NMR spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free milk, and milk substitutes based on soy and grains

Instant quantification of sugars in milk tablets using near-infrared spectroscopy and chemometric tools

Milk tablets are a popular dairy product in many Asian countries. This research aimed to develop an instant and rapid method for determining sucrose and lactose contents in milk tablets using near-infrared (NIR) spectroscopy. For the quantitative analysis, a training set composed of laboratory-scale milk samples was generated based on a central composite design (CCD) and used to establish partial least squares (PLS) regression for the predictions of sucrose and lactose contents resulting in R² values of 0.9749 and 0.9987 with the corresponding root mean square error of calibration (RMSEC) values of 1.69 and 0.35. However, the physical difference between the laboratory-scale powder and the final product milk tablet samples resulted in spectral deviations that dramatically affected the predictive performance of the PLS models. Therefore, calibration transfer methods called direct standardization (DS) and piecewise direct standardization (PDS) were used to adjust the NIR spectra from the real milk tablet samples before the quantitative prediction. Using high-performance liquid chromatography (HPLC) as a reference method, the developed NIR-chemometric model could be used to instantly predict the sugar contents in real milk tablets by producing root mean square error of prediction (RMSEP) values for sucrose and lactose of 5.04 and 4.22 with Q² values of 0.7973 and 0.9411, respectively, after the PDS transformation.

Terahertz Spectroscopic Analysis of Lactose in Infant Formula: Implications for Detection and Quantification

Lactose plays a significant role in daily lives as a constituent of various food and pharmaceutical products. Yet, lactose intolerance conditions demand low-lactose and lactose-free products in the market. These increasing nutritional claims and labels on food products entail simple and reliable methods of analysis that can be used for meeting quality standards, nutritional claims and legal requirements. In this study, terahertz time-domain spectroscopy (THz-TDS) was employed to analyse α-lactose monohydrate qualitatively and quantitatively in food products. Both absorption spectra and absorption coefficient spectra were investigated for their prediction performance. Regression models for lactose quantification using peak area and height of the absorption peaks 0.53 and 1.37 THz were developed and assessed in infant formula samples. Satisfactory prediction results were achieved in ideal conditions with pure standards, but not in all predictions of infant formula samples. Reasons and further implications are discussed.

Application of accelerated heteronuclear single quantum coherence experiments to the rapid quantification of monosaccharides and disaccharides in dairy products

Monosaccharides and disaccharides are important dietary components, but if insufficiently metabolized by some population subgroups, they are also linked to disease patterns. Thus, the correct analytical identification, quantification, and labeling of these food components are crucial to inform and potentially protect consumers. Enzymatic assays and high-performance anion-exchange chromatography with pulsed amperometric detection are established methods for the quantification of monosaccharides and disaccharides that, however, require long measuring times (60-180 min). Accelerated methods for the identification and quantification of the nutritionally relevant monosaccharides and disaccharides d-glucose, d-galactose, d-fructose, sucrose, lactose, and maltose were therefore developed. To realize this goal, the NMR experiments HSQC (heteronuclear single quantum coherence) and acceleration by sharing adjacent polarization (ASAP)-HSQC were applied. Measurement times were reduced to 27 and 6 min, respectively, by optimizing the interscan delay and applying non-uniform sampling. The optimized methods were used to quantify d-glucose, d-galactose, d-fructose, sucrose, and lactose in various dairy products. Results of the HSQC and ASAP-HSQC methods are equivalent to the results of the reference methods in terms of both precision and accuracy, demonstrating that these methods can be used to correctly analyze nutritionally relevant monosaccharides and disaccharides in short times.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Investigation of the Applicability of Raman Spectroscopy as Online Process Control during Consumer Milk Production

Online detection of product defects using fast spectroscopic measurements is beneficial for producers in the dairy industry since it allows readjustment of product characteristics or redirection of product streams during production. Raman spectroscopy has great potential for such application due to the fast and simple measurement. Its suitability as online sensor for process control was investigated at typical control points in consumer milk production being raw milk storage, standardization, and heat treatment. Additionally, the appropriateness of Raman spectroscopy to act as indicator for product application parameters was investigated using the example of barista foam. To assess the suitability of a pure online system, the merit of Raman spectra was evaluated by a principal component analysis (PCA). Thereby, proteolytic spoilage due to the presence of extracellular enzymes of Pseudomonas sp. was detected and samples based on the applied heat treatment (extended shelf life (ESL) and ultra-high temperature (UHT)) could be separated. A correlation of the content of free fatty acids and foam stability with spectra of the respective milk samples was found, allowing a prediction of the technofunctional quality criterion “Barista” suitability of a UHT milk. The results underlined the suitability of Raman spectroscopy for the detection of deviations from a defined product standard of consumer milk.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Variance of Commercial Powdered Milks Analyzed by Proton Nuclear Magnetic Resonance and Impact on Detection of Adulterants

