Characterization of poultry house dust using chemometrics and scanning electron microscopy imaging

Abstract

Poultry house dust is composed of fine particles which likely originate from a diverse range of materials such as feed, litter, excreta, and feathers. Little is known about the contribution of these sources to broiler house airborne dust so the present study was designed to identify the relative contributions of these sources. Samples of feed, excreta, feather, and bedding, known mixtures of these and settled dust from 28 broiler chicken flocks were tested for the concentration of 18 chemical elements. A chemometrics approach (the application of multivariate statistical techniques to chemical analysis data) was used to identify the primary source material in broiler chicken house dust samples. Scanning electron microscopy (SEM) was also used to analyze dust sample particulates based on examination of source materials. Excreta was found to be the main component of broiler chicken house dust, both by SEM and chemometric analysis. SEM of experimental flock dust between 7 and 35 days of age (d) revealed that the contribution of excreta to dust increased with age from 60% at 7 d to 95% at 28 d (P < 0.001). The proportion of bedding and feed in dust declined with age while the contribution of feather material remained low throughout. This study demonstrates that excreta provides the bulk of the material in poultry dust samples with bedding material, feed and feather material providing lower proportions. The relative contributions of these materials to dust varies with age of birds at dust collection. Additional research is required to determine the health and diagnostic implications of this variation.

Keywords: chemistry; chemometric; poultry dust; scanning electron microscopy; source.

Figure 1

The PCA score plot (left) of the first two principal components of the data set comprising the individual component samples. The first component accounts for 45.3% of the variation, and the second component 19.7%. The PCA loading plot (right) of the same data set indicates partial contribution of variables in PCA analysis. Abbreviations: PCA, Principal component analysis.

Figure 2

The PCA score plot of the first two principal components of a data set of individual component samples (feed, excreta, feather, and rice hulls) and mixture samples (Mix 1−8). The first component explains 40.6% of the variation, and the second component 24.1%. Proportions of rice hull: feed: excreta: feather for the mixtures are: Mix 1 (50:30:15:05); Mix 2 (50:15:30:05; Mix 3 (30:15:50:05); Mix 4 (30:50:15:05; Mix 5 (50:15:25:10); Mix 6 (50:30:19:01); Mix 7 (60:10:29:01) and Mix 8 (10:60:29:01).

Figure 3

The PCA score plot of the first two principal components of a data set of individual component samples and dust from commercial broiler flocks housed on various bedding materials (n = 7 wood shavings, n = 7 sawdust, n = 7 rice hulls, n = 7 straw). The first component explains 34.1% of the variation, and the second component 13.3%. Components close to each other have similar profile.

Figure 4

SEM images of individual component samples representing key source materials for poultry house dust: (A) Feed particles: spherical or angular appearance with smooth surface. (B) Excreta particles: irregular appearance with rough surface. (C) Feather: flat or filamentous with barbules and calamus. (D) Rice hulls: plate shape, vascular, thick and flat. (E) Wood shavings: woodchip appearance. (F) A known mixture (mix 2) of rice hulls: feed: excreta: feather. All samples were ground to pass a 0.5-mm sieve and coated with gold before SEM. Abbreviations: Scanning electron microscopy.

Figure 5

(A) Proportion of feed, excreta, feather and bedding particles (LSM±SE) identified in end of batch (35−49 d) broiler house dust (n = 11). (B) Mean proportion of feed, excreta, feather and bedding particles in dust by bird age (7−35d) in an experimental flock on wood shavings with dust samples collected weekly by settle plate. Characterization of particles was done visually using SEM and ImageJ analyses.