Multivariate prediction of clarified butter composition using raman spectroscopy

Chemical Composition and Crystallization Behavior of Oil and Fat Blends for Spreadable Fat Applications

To meet the expectations of European consumers, who prioritize agro-environmental factors and local resources, the substitution of fats (palm, coconut, shea) and achieving a balanced fatty acid profile in spreadable fats are gaining more attention. The crystallization at 4 °C of a lipid blend composed of rapeseed oil, anhydrous dairy fats, and emulsifiers was studied using a multi-scale approach (DSC and X-ray diffraction techniques) to understand the emergence of polymorphic structures. Although the addition of PUFA from rapeseed oil reduces the atherogenicity and thrombogenicity indices in the blend, controlling the cooling kinetics influences the shapes (needles and spherulites) and sizes of the crystalline structures (small crystals form at a cooling rate of 1 °C min-1, while larger crystals form at higher rates of 5 and 10 °C min-1). The crystallization behavior revealed differences in polymorphic forms at 4 °C in the blend, with a transition to different forms occurring more rapidly compared to dairy fat (stop-and-return method). The study shows crystalline coexistence (α, β', and β) in a 2L lamellar structure, with the β' form being predominant. This structure is ideal for formulating a spreadable product, offering good spreadability (SFC < 32% at 10 °C), mouthfeel, and nutritional benefits compared to butter.

Lipidomics in food quality and authentication: A comprehensive review of novel trends and applications using chromatographic and spectroscopic techniques

Lipid analysis is an integral part of food authentication and quality control which provides consumers with the necessary information to make an informed decision about their lipid intake. Recent advancement in lipid analysis and lipidome scope represents great opportunities for food science. In this review we provide a comprehensive overview of available tools for extraction, analysis and interpretation of data related to dietary fats analyses. Different analytical platforms are discussed including GC, MS, NMR, IR and UV with emphasis on their merits and limitations alongside complementary tools such as chemometric models and lipid-targeted online databases. Applications presented here include quality control, authentication of organic and delicacy food, tracing dietary fat source and investigating the effect of heat/storage on lipids. A multitude of analytical methods with different sensitivity, affordability, reproducibility and ease of operation are now available to comprehensively analyze dietary fats. Application of these methods range from studies which favor the use of large data generating platforms such as MS-based methods, to routine quality control which demands easy to use affordable equipment as TLC and IR. Hence, this review provides a navigation tool for food scientists to help develop an optimal protocol for their future lipid analysis quest.

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Raman spectroscopy: A rapid method to assess the effects of pasture feeding on the nutritional quality of butter

The animal diet is a critical variable affecting the composition and functionality of dairy products. As "Grass-Fed" labeling becomes more prominent on the market, rapid and label-free methods for verification of feeding systems are required. This work proposes the use of Raman spectroscopy to study the effects of 3 different experimental cow feeding systems-perennial ryegrass pasture, perennial ryegrass with white clover pasture, and an indoor total mixed ration diet (TMR)-on the nutritional quality of sweet cream butter. The results demonstrate that Raman spectroscopy coupled with multivariate analysis is a promising approach to distinguish butter derived from pasture or conventional TMR feeding systems. A Pearson correlation analysis confirmed high positive correlations between the spectral bin at 1,657 cm^-1, ascribed to the stretching vibrations of C=C bonds, and concentrations of α-linolenic acid and conjugated linolenic acid (CLA) in butter, and in general with the concentration of n-3 and n-3+CLA fatty acids and polyunsaturated fatty acids in the samples. The yellow color indicative of the presence of carotenoids in butter, which has previously been suggested as a biomarker of pasture or "Grass-Fed" feeding, was also positively correlated with the data obtained from the Raman spectra. Raman spectroscopy could also be used to accurately predict indicators of the nutritional quality of butter, such as the thrombogenic index, which showed a strong negative correlation with the spectral bin at 3,023 cm^-1.

Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef

Australian grass and grain-fed beef products attract premium prices at sale and several beef processors market beef underwritten by production system claims. This preliminary investigation assessed the feasibility of using Raman spectroscopy to detect differences in the chemical composition of subcutaneous fat from cattle raised in extensive and intensive production systems. Raman spectra, fatty acid composition, β-carotene composition and objective colour measurements were measured on 150 grass and 150 grain-fed cattle. Spectral differences at peaks including 1069 cm^-1, 1127 cm^-1, 1301 cm^-1 and 1445 cm^-1 suggest that Raman spectra is able to detect differences in saturated fatty acids, which were significantly higher in carcases from grain-fed cattle. Differences in spectra at 1658 cm^-1 were observed, however further research is required to investigate the cause of this spectral feature. Overall, this study indicated that Raman spectroscopy is a potential tool for the authentication of beef carcases from grass and grain-fed production systems.

Evaluation of the quality of local butters: A new approach based on Raman spectroscopy and supported by the classical pycnometer method

In this study, the quality of the local Romanian butters was investigated using the classical pycnometer and optic microscopy methods, combined with Raman spectroscopy. We used a pool of 10 samples with different characteristics, and analyzed them by the three aforementioned methods. Pycnometric measurements showed a direct correlation between the fat content and the density values of the samples. Raman spectroscopy validated the results from the pycnometric measurements and the optical microscopy and indicated several other properties, such as protein content, hydration, saturation level of the polycarbonate chains, as well as the total cis isomer content and the type of arrangement preferred by the aliphatic chains (polymorphic transition). The methods employed in the present study have a strong potential to become analytical tools for the food industry and food safety agencies in order to assess the quality of butters and margarines, in a fast and cost-effective manner.

Detection of Adulterants and Contaminants in Liquid Foods-A Review

Milk and fruit juices have paramount importance in human diet. Increasing demand of these liquid foods has made them vulnerable to economic adulteration during processing and in supply chain. Adulterants are difficult to detect by consumers and thus necessitating the requirement of rapid, accurate and sensitive detection. The potential adulterants in milk and fruit juices and their limits set by different regulatory bodies have been briefly described in this review. Potential advantages and limitations of various techniques such as physicochemical methods, chromatography, immunoassays, molecular, electrical, spectroscopy with chemometrics, electronic nose, and biosensors have been described. Spectroscopy in combination with chemometrics has shown potential for rapid, precise, and sensitive detection of potential adulterants in these liquid foods.

Prediction of total fatty acid parameters and individual fatty acids in pork backfat using Raman spectroscopy and chemometrics: Understanding the cage of covariance between highly correlated fat parameters

This study investigates how Partial Least Squares regression models for predicting individual fatty acids (FAs) and total FA parameters depend on Raman spectral variation associated with the iodine value in pork backfat. The backfat was sampled from pigs, which were fed with different dietary fat sources and levels. Good correlations between the Raman spectra and the total FA composition parameters and most individual FAs were obtained (R(CV)(2)=0.78-0.90). However, the predictions of the individual FAs are indirect and to a high degree depend on co-variance with the total FA parameters. A new procedure was demonstrated for identifying and characterizing such indirect or non-targeted calibrations. This information is very useful when Raman spectroscopy or other vibrational spectroscopic techniques are used to predict non-targeted quality parameters such as individual FAs as they may lead to inaccurate predictions of future sample if the underlying covariance structure is changed e.g. by new dietary regimes or genotypes.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.