Proton nuclear magnetic resonance spectra for 66 commercial powdered milk samples were analyzed by principal component analysis, soft independent modeling of class analogy, and pooled, crossed analysis of variance. It was found that the sample type (skim milk powder or non-fat dry milk), the supplier, the production site, the processing temperature (high, medium, or low temperature), and the day of analysis provided statistically significant sources of variation. Interestingly, inexact alignment (deviations of ±0.002 ppm) of the spectral reference peak was a significant source of variation, and fine alignment was necessary before the variation arising from the other experimental factors could be accurately evaluated. Using non-targeted analysis, the lowest detectable adulteration for dicyandiamide, melamine, and sucrose was 0.05%, the lowest detectable adulteration for maltodextrin and urea was 0.5%, the lowest detectable adulteration for ammonium sulfate and whey was 5%, and the lowest adulteration for soy protein isolate was undetectable using methods described herein. The measurement of variance and detection of adulteration were relatively unaffected by the resolution. Similar results were obtained with unbinned data (0.0003 ppm resolution) and binning of 333 data points (0.1 ppm resolution).

NMR metabolomic fingerprinting distinguishes milk from different farms

A fast and reproducible protocol for milk Nuclear Magnetic Resonance (NMR) metabolomic fingerprinting was developed, allowing for an accurate discrimination among milk samples from large-scale distribution, as well as among milk sample from different farms located in the same restricted geographical area. Seasonal variations in milk composition and correlations with cows' nutritional patterns are also assessed, underlining relationships between feeding and metabolites. The most important difference was related to the use of silage feeding. This finding is relevant to assess the suitability of milk for different dairy products. A prominent example is parmesan cheese, the preparation protocol of which excludes milk from silage-fed cows.

Real-Time Monitoring of Chemical Changes in Three Kinds of Fermented Milk Products during Fermentation Using Quantitative Difference Nuclear Magnetic Resonance Spectroscopy

Fermented milk products are rising in popularity throughout the world as a result of their health benefits, including improving digestion, normalizing the function of the immune system, and aiding in weight management. This study applies an in situ quantitative nuclear magnetic resonance method to monitor chemical changes in three kinds of fermented milk products, Bulgarian yogurt, Caspian Sea yogurt, and kefir, during fermentation. As a result, the concentration changes in nine organic compounds, α/β-lactose, α/β-galactose, lactic acid, citrate, ethanol, lecithin, and creatine, were monitored in real time. This revealed three distinct metabolic processes in the three fermented milk products. Moreover, pH changes were also determined by variations in the chemical shift of citric acid during the fermentation processes. These results can be applied to estimate microbial metabolism in various flora and help guide the fermentation and storage of various fermented milk products to improve their quality, which may directly influence human health.

Comparison of HPLC-RI, LC/MS-MS and enzymatic assays for the analysis of residual lactose in lactose-free milk

Lactose intolerance is the decreased ability to digest lactose, and the population involved is rapidly increasing all over the world. Different procedures have been reported in the literature to quantify lactose in dairy products, but the official method of analysis is based on enzymatic assay. In this paper, the effectiveness of two enzymatic kits in detecting residual lactose in lactose-free milk was investigated, and a comparison with two alternative chromatographic methods was done. The investigation used several samples of UHT milk containing different levels of lactose, and the results highlighted the inadequacy of the enzymatic assays and of the HPLC-RI method to analyse lactose-free milk. An LC-MS/MS method using the formate adduct was developed, and it allowed quantitation of lactose and lactulose in all samples at a high level of precision and repeatability.

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non-negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS)] are applied for simultaneous (1)H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight-component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography-mass spectrometry as well as the MCR-ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures.

Applications of NMR in dairy research

NMR is a robust analytical technique that has been employed to investigate the properties of many substances of agricultural relevance. NMR was first used to investigate the properties of milk in the 1950s and has since been employed in a wide range of studies; including properties analysis of specific milk proteins to metabolomics techniques used to monitor the health of dairy cows. In this brief review, we highlight the different uses of NMR in the dairy industry.

NMR investigation of acrolein stability in hydroalcoholic solution as a foundation for the valid HS-SPME/GC-MS quantification of the unsaturated aldehyde in beverages

Acrolein (propenal) is found in many foods and beverages and may pose a health hazard due to its cytotoxicity. Considerable knowledge gaps regarding human exposure to acrolein exist, and there is a lack of reliable analytical methods. Hydroalcoholic dilutions prepared for calibration purposes from pure acrolein show considerable degradation of the compound and nuclear magnetic resonance (NMR) spectroscopy showed that 1,3,3-propanetriol and 3-hydroxypropionaldehyde are formed. The degradation can be prevented by addition of hydroquinone as stabilizer to the calibration solutions, which then show linear concentration-response behaviour required for quantitative analysis. The stabilized calibration solutions were used for quantitative headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) determination of acrolein in alcoholic beverages with a detection limit of 14 μg L(-1). Of 117 tested alcoholic beverages, 64 were tested positive with the highest incidence in grape marc spirits and whiskey (100%, mean 252 μg L(-1)), followed by fruit spirits (86%, mean 591 μg/L(-1)), tequila (86%, mean 404 μg L(-1)), Asian spirits (43%, mean 54 μg L(-1)) and wine (9%, mean 0.7 μg L(-1)). Acrolein could not be detected in beer, vodka, absinthe and bottled water. Six of the fruit and grape marc spirits had acrolein levels above the World Health Organization (WHO) provisional tolerable concentration of 1.5 mg L(-1).

