Authentication of liquid egg composition using ATR‐FTIR and NIR spectroscopy in combination with PCA

Abstract

BACKGROUND The starting point of this work is to propose a qualitative approach for authenticity confirmation of the composition of liquid egg products. To this end, it was aimed to detect the liquid egg authenticity and adulteration (with water) by using attenuated total reflectance Fourier‐transform infrared (ATR‐FTIR), near infrared (NIR) spectroscopy and chemometrics. RESULTS Liquid (n = 50) and dry (n = 50) samples of whole egg, egg yolk, egg white, and whole egg containing extra egg white (BXEW) and water (BXW) were prepared. Principal component analysis (PCA) models were formed using the data obtained from ATR‐FTIR and NIR measurements of liquid and dry samples. A better classification was achieved with PCA model of ATR‐FTIR measurements formed by using dry samples (100%) instead of liquid ones (80%). The best separation was obtained between dry sample groups of BXEW and BXW (adulterated). The presence of water content in liquid samples showed a negative effect on classification of the samples, while a good classification (100%) was obtained for NIR measurements of both liquid and dry sample groups. The developed PCA models achieved classification regardless of the form of egg samples (liquid or dry). CONCLUSION The results of the study revealed that adulterated egg samples (with water) could be qualitatively detected using ATR‐FTIR and NIR spectroscopy techniques in combination with PCA. © 2019 Society of Chemical Industry

Biocomposite Foams with Multimodal Cellular Structures Based on Cork Granulates and Microwave Processed Egg White Proteins

“…In particular, the peak at 1580 cm −1 , assigned to C=C aromatic bond vibration, and the peak at 1050 cm −1 , assigned to the C-OH vibration, can be ascribed to the presence of phenolics extracted from cork [23,25]. Egg white proteins presents many other peaks: 1082 cm −1 assigned to -C-O stretching, a peak at 1158 cm −1 assigned to -C-O (stretching), -CH 2 -(bending); 1235 cm −1 -C-O (stretching), -CH 2 -(bending), 1458 cm −1 -C-H(CH 2, CH 3 ) bending (scissoring) ~1546 cm −1 to -N-H (bending), -C-N (stretching), 1590-1720 cm −1 -C=O, -C-N stretching, 2851 cm −1 -C-H(CH 2 ) stretching (symmetry), and at 2920 cm −1 -C-H(CH 2 ) stretching (asymmetry) [26].…”

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

“…Another study reported on the combined use of NIR and PLS regression for the determination and evaluation of texture and freshness (pH, total content of volatile basic nitrogen, thiobarbituric acid reactive substances, and ATP-related compounds) of bighead carp (Aristichthys nobilis), with excellent accuracy [184]. The combined use of NIR spectroscopy, PCA, and PLS-DA for fast authentication and classification of European sea bass (Dicentrarchus labrax L.) according to production method, farming system, and geographical origin has been reported [185]. According to the report, PLS-DA models achieved an impressive classification rate of 100% for both wild and farmed sea bass.…”

“…Quality assessment and authentication of eggs and egg products is therefore imperative for the food industry. A fast and accurate protocol involving the use of ATR-FTIR and NIR spectroscopy in combination with PCA for liquid egg authenticity and possible adulteration (with water) has been reported [185]. An accurate method based on UV-VIS-NIR spectroscopy utilizing support vector machine classification as well as linear and quadratic discriminant analysis for rapid fraud detection in hen housing systems declared on egg product labels has also been published [186].…”

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas

“…Statistical classification methods have been used for histopathological studies, which discriminate between pixels of healthy tissue versus pixels of diseased tissue [15,16]. Multifactorial statistical analysis methods related to IR data have been widely implemented for identifying changes in lipids, proteins, nucleic acids, and carbohydrates, such as principal component analysis (PCA) [17,18] and partial least squares (PLS) [19,20] combined with discriminant analysis (DA) [21], hierarchical cluster analysis (HCA) [22], support vector machines (SVMs) [23] and random forest (RF) [24]. As a pattern-recognition-based approach, the (artificial) neural network ((A)NN) proved to be effective in analyzing data obtained from biological specimens, also using IR imaging techniques [25][26][27].…”