Picture of rolled up £5 note

Open Access research that shapes economic thinking...

Strathprints makes available scholarly Open Access content by the Fraser of Allander Institute (FAI), a leading independent economic research unit focused on the Scottish economy and based within the Department of Economics. The FAI focuses on research exploring economics and its role within sustainable growth policy, fiscal analysis, energy and climate change, labour market trends, inclusive growth and wellbeing.

The open content by FAI made available by Strathprints also includes an archive of over 40 years of papers and commentaries published in the Fraser of Allander Economic Commentary, formerly known as the Quarterly Economic Commentary. Founded in 1975, "the Commentary" is the leading publication on the Scottish economy and offers authoritative and independent analysis of the key issues of the day.

Explore Open Access research by FAI or the Department of Economics - or read papers from the Commentary archive [1975-2006] and [2007-2018]. Or explore all of Strathclyde's Open Access research...

Prediction of lamb eating quality using hyperspectral imaging

Qiao, Tong and Ren, Jinchang and Zabalza, Jaime and Marshall, Stephen (2015) Prediction of lamb eating quality using hyperspectral imaging. In: OCM (Optical Characterization of Materials) 2015. KIT scientific publishing, Karlsruhe, Germany, pp. 15-25. ISBN 978-3-7315-0318-7

[img]
Preview
Text (Qiao-etal-OCM2015-prediction-lamb-eating-quality-hyperspectral-imaging)
Qiao_etal_OCM2015_prediction_lamb_eating_quality_hyperspectral_imaging.pdf
Accepted Author Manuscript
License: Creative Commons ShareAlike 4.0 logo

Download (543kB) | Preview

Abstract

Lamb eating quality is related to 3 factors, which are tenderness, juiciness and flavour. In addition to these factors, the surface colour of lamb could influence the purchase decision of consumers. Objective quality evaluation approaches, like near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), have been proved fast and non-destructive in assessing beef quality, compared with conventional methods. However, rare research has been done for lamb samples. Therefore, in this paper the feasibility of HSI for evaluating lamb quality is tested. A total of 80 lamb samples were imaged using a visible range HSI system and the spectral profiles were used for predicting lamb quality related traits. For some traits, noises were removed from HSI spectra by singular spectrum analysis (SSA) for better performance. Support vector machine (SVM) was employed to construct prediction equations. Considering SVM is sensitive to high dimensional data, principal component analysis (PCA) was applied to reduce the dimensionality first. The prediction results suggest that HSI is promising in predicting lamb eating quality