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Background: The pandemic and lockdown caused a slowdown or halt in many work activities across sectors, including academic research, which had to adapt lab procedures to lockdown restrictions. This study aimed to assess an innovative approach to sensory analysis that aligned with the pandemic’s constraints and could enhance traditional methods even in normal conditions. Methods: Remote training of judges was conducted to test the method’s effectiveness. Sensory evaluation of sous-vide chicken breast fillets was conducted at different temperatures (60, 70, 80 °C) and time combinations (60, 90, 120, 150 min), compared to a control (boiled at 100 °C for 60 min). Judges tasted 6 out of 13 randomized samples, recording intensities on a cloud-based sensory card. Results: Judges demonstrated good repeatability and panel homogeneity (RSD ≤ 30%). Significant differences (p < 0.05) in olfactory and flavor characteristics were noted among samples. Higher-temperature samples had stronger boiled meat and chicken flavors, and sous-vide samples showed greater juiciness, especially LT2 and LT3. Conclusions: The remote home-tasting approach proved effective in distinguishing key differences in meat characteristics based on cooking conditions. This method’s reliability and adaptability make it a promising alternative to lab-based sensory evaluation, ensuring research continuity in restrictive conditions and broadening potential for decentralized studies.

The response of a biological process to various factors is generally nonlinear, with many interactions among those factors. Although meta-analyses of data across multiple studies can help in identifying and quantifying interactions among factors, missing latent variables can result in serious misinterpretation. Eventually, all influential factors have to be studied simultaneously in one single experiment. Because of the curvature of the expected response and the presence of interactions among factors, the size of experiments grows very large, even when only 3 or 4 factors are fully arranged. There exists a class of experimental designs, named central composite designs (CCD), that considerably reduces the number of treatments required to estimate all the terms of a second-order polynomial equation without any loss of efficiency compared with the full factorial design. The objective of this technical note is to explain the construction of a CCD and its statistical analysis using the Statistical Analysis System. In short, a CCD consists of 2k treatment points (a first-order factorial design, where k represents the number of factors), augmented by at least one center point and 2×k axial treatments. For 3 factors, the resulting design has 16 treatment points, compared with 27 for a full factorial design. For 4 factors, the CCD has 25 treatment points, compared with 81 for a full factorial design. The CCD can be made orthogonal (no correlation between parameter estimates) or rotatable (the variance of the estimated response is a function only of the distance from the design center and not the direction) by the location of the axial treatments. In spite of the reduced number of treatments compared with a full 3k factorial, the CCD is relatively efficient in estimation of the quadratic and interaction terms. Blocking of experimental units is often desirable and is sometimes required. Randomized block designs for CCD are found in some statistical design textbooks. The construction of incomplete, balanced (or near-balanced) designs for CCD experimental layouts is explained using an example. The Statistical Analysis System statements used to analyze a CCD, to identify the significant parameters in the polynomial equation, and to produce parameter estimates are presented and explained.

Progeny trials in tree breeding are often laid out using blocked experimental designs, in which families are randomly assigned to plots and several trees are planted per plot. Such designs are optimized for the assessment of family effects. However, tree breeders are primarily interested in assessing breeding values of individual trees. This paper considers the assessment of heritability at both the family and tree levels. We assess heritability based on pairwise comparisons among individual trees. The approach shows that there is considerable heterogeneity in pairwise heritabilities, primarily due to the differences in both genetic as well as error variances among within- and between-family comparisons. Our results further show that efficient blocking positively affects all types of comparison except those among trees within the same plot.

The example of the analysis of a collection of trials in diabetes consisting of a sparsely connected network of 10 treatments is used to make some points about approaches to analysis. In particular various graphical and tabular presentations, both of the network and of the results are provided and the connection to the literature of incomplete blocks is made. It is clear from this example that is inappropriate to treat the main effect of trial as random and the implications of this for analysis are discussed. It is also argued that the generalisation from a classic random-effect meta-analysis to one applied to a network usually involves strong assumptions about the variance components involved. Despite this, it is concluded that such an analysis can be a useful way of exploring a set of trials.

This chapter considers experiments with more than one treatment factor or with blocking. These include paired comparison designs, randomized block designs, two‐way layouts with fixed and random effects, multi‐way layouts, Latin square designs, Graeco‐Latin square designs, balanced incomplete block designs, and split‐plot designs. The chapter introduces analysis techniques like data transformation and analysis of covariance. The design employed in the sewage experiment is called a paired comparison design because a pair of treatments are compared in each of the eight samples. An experiment is considered to compare four methods for predicting the shear strength for steel plate girders. This experiment used a randomized block design, which has one experimental factor with k treatments and b blocks of size k. The chapter considers factors whose levels are random; the levels used in the experiment represent a random sample from a large or infinite population of levels.

The procedures of this chapter are designed for statistical analyses of data collected under the auspices of an experimental design involving two factors (the treatment factor and the blocking factor), each at two or more levels. The chapter discusses a case of one observation per treatment–block cell (randomized complete block design). It presents a distribution‐free hypothesis test for general alternatives that is applicable for two‐way layout data representing an arbitrary configuration of either zero or one observation per cell. The chapter discusses a distribution‐free test designed to detect ordered differences among the k treatments. Multiple comparison procedures designed to detect which treatment effects differ from one another, are also presented. Finally, the chapter talks about estimators of contrasts in the treatment effects. The all‐treatments multiple comparison procedure for the most commonly used design is specifically structured to yield less than complete block data, namely, the balanced incomplete block design (BIBD).

Davison et al. (1986) have shown that finite bootstrap simulations can be improved by forcing balance in the aggregate of simulated data sets. Their methods yield first-order balance, which principally affects bootstrap estimation of bias. Here we extend the methodology to second-order balance, which principally affects bootstrap estimation of variance. The particular techniques involve Latin square and balanced incomplete block designs. Numerical examples are given to illustrate both the positive and the negative features of the balanced simulations.

This chapter discusses a basic aspect of variety trials and breeding nurseries, that is, experimental design. This includes designs for different levels of variety and breeding trials, balanced multilocation trials, unbalanced augmented trials, unrandomized nurseries, and experimental plans for earlier generations in the breeding cycle. The chapter demonstrates the user‐friendly GGEbiplot modules for generating the following experimental designs: randomized complete blocks design (RCBD), incomplete blocks design (ICBD), augmented design (AD), row‐column design, AD with varying number of replicates (partially replicated variety trials), and unrandomized breeding nurseries. It discusses the plans for breeding line increase and plans for generation advancement, and the naming of new crosses and renaming breeding lines.

Yates (1939, 1940) pointed out that ordinary randomized block methods can be used, if necessary, to analyse lattice designs. This useful property is shown to extend to any resolvable incomplete block design including designs with blocks of unequal size.

SUMMARY A method is described for constructing nonparametric tests for experiments arranged in blocks when the underlying distributions are of Lehmann's form. In simple eases the resulting test statistics are those that would be obtained by an analysis of variance following a trans formation of the data into exponential scores.