Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef

Journal of Food Engineering 110 (2012) 127–140

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Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng

Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef Gamal ElMasry a,1, Da-Wen Sun a,⇑, Paul Allen b a

Food Refrigeration and Computerised Food Technology (FRCFT), School of Agriculture, Food Science & Veterinary Medicine, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland b Ashtown Food Research Centre, Teagasc, Dublin 15, Ireland

a r t i c l e

i n f o

Article history: Received 21 April 2011 Received in revised form 18 November 2011 Accepted 22 November 2011 Available online 1 December 2011 Keywords: Hyperspectral imaging Imaging spectroscopy Meat Beef pH Colour Tenderness

a b s t r a c t Hyperspectral imaging system operated in the near infrared (NIR) region (900–1700 nm) was developed for non-contact measurement of surface colour, pH and tenderness of fresh beef. Hyperspectral images were acquired for beef samples and their spectral signatures were extracted. The real colour (expressed as L⁄a⁄b⁄), pH and tenderness of the same samples were recorded using traditional contact methods and then modelled with their corresponding spectral data using partial least square regression (PLSR). The L⁄, b⁄, pH and tenderness values were predicted with coefficients of determination (R2CV ) of 0.88, 0.81, 0.73 and 0.83 and root mean square errors estimated by cross validation (RMSECV) of 1.21, 0.57, 0.06 and 40.75, respectively. The weighted regression coefficients of the resulting PLSR models were used to identify the most important wavelengths and to reduce the high dimensionality of the hyperspectral data. By using these important wavelengths, image processing algorithm was developed to transfer the predicting models to every pixel in the image for visualizing colour and pH in all portions of the sample. The results demonstrated that NIR hyperspectral imaging system is a potential technique for non-destructive prediction of beef quality attributes, thus facilitating identification and classification of beef meat in a simple and fast way. With more improvement in terms of speed and processing, the hyperspectral imaging system could be effectively implemented in commercial meat product processing plants for non-destructive and rapid quality measurements. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction In the past few decades the meat industry has immensely thrived as demands for superior food quality continues to grow on both international and domestic markets (Desmond et al., 2000; McDonald et al., 2001; McDonald and Sun, 2001). Interests in meat quality are driven by the need to supply the consumer with a consistent high quality product at an affordable price (Sepúlveda et al., 2011; ElMasry and Sun, 2010). To fulfil consumer’s satisfaction it is very important to provide meat products that can better meet market requirements and to refocus the meat industry on the customer’s needs because it directly impacts on its profitability (Troy and Kerry, 2010). To realize these needs, it is a crucial element within the meat industry to accurately assess meat quality attributes by improving modern techniques for quality evaluation (Herrero, 2008). ⇑ Corresponding author. Tel.: +353 1 7167342; fax: +353 1 7167493. E-mail address: [email protected] (D.-W. Sun). URLs: http://www.ucd.ie/refrig, http://www.ucd.ie/sun (D.-W. Sun). Permanent address: Agricultural Engineering Department, Suez Canal University, Ismailia, Egypt. 1

0260-8774/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2011.11.028

In the production of meat products, the great variability in raw meat often leads to highly variable products being marketed without a controlled level of quality. This variability originates from differences in both animal production and meat processing, which imposes great pressure on the food manufacturing industry to guarantee the quality of meat (Cozzolino et al., 2002). The problem is aggravated when the industry is unable to satisfactorily characterize this level of quality and cannot therefore market their products with a certified quality level (Damez and Clerjon, 2008). Hence, inferior quality meats are essential to be identified on the slaughter line, so that they can be handled and marketed separately from high quality meats (Byrne et al., 1998). Assortment of intrinsic and extrinsic quality cues of fresh meat are usually used by consumers for choosing and purchasing high-quality meat products at the points of sale and consumption. Healthy appearance, colour, visible drip, visible fat and marbling and tenderness are among the main quality attributes usually sought by the consumers. Traditional methods for assessing meat quality attributes are time consuming, destructive and are associated with inconsistency and variability. Instrumental techniques for rapid screening of meat properties to improve control and classification of the