Determination of omega-3 fatty acids in fish oil supplements using vibrational spectroscopy and chemometric methods

The potential of Fourier transform infrared (FT-IR), near-infrared (NIR), and Raman spectroscopic techniques combined with partial least squares (PLS) regression (PLSR) to predict concentrations of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and total omega-3 fatty acids (n-3 FAs) in fish oil supplements was investigated. FT-IR spectroscopy predicted EPA (coefficient of determination (R(2)) of 0.994, standard error of cross-validation (SECV) of 2.90%, and standard error of prediction (SEP) of 2.49%) and DHA (R(2) = 0.983, SECV = 2.89%, and SEP = 2.55%) with six to seven PLS factors, whereas a simpler PLS model with two factors was obtained for total n-3 FAs (R(2) = 0.985, SECV = 2.73%, and SEP = 2.75%). Selected regions in the NIR spectra gave models with good performances and predicted EPA (R(2) = 0.979, SECV = 2.43%, and SEP = 3.11%) and DHA (R(2) = 0.972, SECV = 2.34%, and SEP = 2.60%) with four to six PLS factors. Both the whole and selected NIR regions gave simple models (two PLS factors) with similar results (R(2) = 0.997, SECV = 2.18%, and SEP = 1.60%) for total n-3 FAs. The whole and selected regions of Raman spectra provided models with comparable results and predicted EPA (R(2) = 0.977, SECV = 3.18%, and SEP = 2.73%) and DHA (R(2) = 0.966, SECV = 3.31%, and SEP = 2.56%) with seven to eight PLS factors, whereas a simpler model (three PLS factors) with R(2) = 0.993, SECV = 2.82%, and SEP = 3.27% was obtained for total n-3 FAs. The results demonstrated that FT-IR, NIR, and Raman spectroscopy combined with PLSR can be used as simple, fast, and nondestructive methods for quantitative analysis of EPA, DHA, and total n-3 FAs. FT-IR and NIR spectroscopy, in particular, have the potential to be applied in process industries during production of fish oil supplements.

Quality and statistical classification of Brazilian vegetable oils using mid-infrared and Raman spectroscopy

Palm oil, soy oil, sunflower oil, corn oil, castor oil, and rapeseed oil were analyzed with Fourier transform infrared (FT-IR) and FT-Raman spectroscopy. The quality of different oils was evaluated and statistically classified by principal component analysis (PCA) and a partial least squares (PLS) regression model. First, a calibration set of spectra was selected from one sampling batch. The qualitative variations in spectra are discussed with a prediction of oil composition (saturated, mono- and polyunsaturated fatty acids) from mid-infrared analysis and iodine value from FT-Raman analysis, based on ratioing the intensity of bands at given wavenumbers. A more robust and convincing oil classification is obtained from two-parameter statistical models. The statistical analysis of FT-Raman spectra favorably distinguishes according to the iodine value, while the mid-infrared spectra are most sensitive to hydroxyl moieties. Second, the models are validated with a set of spectra from another sampling batch, including the same oil types as-received and after different aging times together with a hydrogenated castor oil and high-oleic sunflower oil. There is very good agreement between the model predictions and the Raman measurements, but the statistical significance is lower for mid-infrared spectra. In the future, this calibration model will be used to check vegetable oil qualities before using them in polymerization processes.

Fast and minimally invasive determination of the unsaturation index of white fat depots by micro-Raman spectroscopy

In the last 20 years increasing interest has been devoted to the investigation of white adipose tissue (WAT) because hypo- or hyperfunction of WAT is involved in the pathogenesis of obesity and other pathologies. The investigation and discrimination of different characteristics in adipose tissues by means of spectroscopic techniques appears as a topic of current interest, also in view of possible medical-technological applications. The aim of this work was to establish micro-Raman spectroscopy as a tool for the characterization of mammals fat tissue. After preliminary tests aimed at defining a suitable sample preparation protocol, Raman spectra of WAT specimens excised from mice of different ages were recorded in the energy range 750-3,350 cm⁻¹. Quantitative values of the unsaturation index were obtained through the calibration with HR-NMR spectra of lipid extracts. Raman spectroscopy detected a sharp increase in the unsaturation index between 22 and 30 days of age in close correspondence with the weaning of mice (21 days). The present results show that Raman spectroscopy is an inexpensive, fast and robust technique to analyze the unsaturation index of mammals fat tissues that could be routinely used in bioptic samples.

