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.

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

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.