fractions, triacylglycerols (TAGs) and free fatty acids (FFAs), by solid-phase extraction employing sodium carbonate as the sorbent and dichloromethane (20% by volume) in n-hexane as the extracting solvent. The TAG fraction was then saponified, followed by acidification, extraction and tert-butyldimethylsilyl esterification. The FFA fraction was directly acidified, extracted and derivatized. From the lipid extracts of eight microalgal species examined, a total of 13 fatty acids were detected in the TAG fractions and nine were found in the FFA fractions, with at much higher total TAG content in all microalgae. Oleic acid was the most prominent fatty acid in three species, α-linolenic acid was more abundant in two others, and palmitic acid was present in highest concentration in the remaining three species.     

Direct determination of organic acids in wine and wine-derived products by Fourier transform infrared (FT-IR) spectroscopy and chemometric techniques

FT-IR with partial least squares (PLS) was used to establish a full calibration model for tartaric acid, malic acid, lactic acid, succinic acid, citric acid and acetic acid in wines, vinegars and spirits. Sample pre-treatment was not required except for filtering.     

Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares,principal component analysis,and related techniques

Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.     

氨基酸结构描述子矢量VHSE及其在肽QSAR中的应用

从20种天然氨基酸的50个物化性质出发，按照疏水、立体和电性特征将其分类后分别进行主成分分析，并将产生的得分矢量即VHSE(principal component score vector of hydrophilicity,steric，and electronic properties)作为氨基酸结构描述子用于肽的定量构效关系研究。与已有方法相比，VHSE描述子具有物化意义明确、结果更易解释等特点。应用该描述子并结合逐步回归变量筛选和偏最小二乘建模方法，在对苦味二肽和血管舒缓激肽促进剂等体系的定量构效关系研究中，均取得了优于已有文献的结果。     

傅里叶变换红外光谱法应用于水-乙醇溶液中海南霉素的测定

随着化学计量学方法处理光谱重叠和光谱采样技术的发展,红外光谱应用于多组份同时定量分析和过程分析的研究日益受到人们的关注^[1～5].海南霉素(C₄₇H₇₉O₁₅Na,分子量907.122)是国家一类新药,用于防治鸡球虫病,易溶于有机溶剂,不溶于水.     

Determination of the major elements in homogeneous and heterogeneous samples by tandem laser-induced breakdown spectroscopy-partial least square regression

The tandem laser-induced breakdown spectroscopy-partial least square regression (LIBS-PLS) was applied for the analysis of both, a smelting product from a noble metal ore (as example of a homogeneous sample) and a calcareous rock from a Maya building (as a heterogeneous sample). The method was designed for the analysis of both samples without perceptible deterioration of the pieces, by monitoring the emission lines of the major elements present in each sample while subjecting the pieces to a number of laser shots. The results obtained, as well as the precision provided by the LIBS-PLS method, were statistically compared with those obtained by graphite furnace-atomic absorption spectroscopy (for Ag and Cu) and flame atomic emission spectroscopy (for Ca), showing good agreement between methods.     

银离子固相萃取-气相色谱法检测乳脂肪中的反式脂肪酸

建立了分离反式油酸(C18:1)、亚油酸(C18:2)、亚麻酸(C18:3)的银离子固相萃取-气相色谱(Ag+-SPE/GC)方法,并应用于乳脂肪中反式脂肪酸的检测。采用自制的银离子固相萃取柱对样品进行预分离,总脂肪酸甲酯化后上样,依次经9 mL甲苯-正己烷(体积比5:95)、8 mL甲苯-正己烷(体积比17:83)、6 mL甲苯-乙酸乙酯(体积比17:83)、10 mL甲苯-乙酸乙酯(体积比30:70)洗脱并分别收集洗脱液,采用气相色谱分别进行检测。结果显示,除了反式亚麻酸的回收率为69.9%～101.0%、相对标准偏差(RSD)为11.0%～18.1%外,其余的反式脂肪酸的回收率均为88.4%～107.2%、RSD为1.2%～11.9%。该方法通过特异性固相萃取的方法对样品进行前处理,较好地避免了样品中顺式及饱和脂肪酸对反式脂肪酸检测的干扰。     

茶叶浸出液中农药残留表面拉曼光谱检测与初步定量分析

选取甲基对硫磷和水胺硫磷为研究对象,改良了传统的QuEChERS前处理工艺,以自制纳米金溶胶为增强基底,利用表面增强拉曼光谱(SERS)技术,对茶叶浸出液中的农药残留进行检测。通过比对两种有机磷农药的拉曼特征峰进行定性分析。同时,选取570,1034,1107和1202 cm^-1等拉曼位移附近的特征峰光谱数据,利用微分等数学手段,结合偏最小二乘法(PLSR)建立回归方程,预测样品中农药残留含量。所得预测数值与气相色谱-质谱联用(GC-MS)法检测值对比,验证本方法的可行性与可信度。结果表明:基于SERS技术对上述两种有机磷农药的检出限可达0.05 mg/L;通过数学模型分析建立回归方程,其线性相关系数范围为0.9077~0.9824,预测均方根误差(RMSEP)范围为0.77%~2.68%;利用回归方程得到的预测值与GC-MS检测结果基本接近,相对误差范围-5.16%~9.03%,回收率为81.4%~115.1%,说明可以用SERS技术对茶叶浸出液中的有机磷农药残留进行定性和初步定量分析。