Nondestructive single-seed oil determination of meadowfoam by near-infrared transmission spectroscopy

Near-infrared transmission spectroscopy (NITS) was explored for single-seed oil determination of meadowfoam. Two calibration sets were determined by principle component analysis of recorded spectra. Nuclear magnetic resonance (NMR) spectroscopy was used for oil content determination. The calibrations had standard errors of cross-validation of 3.6 and 4.4%, respectively, with an oil content ranging from 0.8 to 45.7%. This error is similar to that reported for NITS of single maize kernels, relative to the respective oil content ranges. Although not as accurate as NMR spectroscopy, NITS does provide a fast, efficient, and nondestructive method of predicting oil content of individual meadowfoam seeds.     

Near‐infrared spectroscopy for analysis of oil content and fatty acid profile in almond flour

Almond kernels show large variability for oil content and fatty acid profile. The objective of this research was to evaluate the potential of near infrared (NIR) reflectance spectroscopy (NIRS) for the analysis of these traits in almond flour. Ground kernels of 181 accessions collected in 2009 were used for developing calibration equations for oil content and concentrations of individual fatty acids. Calibration equations were developed using second derivative transformation and modified partial least squares regression. They were validated with samples from 179 accessions collected in 2010. The accuracy of calibration equations was measured through the coefficient of determination (r²) in external validation and the ratio of the SD in the validation set to the standard error of prediction (RPD). Both r² and RPD were high for oil content (r² = 0.99; RPD = 9.24) and concentrations of oleic (r² = 0.97; RPD = 5.37) and linoleic acids (r² = 0.98; RPD = 7.35), revealing that calibration equations for these traits are highly accurate. Conversely, the accuracy of the calibration equations for palmitic (r² = 0.54; RPD = 1.41) and stearic acids (r² = 0.52; RPD = 1.44) was too low for allowing their application in practice. NIRS discrimination of oil content and concentrations of oleic and linoleic acids was mainly based on the spectral region from 2240 to 2380 nm. Practical applications : NIRS is a high‐throughput analytical technique that allows fast measurement of several traits in a single analysis without using chemical reagents. We evaluated the feasibility of analyzing oil content and concentrations of palmitic, stearic, oleic, and linoleic acids in almond flour using fruits collected during 2 years from a world germplasm collection. The fruits collected in 2009 were used for NIRS calibration, whereas the fruits collected in 2010 were used for validation. NIRS equations were highly accurate for measuring oil content and concentrations of oleic and linoleic acids, which are important traits defining the quality of almond flour for specific uses in the food industry. These results have applications both in the research laboratory and the food industry, where NIRS is becoming a widely used technique for quality control.     

The prediction of clay contents in oil shale using DRIFTS and TGA data facilitated by multivariate calibration

The prediction of clay content in oil shale is important for the optimisation of oil shale processing conditions and process feasibility. The multivariate calibration technique of partial least squares regression (PLSR) was implemented in order to predict clay content in oil shale samples taken from the Stuart oil shale deposit, Queensland, Australia. The calibration data used were the diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) spectra of 34 oil shale samples. DRIFTS data from another set of 20 oil shale samples were used for model validation. The data pre-processing includes the use of derivatives facilitated by the Savitsky-Golay nine-points’ method. A four components model was constructed and it showed a root mean square error of calibration (RMSEC) of 4.79% and a root mean square error of prediction (RMSEP) of 4.35%. TGA data sets were also used to construct a calibration model, which produced less accurate results than DRIFTS. DRIFTS, when combined with multivariate calibration, provided an accurate in situ method of evaluating clay content in oil shale. Clay content measured using XRD was used as a reference.     

Oil and water analysis of sunflower seed by near-infrared reflectance spectroscopy

The applicability of NIR for oil and moisture analyses of sunflower seed was determined using a NIR spectrocomputer system. The method was compared with the wide-line NMR method for oil analysis and with the A.O.C.S. oven method for moisture analysis. The NIR was calibrated with 120 samples for oil (96 for calibration, 24 for prediction) and 63 samples for moisture (55 for calibration, 8 for prediction). Twenty-two sunflower seed samples were analyzed for oil and moisture by NIR and by methods used by industry. The oil contents of the samples by NMR and NIR were not significantly different. The overall mean oil contents and mean of the standard deviations for the samples were: NMR, 44.2%±0.35% and NIR, 44.34%±0.74%. A significant difference was found between the moisture values obtained by the oven-drying method and NIR. The average standard deviation for moisture by NIR was 0.57% compared with 0.07% for the oven-drying method. The variability of the oil content in one of the commercial seed samples was 1.52% oil as determined by NMR and 2.52% as determined by NIR. The advantages and disadvantages of both methods are discussed.     

