Impact of freeze-drying and subsequent storage on milk metabolites based on 1H NMR and UHPLC-QToF/MS

Identification of pasteurized mare milk and powder adulteration with bovine milk using quantitative proteomics and metabolomics approaches

Adulteration in dairy products presents food safety challenges, driven by economic factors. Processing may change specific biomarkers, thus affecting their effectiveness in detection. In this study, proteomics and metabolomics approaches were to investigate the detection of bovine milk (BM) constituents adulteration in pasteurized mare milk (PMM) and mare milk powder (MMP). Several bovine proteins and metabolites were identified, with their abundances in PMM and MMP increasing upon addition of BM. Proteins like osteopontin (OPN) and serotransferrin (TF) detected adulteration down to 1 % in PMM, whereas these proteins in MMP were utilized to identify 10 % adulteration. Biotin and N6-Me-adenosine were effective in detecting adulteration in PMM as low as 10 % and 1 % respectively, while in MMP, their detection limits extend down to 0.1 %. These findings offer insights for authenticating mare milk products and underscore the influence of processing methods on biomarker levels, stressing the need to consider these effects in milk product authentication.

Multi-omics in food safety and authenticity in terms of food components

One of the main goals of food science is to ensure the high quality and safety of food. The inspection technology for known hazards has matured, and the identification of unknown and potential food safety hazards, as well as the identification of their composition and origin, is a challenge faced by food safety. Food safety and authenticity require multi-omics methods to support the implementation of qualitative discrimination to precise quantitative analysis, from targeted screening to non-target detection, and from multi component to full component analysis to address these challenges. The present review aims to provide characterizations, advantages, the latest progress, and prospects of using omics (including genomics, proteomics, and metabonomics) in food safety and authenticity. Multi omics strategies used to detect and verify different standard biomarkers of food will contribute to understanding the basic relationship between raw materials, processing, foods, nutrition, food safety, and human health.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Untargeted Plant Metabolomics: Evaluation of Lyophilization as a Sample Preparation Technique

Lyophilization is a common method used for stabilizing biological samples prior to storage or to concentrate extracts. However, it is possible that this process may alter the metabolic composition or lead to the loss of metabolites. In this study, the performance of lyophilization is investigated in the example of wheat roots. To this end, native and ¹³C-labelled, fresh or already lyophilized root samples, and (diluted) extracts with dilution factors up to 32 and authentic reference standards were investigated. All samples were analyzed using RP-LC-HRMS. Results show that using lyophilization for the stabilization of plant material altered the metabolic sample composition. Overall, 7% of all wheat metabolites detected in non-lyophilized samples were not detected in dried samples anymore, and up to 43% of the remaining metabolites exhibited significantly increased or decreased abundances. With respect to extract concentration, less than 5% of the expected metabolites were completely lost by lyophilization and the recovery rates of the remaining metabolites were slightly reduced with increasing concentration factors to an average of 85% at an enrichment factor of 32. Compound annotation did not indicate specific classes of wheat metabolites to be affected.

Yak (Bos grunniens) milk improves bone mass and microarchitecture in mice with osteoporosis

The effect of milk on bone health is controversial. In this study, the effects of yak milk in mice with retinoic acid-induced osteoporosis (OP) were evaluated. Yak milk was provided to OP mice as a nutrition supplement for 6 wk. The results showed that yak milk significantly reduced bone turnover markers (tartrate acid phosphatase and alkaline phosphatase). The yak milk treatment was also associated with remarkably increased bone mineral density, bone volume, trabecular thickness, and trabecular number, as well as improved biomechanical properties (maximum load and stress) of the tibia. Furthermore, yak milk mitigated the deterioration of the network and thickness of trabecular bone in treated OP mice compared with the OP model group. The results indicated that yak milk could improve bone mass and microarchitecture through the inhibition of bone resorption in OP mice.