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product in mechanised processes are of great interest for both the industry and the consumers. By these techniques, the slaughter industry can gain knowledge regarding the meat quality early after sticking in order to facilitate sorting before further use. One of these techniques is the image scanning system (Belk et al., 2000; Vote et al., 2003; Hopkins et al., 2004) developed as a non-invasive method operating at normal abattoir chain speeds for automatic inspection of carcasses. However, this system has some limitations and needs therefore to be augmented with other suitable systems to measure meat eating quality traits (Cubero et al., 2011). On the other hand, among the numerous techniques which have been proposed for meat quality evaluation on the fresh intact product, near infrared (NIR) spectroscopy proved a great potential for continuous monitoring and controlling of process and product quality in food processing industry (Huang et al., 2008). Since NIR is a rapid method, it is considered as a suitable tool to implement frequent quality control during the entire meat processing chain (Konstantinos and Athanasia, 2004). Furthermore NIR technology can provide complete information on the chemical constituents in a sample scanned, it is thus a convenient tool for characterising foods (Andrés et al., 2007; Wu et al., 2008). In contrast to conventional methods for the determination of meat quality, spectral techniques enable rapid, simple and simultaneous assessment of numerous meat properties without sample preparation, resulting in possibly replacing expensive and slow reference methods (Heigl et al., 2009; Prieto et al., 2009; Prevolnik et al., 2010). Spectral techniques in the visible and near-infrared ranges have already found considerable applications in food and meat products (Osborne et al., 1997). Also, common applications with meats include the quantitative prediction of chemical composition such as fat, water and protein (Tøgersen et al., 1999) as well as for predicting physical characteristics such as colour (Liu et al., 2003), pH (Andersen et al., 1999), tenderness (Shackelford et al., 2005; Rust et al., 2008; Bowling et al., 2009), drip loss and water holding capacity (Prevolnik et al., 2010) and other physical, sensory and technological characteristics (Geesink et al., 2003; Leroy et al., 2003; Liu et al., 2003; Cozzolino and Murray, 2004;). However, spectroscopy measurements suffer critically from the small sample area (limited spatial information) which cannot be representative of such a heterogeneous material as meat (Brøndum et al., 2000). As an extension of both spectroscopy and imaging techniques, hyperspectral imaging technique has been emerged to integrate both techniques in one system to provide spectral and spatial backgrounds simultaneously. In recent years there have been growing interests in this technology from researchers around the world for non-destructive analysis in many research and industrial sectors (ElMasry et al., 2007, 2009; Cluff et al., 2008; Naganathan et al., 2008a,b; Menesatti et al., 2009; Lorente et al., 2011a). The use of hyperspectral imaging has approached to be a viable alternative to the conventional imaging and spectroscopy in a wide range of applications. Therefore, developing quality evaluation system based on hyperspectral imaging technology to assess meat quality attributes and to ensure its authentication would thus bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. Although this technology has not yet been sufficiently exploited in meat processing lines and for quality assessment, its potential is very promising. Indeed, it is important to emphasize that the current ‘hyperspectral’ imaging systems are a laboratory-based system, which is not yet ready for implementation in meat processing lines due to its high dimensionality as well as time constraints for image acquisition and subsequent image analyses. Therefore, the challenge is to seek the most sensitive wavebands to predict the essential meat quality attributes, leading to the development of an optimized ‘multispectral’ imaging system that could be imple-

mented directly in industrial applications (ElMasry and Sun, 2010; Lorente et al., 2011b). Therefore, the prime focus of this study was to develop a NIR hyperspectral imaging system (900–1700 nm) for objective prediction of some quality attributes (colour, pH and tenderness) of intact fresh beef. Specific objectives were to:  establish a NIR hyperspectral imaging system in the NIR spectral range of 900–1700 nm;  extract spectral information, build multivariate analysis models and identify the sensitive wavelengths most related to colour, pH and tenderness prediction; and  develop image processing algorithms for visualizing variations in quality attributes in beef samples. 2. Materials and methods 2.1. Preparation of beef samples A total of 27 bulls from three different breeds (Holstein–Friesian, Jersey  Holstein–Friesian and Norwegian Red  Holstein– Friesian) with nine bulls from each breed were slaughtered at a commercial slaughterhouse (Meadow Meats, Rathdowney, Co. Laois, Ireland). The mean hot carcass weight was 300.60 ± 34.36 kg (ranged from 242.0–386.6 kg). At 24 h post-mortem, three muscles (M. longissimus dorsi (LD), M. semitendinosus (ST) and M. psoas major (PM)) were dissected from each carcass and then sliced to 1-in. thick slices by a mechanical slicer. The slices were labelled and vacuum packed in polyethylene bags and then transported in ice boxes to laboratories of Food Refrigeration & Computerized Food Technology (FRCFT), University College Dublin (UCD), Ireland, and stored at 4 °C until the next day when quality parameters were measured. One slice from each muscle (n = 81 slices in total) was used for tenderness measurement. Colour and pH were measured directly at 2-day post-mortem in some samples; meanwhile some samples were randomly aged for 14 days to create wider ranges of beef properties value to ensure good multivariate models. In total 321 samples were used for colour and pH measurements. Slices excised from different breeds and different muscles to a high extent guarantee a large variation in quality values, which is critically important to build effective multivariate prediction models. 2.2. Measurement of quality parameters Each beef slice was first scanned by the hyperspectral imaging system and then its reference values of colour, pH and tenderness were determined. Each slice was removed from the vacuum package, left for 30 min for blooming, and then its quality parameters were measured. Colour was measured in L⁄a⁄b⁄ colour system with a chromometer (CR-400, Konica Minolta Corp., Japan) calibrated against white ceramic reference tile. Where ‘L⁄’ stands for colour lightness (varies from 100 for perfect white to 0 for black), ‘a⁄’ defines the colour degree between red and green (0 indicates green while 255 indicates red), and ‘b⁄’ indicates the colour degree between yellow and blue (0 indicates blue and 255 indicates yellow). Colour values were obtained as the average of four measurements performed on different locations of each slice. On the other hand, pH was calculated as the average of five pH measurements in each slice using a pH meter (Orion 3 Star, Thermo Fisher Scientific Inc., USA) supported with a glass electrode. Tenderness was measured using the slice shear force ‘‘SSF’’ method (Shackelford et al., 2001). The sample is first imaged by the system and then cooked in a water bath to an internal temperature of 70 °C. A single slice of 5 cm long from the centre of the cooked slice was removed parallel to the long dimension using a double-blade knife, and the slice

