Monitoring lipase-catalyzed interesterification for bulky fat modification with FT-IR/NIR spectroscopy

This work demonstrates the application of FT-IR and FT-NIR spectroscopy to monitor the enzymatic interesterification process for bulky fat modification. The reaction was conducted between palm stearin and coconut oil (70:30, w/w) with the catalysis of Lipozyme TL IM at 70 degrees C in a batch reactor. The blends and interesterified fat samples in liquid form were measured by attenuated total reflectance based FT-IR (spectra region, 1516-781 cm(-1)) and transmission mode based FT-NIR (spectra region, 5369-4752 cm(-1)) with the temperature of both controlled at 70 degrees C. The samples in solid form were also measured by reflectance-based FT-NIR (spectra regions, 7037-6039 and 5995-5612 cm(-1)) at room temperature. Calibrations of FT-IR and FT-NIR for conversion degrees (evaluated by triglyceride profile), solid fat contents (SFC), and dropping points of interesterified products were carried out by using partial least-squares regression. High correlations (r > 0.96) were obtained from cross validations of the data estimated by FT-IR, FT-NIR, and the above-mentioned conventional analytical methods, except for correlations (r = 0.90-0.95) between FT-IR and SFC profiles. Overall, FT-NIR spectroscopy coupled with transmission mode measured at 70 degrees C had the highest correlations, which also had the closest conditions to the sampled products in the process, indicating a great potential for implementation as an on-line control for monitoring the enzymatic interesterification process.     

Quantification of fatty acids in forages by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.     

Measurement of conjugated linoleic acid (CLA) in CLA-rich potato chips by ATR-FTIR spectroscopy

A conjugated linoleic acid (CLA)-rich soy oil has been produced by photoisomerization of soy oil linoleic acid. Nutritional studies have shown that CLA possesses health benefits in terms of reducing certain heart disease and diabetes risk factors. Potato chips are snacks that are readily produced in the CLA-rich soy oil containing CLA levels similar to those of the oil used for frying. The objective of this study was to develop an FTIR method to rapidly determine the CLA content of oil in potato chips. Photoirradiated soy oil samples with ～25% total CLA were mixed with control soy oil, and 100 soy oil samples with total CLA levels ranging from 0.89 to 24.4% were made. Potato chips were fried using each of these 300 g CLA rich soy oil mixtures at 175 °C for approximately 3 min. Duplicate GC-FID fatty acid analyses were conducted on oil extracted from each batch of potato chips. The chip samples were ground and then scanned using ATR-FTIR spectroscopy with the aid of a high-pressure clamp, and duplicate spectra of each sample were averaged to obtain an average spectrum. Calibration models were developed using PLS regression analysis. These correlated the CLA isomer concentrations of potato chips obtained by GC-FID fatty acid analysis with their corresponding FTIR spectral features. The calibration models were fully cross validated and tested using samples that were not used in the calibration sample set. Calibrations for total CLA, trans,trans CLA, trans-10,cis-12 CLA, trans-9,cis-11 CLA, cis-10,trans-12 CLA, and cis-9,trans-11 CLA had coefficients of determinations (R2v) between 0.91 and 0.96 and corresponding root-mean-square error of prediction (RMSEP) ranging from 0.005 to 1.44. The ATR-FTIR technique showed potential as a method for the determination of the CLA levels in unknown potato chip samples.     

Determining milk isolated and conjugated trans-unsaturated fatty acids using fourier transform Raman spectroscopy

The feasibility of Raman spectroscopy in combination with partial least-squares (PLS) regression for the determination of individual or grouped trans-monounsaturated fatty acids (trans-MUFA) and conjugated linoleic acids (CLA) in milk fat is demonstrated using spectra obtained at two temperature conditions: room temperature and after freezing at -80 °C. The PLS results displayed capability for direct semiroutine quantification of several individual CLA (cis-9,trans-11 and trans-10,cis-12 C18:2) and trans-MUFA (trans-4-15 C18:1) in minor concentrations (below 1.0 g/100 g of milk fat). Calibration models were based on reference data cross-correlation or determined by specific scattering signals in the Raman spectra. Distinct bands for trans-MUFA (1674 cm(-1)) and CLA (1653 cm(-1)) from the trans isolated and cis,trans conjugated C ═ C bonds were identified, as well as original evidence for the temperature effect (new bands, peak shifts, and higher intensities) on the Raman spectra of fatty acid methyl ester and triacylglyceride standards, are supplied.     