The effect of hydrogen content on estimation of seed oil by pulsed nuclear magnetic resonance

In the nondestructive estimation of seed oil by pulsed nuclear magnetic resonance (NMR), an assumption generally is made that the hydrogen content of the oil in the seed sample under investigation is the same as that of the oil standard or that of the oil in the standard seed samples. The hydrogen content is defined as the number of hydrogen atoms per unit mass of oil. The validity of this assumption has been investigated by; (i) calculating the Hydrogen contents of various oils on the basis, of their reported fatty acid composition, and (ii) experimentally determining the hydrogen contents of cotton and mustard oils obtained from different varieties of seeds. The FID method was used to monitor the NMR signal intensities. Both calculated and experimental values show that that hydrogen contents can be different not only for different oils but also for the same oil extracted from different varieties of the seeds. This variation in hydrogen contents is shown to introduce an error in the oil content values as estimated by NMR methods. The magnitude of this error increases with oil content, and also with the difference in the hydrogen contents.     

Estimating the content of free fatty acids in high‐oleic sunflower seeds by near‐infrared spectroscopy

The content of free fatty acids (FFA) in vegetable oils represents an important quality factor in oil crops. The objective of the investigation was to develop a near‐infrared (NIR) calibration for estimating the FFA content in high‐oleic sunflower seeds. A sample set of different varieties from the harvest of 2004 as well as of 2005 from two locations in Germany was used; additionally seeds from 2003 were stored under unsuitable conditions to obtain samples utilised for calibration with an extended FFA range. A direct titration method for FFA determination was developed and adjusted to the official AOCS method. The modified method is sufficiently reliable, much faster than the AOCS method and therefore suitable for use in the calibration of NIR spectrometers. The developed NIR spectroscopy (NIRS) calibration was calculated with a modified partial least square algorithm, standard normal variate and detrend scatter correction and the 2^nd derivative of the spectra of ground sunflower seeds. The standard error of prediction of the validated calibration was 0.20, and the multiple coefficient of determination (RSQ_val) reached 0.94. The obtained results demonstrated clearly the efficiency and how cost effective the NIRS method is for the estimation of FFA content in sunflower seeds.     

Determination of tocopherols and phytosterols in sunflower seeds by NIR spectrometry

The objective of this work was to develop a near‐infrared reflectance spectrometry (NIRS) calibration estimating the tocopherol and phytosterol contents in sunflower seeds. Approximately 1000 samples of grinded sunflower kernels were scanned by NIRS at 2‐nm intervals from 400 to 2500 nm. For each sample, standard measurements of tocopherol and phytosterol contents were performed. The total tocopherol content was obtained by high‐performance liquid chromatography coupled with a fluorescence detector, while the total phytosterol content was assessed by gas chromatography. For tocopherol, the calibration data set ranged from 175 to 1005 mg/kg oil (mean value around 510 ± 140 mg/kg oil), whereas for the phytosterol content, the calibration data set ranged from 180 to 470 mg/100 g oil (mean value of 320 ± 50 mg/100 g oil). The NIRS calibration showed a relatively good correlation (R² = 0.64) between predicted by NIRS and real values for the total tocopherol content but a poor correlation for the total phytosterol content (R² = 0.27). These results indicate that NIRS could be useful to classify samples with high and low tocopherol content. In contrast, the estimation of phytosterol contents by NIRS needs further investigation. Moreover, in this study, calibration was obtained by a modified partial least‐squares method; the use of other mathematical treatments can be suitable, particularly for total phytosterol content estimation.     

超临界CO_2萃取迷迭香精油及其化学成分分析

迷迭香是非常受关注的天然抗氧化剂来源植物之一。该研究用超临界CO2流体萃取技术对福建产迷迭香进行精油提取研究,萃取过程采用程序加压和程序升温,进一步采用GC-MS联用技术对萃取得到的迷迭香精油组分进行了分析。共分离出44个组分峰,鉴定出其中43个化学成分,占挥发油总量的97.83%。福建产迷迭香挥发油中含量较高的成分如α-蒎烯(10.54%)、莰烯(4.63%)、柠檬烯(7.27%)、桉叶油素(11.76%)、樟脑(9.25%)等的混合香气构成了迷迭香精油的特征香气。根据国际标准对迷迭香精油的分型,确定福建产迷迭香与突尼斯/摩洛哥型更为相近。     

