Noninvasive Assessment of Chicken Egg Fertility during Incubation Using HSSE-GC-MS VOC Profiling.

Volatile organic compounds (VOCs) carry crucial information about chicken egg fertility. Assessing the fertility before incubation holds immense potential for poultry industry efficiency. Our study used headspace sorptive extraction-gas chromatography-mass spectrometry to analyze egg VOCs before and during the initial 12 incubation days. A total of 162 VOCs were identified. Hexanal was significantly higher in unfertilized eggs, whereas compounds such as propan-2-ol, propan-2-one, and carboxylic acids were higher in fertilized eggs. Furthermore, the obtained multiple logistic regression model outperformed the partial least-squares-discriminant analysis (PLS-DA) model, demonstrating lower complexity and superior performance. Fertile eggs were accurately identified in the validation set in 68-75% of the cases during the initial 4 days, to 85 and 100% on days 6 and 8. Finally, hierarchical cluster analysis in fertilized eggs revealed the clustering of VOCs of the same chemical class, indicative of their shared biochemical origin. This suggests a promising direction for future research aimed at understanding the biological information embedded in VOCs and their relationship to biochemical processes during embryo development.

Non-destructive egg breed separation using advanced VOC analytical techniques HSSE-GC-MS, PTR-TOF-MS, and SIFT-MS: Assessment of performance and systems' complementarity.

Over the past decade, advanced analytical techniques have been utilized to examine volatile organic compounds (VOCs) in eggs. These VOCs offer valuable insights into factors such as freshness, fertility, the presence of cracks, embryo sex, and breed. In our study, we assessed three mass spectrometry-based systems (headspace sorptive extraction gas chromatography-mass spectrometry; HSSE-GC-MS, proton transfer reaction time-of-flight-mass spectrometry; PTR-TOF-MS; and selected ion flow tube mass spectrometry; SIFT-MS) to analyze and identify VOCs present in intact hatching eggs from three distinct breeds (Dekalb white layer, Shaver brown layer, and Ross 308 broiler). The eggs were sampled on incubation days 2 and 8, to identify VOCs that distinguish breeds irrespective of incubation day. VOC measurements were conducted on 15 eggs per breed by placing them together with PDMS-coated stir bars inside inert Teflon® air sampling bags. After an accumulation period of 2 h, the headspace was analyzed using PTR-TOF-MS and SIFT-MS, while the VOCs adsorbed onto the stir bars were analyzed using GC-MS for additional compound identification. Partial least squares discriminant analysis (PLS-DA) models were constructed for breed differentiation, and variable selection was performed. As a result, 111 VOCs were identified using HSSE-GC-MS, with alcohols and esters being the most abundant. The PLS-DA models demonstrated the efficacy of breed discrimination, with the HSSE-GC-MS and the PTR-TOF-MS exhibiting the highest balanced accuracy of 95.5 % using a reduced set of 11 VOCs and 5 product ions, respectively. The SIFT-MS model had a balanced accuracy of 92.8 % with a reduced set of 11 product ions. Furthermore, complementarity was observed between HSSE-GC-MS, which primarily selected higher molecular weight VOCs, and PTR-TOF-MS and SIFT-MS. A higher correlation was found for compound abundances between the HSSE-GC-MS and the PTR-TOF-MS relative to the SIFT-MS, indicating that the PTR-TOF-MS was better suited to quantify specific compounds identified by the HSSE-GC-MS. Finally, the findings support the presence of VOCs originating from both synthetic and natural sources, highlighting the ability of the VOC analysis systems to non-destructively perform quality control and reveal differences in management practices or biological information encoded in eggs.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory Part I: Theory and Applicability.

In the egg industry, fast and highly reliable quality measurements are crucial. This study presents a novel method based on Hertz contact theory that allows for non-destructive determination of eggshell strength. The goal of the study was to evaluate the material strength (Young's Modulus) and structural strength (stiffness) of eggshells. To this end, an experimental setup was constructed to measure the collision of an eggshell with a small steel ball, which was recorded using a laser vibrometer. The study analyzed a sample of 120 eggs and found a correlation of 0.85 between the traditional static stiffness measured during quasi-static compression tests and the stiffness obtained from the Hertz contact theory. The results show that Hertz contact theory is valid for small steel spheres impacting eggshells, while a sensitivity analysis indicated that the most important factor in determining the strength of the eggshell is the contact duration between the egg and the impactor. These results open up the possibility of grading eggs based on their shell strength in a non-destructive manner.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory-Part II: Implementation and Validation.

Eggshell strength is a critical quality factor for consumption eggs as it affects the probability of breakage in practice. In this study, a fast and low-cost methodology for the non-destructive determination of eggshell strength is presented. The method utilized a small steel ball to impact the egg and a microphone to analyse the impact characteristics. Hertz contact theory was applied to relate the measured impact characteristics to the local stiffness of the eggshell. Therefore, a total of 150 eggs were studied on which eight consecutive measurements per egg were taken around the equator at equidistant places. The results showed a strong correlation of 0.93 between the traditional static stiffness measured during quasi-static compression tests and the average stiffness obtained from the new methodology. This paves the way towards fast, low-cost and non-destructive in-line shell strength measurements to reduce the number of cracked eggs reaching the consumer.

Short communication: Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows using cost-effective, on-farm measured data.

The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows.

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring.

Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations.

Assessment of embryonic growth in chicken eggs by means of visible transmission spectroscopy.

During this work, it was investigated whether spectral measurements can be used to monitor embryonic growth. An experiment was conducted in which both the transmission spectra and embryonic weight were determined on 240 eggs (Cobb, 37 weeks) between Day 5 and Day 10 of incubation. The spectral data were linked to embryonic weight by means of a partial least squares analysis. Different preprocessing procedures were compared during this work, that is, smoothing, multiplicative scatter correction (MSC), and first- and second-order derivative. Compared to the remainder of the preprocessing procedures, MSC leads to a considerable improvement of the prediction capability of the embryonic weight. The ratio of performance to deviation obtained for the MSC spectra equaled 4.5 indicating that a very accurate prediction of embryonic weight is feasible based on the VIS/NIR transmission measurements. Important regions for the prediction are situated around 685-740 nm. It is suggested that the spectral changes in these spectral regions result from the displacement of carotenoids from the yolk into the blood circuitry.