Formation of modified fatty acids and oxyphytosterols during refining of low erucic acid rapeseed oil

Formation of trans fatty acids and cyclic fatty acid monomers was investigated during refining of low erucic acid rapeseed oil. The first steps of the refining process, that is, degumming, neutralization, and bleaching, hardly modified the fatty acid profile. In contrast, deodorization produced substantial quantities of trans fatty acids (>5% of total fatty acids) and small amounts of cyclic fatty acid monomers (650 mg of cyclic fatty acid monomers/kg of oil) when severe conditions (5-6 h at 250 degrees C) were used. Alpha-linolenic acid was the main precursor of cyclic fatty acid monomers. The influence of deodorization on the chemical composition of low erucic acid rapeseed oil was studied additionally. Whereas free fatty acids, peroxides, and tocopherols decreased, neither total polar compounds nor oxyphytosterols changed during deodorization. Oxyphytosterols were identified by GC-MS. Three oxyphytosterols not yet observed in oil were tentatively identified as 6beta-hydroxycampestanol, 6beta-hydroxysitostanol, and 6beta-hydroxybrassicastanol. Brassicasterol oxides were the most abundant oxyphytosterols.     

Rapid determination of the attenuation limit of beer using middle-infrared (MIR) spectroscopy and a multivariate model

A new approach for the determination of the attenuation limit of beer samples using the specific fingerprint region of middle-infrared (MIR) spectroscopy in combination with multiple regression by partial least-squares (PLS) was developed using an attenuated total reflectance (ATR) module. A specific spectral region between 1200 and 800 cm(-1) was identified as highly informative for the quantification of the limit of attenuation. The absorptions in this region are induced by vibrational bands of ethanol (1080, 1040, and 880 cm(-1)) and dissolved extract, in majority maltotriose (1160-1140 and 1040-980 cm(-1)). The multivariate calibration results in a root mean squared error of calibration (RMSEC) of 0.40% and a validation procedure with independent samples results in a root mean squared error of validation (RMSEV) of 0.50%. A repeatability test, concerning the precision of the developed MIR method as well as the reference method, was analyzed using Student's t test. The test has shown no significant difference between the two random samples.     

Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples

Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.     

Correcting for underlying absorption interferences in Fourier transform infrared trans analysis of edible oils using two-dimensional correlation techniques

Substantive improvement in the sensitivity of the AOAC/AOCS spectral ratioing method for the determination of isolated trans isomers in edible oils was recently achieved by the application of a new spectral reconstitution (SR) technique that facilitates the FTIR analysis of edible oils in the transmission mode. However, the general applicability of the spectral ratioing method is still severely limited by the requirement to know the provenance of the oil to be analyzed and have on hand its trans-free counterpart so that the underlying triacylglycerol absorptions in the trans measurement region (990-945 cm(-1)), henceforth referred to as UAt , may be ratioed out. To eliminate the need for a trans-free reference oil, we have employed two-dimensional (2D) correlation spectroscopy to search for other spectral features that might correlate with and serve to estimate the UAt . The three-dimensional contour maps obtained by 2D correlation analysis of the spectra of 10 trans-free oils of different oil types, recorded using the SR procedure, revealed such correlations in two spectral regions, 1700-1600 and 4500-4300 cm(-1), exhibiting one maximum and two maxima, respectively, with wavenumber coordinates of (968, 4407), (968, 4299), and (968, 1650). The latter two correlations, when optimized, produced excellent linear regression relationships (r>0.95) with the UAt . The spectra of five sets of trielaidin-spiked oils were corrected for the UA t using these relationships, and their trans contents were predicted from the calibration equation generated for the spectral ratioing procedure. Linear regression of predicted versus added trans over the range of 0-1.6% trans, which is below the limit of quantitation of the AOAC/AOCS spectral ratioing method, yielded r=0.88-0.90 with an SD of approximately 0.2% trans. These results indicate that the combination of the SR technique with the UA t correction approach may provide a simple and accurate FTIR method for the analysis of the trans content of fats and oils that would be competitive with GC.     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

Relationship between fatty acid profile and vitamin E content in maize hybrids (Zea mays L.)