Two-dimensional ³¹P,¹H NMR spectroscopic profiling of phospholipids in cheese and fish

Phospholipids (PLs) comprise an important lipid class in food because of their technological use as emulsifiers and their nutritional value. This study used one-dimensional (31)P NMR and two-dimensional (2D) (31)P,(1)H COSY NMR spectroscopy for the determination of the PL composition of cheese and fish after liquid-liquid enrichment. This extraction step enabled the identification of 10 PLs in cheese and 9 PLs in fish by 2D (31)P,(1)H NMR. Variations in the (31)P shifts indicated differences in the fatty acids attached to the individual PLs. The total PL content in cheese fat and fish oil ranged from 0.3 to 0.4% and from 5 to 12%, respectively. Phosphatidylcholine was the most prominent PL in both matrices (up to 65%). Minor PLs (limit of detection = 4 nmol, i.e. 500 μL of an 8 μM solution) were identified in forms of phosphatidic acid, lysophosphatidic acid, and phosphatidylglycerol. Specific cross couplings and (1)H fine structures in the 2D (31)P,(1)H NMR spectra proved to be valuable for the assignment and verification of known and uncommon PLs in the samples.

Identification of Imitation Cheese and Imitation Ice Cream Based on Vegetable Fat Using NMR Spectroscopy and Chemometrics

Vegetable oils and fats may be used as cheap substitutes for milk fat to manufacture imitation cheese or imitation ice cream. In this study, 400 MHz nuclear magnetic resonance (NMR) spectroscopy of the fat fraction of the products was used in the context of food surveillance to validate the labeling of milk-based products. For sample preparation, the fat was extracted using an automated Weibull-Stoldt methodology. Using principal component analysis (PCA), imitation products can be easily detected. In both cheese and ice cream, a differentiation according to the type of raw material (milk fat and vegetable fat) was possible. The loadings plot shows that imitation products were distinguishable by differences in their fatty acid ratios. Furthermore, a differentiation of several types of cheese (Edamer, Gouda, Emmentaler, and Feta) was possible. Quantitative data regarding the composition of the investigated products can also be predicted from the same spectra using partial least squares (PLS) regression. The models obtained for 13 compounds in cheese (R (2) 0.75-0.95) and 17 compounds in ice cream (R (2) 0.83-0.99) (e.g., fatty acids and esters) were suitable for a screening analysis. NMR spectroscopy was judged as suitable for the routine analysis of dairy products based on milk or on vegetable fat substitutes.

NMR-Based Milk Metabolomics

Milk is a key component in infant nutrition worldwide and, in the Western parts of the world, also in adult nutrition. Milk of bovine origin is both consumed fresh and processed into a variety of dairy products including cheese, fermented milk products, and infant formula. The nutritional quality and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking the milk metabolite profiling with nutritional aspects, and applications which aim to link the milk metabolite profile to various technological qualities of milk. The metabolite profiling studies encompass the identification of novel metabolites, which potentially can be used as biomarkers or as bioactive compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining a better understanding of how milk composition is linked to nutritional or quality traits.

Formaldehyde in hair straightening products: rapid ¹H NMR determination and risk assessment

Despite official regulations, the illegal use of formaldehyde-containing or releasing hair straightening products has become a popular practice in Europe and high contents of formaldehyde in such products have been reported. In this study, a methodology utilizing (1)H NMR spectroscopy has been developed to measure the concentration of formaldehyde in hair straightening products. For sample preparation, a dilution and alkaline hydrolysis is required. The total formaldehyde content can then be quantified by a distinct peak of the CH2 group of the methanediol molecule in the δ4.84-4.82 ppm range. The developed methodology was applied for the analysis of 10 hair straightening products. Seven of these products contained detectable amounts of formaldehyde that were higher than the maximum allowed concentration of 0.2%. The formaldehyde content of these products was found to be in the range 0.42-5.83% with an average concentration of 1.46%. The accuracy and reliability of the NMR results were confirmed by the EU reference photometric method. The air formaldehyde concentrations after application of hair straightening products were estimated in ranges 20-423 ppm and 1-18 ppm (for 1 and 24 m(3) salon volume). A probabilistic exposure estimation using Monte Carlo simulation found the average formaldehyde concentration to be 6 ppm (standard deviation 15 ppm). All exposure scenarios considerably exceeded the safe level of 0.1 ppm. Our findings confirmed that the risk of cosmetic formulations with formaldehyde above 0.2% is not negligible, as these products may facilitate considerable exposure of formaldehyde for consumers especially for salon workers.