Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools

Mass spectrometry unveils heat-induced changes in yolk oxylipins and key lipid molecules during home cooking

INTRODUCTION

Oxylipins, as a widespread class of metabolic markers following oxidative stress, and several studies have reported dietary regulation of lipid metabolism. However, there is a lack of investigation of dietary oxylipins, especially cooking-induced changes in food lipid oxidation.

OBJECTIVES

Investigated the effects of cooking methods and lipid profiles on polyunsaturated fatty acids derived oxylipins generation within egg yolks.

METHODS

The lipid profile of egg yolk was determined by UPLC-QTOF-MS/MS, oxylipins were detected by HPLC-QTRAP-MS/MS, while the total fatty acid content was quantified by GC-FID. Random Forest (RF) and Partial Least Squares (PLS) regression models were employed to explore the association between oxidized lipids and key lipid species.

RESULTS

Heating reduced egg yolk docosahexaenoic acid (DHA) content, and no consistent trends for arachidonic acid (AA), linoleic acid (LA), and linolenic acid (ALA). Yolk lipid composition affected triacylglycerol (TG), phosphatidylethanolamine (PE), and LA-monoepoxide contents after cooking. 9- and 13-HODE (hydroxyoctadecadienoic acid), 9,10,13-TriHOME (trihydroxyoctadecenoic acid), 9,10- and 12,13-EpOME (epoxyoctadecenoic acid), 9,10- and 12,13-DiHOME (dihydroxyoctadecenoic acid), 5-HETE (hydroxyeicosatetraenoic acid), and 4-HDHA (hydroxydocosahexaenoic acid) were the prevalent oxylipins with high concentrations, accounting for 1.08 %-29.58 % of the total content of 29 oxylipins. Steaming resulted in a 1.9-fold increase in oxylipin concentrations in yolks compared to raw yolks, and boiling with or without shells (poaching) resulted in a 1.30- to 1.76-fold increase in oxylipin concentrations. In contrast, pan-fried yolks exhibited the lowest and least variable levels of total oxylipins, while still retaining some epoxides, including epoxyeicosatrienoic acid (EET) and EpOME. Utilizing big data analysis, we mapped the oxylipin network in both ordinary and DHA-enriched egg yolks, revealing a strong correlation between cooking-induced oxylipin production and variations in 24 lipid species.

CONCLUSION

Revealed the potential mechanisms and key lipid molecules for heating-induced oxylipin production of yolk through lipidomics and big data analysis.

Microwave-assisted esterification and electro-enhanced solid-phase microextraction of omega-3 polyunsaturated fatty acids in eggs

Omega-3 polyunsaturated fatty acids (ω-3 PUFAs), a type of fatty acid that has many health benefits, are of increasing concern. Herein, we developed a method for the rapid esterification and enrichment of ω-3 PUFAs in eggs, which includes microwave-assisted esterification (MAE) and electrically enhanced solid-phase microextraction (EE-SPME). Combined with gas chromatographic, efficient detection of ω-3 PUFAs was achieved in eggs. Under microwave radiation, the esterification efficiency exhibited a significant increase ranging from 5.06 to 10.65 times. The EE-SPME method reduced extraction time from 50 to 15 min. In addition, improvements in extractive fiber coating materials were explored, which ensured efficient extraction of ω-3 PUFAs. Under the optimal conditions, the method displayed a low detection limit (1.01-1.54 μg L-1), good recoveries (85.82%-106.01%), and wide linear range (7.5-1000 μg L-1), which was successfully applied to determine ω-3 PUFAs in real egg samples.