The balance between the vitamin E (tocochromanols) and polyunsaturated fatty acid (PUFA) contents mainly determines the susceptibility to lipid peroxidation and the storage stability of corn oil. In 1997, field experiments were conducted at two different locations to evaluate a collection of 30 corn hybrids for fatty acid profiles and tocochromanol contents. Hybrids differed significantly (p < 0.01) for major fatty acids, as well as for tocochromanol contents and composition. The major fatty acids were palmitic, oleic, and linoleic acids, whose contents were in the ranges 9.2-12.1%, 19.5-30.5%, and 53.0-65.3%, respectively. The tocopherol contents ranged as follows: alpha-tocopherol, 67-276 mg (kg of oil)(-1); beta-tocopherol, 0-20 mg (kg of oil)(-1); gamma-tocopherol, 583-1048 mg (kg of oil)(-1); delta-tocopherol, 12-71 mg (kg of oil)(-1); total tocopherol, 767-1344 mg (kg of oil)(-1). gamma-Tocopherol was the predominant derivative among all tocopherols. The tocotrienol contents were in the ranges 46-89, 53-164, and 99-230 mg (kg of oil)(-1) for alpha-, gamma-, and total tocotrienol contents, respectively. The tocotrienol profile was not characterized by the predominance of any tocotrienol homologue. alpha-Tocopherol was positively correlated with PUFA (r = 0.41) and with the vitamin E equivalent (vit E equiv) (r = 0.84), and it was not correlated with gamma-tocopherol. gamma-Tocopherol was highly correlated with total tocopherol and tocochromanol contents (r = 0.93 and r = 0.90, respectively), indicating that the contribution of this vitamer to the total tocochromanol content is the most important among all tocochromanols. The high positive correlation found between the vit E/PUFA ratio and the vit E equiv, as well as the absence of correlation between this ratio and PUFA indicates that a higher vit E/PUFA ratio can be easier achieved be increasing the vitamin E content than by modifying fatty acid profile in corn oil.     

拉曼光谱结合UVE-SVR算法预测加热食用油反式脂肪酸的含量

为实现拉曼光谱技术对食用油加热后反式脂肪酸(TFAs)含量的快速预测,将3种食用油在190℃下(常用煎炸温度)进行不同时间加热,每个样品采集36条拉曼光谱。首先,采用多项式平滑与标准正态变量变换(SNN)对原始光谱数据进行预处理,以去除背景和噪音的干扰,然后采用无信息变量消除法(UVE)对光谱数据进行特征变量筛选,最后分别基于全谱数据和筛选的特征光谱变量建立定性和定量分析模型,并对试验结果进行对比分析。结果表明,基于筛选后的变量,运用Fisher判别分析(FDA)建立定性判别模型,其判别正确率由40%~50%提升至90%以上,表明筛选后的变量能较好表征样品的特征信息;分别基于筛选变量和全谱数据,运用偏最小二乘回归(PLSR)、BP神经网络(BPNN)和支持向量回归机(SVR)方法,建立不同样品中TFAs含量的数学预测模型。通过对预测结果对比分析,表明UVE结合SVR方法具有良好的检测效果,菜籽油、大豆油、玉米油的测试集R²分别从0.850 4、0.943 5和0.753 4升至0.952 6、0.954 8和0.958 5。因此,利用UVE-SVR方法不仅简化了预测模型,提高了模型的稳定性和精度,也为食用油中TFA的快速检测提供了一种可行的方法。     

Comparison of the effect of fish oil and corn oil on chemical-induced hepatic enzyme-altered foci in rats

The effects of fish oil and corn oil diets on diethylnitrosamine initiation/phenobarbital promotion of hepatic enzyme-altered foci in female Sprague-Dawley rats were investigated. Groups of 12 rats were initiated with diethylnitrosamine (15 mg/kg) at 24 h of age. After weaning, they received diets containing either 13.5% fish oil plus 1. 5% corn oil or 15% corn oil for 24 weeks. Rats fed fish oil had significantly greater liver weight, relative liver weight, spleen weight, and relative spleen weight than rats fed corn oil (p < 0.05). Hepatic phospholipid fatty-acid profile was significantly affected by the type of dietary lipid. The rats fed fish oil had significantly greater hepatic phospholipid 20:5 and 22:6 than rats fed corn oil; in contrast, the rats fed corn oil had significantly greater hepatic phospholipid 18:2 and 20:4 than rats fed fish oil (p < 0.05). Rats fed fish oil had significantly lower hepatic vitamin E and PGE(2) content but significantly greater hepatic lipid peroxidation than rats fed corn oil (p < 0.05). The hepatic levels of antioxidant enzymes (GSH reductase and GST) were significantly greater in rats fed fish oil than in rats fed corn oil (p < 0.05). Except for PGST-positive foci (foci area/tissue area), all the other foci parameters (GGT-positive foci area/tissue area, GGT-positive foci no./cm(2), GGT-positive foci no./cm(3), PGST-positive foci no. /cm(2), and PGST-positive foci no./cm(3)) measured in the fish oil group were 10-30% of those in the corn oil group (p < 0.05). Analyses of Pearson correlation coefficient revealed a positive correlation between hepatic GGT- or PGST-positive foci number (no. /cm(2)) and PGE(2) content (r = 0.66, P = 0.01; r = 0.56, P = 0.02, respectively) but a negative correlation between GGT- and PGST-positive foci (no./cm(2)) and lipid peroxidation (r = -0.8, P = 0.0006; r = -0.58, P = 0.01, respectively), GSH/(GSH + GSSG) ratio (r = -0.61, P = 0.05; r = -0.4, P = 0.14, respectively), GSH reductase (r = -0.75, P = 0.002; r = -0.53, P = 0.02, respectively), and GST activities (r = -0.65, P = 0.01; r = -0.44, P = 0.07, respectively). Similar correlation between foci number (no./cm(3)) and PGE(2), lipid peroxidation, GSH/(GSH + GSSG) ratio, GSH reductase, and GST activities were obtained. The results of this study show that dietary fish oil significantly inhibited hepatic enzyme-altered foci formation compared with corn oil in rats. These results suggest that the possible mechanisms involved in this process are the stimulation of hepatic detoxification system, changes in membrane composition, inhibition of PGE(2) synthesis, the enhancement of GSH-related antioxidant capacity, and the enhancement of lipid peroxidation by fish oil.     

