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.     

Non-destructive Determination of High Oleic Acid Content in Single Soybean Seeds by Near Infrared Reflectance Spectroscopy

Soybean [Glycine max (L.) Merr] with increased oleic acid is desirable to improve oxidative stability and functionality of soybean seed oil. Recently, soybean genotypes with high oleic acid (≥70 %) were developed by breeding programs. Efficient and effective identification of high oleic acid soybean genotypes using non-destructive near infrared reflectance (NIR) on whole seeds would greatly enhance progress in breeding programs. The objective of this study was to develop a calibration equation for NIR determination of high oleic acid from single soybean seeds. A total of 600 intact, single F₂ seeds were scanned by NIR. Spectral data were collected between 400 and 2,500 nm at 2 nm intervals. The relationship between NIR spectral patterns of each soybean seed and its oleic acid content was examined. The best predicted equations for oleic acid were selected on the basis of minimizing the standard error of cross-validation and increasing the coefficient of determination. Validation demonstrated that the equations for determining total oleic acid and over 50 % oleic acid content had high predictive ability (r ² = 0.91 and r ² = 0.99, respectively). To validate the newly developed equation, F₂ seeds from a different genetic background were tested. Again, high oleic acid from single soybean seeds was accurately predicted from various genetic backgrounds. Therefore, applying the calibration equations to NIR will be useful to rapidly and efficiently select high oleic acid soybean genotypes in breeding programs.     

Comparison of three whole seed near-infrared analyzers for measuring quality components of canola seed

Whole seed near-infrared (NIR) analyzers are capable of high-speed compositional analysis of oilseed commodities. This study compared the PerCon Inframatic 8144 (Perten Instruments, North America Inc., Reno, NV), the Tecator Infratec 1225 (Tecator AB, Hoganas, Sweden) and the NIR-Systems 6500 (NIR Systems, Inc., Silver Spring, MD) analyzers for measurement of oil, protein, chlorophyll and glucosinolates in intact canola seed of composite samples from the Grain Research Laboratory's (Winnipeg, Manitoba, Canada) annual Western Canada Harvest Surveys (1985–1989) for assembly of calibration and prediction sets. No significant differences were found between the three instruments for oil [standard error of prediction (SEP 0.43–0.55%)], protein (SEP 0.35–0.42%) and glucosinolates (SEP 2.4–3.8 mM/g). Neither the Tecator nor the PerCon instruments were effective for determining chlorophyll. By combining oil content and fatty acid composition data to give an estimate of the total level of each fatty acid in the sample, high correlations were obtained for total saturates, linolenic acid, and linoleic acid although the RPD (ratio of the S.E. of prediction to the S.D. of the original data) values were not high enough to enable routine use of the method to predict results. Presented at the 84th AOCS Annual Meeting & Expo, April 27, 1993, Anaheim, California.     

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.     

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.     

Screw pressing of whole and dehulled flaxseed for organic oil

Flaxseed oil, a rich source of dietary n−3 FA, is commonly obtained by cold pressing whole seed. Furthermore, flaxseed hulls are emerging as a valuable lignan-rich product for functional food use; therefore, the pressing characteristics of dehulled seed need to be understood. Screw press performance was measured for pressing of whole and dehulled flaxseed. When whole Omega flaxseed was pressed through a 6-mm choke, an inverse relationship between seed moisture content (6.1–11.6% range) and oil recovery (70.1–85.7%) was observed. However, peak oil recovery from pressing dehulled Omega flaxseed of 72.0% was found at 10.5% moisture content in the moisture content range of 7.7–11.2%. Although oil recovery from dehulled Omega flaxseed was lower than from whole Omega flaxseed, the weight of oil produced from dehulled Omega flaxseed per unit time was higher. The dependence of capacity on moisture content was less evident with the 6-mm choke than with the 8-mm choke. An inverse relationship between moisture content of whole flaxseed and oil and meal temperature was observed. The oil and meal temperatures from pressing dehulled flaxseed were significantly lower than those from whole flaxseed. Therefore, pressing dehulled flaxseed appears to offer advantages in organic flaxseed oil production.     

