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

A Simple and Rapid Quantitative Assay for Gossypol via Reactive Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

The development of simple and rapid analytical tools for gossypol (GSP) is important to the food industry and medical field. Here, we report a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) method for the detection of GSP by using a reactive matrix 4-hydrazinoquinazoline (4-HQ). The two aldehyde groups of GSP react with the 4-HQ and therefore improve the detection sensitivity and selectivity of GSP. Moreover, GSP forms homogeneous crystals with the 4-HQ matrix, allowing the quantification of the GSP by the proposed method. With the optimized experimental conditions, GSP could be detected at concentrations as low as 0.1 μM and quantified in a wide linear range (1-500 μM). After a brief extraction with an organic solvent, the GSP contents in cottonseeds and cottonseed kernels from different provinces of China were determined successfully. The spiked recovery of GSP in cottonseed/cottonseed kernel samples was obtained as 97.88-105.80%, showing the reliability of the assay for GSP determination in real samples.

Identification of the Wound Healing Activity Peptidome of Edible Bird's Nest Protein Hydrolysate and the In Silico Evaluation of Its Transport and Absorption Potential in Skin

In this study, edible bird's nest (EBN) was proven to be a suitable source of bioactive peptides via enzymatic hydrolysis. The ultrafiltration component of the EBN peptides (EBNPs, Mw < 3 000 Da) could be responsible for moderate moisture retention and filaggrin synthesis. It was found that EBNP had a great capacity to protect HaCaT keratinocytes from DNA damage caused by UVB-irradiation and enhance wound healing by increasing the migratory and proliferative potential of cells. Furthermore, the external application of EBNP could effectively repair high glycolic acid concentration-induced skin burns in mice. A total of 1 188 peptides, predominantly the hydrophobic amino acids (e.g., Leu, Val, Tyr, Phe), were identified in the EBNP by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Molecular docking showed that hydrophobic tripeptides from EBNP had a good binding affinity to proton-dependent oligopeptide transporter PepT1. Our data indicated that the hydrophobic amino acid-rich EBNP plays an important role in skin wound healing.

Assessment of cheese frauds, and relevant detection methods: A systematic review

Dairy products are widely consumed in the world due to their nutritional and functional characteristics. This group of food products are consumed by all age groups due to their health-giving properties. One of these products is cheese which has a high price compared to other dairy products. Because of this, it can be prone to fraud all over the world. Fraud in food products threatens the world's food safety and can cause serious damage to human health. There are many concerns among food authorities in the world about the fraud of food products. FDA, WHO, and the European Commission provide different legislations and definitions for fraud. The purpose of this review is to identify the most susceptible cheese type for fraud and effective methods for evaluating fraud in all types of cheeses. For this, we examined the Web of Science, Scopus, PubMed, and ScienceDirect databases. Mozzarella cheese had the largest share among all cheeses in terms of adulteration due to its many uses. Also, the methods used to evaluate different types of cheese frauds were PCR, Spectrometry, stable isotope, image analysis, electrophoretic, ELISA, sensors, sensory analysis, near-infrared and NMR. The methods that were most used in detecting fraud were PCR and spectrometry methods. Also, the least used method was sensory evaluation.

Geographical traceability of gelatin in China using stable isotope ratio analysis

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ¹³C, δ¹⁵N, and δ²H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ¹³C, δ¹⁵N, and δ²H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ¹³C, δ¹⁵N, and δ²H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Online trypsin digestion coupled with LC-MS/MS for detecting of A1 and A2 types of β-casein proteins in pasteurized milk using biomarker peptides

Bovine A1-or A2-type β-caseins have attracted a growing interest due to their variation in beta-casomorphin-7 (BCM-7) formation, which may affect health. In the present work, identification and quantification of A1 and A2 types of β-casein proteins at the peptide level was achieved for the first time. An automated and online immobilized trypsin digestion system was employed for high throughput digesting of proteins into peptides. Tryptic peptides were separated and analyzed subsequently by liquid chromatography coupled to mass spectrometry platform. Two specific peptides ranging from the position of 49 to 97 in the peptide chain were selected for the identification and quantification of A1 and A2 β-casein, which covered the different amino acids between them. Synthetic isotopically labeled winged peptides were used for absolute quantification. Compared with traditional in-solution digestion, online digestion shortens digestion times from 2 to 24 h to 4 min. The limits of quantification (LOQ) of A1 and A2 β-casein in pasteurized milk are 0.8 and 2.4 µg/g, respectively. To further demonstrate the applicability of the proposed method, commercial pasteurized milk tests were performed with satisfactory results.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13197-022-05376-6.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

Enzyme-assisted ReMALDI-MS assay for quantification of cholesterol in food

Cholesterol is a vital building block for animal cell membranes and participates in the synthesis of various hormones. Accurate quantitation of cholesterol in food is crucial for healthy diets. Here, we describe an enzyme-assisted reactive matrix-assisted laser desorption/ionization mass spectrometry (ReMALDI-MS) assay for the quantification of cholesterol in food. First, cholesterol was converted to 4-cholesten-3-one using the cholesterol oxidase, and then reacted with a reactive matrix, 4-hydrazinoquinazoline (4-HQ), to form a hydrazone bond. Utilizing 4-HQ significantly improved the ionization efficiency of cholesterol, which possesses poor ionization efficiency in MALDI-MS, and no additional tedious derivatization/purification steps were needed. Thus, the proposed assay was successfully used for the quantification of cholesterol in bovine milk and cream. The standard recovery tests show a recovery range of 95.3-103.0% with a relative standard deviation of 0.3-3.1%. Therefore, the proposed enzyme-assisted ReMALDI-MS assay has great potential for quantification of cholesterol in other foods.

Chemometrics-assisted isotope ratio fingerprinting based on gas chromatography/combustion/isotope ratio mass spectrometry for saffron authentication

In the present contribution, the capability of isotopic ratio mass spectrometry (IRMS) for saffron authentication and detection of four common plant-derived adulterants (marigold flower, safflower, rubia, and saffron style) was investigated. For this purpose, 62 authentic saffron samples were analyzed by elemental analyzer-IRMS (EA-IRMS) and gas chromatography-combustion-IRMS (GC-C-IRMS). In this regard, EA-IRMS and GC-C-IRMS isotope fingerprints of carbon-13 and nitrogen-15 isotopes of saffron components were provided and then analyzed by chemometric methods. Principal component analysis (PCA) showed two different behaviors regarding two main regions. Then, a representative saffron sample was provided to study adulteration. On this matter, binary mixtures of saffron and adulterants were prepared at five different weight percentages (5%, 10%, 15%, 25%, and 35%) and analyzed by EA-IRMS and GC-C-IRMS. Data-driven soft independent modeling of class analogy (DD-SIMCA) was used to model authentic saffron samples and find a boundary between authentic and adulterated samples with a sensitivity of 100% by GC-C-IRMS. After that, discriminant models of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least squares-discriminant analysis (PLS-DA) were tested to find the best discrimination line and also detection of the lowest level of adulterants. Among different models, the QDA model outperformed other methods and showed the ability to predict adulterants at 5% w/w level with 100% accuracy and precision. Finally, the developed QDA model was successfully used to discriminate a set of mixed samples of saffron and four adulterants as well as some commercial samples.