Near-infrared spectroscopy for measurement of total dietary fiber in homogenized meals

Near-infrared (NIR) reflectance spectroscopy was investigated as a method for prediction of total dietary fiber (TDF) in mixed meals. Meals were prepared for spectral analysis by homogenization only (HO), homogenization and drying (HD), and homogenization, drying, and defatting (HDF). The NIR spectra (400-2498 nm) were obtained with a dispersive NIR spectrometer. Total dietary fiber was determined in HDF samples by an enzymatic-gravimetric assay (AOAC 991.43), and values were calculated for HD and HO samples. Using multivariate analysis software and optimum processing, partial least squares models (n = 114) were developed to relate NIR spectra to the corresponding TDF values. The HO, HD, and HDF models predicted TDF in independent validation samples (n = 37) with a standard error of performance of 0.93% (range 0.7-8.4%), 1.90% (range 2.2-18.9%), and 1.45% (range 2.8-23.3%) and r(2) values of 0.89, 0.92, and 0.97, respectively. Compared with traditional analysis of TDF in mixed meals, which takes 4 days, NIR spectroscopy provides a faster method for screening samples for TDF. The accuracy of prediction was greatest for the HDF model followed by the HD model.     

Fatty acids including trans content of commercial bakery products manufactured in Spain.

Fifteen samples of the most common industrial bakery products sold in Spain were analyzed for their fatty acid composition. Saturated fatty acids occurred in the largest proportions in all samples (mean = 52.8%), followed by monounsaturated (mean = 23.5%) and polyunsaturated fatty acids (mean = 17.2%). A small percentage of trans fatty acids, which were found in all samples, showed a mean value of 5.7%. According to their fatty acid composition, that is, their saturated, monounsaturated, and polyunsaturated fatty acid contents, the statistical analysis (discriminant analysis) showed three groups of samples, suggesting that the fat incorporated in these samples was obtained from different sources.     

Analysis of Fatty Acids in Kernel,Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods

High cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R² = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R² = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.     

Near-infrared reflectance spectroscopy for predicting amino acids content in intact processed animal proteins

Near-infrared calibrations were developed for the instantaneous prediction of amino acids composition of processed animal proteins (PAPs). Two sample presentation modes were compared (ground vs intact) for demonstrating the viability of the analysis in the intact form, avoiding the need for milling. Modified partial least-squares (MPLS) equations for the prediction of amino acids in PAPs were developed using the same set of samples (N = 92 PAPs) analyzed in ground and intact form and in three cups differing in the optical window size. The standard error for cross validation (SECV) and the coefficient of determination (1-VR) values yielded with the calibrations developed using the samples analyzed in the intact form showed similar or even better accuracy than those obtained with finely ground samples. The excellent predictive ability (1-VR > 0.90; CV < 3.0%) obtained for the prediction of amino acids in intact processed animal proteins opens an enormous expectative for the on-line implementation of NIRS technology in the processing and marketing of these important protein feed ingredients, alleviating the costs and time associated with the routine quality controls.     

