A waveguide technique for non-destructive determination of egg quality parameters

Microwave spectroscopy in a free-space arrangement for nondestructive quality assessment of chicken eggs: Comparing different measurement modes and feature selection approaches

A free-space dielectric arrangement with X-band coaxial-to-waveguide adapters was used to non-destructively assess shell egg quality. Scattering parameters in the microwave spectral range (8-12 GHz) were measured in reflectance and transmittance modes from eggs placed in three orientations. Partial least squares regression was applied to predict egg quality indices, including air cell height (ACH), yolk coefficient (YC), thick albumen height (TAH), Haugh unit (HU), and albumen pH. Prioritizing PR_S22 spectrum in horizontal 1 orientation, several feature selection methods were employed to identify the most effective frequencies. Artificial neural networks (ANNs) were then used to develop predictive models based on influential frequencies. The competitive adaptive reweighted sampling method consistently outperformed others, yielding robust ANN models with excellent residual predictive deviation values of 4.80, 4.00, 3.27, 3.03, and 3.72 for ACH, YC, TAH, HU, and albumen pH, respectively. This study demonstrates the effectiveness of free-space dielectric arrangements in predicting egg quality.

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.

Inactivating Salmonella Enteritidis on shell eggs by using ozone microbubble water

The major pathogen associated with eggs is Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) and chlorine washing is the most widely used for sanitization. Microbubble, a novel technique and able to operate in large quantity, has been presented to be an alternative method. Thus, microbubble water combining with ozone (OMB) was applied to disinfect S. Enteritidis spiked on shells at 10⁷ cells per egg. OMB was generated by injecting ozone into a Nikuni microbubble system, then delivered into 10 L of water. After 5, 10, or 20 min of activation time, the eggs were placed into OMB and washed for 30 or 60 s. The controls involved unwashed, water washing, ozone only, and microbubble only (MB). The highest reduction, 5.19 log CFU/egg, was achieved by the combination of 20-min activation and 60-s washing, which was used for following tests of large water quantities. Comparing with the unwashed control, 4.32, 3.73 and 3.07 log CFU/egg reductions were achieved in 25, 80, and 100 L of water, respectively. The other system, Calpeda, with higher motor power was tested in 100 L and obtained a reduction of 4.15 log CFU/egg. The average diameter of bubbles generated by Nikuni and Calpeda pump systems were 29.05 and 36.50 μm, respectively, which both were within the microbubble definition of ISO. Much lower reductions, around 1-2 log₁₀ CFU/egg, were shown with the treatments of ozone only and MB by the same operative parameters. After 15-day storage at ambient temperature, the OMB-treated eggs showed similar sensory quality with the unwashed ones. This is the first study demonstrating that OMB effectively inactivates S. Enteritidis on shell eggs in large quantity of water and does not diminished the sensory characteristics of eggs. Furthermore, bacterial population was under the detection limit in the OMB-treated water.

Design and Fabrication of Modified SMA-Connector Sensor for Detecting Water Adulteration in Honey and Natural Latex

A detection system for water adulteration in honey is proposed. It consists of a modified SMA-connector sensor and a vector network analyzer. A modified SMA-connector sensor is applied to measure complex relative permittivity, electrical conductivity, and phase constant of honey samples with the open-ended method. The system is tested in the frequency range of 0.5–4.0 GHz at the sample temperature of 25 °C. The relationships between the complex relative permittivity, electrical conductivity, the phase constant, and the honey samples with different concentrations (0–30%w/w) are determined. The experimental results show that the real part of the complex relative permittivity is significantly proportional in honey samples with adulteration of water in the range of 0–30%w/w. The frequency of 0.6 GHz is a suitable frequency for detection with a real part of complex relative permittivity as an indicator. The frequency of 3.74 GHz is an appropriate frequency for detection with electrical conductivity as in indicator while the frequency of 4.0 GHz is suitable for detection with phase constant as an indicator. In addition, the data are analyzed with regression analysis. This technique is also performed on natural latex samples to determine the dry rubber content. The frequency of 0.5 GHz is a suitable frequency with a real part of complex relative permittivity as an indicator while the frequency of 4.0 GHz is a suitable frequency with an imaginary part of complex relative permittivity, electrical conductivity, and phase constant as the indicators. The results demonstrate that it is possible to apply this technique to determine the dry rubber content in the natural latex samples as well.

Microwave spectroscopy based on the waveguide technique for the nondestructive freshness evaluation of egg

A rectangular waveguide equipped with a network analyzer was used to assess the quality indices of shell egg. The scattering parameters of the eggs were acquired in the range of 0.9-1.7 GHz and they were then used to calculate microwave spectra of the samples. PLS and ANN regression methods were implemented to predict the egg quality indices and SIMCA and ANN classification methods were applied to classify the eggs based on their storage time. The best predictive models, however, obtained from ANN analysis where the yolk coefficient, air cell height, thick albumen height, Haugh unit, and albumen pH could be predicted with the residual predictive deviation (RPD) values of 3.500, 3.000, 2.411, 2.033, and 1.829, respectively. To classify the eggs according to their storage time, both SIMCA and ANN analyses resulted in the total accuracy of 100% when return loss spectra were used as the input.

Non-destructive identification of native egg by near-infrared spectroscopy and data driven-based class-modeling

Eggs are very important parts of human diets worldwide. It is very common to pass feed eggs off as native ones of high commercial values in Chinese markets. One urgent and challenging work is to develop a non-destructive method for verifying the authenticity of native eggs. The present work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy with data driven-based class-modeling (DDCM) and model-independent variable selection, i.e., joint mutual information (JMI). A total of 122 eggs of three types were collected. Principal component analysis (PCA) was utilized for exploratory analysis. The JMI algorithm selected only 20 informative variables out of 1557 original variables for class-modeling. DDCM constructed a class-model for each kind of eggs by optimizing parameters such as degrees of freedom (DoF) and the number of principal components (NPC). All class-models and the decision rules were validated on the corresponding test sets. In short, these models achieved an acceptable performance and are also more consistent with actual needs than classification models. The results show that NIR spectroscopy combined with class-modeling is a potential tool for detecting the authenticity of a specific kind of native eggs.

Determination of the concentration of alum additive in deep-fried dough sticks using dielectric spectroscopy

The concentration of alum additive in deep-fried dough sticks (DFDSs) was investigated using a coaxial probe method based on dielectric properties in the 0.3–10-GHz frequency range. The dielectric spectra of aqueous solutions with different concentrations of alum, sodium bicarbonate, and mixtures thereof were used. The correspondence between dielectric loss and alum concentration was thereby revealed. A steady, uniform correspondence was successfully established by introducing ω·ɛ″(ω), the sum of dielectric loss and conductor loss (i.e., total loss), according to the electrical conductivity of the alum-containing aqueous solutions. Specific, resonant-type dielectric dispersion arising from alum due to atomic polarization was identified around 1 GHz. This was used to discriminate the alum additive in the DFDS from other ingredients. A quantitative relationship between alum and sodium bicarbonate concentrations in the aqueous solutions and the differential dielectric loss Δɛ″(ω) at 0.425 GHz was also established with a regression coefficient over 0.99. With the intention of eliminating the effects of the chemical reactions with sodium bicarbonate and the physical processes involved in leavening and frying during preparation, the developed technique was successfully applied to detect the alum dosage in a commercial DFDS (0.9942 g/L). The detected value agreed well with that determined using graphite furnace atomic absorption spectrometry (0.9722 g/L). The relative error was 2.2%. The results show that the proposed dielectric differential dispersion and loss technique is a suitable and effective method for determining the alum content in DFDSs.