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

Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning

(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.

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.

Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs

Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.

Research on Online Nondestructive Detection Technology of Duck Egg Origin Based on Visible/Near-Infrared Spectroscopy

As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world's largest producer and consumer of duck eggs, China's duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400-1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology's implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice.

1H NMR to analyse the lipid profile in the glyceride fraction of different categories of Iberian dry-cured hams

The extraordinary organoleptic qualities of Iberian ham derive from the singular producing pig breed and from the traditional rearing conditions, both of which define its lipid content and composition. In this work ¹H NMR spectroscopy is used for the first time to analyse the lipid profile of Iberian hams as determinant of quality. Quantification of fatty acids is readily obtained from the spectra, with the monounsaturated fatty acids standing out, especially in the higher quality hams. Unprecedently, triacylglyceride hydrolysis products formed during the curing process can also be directly detected and quantified. Furthermore, chemometric analysis of the NMR data allows to classify Iberian hams according to the pig's crossbreed and feeding regime. Principal component analysis shows fatty acid unsaturation and triacylglyceride hydrolysis as discriminating variables. ¹H NMR spectroscopy has thus revealed as a convenient and powerful tool for the lipid analysis and classification of Iberian hams and for detection of fraud.

Cheese characterization and authentication through lipid biomarkers obtained by high-resolution 1H NMR profiling

Food quality and safety are at the heart of consumers' concerns across the world. Dairy products, because of their large consumption, are fertile ground for fraudulent acts. This fact justifies the development of effective, accessible, and rapid analytical methods for their authentication. A high-resolution spectral treatment method previously developed by our team was applied to ¹H NMR spectra of cheese triacylglycerols. 178 Peaks were thus quantitated and successfully used in the construction of multivariate models for the quantitation of individual fatty acids and for the classification of cheese samples according to the producing species, to their origin and variety. Besides, several peaks related to the amount and position of anteisopentadecanoic, butyric, α-linolenic, myristoleic, rumenic, and vaccenic acids were, among others, specific biomarkers of cheese groups. For the first time in ¹H NMR, we were able to identify and to quantitate signals related to minor fatty acids within cheese triacylglycerols.