Boar taint detection: A comparison of three sensory protocols

Trautmann, Johanna; Meier-Dinkel, Lisa; Gertheiss, Jan; Mörlein, Daniel

doi:10.1016/j.meatsci.2015.08.011

Cited by 27 publications

(35 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extent to which a carcass is tainted can be evaluated at the abattoir. The evaluation can be conducted either by a human nose method (Trautmann, Meier-Dinkel, Gertheiss, & Mörlein, 2016) or by a chemical analysis. In Denmark, a chemical analysis of skatole is used for sorting the tainted carcasses (Mortensen & Sørensen, 1984).…”

Section: The Boar Taint Casementioning

confidence: 99%

Meat flavour in pork and beef – From animal to meal

2017

View full text Add to dashboard Cite

Section: The Boar Taint Casementioning

confidence: 99%

Meat flavour in pork and beef – From animal to meal

2017

View full text Add to dashboard Cite

“…For the definition of boar‐tainted samples, we refer to Meier‐Dinkel et al and Trautmann et al The original observations of skatole and androstenone concentrations are converted into a binary variable indicating whether a meat sample is boar tainted or not. For this conversion, two threshold values τ sk and τ an are chosen, and sample i is defined as boar tainted if

y_{i}^{sk} > τ_{sk}

and/or

y_{i}^{an} > τ_{an}

.…”

Section: Application To Real Datamentioning

confidence: 99%

Random forests for functional covariates

Möller

Tutz

Gertheiss

2016

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

We propose a form of random forests that is especially suited for functional covariates. The method is based on partitioning the functions' domain in intervals and using the functions' mean values across those intervals as predictors in regression or classification trees. This approach appears to be more intuitive to applied researchers than usual methods for functional data, while also performing very well in terms of prediction accuracy. The intervals are obtained from randomly drawn, exponentially distributed waiting times. We apply our method to data from Raman spectra on boar meat as well as near‐infrared absorption spectra. The predictive performance of the proposed functional random forests is compared with commonly used parametric and nonparametric functional methods and with a nonfunctional random forest using the single measurements of the curve as covariates. Further, we present a functional variable importance measure, yielding information about the relevance of the different parts of the predictor curves. Our variable importance curve is much smoother and hence easier to interpret than the one obtained from nonfunctional random forests.

show abstract

“…The university Panel was from the Laboratory for Sensory Analysis and Consumer Research at the University of Göttingen. Its members were carefully selected on the basis of their individual olfactory towards androstenone and skatole [13],[14] and then trained for sensory quality control of boar fat for a period of 8 months up to 3.5. The abattoir panel consisted of staff members from a slaughterhouse, who work in the daily production process controlling the quality of boar carcasses on-line.…”

Section: Methodsmentioning

confidence: 99%

“…The samples were taken at a commercial abattoir (Tönnies Lebensmittel GmbH & Co. KG–Home; Long: 8.32240°E; Lat: 51.86262°N). Chemical analysis of boar taint compounds was done in duplicate using gas chromatography mass spectrometry (GC-MS) and deuterated standards [14]; results are given as μg/g (ppm) melted back fat. Samples were presented in a randomized order for each assessor/each condition and identified with a three digit code.…”

Section: Methodsmentioning

confidence: 99%

“…Each panel already had implemented their evaluation system for boar taint in order to cope with their daily evaluation routine. Therefore, each panel applied its individual protocol: the university panel evaluated the samples on a 6-point scale from 0 (=“no deviation from standard fat”) to 5 (=“very strong deviation from standard fat”) [14]. The abattoir panel evaluated the samples on a binary scale (0 = “standard”/no boar taint; 1 = “deviant”/boar tainted).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Noise and accustomation: A pilot study of trained assessors’ olfactory performance

et al. 2017

Self Cite

View full text Add to dashboard Cite

While recent studies suggest an influence of noise on olfactory performance, it is unclear as to what extent the influence varies between subjects who are accustomed to noise and those who are not. Two groups of panelists were selected: a University panel usually working under silent conditions and an abattoir panel usually working on the slaughter line with abattoir noise. Odor discrimination, odor identification, and odor detection thresholds were studied. Furthermore, a sensory quality control task using 40 boar samples was performed. All tests were accomplished both with and without extraneous noise recorded at an abattoir (70 dB) using headphones. Contrary to the researchers’ expectations, abattoir noise hardly affected the olfactory tests nor was the quality control task impaired. Abattoir noise did not influence the perceived intensity of boar taint and the classification results of the testers, regardless of whether they were accustomed to such noise or not. The results indicate that sensory quality control can be conducted in a manufacturing environment with constant noise without diminishing the assessors’ performance.

show abstract

Boar taint detection: A comparison of three sensory protocols

Cited by 27 publications

References 29 publications

Meat flavour in pork and beef – From animal to meal

Meat flavour in pork and beef – From animal to meal

Random forests for functional covariates

Noise and accustomation: A pilot study of trained assessors’ olfactory performance

Contact Info

Product

Resources

About