NMR-based foodomics of common tubers and roots

Common roots and tubers such as arracacha, Asterix potato, cassava, potato, sweet potato, taro, and yam are consumed by millions of people. These foods are an integral part of the diet in developing countries and are nutritionally important as energy reserves due to their carbohydrate content. Although many studies have been performed on these foods, comparative chemical profiles have been still poorly evaluated. In this work, we applied nuclear magnetic resonance (NMR) analysis associated with chemometrics to evaluate the chemical composition of extracts obtained in deuterated water from roots and tubers that are commercially consumed in Brazil and the rest of the world. From the 31 metabolites characterized in the extracts, 22 were quantified. Multivariate analyses showed 8 metabolites which were primary responsible for the distinction between samples, including choline, γ-aminobutyrate (GABA), glutamine, asparagine, isoleucine, fructose, glucose, and sucrose. Thus, our work shows important information on the chemical composition in addition to the mere carbohydrate content of these food matrices. This knowledge can provide information about food safety and beneficial nutritional values of the studied tubers and roots, which can be useful to consumers and the food industry.

Foodomic-Based Approach for the Control and Quality Improvement of Dairy Products

The food quality assurance before selling is a needed requirement intended for protecting consumer interests. In the same way, it is also indispensable to promote continuous improvement of sensory and nutritional properties. In this regard, food research has recently contributed with studies focused on the use of 'foodomics'. This review focuses on the use of this technology, represented by transcriptomics, proteomics, and metabolomics, for the control and quality improvement of dairy products. The complex matrix of these foods requires sophisticated technology able to extract large amounts of information with which to influence their aptitude for consumption. Thus, throughout the article, different applications of the aforementioned technologies are described and discussed in essential matters related to food quality, such as the detection of fraud and/or adulterations, microbiological safety, and the assessment and improvement of transformation industrial processes (e.g., fermentation and ripening). The magnitude of the reported results may open the door to an in-depth transformation of the most conventional analytical processes, with the introduction of new techniques that allow a greater understanding of the biochemical phenomena occurred in this type of food.

Effects of the Oxygen Content and Light Intensity on Milk Photooxidation Using Untargeted Metabolomic Analysis

Photooxidation is the main cause of milk quality deterioration during processing and transportation. Oxygen and light are responsible for dairy milk photooxidation. The objective of this study was to determine the characteristic metabolites after photooxidation and how they are affected by oxygen exposure and light intensity. Ultra-performance liquid chromatography coupled with triple time-of-flight mass spectrometry and multivariate data analysis were used for the high-throughput evaluation of milk photooxidation. Four products were identified as biomarkers: uric acid, riboflavin, lumichrome, and indole-3-carboxaldehyde. Afterward, the effects of oxygen content and light intensity on the biomarkers were investigated, and a sensory evaluation was performed. Both oxygen exposure and light intensity affected the contents of photooxidation biomarkers in milk samples. The sensory score correlated well with the oxygen content but not with the light intensity. The untargeted metabolomic method was an effective tool to identify biomarkers for milk photooxidation evaluation.

Metabolomic Approaches to Studying the Response to Drought Stress in Corn (Zea mays) Cobs

Metabolomics is a technique that allows for the evaluation of the entire extractable chemical profile of a plant, for example, using high-resolution mass spectrometry (HRMS) and can be used to evaluate plant stress responses, such as those due to drought. Metabolomic analysis is dependent upon the efficiency of the extraction protocol. Currently, there are two common extraction procedures widely used in metabolomic experiments, those that extract from plant tissue processed in liquid nitrogen or extraction from lyophilised plant tissues. Here, we evaluated the two using non-targeted metabolomics to show that lyophilisation can stabilise the maize (Zea mays) extractable metabolome, increasing throughput and efficiency of extraction as compared to the more traditional processing in liquid nitrogen. Then, we applied the lyophilisation approach to explore the effect of drought upon the maize metabolome in a non-targeted HRMS metabolomics approach. Metabolomics revealed differences in the mature maize metabolome having undergone three drought conditions imposed at two critical development stages (three-leaf stage and grain-fill stage); moreover, this difference was observed across two tissue types (kernel and inner cob/pith). It was shown that under ideal conditions, the biochemical make-up of the tissue types is different. However, under stress conditions, the stress response dominates the metabolic profile. Drought-related metabolites known from other plant systems have been identified and metabolomics has revealed potential novel drought-stress indicators in our maize system.