Discrimination of various poly(propylene) copolymers and prediction of their ethylene content by near‐infrared and Raman spectroscopy in combination with chemometric methods

Near‐infrared (NIR) diffuse reflectance (DR) spectra and Fourier‐transform (FT) Raman spectra were measured for 12 kinds of block and random poly(propylene) (PP) copolymers with different ethylene content in pellets and powder states to propose calibration models that predict the ethylene content in PP and to deepen the understanding of the NIR and Raman spectra of PP. Band assignments were proposed based calculation of the second derivatives of the original spectra, analysis of loadings and regression coefficient plots of principal component analysis (PCA) and principal component regression (PCR) (predicting the ethylene content) models, and comparison of the NIR and Raman spectra of PP with those of linear low‐density polyethylene (LLDPE) with short branches. PCR and partial least squares (PLS) regression were applied to the second derivatives of the NIR spectra and the NIR spectra after multiplicative scatter correction (MSC) to develop the calibration models. After MSC treatment, the original spectra yield slightly better results for the standard error of prediction (SEP) than the second derivatives. A plot of regression coefficients for the PCR model shows peaks due to the CH₂ groups pointing upwards and those arising from the CH₃ groups pointing downwards, clearly separating the bands due to CH₃ and CH₂ groups. For the Raman data, MSC and normalization were applied to the original spectra, and then PCR and PLS regression were carried out to build the models. The PLS regression for the normalized spectra yields the best results for the correlation coefficient and the SEP. Raman bands at 1438, 1296, and 1164 cm^?1 play key roles in the prediction of the ethylene content in PP. The NIR chemometric evaluation of the data gave better results than those derived from the Raman spectra and chemometric analysis. Possible reasons for this observation are discussed. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 616–625, 2003     

Prediction of Lignin and Extractive Content of Pinus nigra Arnold. var. Pallasiana Tree Using Near Infrared Spectroscopy and Multivariate Calibration

Abstract

Determination of quality parameters such as lignin and extractive content of wood samples by wet chemistry analyses takes a long time. Near infrared (NIR) spectroscopy coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus nigra Arnold. Var. pallasiana is the second most growing pine species in Turkey. Even though its rotation period is very high, around 120 years, the forest products industry has widely accepted the use of Pinus nigra because of its ability to grow on a wide range of sites and its suitability to produce desirable products. In this study, 51 samples of Pinus nigra trees were collected and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near infrared spectrometer between 1000 and 2500 nm in diffuse reflectance mode. Multivariate calibration models were built with genetic inverse least squares method for both lignin and extractive content using the concentration information obtained from wet standard reference method. Overall, standard error of calibration (SEC) and standard error of prediction (SEP) were ranged between 0.35% (w/w) and 2.4% (w/w).     

Nondestructive Estimation of Fat Constituents of Sesame (Sesamum indicum L.) Seeds by Near‐Infrared Reflectance Spectroscopy

A rapid and efficient method for oil constituent estimation in intact sesame seeds was developed through near‐infrared reflectance spectroscopy (NIRS) and was used to evaluate a sesame germplasm collection conserved in China. A total of 342 samples were scanned by reflectance NIR in a range of 950–1650 nm, and the reference values for oil content and fatty acid (FA) profiles were measured by Soxhlet and gas chromatograph methods. Useful chemometric models were developed using partial least squares regression with full cross‐validation. The equations had low standard errors of cross‐validation, and high coefficient of determination of cross‐validation (R_c²) values (>0.8) except for stearic acid (0.794). In external validation, r² values of oil and FA composition equations ranged from 0.815 (arachidonic acid) to 0.877 (linoleic acid). The relative predictive determinant (RPD_v) values for all equations were more than 2.0. The whole‐seed NIR spectroscopy equations for oil content and FA profiles can be used for sesame seed quality rapid evaluation. The background information of the 4399 germplasm resources and accessions with high linoleic acid content identified in this study should be useful for developing new sesame cultivars with desirable FA compositions in future breeding programs.     

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.     