Acid detergent fiber analysis in oilseed Brassicas by near-infrared spectroscopy

The potential of near infrared spectroscopy (NIRS) for determining the acid detergent fiber (ADF) in the seed of oilseed Brassica (fam. Brassicaceae) was assessed. One hundred and fifty accessions belonging to the species Indian mustard (Brassica juncea L. Czern.& Coss.), Ethiopian mustard (B. carinata A. Braun) and rapeseed (B. napus L.) were scanned by NIRS as intact and ground seed, and their ADF values were regressed against different spectra transformations by modified partial least squares regression. The coefficients of determination in the external validation (r(2)) for intact and ground seed were 0.83 and 0.85, respectively. The standard deviation to standard error of prediction ratio and range to standard error of prediction ratio were 2.40 and 10.75 for intact seed and 2.62 and 11.76 for ground seed. No significant differences in the prediction were found for both sample presentations. Effects of the C-H and O-H groups of lipids and water, respectively, as well as protein and chlorophyll, were most important in modeling these equations.     

Effect of different extraction methods on fatty acids,volatile compounds,and physical and chemical properties of avocado (Persea americana Mill.) oil

Because Mexico is the number one producer of avocados in the world, this fruit has potential as a source for oil extraction. It is appropriate to further investigate the detailed changes that the oil undergoes when different extraction methods are applied. This research paper presents the study of the physical and chemical changes, the fatty acids profile, the trans fatty acid content, and the identification of volatile compounds of the oils from avocado pulp (Persea americana Mill.), obtained by four different extraction methods. The method with the greatest extraction yield was the combined microwave-hexane method. The amount of trans fatty acids produced in the microwave-squeezing treatment was <0.5 g/100 g. On the other hand, the amounts of trans fatty acids produced with the hexane and acetone treatments were 0.52 and 0.87 g/100 g, respectively. The method that caused the slightest modification to the oil quality was a novel combined extraction method of microwave-squeezing proposed by the authors.     

Quantitation of fatty acids, sterols, and tocopherols in turpentine (Pistacia terebinthus Chia) growing wild in Turkey

The chemical composition (fatty acids, tocopherols, and sterols) of the oil from 14 samples of turpentine (Pistacia terebinthus L.) fruits is presented in this study. The oil content of the samples varied in a relatively small range between 38.4 g/100 g and 45.1 g/100 g. The dominating fatty acid of the oil is oleic acid, which accounted for 43.0 to 51.3% of the total fatty acids. The total content of vitamin E active compounds in the oils ranged between 396.8 and 517.7 mg/kg. The predominant isomers were alpha- and gamma-tocopherol, with approximate equal amounts between about 110 and 150 mg/kg. The seed oil of P. terebinthus also contained different tocotrienols, with gamma-tocotrienol as the dominate compound of this group, which amounted to between 79 and 114 mg/kg. The total content of sterols of the oils was determined to be between 1341.3 and 1802.5 mg/kg, with beta-sitosterol as the predominent sterol that accounted for more than 80% of the total amount of sterols. Other sterols in noteworthy amounts were campesterol, Delta5-avenasterol, and stigmasterol, which came to about 3-5% of the total sterols.     

Classification and structural analysis of live and dead Salmonella cells using Fourier transform infrared spectroscopy and principal component analysis

Fourier transform infrared spectroscopy (FT-IR) was used to detect Salmonella Typhimurium and Salmonella Enteritidis food-borne bacteria and to distinguish between live and dead cells of both serotypes. Bacteria cells were prepared in 10(8) cfu/mL concentration, and 1 mL of each bacterium was loaded individually on the ZnSe attenuated total reflection (ATR) crystal surface (45° ZnSe, 10 bounces, and 48 mm × 5 mm effective area of analysis on the crystal) and scanned for spectral data collection from 4000 to 650 cm(-1) wavenumber. Analysis of spectral signatures of Salmonella isolates was conducted using principal component analysis (PCA). Spectral data were divided into three regions such as 900-1300, 1300-1800, and 3000-2200 cm(-1) based on their spectral signatures. PCA models were developed to differentiate the serotypes and live and dead cells of each serotype. Maximum classification accuracy of 100% was obtained for serotype differentiation as well as for live and dead cells differentiation. Soft independent modeling of class analogy (SIMCA) analysis was carried out on the PCA model and applied to validation sample sets. It gave a predicted classification accuracy of 100% for both the serotypes and its live and dead cells differentiation. The Mahalanobis distance calculated in three different spectral regions showed maximum distance for the 1800-1300 cm(-1) region, followed by the 3000-2200 cm(-1) region, and then by the 1300-900 cm(-1) region. It showed that both of the serotypes have maximum differences in their nucleic acids, DNA/RNA backbone structures, protein, and amide I and amide II bands.