Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction

Abstract

Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral images of fresh ribeye steaks (n = 319) acquired at 3-5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900-1700 nm) with a diffuse-flood lighting system was developed. After imaging, steaks were vacuum-packaged and aged until 14 days postmortem. After aging, the samples were cooked and slice shear force (SSF) values were collected as a tenderness reference. After reflectance calibration, a Region-of-Interest (ROI) of 150 9 300 pixels at the center of longissimus muscle was selected. Partial least squares regression (PLSR) was carried out on each ROI image to reduce the dimension along the spectral axis. Gray-level textural co-occurrence matrix analysis with two quantization levels (64 and 256) was conducted on the PLSR bands to extract second-order statistical textural features. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF B 205.80 N), intermediate (205.80 N \ SSF \ 254.80 N), and tough (SSF C 254.80 N). The model with a quantization level of 256 performed better than the one with a quantization level of 64. This model correctly classified 242 out of 314 samples with an overall accuracy of 77.0%. Fat, protein, and water absorption bands were identified between 900 and 1700 nm. Our results show that NIR hyperspectral imaging holds promise as an instrument for forecasting beef tenderness.

NIR hyperspectral imaging spectroscopy and chemometrics for the discrimination of roots and crop residues extracted from soil samples

“…Partial least squares discriminant analysis is a dimensionality reduction technique that defines new latent variables such that they explain maximum variation of independent and dependent variables. 31 Support vector machine is an algorithm designed to derive a function based on high-dimensional space that describes the hyperplane that optimally separates 2 classes of data. Support vector machine tries to correctly classify the training data by maximizing the wide of the margin between the groups and by penalizing for errors to get a better generalization performance.…”

Hyperspectral imaging (hsi): applications in animal and dairy sector

“…Particularly in the meat industry, the technology has gained significant attraction. Quality aspects of meat and meat products which are currently studied using HIS includes  Examination of tenderness (Naganathan et al, 2008a;Naganathan et al,2008b;ElMasry et al, 2012a;Wu et al, 2012a), pH (ElMasry et al,2012a, color (ElMasry et al, 2012a, Wu et al, 2012a, water content and water holding capacity (ElMasry et al, 2011b), chemical composition (Kobayashi et al, 2010;ElMasry et al,2012b), and spoilage by microbes in beef;  Classification, grading (Qiao et al, 2007a;Barbin et al, 2012a), and prediction of sensory and quality characteristics (Qiao et al, 2007b;Barbin et al, 2012b), chemical composition (Barlocco et al, 2006Barbin et al,2012c,) and microbial spoilage Barbin et al, 2012c;Tao et al, 2012), in pork;  Muscle discrimination (Kamruzzaman et al, 2011), determination of sensory and quality characteristics (Kamruzzaman et al, 2012a;Kamruzzaman et al, 2013a), and chemical composition (Kamruzzaman et al, 2012b);  Authenticity analysis (Kamruzzaman et al, 2012c), & adulteration (Kamruzzaman et al, 2013b in lamb; detection of faecal contaminants , tumors (Nakariyakul & Casasent, 2008;Nakariyakul & Casasent, 2009), bacterial spoilage (Feng et al, 2013aFeng et al, 2013b),and freshness of chicken (Grau et al, 2011);  Prediction of contaminants (Segtnan et al, 2009a;Segtnan et al, 2009b), composition (ElMasry & Wold, 2008, Wu et al, 2012, and freshness (Sivertsen et al, 2011b;…”

Spectral discrimination of bulloak (Allocasuarina luehmannii) and associated woodland for habitat mapping of the endangered bulloak jewel butterfly (Hypochrysops piceata) in southern Queensland

“…A series of "cleaning" operations was applied for the elimination of: (a) very short wavelengths (350 to 399 nm) and strong water vapor absorption bands: 1356 to 1480, 1791 to 2021, and 2396 to 2500 nm, 18,21 (b) outliers that indicated abnormal reflectance response as compared with other samples. 20,30,31 Pretreatment or transformation of the spectral data was a significant component of a number of spectral analyses to improve the accuracy of results. In this study, two chemometrics pretreatment methods [moving average smoothing 8,18,20 and multiplicative scatter correction (MSC) 20,31 ] were applied to reduce the noise and to normalize the data.…”

“…30,31,33 The PLS model is comparatively better than the PCA because it does not include latent variables that are less important to describe the variance of the quality measurement. 20,30,31,34 The PLS regression can be defined as a bilinear modeling method for relating the variations in one or numerous response variables (Y-variable) to numerous predictors (X-variable). 33,35 In PLS regression, information of the independent (X-variable) variables is projected onto a small number of latent (Y-variable) variables named PLS factors or components.…”

“…33,35 In PLS regression, information of the independent (X-variable) variables is projected onto a small number of latent (Y-variable) variables named PLS factors or components. 30,35 This meant that the Y-data are used in estimating the latent variables to ensure that the first components are those that are most relevant for predicting the Y-variables.…”