Quality Assessment and Antioxidant Activities of the Blossoms of Inula Nervosa Wall

BACKGROUND

Currently, although Inula nervosa Wall is substantially investigated, little is understood about blossoms of Inula nervosa Wall (BINW).

OBJECTIVE

In this work, we systematically investigated the antioxidant activity of the extract from BINW by various standard assays including 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical ability, 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) di-ammonium salt radical cation (ABTS), and ferric reducing antioxidant potential (FRAP).

METHODS

Chemical compounds were tentatively identified through an UHPLC-QTOF-MS system. Furthermore, the contents of nine compounds were detected with UHPLC method coupled with photodiode array (PDA) detector. By carefully analyzing the quantitative data via clusters analysis and principal component analysis (PCA).

RESULTS

Forty-six compounds were tentatively identified, and our results showed that nine compound samples in 21 batches of BINW collected from different areas could be differentiated and analyzed by a heatmap visualization. In addition, the contents of nine compounds (flavonoids, phenolic acids) exhibited a total of higher amounts and better antioxidant activities from Yunnan than those from the other three origins.

CONCLUSIONS

Our study not only developed a powerful platform to explain the difference between traditional Chinese medicines species that are closely related through the chemometric and chemical profiling, but also presented a useful method to establish quality criteria of BINW with multiple origins.

HIGHLIGHTS

To characterize the BINW in detail, we not only performed DPPH, FRAP, and ABTS assays to investigate its antioxidant activity, but also established UHPLC-QTOF-MS/MS- and UHPLC-PDA-based methods to comprehensively identify and qualitatively analyze its components.

Detection of bovine milk adulteration in caprine milk with N-acetyl carbohydrate biomarkers by using 1H nuclear magnetic resonance spectroscopy

In a return to tradition, the popularity of caprine milk is on the rise. However, particularly in countries with developed dairy industries based on bovine milk, there is the risk of adulteration with bovine milk, which is a cheaper alternative. Thus, a rapid, robust, and simple method for the detection of bovine milk added to caprine milk is necessary, and ¹H nuclear magnetic resonance spectroscopy appears to provide a solution. A matrix of 115 pure and artificially adulterated pasteurized milk samples was prepared and used to discover biomarkers of bovine milk that are independent of chemical and biological variation caused by factors such as genetics, diet, or seasonality. Principal component analysis and orthogonal projections to latent structures discriminant analysis of pure bovine milk and pure caprine milk revealed spectral features that were assigned to the resonances of 4 molecules. Of these, the peaks corresponding to protons in the N-acetylglucosamine and N-acetylgalactosamine acetyl moieties showed significant applicability for our method. Receiver operating characteristic curve analysis was used to evaluate the performance of the peak integrals as biomarkers of adulteration. This approach was able to distinguish caprine milk adulterated with 5% of bovine milk with 84.78% accuracy and with 10% of bovine milk an excellent 95.65% accuracy. This study demonstrates that N-acetyl carbohydrates could be used as biomarkers for the detection of bovine milk in caprine milk and could help in protecting caprine milk authenticity.

Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage

The application of spectroscopic techniques can help in alleviating problems encountered during the processing of milk and dairy products. Indeed, traditional analytical methods (e.g., physicochemical measurements, sensory, chromatography) are relatively expensive, time-consuming, and require chemicals and sophisticated analytical equipment, and skilled operators. Hence, there is a need to develop faster and less costly methods for accurately monitoring changes in the quality of milk and other dairy products during processing and storage.Many nondestructive and noninvasive instrumental techniques are available for inline and online monitoring of food. These include fluorescence spectroscopy, mid-infrared (MIR), near-infrared (NIR), nuclear magnetic resonance (NMR), etc. These techniques are usually used in combination with chemometric tools a to explore the information present in spectral data.This review article will discuss the potential of the above-mentioned spectroscopic techniques for monitoring chemical modifications of dairy products and the prediction of their functional properties during processing. The advantages and disadvantages of each technique are also discussed in this review. Finally, some conclusions are drawn, and the future trends of these methods are presented.