Analysis of oil content of maize by wide-line NMR

A series of experiments to define feasibility, accuracy, and precision of wide-line nuclear magnetic resonance spectroscopy as a nondestructive analytical tool for the oil content of living seeds is described. Corn samples, ranging from single seeds to 25 g, were scanned by NMR and gravimetrically analyzed. A high positive correlation (r = 0.99⁺) was invariably encountered. Single, 30-sec NMR scans on 25-g corn samples gave estimates within ±33 ing of the “true” oil content 95% of the time. Error associated with single, 30-sec scans of individual corn kernels amounted to ±1.3 mg of oil at the same confidence level. Samples containing more than 4.5% moisture contribute to the NMR signal, and therefore oil content is overestimated. A new dimension is added to the breeding and genetics of oil crops by the application of the NMR technique in that the process is non-destructive and feasible even for single seeds. The usefulness of the technique in studies involving the nature of water and its surrounding medium is suggested.     

Measurement of Whole Soybean Fatty Acids by Near Infrared Spectroscopy

Whole soybean fatty acid contents were measured by near infrared spectroscopy. Three calibration algorithms—partial least squares (PLS), artificial neural networks (ANN), and least squares support vector machines (LS-SVM)—were implemented. Three different validation strategies using independent sets and part of calibration samples as validation sets were created. There was a significant improvement of the prediction precision of all fatty acids measured on relative concentration of oil compared with previous literature using PLS (standard error of prediction of 0.85, 0.42, 1.64, 1.67, and 0.90% for palmitic, stearic, oleic, linoleic and linolenic acids respectively). ANN and LS-SVM methods performed significantly better than PLS for palmitic, oleic and linolenic acids. Calibration models developed on relative concentrations (% of oil) were compared to prediction models created on absolute fatty acid concentration (% of weight) and corrected to relative concentration by multiplying by the predicted oil content. While models were easier to develop in absolute concentration (higher coefficients of determination), the multiplication of errors with the total oil content model resulted in no net precision improvement.     

Quantitative analysis of palm carotene using fourier transform infrared and near infrared spectroscopy

β-Carotene content is usually determined by using ultraviolet (UV)-visible spectrophotometry at 446 nm. In this study, two spectroscopic techniques, namely, Fourier transform infrared (FTIR) and near infrared (NIR) spectroscopy, have been investigated and compared to UV-visible spectrophotometry to measure the β-carotene content of crude palm oil (CPO). Calibration curves ranging from 200 to 800 ppm were prepared by extracting β-carotene from original CPO using open-column chromatography. Separate partial least squares calibration models were developed for predicting β-carotene based on the spectral region from 976 to 926 cm⁻¹ for FTIR spectroscopy and 546 to 819 nm for NIR spectroscopy. The correlation coefficient (R ²) and standard error of calibration obtained were 0.972 and 25.2 for FTIR and 0.952 and 23.6 for NIR techniques, respectively. The validation set gave R ² of 0.951 with standard error of performance (SEP) of 25.78 for FTIR technique and R ² of 0.979 with SEP of 19.96 for NIR technique. The overall reproducibility and accuracy did not give comparable results to that of spectrophotometric method; however, the standard deviation of prediction was still within ±5% β-carotene content over the range tested. Because of their rapidness and simplicity, both FTIR and NIR techniques provide alternative means of measuring β-carotene content in CPO. In addition, these two spectroscopic techniques are environmentally friendly since no solvent is involved.     

Measurement of oil in whole flaxseed by near-infrared reflectance spectroscopy

A near-infrared reflectance (NIR) Infralyzer 500 was calibrated for determination of oil with samples of ground and whole flaxseed grown over three years. Wavelength selection by the computer software interfaced with the Infralyzer, analytical and regression statistic data, such as standard deviation of laboratory analysis (SD_x), correlation coefficient, standard error of estimate (SEE), standard error of prediction (SEP), and the SD_x/SEP ratio showed that calibration of the instrument with whole flaxseed was equal in precision to that obtained with the ground flaxseed. Growth location or seed moisture content had no effect on oil content of whole flaxseed determined by the NIR. The whole seed calibration allowed rapid, nondestructive screening for oil in flaxseed at greatly reduced cost.     

