Cold atmospheric pressure air plasma jet disinfection of table eggs: Inactivation of Salmonella enterica, cuticle integrity and egg quality

Eggshell cuticles are first lines of defense against egg-associated pathogens, such as Salmonella enterica serovar Enteritidis (SE). Infections from eggs contaminated with this strain remain a significant risk. In addition, changes in the cuticle are closely related to changes in egg safety. The emerging non-thermal atmospheric pressure plasma technology enables a high rate of microbial inactivation at near-ambient temperatures, making it ideal for food safety applications. This study examines the effects of a cold atmospheric pressure air plasma jet (CAAP-J) on eggshell cuticle and egg quality whilst inactivating SE. Shell eggs inoculated with SE (7 log10 cfu/egg) were used as the samples to test the decontamination performance of the device. The tests were conducted using an industrial CAAP-J with different power levels (600-800 W), exposure times (60-120 s), at a fixeddistance of 20 mm from the plasma jet and an air flow rate of 3600 L/h. It was found that the best results were obtained after 120 s at maximum plasma power (800 W). Subsequent to the implementation of this plasma procedure, it was determined that no viable cells could be detected. After CAAP-J treatment, the temperature remains below 50.5 °C, thereby minimizing the risk of altering egg quality. All specific measurements (egg white pH, yolk pH, yolk color, HU, and eggshell breaking strength) have shown that CAAP-J treatment has no negative effect on egg quality. No changes in eggshell cuticle quality after CAAP-J treatment was confirmed through scanning electron microscope (SEM).

Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil

In this study, an innovative odor imaging system capable of detecting adulteration in quince seed edible oils mixed with sunflower oil and sesame oil based on their volatile organic compound (VOC) profiles was developed. The system comprises a colorimetric sensor array (CSA), a data acquisition unit, and a machine learning algorithm for identifying adulterants. The CSA was created using a method that involves applying a mixture of six different pH indicators (methyl violet, chlorophenol red, Nile blue, methyl orange, alizarin, cresol red) onto a Thin Layer Chromatography (TLC) silica gel plate. Subsequently, difference maps were generated by subtracting the "initial" image from the "final" image, with the resulting color changes being converted into digital data, which were then further analyzed using Principal Component Analysis (PCA). Following this, a Support Vector Machine was employed to scrutinize quince seed oil that had been adulterated with varying proportions of sunflower oil and sesame oil. The classifier was progressively supplied with an increasing number of principal components (PCs), starting from one and incrementally increasing up to five. Each time, the classifier was optimized to determine the hyperparameters utilizing a random search algorithm. With one to five PCs, the classification error accounted for a range of 37.18% to 1.29%. According to the results, this novel system is simple, cost-effective, and has potential applications in food quality control and consumer protection.

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT-IR spectroscopy

Recently, the application of Fourier transform infrared (FT-IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT-IR spectra were acquired in the spectral range 1000-8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2 = .84, RMSE = 0.29), acidity (R 2 = .71, RMSE = 0.0004), phenol (R 2 = .35, RMSE = 0.19), total anthocyanin (R 2 = .93, RMSE = 5.85), and browning (R 2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2 = .98, RMSE = 0.003) and pH (R 2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT-IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.

Young Broiler Feeding Kinematic Analysis as A Function of the Feed Type

Simple Summary

The present study aims to compare the kinematic feeding variables of 3–4 days old broiler chickens using three different feed types: fine mash (F1), coarse mash (F2), and crumbled (F3); size was 476 µm, 638 µm, and 1243 µm, respectively. The head displacement and the maximum beak gape were automatically calculated by computational image analysis to find the feeding behavior of broilers. The results did not show strong correlations between birds’ weight, beak size (length and width), and the kinematic variables. The “catch-and-throw” movements in F1 (the smallest feed particle) generally occurred in the first mandibulation, while in F3 (the largest feed particle) occurred in the latest mandibulation. It can be suggested that the adoption of “catch-and-throw” in the latest mandibulations increases with larger particles.

Abstract

Past publications describe the various impact of feeding behavior of broilers on productivity and physiology. However, very few publications have considered the impact of biomechanics associated with the feeding process in birds. The present study aims at comparing the kinematic variables of young broiler chicks (3–4 days old; 19 specimens) while feeding them with three different feed types, such as fine mash (F1), coarse mash (F2), and crumbled feed (F3). The feeding behavior of the birds was recorded using a high-speed camera. Frames sequences of each mandibulation were selected manually and classified according to the temporal order that occurred (first, second, third, or fourth, and further). The head displacement and the maximum beak gape were automatically calculated by image analysis. The results did not indicate strong correlations between birds’ weight, beak size (length and width), and the kinematic variables of feeding. The differences between the tested feed were found mostly in the first and second mandibulations, probably explained by the higher incidence of “catch-and-throw” movements in F3 (33%) and F1 (26%) than F2 (20%). The “catch-and-throw” movements in F1 (the smallest feed particle) mostly occurred in the first mandibulation, as in F3 (the largest feed particle) also occurred in the latest mandibulations. It might be suggested that the adoption of “catch-and-throw” in the latest mandibulations increases with larger particles. The kinematic variables in the latest mandibulations (from the third one on) seem to be similar for all feed types, which represent the swallowing phase. It might be inferred that the temporal sequence of the mandibulations should be essential to describe the kinematics of a feeding scene of broiler chickens, and the first and second mandibulations are potentially the key factors for the differences accounted by the diverse feed particle sizes.

Experimental and Numerical Analysis for Prediction of Mechanical Properties of Eggshell

Egg is a complete food package. Importance and quality of this sealed food package rely on its invulnerability to external contaminants. Therefore, eggshell with no cracks and high resistance against loads during transportation, packaging, and so on is recommended. There are two methods for measurement of shell resistance: destructive and nondestructive methods. Modeling and simulation of eggshell by computer and finite element software (FEM) is one of the nondestructive methods which was used in this study to measure eggshell resistance. ANSYS 10 software was utilized to perform finite element analysis. In destructive testing of 90 hen eggs, the average eggshell rupture force was determined to be 22.7 N at 0.144 mm displacement when applying parallel plate force. Results of the finite element analysis showed that for 0.144 mm displacement, the estimated rupture force was 21.7 N. Results of FE analysis showed that maximum stress and displacement occurred at the point of application of the force interface with the supports and at the equator. Stress value at failure point was 14.307 MPa for the first principal stress, 8.75 MPa for the second and –0.028 MPa for the third principal stress by finite element method.

The effect of job mobility on pension wealth

This study examined the extent to which labor market mobility affects workers' ability to amass retirement wealth through employer-sponsored pension plans. This is done by calculating the total pension wealth accumulated over a worker's entire career using different patterns of pension coverage in a variety of jobs. The study indicates that, with a mobile workforce, accumulation of pension wealth depends critically on the type and timing of pension coverage during a career.