Identifying the spatial distribution of vitamin E, pulmonary surfactant and membrane lipids in cells and tissue by confocal Raman microscopy

Every organ compromises of several different cell types. When studying the effects of a chosen compound within this organ or tissue uptake, localisation, metabolism, and the effect itself can be expected to differ between cells. Using the example of Vitamin E in pulmonary tissue we introduce confocal Raman Microscopy as a superior method to localise lipid-soluble compounds within tissues and cells. We describe the analyses of vitamin E, its oxidation products, and metabolites as well as pulmonary surfactant phospholipids in fixed lung tissue sections. Examples of main structural membrane lipids (PC, cholesterol) and an example of a lipid-signalling molecule (ceramide) are also included. Confocal Raman microscopy is a non-destructive optical method of analysing chemical and physical composition of solids, liquids, gases, gels, and solutions. The method is rich in information allowing discrimination of chemically similar molecules (including geometric isomers) and sensitive monitoring of subtle physical interactions. Additionally, Raman spectroscopy is relatively insensitive to water allowing the analysis of aqueous solutions and suspensions typical in biochemistry. In contrast, Raman spectroscopy is sensitive to non-polar molecules making it ideal for lipidomics research.

Raman spectroscopic prediction of the solid fat content of New Zealand anhydrous milk fat

The functionality of anhydrous milk fat (AMF) is determined from solid fat content (SFC) and triacylglycerol (TG) profiles, parameters traditionally measured using nuclear magnetic resonance and high pressure liquid chromatography respectively. Raman spectroscopy coupled with partial least squares (PLS) analysis has been assessed as an alternative method for SFC and TG class quantification. Sample temperature at which the Raman spectra were collected, method of spectral preprocessing and type of PLS analysis were all investigated and found to significantly affect the resulting calibrations (as parameterized by root mean square error of cross validation). Physically heterogeneous AMF samples held at 20 °C were shown to allow reliable SFC predictions on the basis of collected Raman spectra. In contrast to SFC calibrations, physically homogenous samples in a liquid form were ideal for TG class concentration predictions, however, not all TG classes could be reliably predicted.

Determination of omega-6 and omega-3 fatty acids in pork adipose tissue with nondestructive Raman and fourier transform infrared spectroscopy

In order to predict omega-6 and omega-3 fatty acids in the diet of humans, seventy-three pork back fat adipose tissue samples were measured with Raman spectroscopy directly on adipose tissue and on melted fat. Melted fat samples were, in addition, measured with Fourier transform infrared (FT-IR) spectroscopy. Gas chromatography analyses were conducted as the reference analysis. Partial least squares regression (PLSR) was used to calibrate and validate all models predicting omega-3 and omega-6 fatty acids contents from spectra. Omega-6 fatty acids in melted fat measured with FT-IR was predicted with a correlation coefficient (R) of 0.93 and a root mean square error of cross-validation (RMSECV) of 1.61% of the total amount of fatty acids. Raman spectra measured on melted fat gave a prediction of omega-6 fatty acids with R=0.97, and RMSECV=0.99% of total amount of fatty acids. Omega-6 fatty acids were predicted with R=0.94, and RMSECV=1.50% of the total amount of fatty acids using Raman spectra recorded on adipose tissue. For omega-3 fatty acids, the highest R=0.91, and lowest RMSECV=0.23% of the total amount of fatty acids were obtained from Raman spectra acquired on melted fat. FT-IR and Raman spectroscopy may be used as rapid, nondestructive methods to determine omega-6 and omega-3 fatty acids in melted fat. Raman spectroscopy can also be used directly on adipose tissue.