G. ElMasry et al. / Journal of Food Engineering 110 (2012) 127–140 Table 1 Reference values of colour (L⁄, a⁄ and b⁄), pH and slice shear force (SSF) of beef samples measured by traditional methods. Statistics

Colour L⁄

Mean Standard deviation Minimum Maximum Range n

pH a⁄

Slice shear force (N)

b⁄

33.70 3.53

18.16 2.03

6.97 1.33

5.58 0.12

296.44 99.89

25.74 44.82 19.08 321

11.53 24.26 12.73 321

3.25 10.07 6.82 321

5.37 6.13 0.76 321

138.76 525.00 386.24 81

was then sheared once perpendicular to the muscle fibres using a universal testing machine (Instron, Model 4301, Instron Ltd., UK) equipped with a flat, blunt-end blade. The maximum shear force in Newton was recorded at the highest peak of the force–deformation curve to represent sample tenderness. The values for each attribute for all tested samples were then used as reference values for calibrating the system for predicting these attributes. In Table 1 the relevant statistics of colour, pH and tenderness for the examined beef samples are presented.

2.3. Hyperspectral imaging system The hyperspectral imaging system used in this study was a linescanning configuration (also called ‘Pushbroom’) which records a whole line of an image rather than a single pixel at a time. A narrow line of the specimen is imaged onto a row of pixels on the sensor chip and the spectrograph generates a spectrum for each point on the line, spread across the second dimension of the chip. This configuration is normally used when either the specimen or the imaging unit is moving one in respect to the other such as those used in real-time applications. Since no filter change is required (as typically used in tunable filter systems), the speed of image acquisition is limited only by camera read out speeds. The prime advantage of this method is that both spectral and spatial data are acquired simultaneously and are immediately available for processing. As shown in Fig. 1, the system consists of a 12-bit CCD camera along with focusing lens (Xeva 992, Xenics Infrared Solutions, Belgium), a spectrograph (ImSpector N17E, Specim, Spectral Imaging Ltd., Oulu, Finland), a conveying stage operated by a stepper motor (GPL-DZTSA-1000-X, Zolix Instrument Co.

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Ltd., China) and a computer supported with SpectralCube data acquisition software (Spectral Imaging Ltd., Finland). In order to illuminate the target sample and the field of view of the camera an illumination unit consisting of two 500 W halogen lamps (Lowel Light Inc., NY, USA) was fixed above the sample from both sides at the height of 60 cm and at an angle of 45° to reduce the shadowing effects. The camera has 320  256 pixels, the first coordinate is assigned for the spatial data and the other coordinate is allocated for the spectral information. Based on the system configuration, the proper speed required to move the stage in order to obtain the same spatial resolution (0.578 mm/pixel) in the horizontal and vertical directions was calculated as 2.8 cm/s. All image acquisition parameters such as motor speed, exposure time, binning mode and wavelength range were entirely controlled by the data acquisition software. The system images a line of vision at a time and diffracts the light onto a two-dimensional thermo-electrically cooled indiumgallium-arsenide (InGaAs) sensor array using a diffraction grating capable of dispersing the light in the spectral range from 897 to 1752 nm with a spectral increment of about 3.34 nm between the contiguous bands producing a total of 256 bands. To avoid low signal-to-noise ratio, only the wavelengths ranging from 910 to 1700 nm with 237 spectral wavebands were used in this investigation. By scanning the entire surface of the specimen (line by line), a complete three-dimensional hyperspectral image cube ‘hypercube’ was then created by translating the sample at a constant speed and line-imaging synchronously. In practise, beef sample was placed on the conveying stage to be scanned line by line using 10 ms exposure time to build a hyperspectral image (R0) which is then stored in a Band Interleaved by Line (BIL) format. The formed image consists of several congruent sub-images (237) representing intensities at 237 wavelength bands spanned from 910 to 1700 nm. As a 3D image, the acquired hyperspectral image includes both spatial and spectral information from which physical and geometric features as well as chemical information could be pulled out from the image. The acquired images were then corrected against dark and white references using the following equation:

