Search

BACKGROUND: Early and non‐destructive identification of fertile (F) eggs is a difficult task in the process of breeding laying hens. The odors emitted from unfertilized (UF), infertile (IF), and fertile (F) eggs were characterized by solid‐phase microextraction / gas chromatograph‐mass spectrometry (SPME/GC‐MS) and electronic nose (E‐nose) to determine their differences by principal component, partial least squares, and canonical discriminant analyses. RESULTS: A total of 14 volatiles were identified in unhatched shell white Leghorn eggs, such as nonanal, decanal, 6‐methyl‐5‐hepten‐2‐one, and 6,10‐dimethyl‐5,9‐undecadien‐2‐one. Cedrene and decanal contributed greatly to the classification of UF and fertilized (Fd)/IF eggs; cedrene, decanal, 1‐octanol and hexanal contributed greatly to the distinction between UF and IF eggs; heptanal might be the potential marker to determine F/IF eggs. P40/1, P10/2, P10/1, TA/2, T40/2 and T30/1, P30/1, P40/2, PA/2, T40/2 mostly contributed to the distinction between UF and Fd eggs and between F and IF eggs, respectively. Canonical discriminant analysis presented superior differentiating efficiency for almost all groups, and the odor differences between UF and Fd eggs were significantly larger than the differences between F and IF eggs. CONCLUSION: Solid‐phase microextraction / gas chromatograph‐mass spectrometer combined with E‐nose may have the potential to non‐destructively distinguish UF, F, and IF eggs, which will provide a new perspective to understand the differences among them. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

This study proposed a method using hyper-spectral imaging technology in determining eggs' quality in term of freshness from a biochemical perspective by estimating the S-ovalbumin content. This method has the potential in assessing eggs' quality rapidly and non-destructively. Hyper-spectral image of egg was captured using a hyper-spectral imaging system and regression model was built to estimate the S-ovalbumin content. The successive projections algorithm (SPA) was used to select significant wavebands followed by building a partial least squares regression (PLSR) model and a multiple linear regression (MLR) model. The MLR model could predict S-ovalbumin content better than PLSR model with a higher correlation coefficient (0.922) and lower root mean square error (0.086) of the calibration set, a higher correlation coefficient (0.911) and lower root mean square error (0.119) of the validation set, and a higher residual predictive deviation (2.348). The regression equation from the MLR model was used to compute each pixel of the image in the validation set and visualisation of S-ovalbumin content distribution in the egg was obtained using pseudo-color image. The findings implied that the proposed hyper-spectral imaging system with the regression model developed has the potential in determining and visualising the eggs' quality. [ABSTRACT FROM AUTHOR]

ABSTRACT Fresh egg yolk was separated into plasma and granules. Then, the egg yolk and its fractions were enzymatically modified with phospholipase A1 (PLA1). Changes in physicochemical and functional properties were investigated. We observed that enzymatic treatment showed a significant impact on the properties of egg yolk and its fractions with regard to emulsifying properties, protein solubility and foaming properties. Meanwhile, the color changes and the microstructure and the thermal behavior of egg yolk, plasma and granules before and after enzymatic treatment were also studied. This study indicated that the existence of lysophospholipids and structural changes in egg yolk granules could be responsible for significant differences in the functional properties of untreated and PLA1-modified egg yolk, plasma and granules. PRACTICAL APPLICATIONS This study aimed to investigate the physicochemical and functional properties of egg yolk, including its fractions (plasma and granules), before and after phospholipase A1 (PLA1) treatment. Such modified egg yolk would have higher protein solubility, thermal stability and emulsifying properties, and present high resistance to extreme temperatures. As these properties of egg yolk powder were improved through PLA1 treatment, the application of PLA1 could make egg yolk have a good practical effect and extend the scope of application. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging technology was used to nondestructively predict S‐ovalbumin content by obtaining transmission spectral and fused image data. Hyperspectral images of eggs with spectral wavelengths between 300 and 1,100 nm were collected. Three image features were selected to use as variables to model. After spectral pretreatment, 14 characteristic wavelengths were selected using competitive adaptive reweighted sampling algorithm (CARS). Nondestructive prediction models of S‐ovalbumin content were developed based on either image feature parameters, a combination of spectral characteristic wavelengths, or image‐spectral fusion data after dimensionality reduction. A partial least squares (PLS) model and genetic algorithm to optimize a back propagation neural network (GA‐BP) model was established based on these three kinds of data. From comparisons between PLS and GA‐BP models based on the same type of each of the three data types, we determined that hyperspectral fusion data and a GA‐BP model can improve the performance of a S‐ovalbumin nondestructive prediction model. It had a simpler structure with fewer variables. So, it can be used to nondestructively and rapidly predict protein content in egg white. Practical applications: Current nondestructive methods to evaluate egg quality primarily focus on the changes of simple external and internal quality, such as egg weight and simple chemical indicators. Due to the individual differences between eggs, the above methods are under a limited application scope. Changes in S‐ovalbumin content do not depend on egg weight, and so forth, individual differences have little effect on it. Changes in S‐ovalbumin content of various varieties of eggs are alike because the biochemical processes that affect egg white protein content is fundamentally the same across egg varieties, and its traditional detection method is to perform complex biochemical tests after breaking. Therefore, if the egg's internal protein content can detected quickly without breaking it, it is more accurate and more profound from the biochemical essence and it can not only solve the shortcomings of biochemical detection, but also provide important theory and application technology for the detection and classification of egg quality. [ABSTRACT FROM AUTHOR]

The freshness of egg is an important index to reflect the internal quality. In order to achieve non-destructive detection of freshness, micro fiber spectrometer was used to sample 550～950 nm transmittance spectra of eggs which performed quantitative analysis with haugh unit of eggs. Different pretreatment was combined with partial least squares regression(PLS) and support vector regression(SVR) respectively to find that first derivative combined with SVR predicted better than others through comparison, and it was better to model by SVR than by PLS. In order to improve efficiency and decrease adverse effects of useless information for modeling, the linear dimensionality reduction with principal component analysis (PCA) and the nonlinear dimensionality reduction with locally linear embedding(LLE) were used for the data of first derivative respectively. It indicated that LLE was better than PCA after comparison, and the correlation coefficient of calibration and prediction were 92.2%, 91.1%, and the root mean square error were 7.21, 8.80. The root mean square error of cross validation decreased 0.79.The experimental result illustrated that the nonlinear model of LLE combined with SVR improved predictive performance of egg freshness. It is feasible for the detection of visible/near-infrared spectrum of egg freshness to apply this method.