Rapid Method for the Determination of Moisture Content in Biodiesel Using FTIR Spectroscopy

A new, rapid, and direct method was developed for the determination of moisture content in biodiesel produced from various types of oils using Fourier transform infrared (FTIR) spectroscopy with an attenuated total reflectance (ATR) element. Samples of biodiesels used in this study were produced using sludge palm oil (SPO). The calibration set was prepared by spiking double-distilled water into dried biodiesel samples in ratios (w/w) between 0 and 10% moisture. Absorbance values from the wavelength regions 3,700–3,075 and 1,700–1,500 cm⁻¹, and the partial least square (PLS) regression method were used to derive a FTIR spectroscopic calibration model for moisture content in biodiesel samples. The coefficient of determinations (R ²) for the models was computed by comparing the results obtained from FTIR spectroscopy against the values of the moisture concentrations (%) determined using the American Oil Chemists’ Society (AOCS) oven method Ca 2d-25. Same comparison was done using International Union of Pure and Applied Chemistry (IUPAC) distillation method 2.602. R ² was 0.9793 and 0.9700 using AOCS and IUPAC methods, respectively. The standard error (SE) of calibration was 1.84. The calibration model was cross validated within the same set of samples, and the standard deviation (SD) of the difference for repeatability (SDDr) and accuracy (SDDa) of the FTIR method was determined. With its speed and ease of data manipulation, FTIR spectroscopy is a useful alternative method to other methods for rapid and routine determination of moisture content in biodiesel for quality control.     

High-throughput determination of oil content in corn kernels using nuclear magnetic resonance imaging

A new high-throughput method for measuring oil content in intact, single corn kernels is demonstrated using nuclear magnetic resonance imaging (MRI) methods. This nondestructive technique enables the evaluation of relative oil content in up to 2,592 corn kernels in less than 40 min using a 1.5 T clinical MRI scanner. Custom software was developed to process and analyze 3-D magnetic resonance (MR) image data rapidly. The precision and accuracy of the MR method for measuring oil content are discussed. The precision of the MRI results is shown to be dependent on MR scanner noise. The MRI results show very good relative accuracy compared with low-field NMR, NIR transmission, and accelerated solvent extraction measurements. Minor differences between the MRI and low-field NMR experimental protocols were shown to be inconsequential to the oil content measurement. Extending the MRI method to the analysis of other oilseeds and/or the use of other magnetic field strengths is discussed, as is a comparison of this MRI method relative to other high-throughput magnetic resonance screening techniques.     

Monitoring PV in corn and soybean oils by NIR spectroscopy

NIR spectroscopy was used successfully in our laboratory to monitor oxidation levels in vegetable oils. Calibration models were developed to measure PV in both soy and corn oils, using partial least squares (PLS) regression and forward stepwise multiple linear regression, from NIR transmission spectra. PV can be measured successfully in both corn and soy oils using a single calibration. The most successful calibration was based on PLS regression of first derivative spectra. When this calibration was applied to validation sample sets containing equal numbers of corn and soy oil samples, with PV ranging from 0 to 20 meq/kg, a correlation coefficient of 0.99 between titration and NIR values was obtained, with a standard error of prediction equal to 0.72 meq/kg. For both types of oil, changes occurred in the 2068 nm region of the NIR spectra as oxidation levels increased. These changes appear to be associated with the formation of hydroperoxides during oxidation of the oils.     

Near infrared reflectance spectroscopy and multivariate analysis to monitor reaction products during biodiesel production

In agreement with the principles of green chemistry, near infrared spectroscopy (NIRS) allows multi-component analysis in a fast and nondestructive way, without requiring complex pre-treatments, being a safe, clean and energy saving technique. In this work, a preliminary study to develop near infrared calibration models to predict methyl esters (ME) yield, monoglycerides (MG), diglycerides (DG), triglycerides (TG), free glycerol (FG) and total glycerol (TotalG) content in biodiesel has been carried out. These parameters are considered key factors to determine biofuel quality, its commercialization and to study and monitor the transesterification reaction. For this purpose, samples of biodiesel produced from three different vegetable oils (maize oil, sunflower oil and olive–pomace oil) were analyzed following the EN14103 and EN14105 European standards as reference methods. NIRS calibration equations were validated with a group of validation samples. The mean spectra showed that the main variability on biodiesel NIR spectra occurred around 1700 and 2300 nm. Moreover, the principal components analysis (PCA) applied to the spectra revealed the grouping of samples according to the type of oils used for biodiesel production. The standard deviation of the prediction (cross validation) errors (RMSEP_CV) of the calibration models and the standard deviation error (RMSEP) of the validation set resulted similar to the measurement errors (intra lab SEL_r) and repeatability (inter lab SEL_R) of each analyte. Results confirm the accuracy of the developed NIRS models for determination of glycerides content and methyl esters yield in biodiesel.     