Screening Tablets for DOB Using Surface-Enhanced Raman Spectroscopy

2,5,-Dimethoxy-4-bromoamphetamine (DOB) is of particular interest among the various "ecstasy" variants because there is an unusually long delay between consumption and effect, which dramatically increases the danger of accidental overdose in users. Screening for DOB in tablets is problematic because it is pharmacologically active at 0.2-3 mg, which is c. 50 times less than 3,4-methylenedioxy-N-methylamphetamine (MDMA) and makes it more difficult to detect in seized tablets using conventional spot tests. The normal Raman spectra of seized DOB tablets are dominated by the bands of the excipient with no evidence of the drug component. Here we report the first use of on-tablet surface-enhanced Raman spectroscopy (SERS) to enhance the signal from a low concentration drug. Raman studies (785-nm excitation) were carried on series of model DOB/lactose tablets (total mass c. 400 mg) containing between 1 mg and 15 microg of DOB and on seized DOB tablets. To generate surface-enhanced spectra, 5 microL of centrifuged silver colloid was dispensed onto the upper surface of the tablets, followed by 5 microL of 1.0 mol/dm(3) NaCl. The probe laser was directed onto the treated area and spectra accumulated for c. 20 sec (10 sec x 2). It was found that the enhancement of the DOB component in the model tablets containing 1 mg DOB/tablet and in the seized tablets tested was so large that their spectra were completely dominated by the vibrational bands of DOB with little or no contribution from the unenhanced lactose excipient. Indeed, the most intense DOB band was visible even in tablets containing just 15 microg of the drug. On-tablet surface-enhanced Raman spectroscopy is a simple method to distinguish between low dose DOB tablets and those with no active constituent. The fact that unique spectra are obtained allows identification of the drug while the lack of sample preparation and short signal accumulation times mean that the entire test can be carried out in <1 min.

Classification of adipose tissue species using Raman spectroscopy

In this study multivariate analysis of Raman spectra has been used to classify adipose tissue from four different species (chicken, beef, lamb and pork). The adipose samples were dissected from the carcass and their spectra recorded without further preparation. 102 samples were used to create and compare a range of statistical models, which were then tested on 153 independent samples. Of the classical multivariate methods employed, Partial Least Squares Discriminant Analysis (PLSDA) performed best with 99.6% correct classification of species in the test set compared with 96.7% for Principal Component Linear Discrimination Analysis (PCLDA). Kohenen and Feed-forward artificial neural networks compared well with the PLSDA, giving 98.4 and 99.2% correct classification, respectively.

The use of Raman microscopy to determine and localize vitamin E in biological samples

Alpha-tocopherol (aT), the predominant form of vitamin E in mammals, is thought to prevent oxidation of polyunsaturated fatty acids. In the lung, aT is perceived to be accumulated in alveolar type II cells and secreted together with surfactant into the epithelial lining fluid. Conventionally, determination of aT and related compounds requires extraction with organic solvents. This study describes a new method to determine and image the distribution of aT and related compounds within cells and tissue sections using the light-scattering technique of Raman microscopy to enable high spatial as well as spectral resolution. This study compared the nondestructive analysis by Raman microscopy of vitamin E, in particular aT, in biological samples with data obtained using conventional HPLC analysis. Raman spectra were acquired at spatial resolutions of 2-0.8 microm. Multivariate analysis techniques were used for analyses and construction of corresponding maps showing the distribution of aT, alpha-tocopherol quinone (aTQ), and other constituents (hemes, proteins, DNA, and surfactant lipids). A combination of images enabled identification of colocalized constituents (heme/aTQ and aT/surfactant lipids). Our data demonstrate the ability of Raman microscopy to discriminate between different tocopherols and oxidation products in biological specimens without sample destruction. By enabling the visualization of lipid-protein interactions, Raman microscopy offers a novel method of investigating biological characterization of lipid-soluble compounds, including those that may be embedded in biological membranes such as aT.

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.

Prediction of adipose tissue composition using raman spectroscopy: Average properties and individual fatty acids

Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (> or = 2 double bonds per chain) was also well predicted with R2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.