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

1H NMR as a simple methodology for differentiating barn and free-range chicken eggs

The conventional intensive system produces cheap and safe chicken eggs, but exposes the animals to stress due to overcrowding on farms. This work compared the ¹HNMR lipidic profile of chicken eggs produced in conventional and free-range systems. Sample preparation consisted of a single-step extraction and centrifugation, and the ¹H NMR experimental time was just 3 min per sample. Eggs from free-range chickens had higher concentrations of ω-3 and ω-6 polyunsaturated fatty acids. The ratio between the signals at δ2.85 and 4.14 from bis-allylic polyunsaturated fatty acids and glycerol moiety, respectively, was able to correctly classify 93.8 % of the samples. These results were similar to those of PLS-DA, used for comparative purposes. Therefore, the proposed method could be easily used to assist quality control and fraud prevention in the egg industry. Free-range eggs had higher concentrations of cholesterol but, as they are smaller, similar amounts to conventional ones.

Discriminant Analysis of the Nutritional Components between Organic Eggs and Conventional Eggs: A 1H NMR-Based Metabolomics Study

The difference of nutrient composition between organic eggs and conventional eggs has always been a concern of people. In this study, ¹H nuclear magnetic resonance (NMR) technique combined with multivariate statistical analyses was conducted to identify the metabolite different in egg yolk and egg white in order to reveal the nutritional components information between organic and conventional eggs. The results showed that the nutrient content and composition characteristics were different between organic and conventional eggs, among which the content of glucose, putrescine, amino acids and their derivatives were found higher in the organic eggs yolk, while phospholipids were demonstrated higher in conventional eggs yolk. Organic acid, alcohol, amine, choline and amino acids were higher in conventional eggs white, but glucose and lactate in organic egg were higher. Our study demonstrated that there are more nutritive components and higher nutritional value in organic eggs than conventional eggs, especially for the growth and development of infants and young children, and conventional eggs have more advantages in promoting lipid metabolism, preventing fatty liver, and reducing serum cholesterol. Eggs have important nutritional value to human body, and these two kinds of eggs can be selected according to the actual nutrient needs.

High-resolution 1H NMR profiling of triacylglycerols as a tool for authentication of food from animal origin: Application to hen egg matrix

Metabolomics of complex biological matrices conducted by means of ¹H NMR leads to spectra suffering from severe signal overlapping. Previously, we have developed a high-resolution spectral treatment method to help solving this issue in ¹H NMR of triacylglycerols. In this work, we tested the potential of the developed method in the characterization and authentication of food products from animal origin using egg yolk as a model matrix. The approach consisted in a spectral deconvolution guided by the precision obtained on the deconvoluted peaks after reference lineshape adjustment of spectra. Thus, 135 peaks were quantitated and successfully used as biomarkers of origin, of hens breed, and of farming system. This required multivariate statistical analyses for classification. The same pool of variables allowed construction of multivariate quantitation models for individual fatty acids. Furthermore, minute amounts of conjugated fatty acids were quantitated and used as fingerprints of samples from backyard and free-range farming.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis

The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400-1800 cm-1 and 500-1100 cm-1) to classify different types of adulterated samples. The most effective was the SIMCA model in 500-1100 cm-1 with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400-1800 cm-1 and selected specific region) and data pretreatment methods. The determination coefficients (R2) of all models were higher than 0.96. PLSR models based on typical Raman regions (500-1100 cm-1 for creatine and taurine, the combination of range 800-1000 cm-1 and 1300-1500 cm-1 for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC.

Raman spectroscopy for the differentiation of Arabic coffee genotypes

The objective of this study was to evaluate the ability of Raman spectroscopy to identify the genotype of green coffee beans. Four genotypes of Arabic coffee: one Mundo Novo line (G1) and three Bourbon lines (G2, G3, and G4). The harvest was selected using a wet processing method. Raman spectra of the samples were obtained using a FT-Raman RFS/100 spectrometer in the spectral range of 3500-400 cm^-1. The data were treated using chemometric unsupervised classification tools and supervised analysis. Using the unsupervised analysis (PCA), the apparent tendency of agglomeration between samples G1 and G3 was verified. These differences were present in the spectral bands that are characteristic of fatty acids and kahweol. Based on this information, a classification model to discriminate (PLS-DA) the Mundo Novo and Bourbon samples was utilized. Raman spectroscopy allowed the building of an adequate model to differentiate between coffee genotypes.