Comparative study of methods of determining oil content of sunflower seed

An extraction-gravimetric method (AOCS Official Method Ai 3-75) was compared with 2 instrumental techniques, near-infrared reflec-tance (NIR) spectroscopy and wide-line nuclear magnetic resonance (NMR), for the determination of the oil content of oilseed-type hybrid sunflower seed. Eight sunflower seed samples of varying oil contents, replicated 5 times, were analyzed by the 3 procedures. The overall mean oil contents and standard deviations for the 8 samples were: AOCS method, 44.5% ± 0.33%; NMR, 44.8% ± 0.27%; and NIR, 44.2% ± 0.81%. Analysis of variance of the means of the 3 methods of analysis indicated no difference (p>0.05) in oil content due to the method. However, there was a difference (p>0.001) in total oil content due to replicated analyses of the same sample with the NIR method. With the AOCS and NMR methods, no effect (p>0.05) of replicated analyses of the same sample was found. The NMR method was more precise and repro-ducible than the other 2 methods. Although the NIR mean oil contents were not significantly different from the means of the other 2 methods, the coefficient of variations for all samples were consistently higher for the NIR analyses than for the AOCS and NMR analyses.     

Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near-infrared reflectance spectroscopy

A methodological study was conducted to test the potential of near-infrared reflectance spectroscopy (NIRS) to estimate the oil content and fatty acid composition of sunflower seeds. A set of 387 intact-seed samples, each from a single plant, were scanned by NIRS, and 120 of them were selected and further scanned as husked seed, meal, and oil. All samples were analyzed for oil content (nuclear magnetic resonance) and fatty acid composition (gas chromatography), and calibration equations for oil content and individual fatty acids (C_16:0, C_16:1, C_18:0, C_18:1, and C_18:2) were developed for intact seed, husked seed, meal, and oil. For intact seed, the performance of the calibration equations was evaluated through both cross- and external validation, while cross-validation was used in the rest. The results showed that NIRS is a reliable and accurate technique to estimate these traits in sunflower oil (validation r ² ranged from 0.97 to 0.99), meal (r ² from 0.92 to 0.98), and husked seeds (r ² from 0.90 to 0.97). According to these results, there is no need to grind the seeds to scan the meal; similarly accurate results are obtained by analyzing husked seeds. The analysis of intact seeds was less accurate (r ² from 0.76 to 0.85), although it is reliable enough to use for pre-screening purposes to identify variants with significantly different fatty acid compositions from standard phenotypes. Screening of intact sunflower seeds by NIRS represents a rapid, simple, and cost-effective alternative that may be of great utility for users who need to analyze a large number of samples.     

Moisture Adsorption Isotherms of Watermelon Seed and Kernels

The moisture adsorption isotherms of watermelon seeds and kernels from Citrullus lanatus Cv Mateera and Citrullus vulgaris Cv Sugar baby were obtained using standard static method with saturated salt solutions over a range of water activities from 0.113 to 0.92 at 20-60°C. The adsorption capacity of seeds decreased with the increase in temperature at constant water activity. Sorption models were used to explain the adsorption behavior involving water activity and moisture content (Type I) and also temperature (Type II). Oswin's models gave best fit among Type I with coefficient of determination of 0.953-0.995, standard error of 0.031-0.0571, mean relative error of 0.071-0.152, and scattered residual plots. Modified Oswin was the best fit model among Type II for the seeds and kernels of both the cultivars with coefficient of determination of 0.997-0.999, standard error of 0.151-0.255, mean relative error of 0.018-0.244, and scattered residual plots. The net isoelectric heat of adsorption, estimated from Clausius-Clapeyron decreased from about 27.0 to 0.5 kJ/mol in kernels and 18.0 to 0.5 kJ/mol in seeds of both the cultivars as the moisture content increased from 5 to 25% (dry basis).     

Quantification of soybean oil ethanolysis with <Superscript>1</Superscript>H NMR

In this study, proton NMR spectroscopy (200 MHz) was used for quantifying the content of ethyl esters in known mixtures of soybean oil and ethyl soyate (biodiesel). For this purpose, the peak areas of ester ethoxy and glycerol methylenic peaks in the region of 4.05–4.40 ppm were measured and a calibration plot of the respective peak areas vs. the known composition of the oil/ethyl ester mixtures was used. The transesterification values determined in this way were compared with viscosity and total glycerol determinations and a good correlation was obtained. Therefore, for routine analysis, the conversion (in %) of oil to ethyl esters was determined. The methodology presented in this work proved to be quicker and simpler than others reported in the literature, such as GC and/or HPLC.