Robust on‐line measurement of conversion and molecular weight using NIR spectroscopy during solution polymerization

Fiber‐optic near‐infrared (NIR) spectroscopy was used to monitor the monomer conversion and the weight‐average molecular weight of the polymer produced during solution polymerization of methyl methacrylate (MMA) carried out in a lab‐scale reactor. NIR spectra were recorded during batch and semi‐continuous reactions using an in situ transmission probe. Off‐line gravimetry and GPC were used as reference methods to provide the conversion and the average molecular weight data set required for the calibration procedure. A statistical model was generated using partial least‐squares regression (PLS) to relate the NIR spectral data to the two polymerization variables of interest. The measurements were then validated for various operating conditions (i.e., different solvent, initiator, MMA, and chain‐transfer agent concentrations) and for both batch and semi‐continuous modes. The conversion was predicted during three validation experiments with an average standard error of prediction (SEP) of 2.1%. The on‐line evaluation of M?_w was obtained with an average relative SEP of 5.7%; such on‐line NIR measurement was thus demonstrated to be robust and accurate, even in the case of versatile use of the polymerization plant. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2510–2520, 2002     

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Inexpensive and rapid methods for measurement of seed oil content by near infrared reflectance spectroscopy (NIRS) are useful for developing new oil seed cultivars. Adopting default multiple linear regression (MLR), the predictions of safflower oil content were made by 20–140 samples using a Perten Inframatic 8620 NIR spectrometer. Although the obtained interpolation results of MLR had desired accuracy, the extrapolation was extremely poor. The extrapolation determination coefficient (R²) and standard error (SE) of cross validation for MLR models were 0.63–0.78 and 3.71–4.44, respectively. In order to overcome the accuracy limitation of linear MLR models, a common suggestion is to use a nonlinear artificial neural network (ANN); however, it needs a large number of data to yield significant accurate results. We developed a novel robust hybrid fuzzy linear neural (HFLN) network to capture simultaneously linear and nonlinear patterns of data with a limited number of safflower samples. Empirical extrapolation results showed that the HFLN had higher R² (=0.85) and lower SE (=1.83) compared to those obtained by MLR and ANN models. It is concluded that hybrid methodologies could be used to construct efficient and appropriate models for estimation of seed oil content set up on NIR system.     

Near-infrared spectroscopic analysis of deterioration indices of soybeans for process control in oil milling plant

The feasibility of near infrared (NIR) spectroscopy was examined for analyzing quality criteria or deterioration induces of soybeans, such as the nitrogen solubility index (NSI), acid value, digestibility and percentage germination, as well as the contents of their major constituents (moisture, oil and total nitrogen). Because all the criteria are closely related to yield and quality of the final product, their rapid analysis is required for process control in the oil milling plant. NIR diffuse-reflectace spectra were measured on ground samples in an InfraAlyzer 500 (Bran+Luebbe Co., Norderstedt, Germany). As for the major constituents, the standard errors of prediction (SEP) were 0.34% (moisture), 0.24% (oil) and 0.067% (total nitrogen). There results show the ability of NIR for analyzing these parameters, as many researchers reported previously. As for the other quality criteria, the SEP values were 2.40 (NSI), 5.58 (digestibility), 0.29 (acid value) and 15.27 (germinatio percentage). Further, NIR analysis for some of these criteria of the extracted residue and defatted soybeans was also examined, and almost the same level of SEP values was obtained. Although it is still necessary to improve the accuracy, we concluded that the NIR method has the feasibility to measure quality criteria for the purpose of process control in the plant in place of the time-consuming chemical analysis that are conventionally used. Part of this study was presented at 84th AOCS Annual Meeting & Expo, April 27, 1993, Anaheim, California.     

NIR reflectance spectroscopic analysis of the FA composition in sesame (<Emphasis Type="Italic">Sesamum indicum</Emphasis> L.) seeds

The feasibility of using NIR reflectance spectroscopy to estimate the FA composition of sesame seed (Sesamum indicum L.) samples from the National Institute of Crop Science of Japan and from Myanmar was examined. Multiple linear-regression analyses of NIR sepctral data and chemical data for whole seeds were carried out to develop calibration equations for predicting the proportion of each of the four major FA in sesame seeds from the total FA composition. The SE of prediction (SEP) was 0.616% for palmitic acid, 0.348% for stearic acid, 1.051% for oleic acid, and 0.826% for linoleic acid. This NIR method provides a simple, rapid, and nondestructive means of estimating the FA composition of sesame seeds for breeding selection, regardless of the color of the sesame seed coats. However, the proportions of palmitic and stearic acids could not be reliably measured because their SEP were almost as great as the SD of their concentrations in the set of prediction samples. The relationship between NIR spectral patterns and the FA composition of sesame seeds also was examined. The correlation coefficient calculated for the standardized second-derivative NIR spectral readings at 1708 nm and the percentages of linoleic acid was −0.830. A rough estimate of the proportion of linoleic acid in the total FA composition of sesame seeds could be obtained even with single sesame seeds, except for those with a black coat, based on NIR spectral pattern analysis using the wavelength assignments of linoleic acid.     

