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

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1^st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (R_p) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.

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.

Quantitative fusion omics reveals that refrigeration drives methionine degradation through perturbing 5-methyltetrahydropteroyltriglutamate-homocysteine activity

Postharvest senescence and quality deterioration of fresh tea leaves occurred due to the limitation of processing capacity. Refrigerated storage prolongs the shelf life of fresh tea. In this study, quantitative fusion omics delineated the translational landscape of metabolites and proteins in time-series (0-12 days) refrigerated tea by UHPLC-Q-Orbitrap HRMS. Accurate quantification results showed the content of amino acids, especially l-theanine, decreased with the lengthening of the storage duration (15.57 mg g^-1 to 7.65 mg g^-1) driven by theanine synthetase. Downregulation of enzyme 5-methyltetrahydropteroyltriglutamate-homocysteine methyltransferase expression led to methionine degradation (6.29 µg g^-1 to 1.78 µg g^-1). Refrigerated storage inhibited serine carboxypeptidase-like acyltransferases activity (59.49 % reduction in 12 days) and induced the polymerization of epicatechin and epigallocatechin and generation of procyanidin dimer and δ-type dehydrodicatechin, causing the manifestation of color deterioration. A predictive model incorporating zero-order reaction and Arrhenius equation was constructed to forecast the storage time of green tea.

Design and Experiment of Online Detection System for Water Content of Fresh Tea Leaves after Harvesting Based on Near Infra-Red Spectroscopy

Fresh tea leaves continuously lose water after harvesting, and the level of water content directly affects the configuration of tea processing parameters. To address this problem, this study established an online detection system for the water content of fresh tea leaves after harvesting based on near-infrared spectroscopy. The online acquisition and analysis system of the temperature and humidity sensor signal data was developed based on LabVIEW and Python software platforms. Near-infrared spectral data, environmental temperature, and humidity were collected from fresh leaves after harvesting. Spectral data were combined with PLS (partial least squares) to develop a prediction model for the water content of fresh tea leaves. Simultaneously, data communication between LabVIEW and PLC was established, laying the foundation for establishing a feedback mechanism to send the prediction results to the main platform of the lower computer. This provides a more objective and accurate basis for the detection of fresh leaves before processing and regulation during processing, thereby effectively promoting the standardisation and intelligent development of tea-processing equipment.

Water and lipid migration in salted duck eggs during storage with different packaging conditions as studied using LF-NMR and MRI techniques

Herein, the water and lipid migration of salted duck eggs during storage were systematically explored in three different packaging conditions of long-term salting, no packaging, and vacuum packaging. Bound water, multilayer bound water, lipid, and bulk water were observed in the whole duck egg by low-field nuclear magnetic resonance (LF-NMR) relaxation. Five weeks of salting process led to the redistribution of water and lipid due to the watery state of egg white and the gelation of egg yolk due to the permeation of salt, and boiling mainly caused an obvious decrease in the mobility of bulk water due to the gelation of egg white. Among these three conditions, long-term salting with 6 months storage caused the most serious redistribution of water and lipid as well as the rupture of the vitelline membrane, but could prevent the oxidation of egg yolk. Vacuum packaging had the least influence on the water and lipid distribution, mass change, and water content but led to lipid oxidation with high degree in egg yolk. However, the most obvious mass loss was observed in the salted duck eggs during the storage without packaging. In addition, principal component analysis of Carr-Purcell-Meiboom-Gill data suggested that LF-NMR could distinguish the salted duck eggs with different storage times during the early stage of the storage. Practical Application Water and lipid migration of salted duck eggs during storage with three packaging conditions were explored by using low-field nuclear magnetic resonance and magnetic resonance imaging. Understanding the impacts of packaging conditions on water and lipid migration of salted duck eggs during storage could provide a new method for the quality identification.

A Non-Invasive Soil Moisture Sensing System Electronic Architecture: A Real Environment Assessment

This paper will show the electronic architecture of a portable and non-invasive soil moisture system based on an open rectangular waveguide. The spectral information, measured in the range of 1.5–2.7 GHz, is elaborated on by an embedded predictive model, based on a partial least squares (PLS) regression tool, for the estimation of the soil moisture (%) in a real environment. The proposed system is composed of a waveguide, containing Tx and Rx antennas, and an electronic circuit driven by a microcontroller (MCU). It will be shown how the system provides a useful and fast estimation of moisture on a silty clay loam soil characterized by a moisture range of about 9% to 32% and a soil temperature ranging from about 8 °C and 18 °C. Using the PLS approach, the moisture content can be predicted with an R² value of 0.892, a root mean square error (RMSE) of 1.0%, and a residual prediction deviation (RPD) of 4.3. The results prove that it is possible to make accurate and rapid moisture assessments without the use of invasive electrodes, as currently employed by state-of-the-art approaches.