Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. (May 2018)

• Record Type: Journal Article
• Authors: Zhao, Ming; Nian, Yingqun; Allen, Paul; Downey, Gerard; Kerry, Joseph P.; O'Donnell, Colm P.
• Is Part Of: Food research international. Volume 107(2018)
• Page Start: 27
• Page End: 40
• Publication Date: 2018-05
• Subjects: Beef -- Chemometrics -- Eating quality -- Raman spectroscopy -- Sensory attributes; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2018.02.007
• Languages: English
• ISSNs: 0963-9969

Abstract

Abstract: This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples ( n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300–2800 cm −1 . Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (R 2 CV values of 0.50–0.84 and RMSECV values of 1.31–9.07) and were particularly high for desirable flavour attributes (R 2 CVs of 0.80–0.84, RMSECVs of 4.21–4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or breed type, significant improvements on prediction performances were achieved for overall sensory attributes (R 2 CVs of 0.63–0.89 and RMSECVs of 0.38–6.88 for each breed type; R 2 CVs of 0.52–0.89 and RMSECVs of 0.96–6.36 for each age group). Chemometric analysis revealed strong correlations between sensory attributes. Raman spectroscopy combined with chemometric analysis was demonstrated to have high potential as a rapid and non-destructive technique to predict the sensory quality traits of young dairy bull beef. Graphical abstract: Highlights: RS (1300–2800 cm −1 ) has strong potential to assess beef eating quality. RS with chemometrics is capable of predicting 16 sensory attributes of beef. Good prediction performances were achieved using PLSR on identical beef type. Chemometric methods were used to establish correlations between sensory attributes.