Isolation of High Oleate Recombinants in Peanut by Near Infra‐Red Spectroscopy and Confirmation With Allele Specific Polymerase Chain Reaction Marker

Near infrared (NIR) spectrophotometer offers rapid, noninvasive, nondestructive, and high‐throughput phenotyping of seed samples for use in agriculture and industry. In this study, a reflectance‐based NIR spectrophotometer was calibrated and used for the isolation of desirable higher‐oleic‐acid peanut recombinants from single‐seed‐derived segregating populations at F₇ and F₈ generations. A calibration model was developed through partial least‐square regression using wet chemistry data from 158 peanut genotypes. Desirable prediction for oil, palmitic acid, oleic acid, and linoleic acid in intact seed was obtained based on this calibration. It detected significant high correlations (r) and coefficient of determination (R²) between the actual gas chromatography values and NIR predicted values of fatty acid profile in another 123 peanut genotypes that were generated from crosses involving a high‐oleate mutant and Spanish bunch varieties with early maturity. From this recombinant single‐seed‐derived progenies, 15 higher‐oleate recombinants were isolated and later genotyped through an in‐house developed polymerase chain reaction‐based allele specific marker. The present study has generated high‐oleate peanut recombinants with early maturity in Spanish bunch background. The breeding materials generated here will be evaluated for yield attributing traits at different locations in future.     

Supercritical CO2 extraction of flaxseed

Extraction of flaxseed oil was performed with supercritical carbon dioxide (SC-CO₂). To investigate the effects of pressure and temperature on the solubility of oil and oil yield, three isobaric (21, 35, and 55 MPa) and two isothermal (50 and 70°C) extraction conditions were selected. Although the maximal solubility of flaxseed oil, 11.3 mg oil/g CO₂, was obtained at 70°C/55 MPa, the oil yield obtained after 3 h of extraction at this condition was only 25% (g oil/g seed×100), which represented 66% of the total available oil of the flaxseed. Lipid composition and FFA and tocol (tocopherol and tocotrienol) contents of the oils obtained by both SC-CO₂ and petroleum ether extraction were determined. The α-linolenic acid content of the SC-CO₂-extracted oil was higher than that obtained by solvent extraction.     

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.     

Multivariate calibration of fourier transform infrared spectra for determining thiobarbituric acid-reactive substance content in palm oil

Fourier transform infrared (FTIR) spectra of palm oil samples between 2900 and 2800 cm⁻¹ and 1800 and 1600 cm⁻¹ were used to compare different multivariate calibration techniques for quantitative determination of their thiobarbituric acid-reactive substance (TBARS) content. Fifty spectra (in duplicate) of palm oil with TBARS values between 0 and 0.25 were used to calibrate models based on partial least squares (PLS) and principal components regression (PCR) analyses with different baselines. The methods were compared for the number of factors, coefficients of determination (R ²), and accuracy of estimation. The standard errors of prediction (SEP) were calculated to compare their predictive ability. The calibrated models generated three to eight factors, R ² of 0.9414 to 0.9803, standard error of estimation (SEE) of 0.0063 to 0.0680, and SEP of 1.20 to 6.67.     

Determination of peroxide value in thermally oxidized crude palm oil by near infrared spectroscopy

A near infrared (NIR) spectroscopic method was developed to measure peroxide value (PV) in crude palm oil (CPO). Calibration standards were prepared by oxidizing CPO in a fermentor at 90°C. A partial least squares (PLS) calibration model for predicting PV was developed based on the NIR spectral region from 1350 to 1480 nm with reference to single-point baseline at 1514 nm. The optimization of calibration factors was guided by the predicted residual error sum of squares test. The standard error of calibration obtained was 0.156 over the analytical range of 2.17–10.28 PV and the correlation coefficient (R ²) was 0.994. The method was validated with an independent set of samples which was prepared in the same manner on a different day. A linear relationship between the American Oil Chemists’ Society and the NIR methods was obtained with R ² of 0.996 and standard error of performance of 0.17. This study has demonstrated that the prediction of PV in the NIR region is possible. The method developed is rapid, with total analysis time less than 2 min, is environmentally friendly, and its accuracy is generally good for quality control of CPO.     

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.