Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy

Abstract

Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.

Rapid analysis technologies with chemometrics for food authenticity field: A review

“…In addition, NIR was used to identify the quality of eggs to differentiate the feeding practices of hens. Egg whites and yolks were classified to trace the egg sources (free range and cage) based on the spectral analysis of the protein, carbohydrate, and ash content differences, providing a theoretical reference for evaluating egg quality ( Hoffman et al, 2022 ). Nevertheless, for quality fraud identification of plant-derived foods, NIR focuses on the identification of the growth and feeding system of the product.…”

Novel Contactless Sensors for Food, Beverage and Packaging Evaluation

“…This special issue (SI) focused on the novel methods developed using contactless sensors for food, beverages and packaging. This SI is composed of nine papers related to the use of spectral analysis using different methods, such as near-infrared spectroscopy to classify eggs into cage or free-range production practices [1], a semi-automatic low field nuclear magnetic resonance (LF-NMR) coupled with machine learning modelling to predict oxidation in edible oil [2], the use of vibrational spectroscopy to measure ethanol and methanol levels in pisco [3], and the use of an infrared laser sensor to monitor the gasphase CO 2 in Champagne headspace when swirling [4]. Other papers focus on the use of other specific sensors, such as a low-cost and portable electronic nose (e-nose) to predict aromas and roasting intensity in coffee [5] and the use of flexible sensors for monitoring oyster survival rates [6].…”

“…This has been a very useful contactless tool for developing predictive models. Hoffman et al [1] used a portable NIR device (950-1600 nm) to assess eggs chemical fingerprinting and used principal components analysis (PCA) and linear discriminant analysis (LDA) to analyze results. The authors successfully classified samples in cage and free-range production with an overall accuracy of 76% for whole eggs and 86% for egg whites and yolks separately.…”