Detection of plant protein in adulterated milk using nontargeted nano-high-performance liquid chromatography-tandem mass spectroscopy combined with principal component analysis

Detection of Ovine or Bovine Milk Components in Commercial Camel Milk Powder Using a PCR-Based Method

Food ingredient adulteration, especially the adulteration of milk and dairy products, is one of the important issues of food safety. The large price difference between camel milk powder, ovine, and bovine milk powder may be an incentive for the incorporation of ovine and bovine derived foods in camel milk products. This study evaluated the use of ordinary PCR and real-time PCR for the detection of camel milk powder adulteration based on the presence of ovine and bovine milk components. DNA was extracted from camel, ovine, and bovine milk powder using a deep-processed product column DNA extraction kit. The quality of the extracted DNA was detected by amplifying the target sequence from the mitochondrial Cytb gene, and the extracted DNA was used for the identification of milk powder based on PCR analysis. In addition, PCR-based methods (both ordinary PCR and real-time PCR) were used to detect laboratory adulteration models of milk powder using primers targeting mitochondrial genes. The results show that the ordinary PCR method had better sensitivity and could qualitatively detect ovine and bovine milk components in the range of 1% to 100% in camel milk powder. The commercial camel milk powder was used to verify the practicability of this method. The real-time PCR normalization system has a good exponential correlation (R² = 0.9822 and 0.9923) between ovine or bovine content and Ct ratio (specific/internal reference gene) and allows for the quantitative determination of ovine or bovine milk contents in adulterated camel milk powder samples. Accuracy was effectively validated using simulated adulterated samples, with recoveries ranging from 80% to 110% with a coefficient of variation of less than 7%, exhibiting sufficient parameters of trueness. The ordinary PCR qualitative detection and real-time PCR quantitative detection method established in this study proved to be a specific, sensitive, and effective technology, which is expected to be used for market detection.

Identification and quantification of fox meat in meat products by liquid chromatography-tandem mass spectrometry

Over the years, food adulteration has become an important global problem, threatening public health safety and the healthy development of food industry. This study established a liquid chromatography-tandem mass (LC-MS/MS) method for accurate identification and quantitative analysis of fox meat products. High-resolution mass was used for data collection, and Proteome Discoverer was used for data analysis to screen fox-specific peptides. Multivariate statistical analysis was conducted using the data obtained from the label-free analysis of different contents of simulated samples. Samples with different contents were distinguished without interfering with each other, suggesting the feasibility of quantitative analysis of fox meat content. The linear correlation coefficient and recovery rate were calculated to determine the fox peptides that can be used for accurate quantification. The established LC-MS/MS method can be used for the accurate identification and quantification of actual samples. In addition, this method can provide technical support for law enforcement departments.

Identification of milk from different animal and plant sources by desorption electrospray ionisation high-resolution mass spectrometry (DESI-MS)

This study used desorption electrospray ionisation mass spectrometry (DESI-MS) to analyse and detect and classify biomarkers in five different animal and plant sources of milk for the first time. A range of differences in terms of features was observed in the spectra of cow milk, goat milk, camel milk, soya milk, and oat milk. Chemometric modelling was then used to classify the mass spectra data, enabling unique or significant markers for each milk source to be identified. The classification of different milk sources was achieved with a cross-validation percentage rate of 100% through linear discriminate analysis (LDA) with high sensitivity to adulteration (0.1-5% v/v). The DESI-MS results from the milk samples analysed show the methodology to have high classification accuracy, and in the absence of complex sample clean-up which is often associated with authenticity testing, to be a rapid and efficient approach for milk fraud control.

Investigating changes of proteome in the bovine milk serum after retort processing using proteomics techniques

The objective of this study was to investigate the changes of the proteins in bovine milk serum after retort processing by label-free quantification proteomics techniques. A total of 96 and 106 proteins were quantified in control group (CG) and retort group (RG), respectively. Hierarchical clustering analysis of the identified milk serum proteins showed a decrease in the abundance of most proteins, such as serum albumin, lactoperoxidase, lactotransferrin, and complement C3, and an increase in the abundance of other proteins such as κ-casein, lipocalin 2, and Perilipin. Student's t-test showed 21 proteins significantly differential abundance between CG and RG (p < .05), of which intensity-based absolute quantification (iBAQ) of five proteins decreased and iBAQ of 16 proteins increased. Bioinformatics analysis demonstrated that retort processing increased the digestibility of proteins, but this improvement was offset by a decrease in the digestibility of proteins caused by protein modification. Our results provide insight into the proteome of retort sterilized milk for the first time. Given the extremely high security of retort sterilized milk, the proteome of bovine milk serum changes after retort sterilization exposed in this study will contribute to the formula design of retort sterilized milk products.

Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment

The ever-growing use of mass spectrometry (MS) methodologies in food authentication and traceability originates from their unrivalled specificity, accuracy and sensitivity. Such features are crucial for setting up analytical strategies for detecting food frauds and adulterations by monitoring selected components within food matrices. Among MS approaches, protein and peptide profiling has become increasingly consolidated. This review explores the current knowledge on recent MS techniques using protein and peptide biomarkers for assessing food traceability and authenticity, with a specific focus on their use for unmasking potential frauds and adulterations. We provide a survey of the current state-of-the-art instrumentation including the most reliable and sensitive acquisition modes highlighting advantages and limitations. Finally, we summarize the recent applications of MS to protein/peptide analyses in food matrices and examine their potential in ensuring the quality of agro-food products.

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.

A Comprehensive Workflow for the Analysis of Bio-Macromolecular Supplements: Case Study of 20 Whey Protein Products

The presence of bio-macromolecules as major ingredients is a primary factor in marketing many biologically derived macromolecular supplements. Workflows for analyzing these supplements for quality assurance, adulteration, and other supply-chain difficulties must include a qualitative assessment of small-molecule and macromolecular components; however, no such integrated protocol has been reported for these bio-macromolecular supplements. Twenty whey protein supplements were analyzed using an integrated workflow to identify protein content, protein adulteration, inorganic elemental content, and macromolecular and small-molecule profiles. Orthogonal analytical methods were employed, including NMR profiling, LC-DAD-QToF analysis of small-molecule components, ICP-MS analysis of inorganic elements, determination of total protein content by a Bradford assay, SDS-PAGE protein profiling, and bottom-up shotgun proteomic analysis using LC-MS-MS. All 20 supplements showed a reduced protein content compared to the claimed content but no evidence of adulteration with protein from an unclaimed source. Many supplements included unlabeled small-molecule additives (but nontoxic) and significant deviations in metal content, highlighting the importance of both macromolecular and small-molecule analysis in the comprehensive profiling of macromolecular supplements. An orthogonal, integrated workflow allowed the detection of crucial product characteristics that would have remained unidentified using traditional workflows involving either analysis of small-molecule nutritional supplements or protein analysis.

Identification of milk adulteration by a sensor array based on cationic polymer induced aggregation of a perylene probe

A novel fluorescence sensor array based on cationic polymer induced self-assembly of a perylene probe is developed. Cationic polymer induced aggregation of the carboxyl modified negatively charged perylene probe, and resulted in large quenching of monomer emission and generation of excimer emission. Upon the addition of negatively charged protein, monomer fluorescence restored with a decrease in excimer fluorescence. Based on these observations, we developed a six-channel sensor array to discriminate five main proteins in milk. In addition, we successfully identified pure milk out of different drinks using the developed sensor array since different drinks contained distinct species and contents of proteins. Furthermore, the sensor array exhibited excellent performance to discriminate milk adulterated by different concentrations of adulterants with 100% accuracy of cross validation. The analysis results also presented excellent linear correlation of adulterants contents and thus the developed sensor array shows great potential for quantitative detection of milk adulteration.

Electrochemical Profile Recording for Pueraria Variety Identification

The rapid identification of plant variety is valuable in both academic studies and crop production. However, rapid and accurate identification has been difficult because many varieties have very similar morphological characteristics and are susceptible to the effects of the growing environment. In this work, we established an electrochemical method for recording the electro-active profile of compounds in plant tissue. Because the chemical composition of different varieties is largely controlled by their genes, rather than a growing environment, this method has considerable potential for variety identification. Three varieties of Pueraria with sixteen locations were collected for confirming the feasibility of the proposed methodology. Principal component analysis and peak ratio analysis have been used for grouping the sample data. The results indicate the electrochemical profiles of three varieties can be distinguished using their voltammetric data.

Discrimination of bovine milk from non-dairy milk by lipids fingerprinting using routine matrix-assisted laser desorption ionization mass spectrometry

An important sustainable development goal for any country is to ensure food security by producing a sufficient and safe food supply. This is the case for bovine milk where addition of non-dairy milks such as vegetables (e.g., soya or coconut) has become a common source of adulteration and fraud. Conventionally, gas chromatography techniques are used to detect key lipids (e.g., triacylglycerols) has an effective read-out of assessing milks origins and to detect foreign milks in bovine milks. However, such approach requires several sample preparation steps and a dedicated laboratory environment, precluding a high throughput process. To cope with this need, here, we aimed to develop a novel and simple method without organic solvent extractions for the detection of bovine and non-dairy milks based on lipids fingerprint by routine MALDI-TOF mass spectrometry (MS). The optimized method relies on the simple dilution of milks in water followed by MALDI-TOF MS analyses in the positive linear ion mode and using a matrix consisting of a 9:1 mixture of 2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic acid (super-DHB) solubilized at 10 mg/mL in 70% ethanol. This sensitive, inexpensive, and rapid method has potential for use in food authenticity applications.

Detection of milk powder in liquid whole milk using hydrolyzed peptide and intact protein mass spectral fingerprints coupled with data fusion technologies

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.