Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat/Espacenet databases for papers and patents, respectively. Based on a total of 49 papers and 115 patent families reported until May 2023 worldwide, 11 technology categories were defined: 6 non-optical and 5 optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements, although 5 companies already entered the market. On the one hand, more research and harmony between consumers, industry, and governments is necessary. On the other hand, close monitoring of the market performance of the currently available techniques will offer valuable insights into the potential and expectations of in ovo sexing techniques in the poultry industry.

The culling of day-old male chicks is an animal welfare issue within the laying hen industry that raises substantial ethical concern. Alternative methods are sought to pre-select males during embryonic development. This method is called in ovo sexing and allows more humane male culling. A robust and non-invasive in ovo color sexing technique was developed and validated in this research. To this end, visible-near-infrared (vis-NIR) point spectroscopy was used, which has advantages over state-of-the-art hyperspectral imaging in terms of accuracy and cost. Two independent experiments were each conducted on a batch of 600 Isa Brown eggs. These eggs were individually illuminated on d 8 to 14, and d 18 of incubation by a halogen lamp and the signal was measured in the vis-NIR range from 300 to 1,145 nm. Next, optimal preprocessing strategies were applied and partial least squares discriminant analysis (PLS-DA) models were built and further optimized after performing a forward interval partial least squares (FiPLS) for variable selection. Results demonstrated that d 12 is too early for vis-NIR in ovo sexing, resulting in a prediction accuracy of 86.49%. However, after 13 d of incubation, an accuracy of 97.78% was obtained, increasing to 99.52% on d 14. Furthermore, these accuracies were higher than earlier reported percentages from hyperspectral imaging and successful sexing was expedited from d 14 to d 13. Moreover, prediction improvement up to 99.05% was obtained on d 13 by correcting for the variability in eggshell properties using d 0 eggshell corrections. Applying the method on d 18 resulted in a lower accuracy of 94.62% due to stronger light attenuation by the growing embryos. Finally, a reduced spectral range of 749 to 861 nm was found to be sufficient for correct classification of 98.46% of the eggs. This paves the way for high-throughput and cost-efficient usage of smaller and cheaper spectrophotometers in commercial hatcheries.

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T², EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T² procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Key Points This article draws attention to the importance of considering type II error in Design of Experiments contexts and shows, through two examples, an easy way to do that.

Key Points In this Quality Quandaries, we briefly introduce the concept of Digital Twins and provide the reader two real-life examples to get an idea about the versatility of it. We also highlight the role of statistics in this rapidly emerging field.