RðkÞ ¼

R0 ðkÞ  Br  100% WðkÞ  Bw

ð1Þ

where R(k), R0(k) and W(k) are the calibrated, raw and white reference images at wavelength k, respectively. In addition, Br and Bw are the dark current noise images obtained for the raw and white

Fig. 1. Configuration of the hyperspectral imaging system.

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reference images. The standard white reference image was obtained by acquiring a spectral image from a uniform, high reflectance white calibration tile (about 99.99% reflectance), and the dark current noise image (about 0% reflectance) was acquired by recording a spectral image when the light source was turned off and the camera lens was completely covered with its own non-reflective opaque black cap to nullify the thermal activities of the detector. Furthermore, the reflection intensity hyperspectral data was converted to absorbance spectra by taking the base-10 logarithm of the reciprocal reflectance spectra (log10(1/R)). 2.4. Spectral analysis 2.4.1. Spectral data extraction The average spectrum of each sample was extracted by locating the lean parts of the beef sample as the main region of interest (ROI). Isolating the entire beef sample from the background (conveying stage) and the adjoining fat portion of the sample was carried out by subtracting an image of very low reflectance intensity from an image of a very high reflectance intensity followed by a simple thresholding at a constant value of 0.12. Also, image at wavelength of 1215 nm (absorption band of fat) was subtracted from the image at wavelength of 1270 nm (of higher reflectance value) followed by a simple thresholding at a value of 0.04 to isolate fat portions from the final mask. This procedure resulted in a mask containing only the lean part of the sample without fat, which was then used as the main region of interest (ROI) from which spectral information of the beef sample was extracted. Only one average spectrum was used to represent each sample and the same routine was repeated for all hyperspectral images of beef samples. The extracted spectral data from all slices were then arranged in a matrix (X) where the rows of this matrix represent the number of samples and the columns represent the number of variables (237 wavelengths). All processes of image correction, segmentation and spectral data extraction were programmed in Matlab 7.7 (The Mathworks Inc., Natick, MA, USA). 2.4.2. Multivariate data analysis Multivariate data analysis was carried out to predict colour, pH and tenderness of beef samples using their corresponding spectral information. First, principal component analysis (PCA) was performed on the entire spectral data (X-matrix) to identify the deviant spectral outliers. PCA is based on identifying the most important directions of variability in a multivariate data space (X-matrix) and to find the main phenomena in the dataset. The same spectral dataset can be used together with a block of quality values (colour, pH or tenderness) to build a predictive partial least squares regression (PLSR) model, so that these attributes can be predicted in the future directly from the measured spectra. Partial least square regressions were carried out to perform linear models of prediction between spectral data (X-matrix) and the values of one of the quality parameters (colour, pH or tenderness) obtained from the traditional measurement (Y-matrix). The PLSR model is a quantitative spectral decomposition technique that is used to optimise the covariance between Y and linear combinations of X by performing the decomposition on both the spectral and quality data simultaneously. The PLSR compresses the spectral data into orthogonal structures called latent factors (lf) which describe the maximum covariance between the spectral information and the reference values of beef quality attributes (Geesink et al., 2003). Instead of first decomposing the spectral matrix into a set of eigenvectors and scores and make the regression against the Y-matrix values in a separate step, PLSR actually uses the quality parameters in Y-matrix during the decomposition process. This causes spectra containing higher values of the studied quality parameters to be weighted more heavily than those with low values. In this study

predictions were validated with full cross-validation (leave-oneout) and for estimating the correct number of latent factors (lf). In general, to get efficient and reliable models, a minimum number of latent variables should be used in the models (Peirs et al., 2003). The optimum number of factors for creating a PLSR model was defined either at the maximum value of the explained variance or at the lowest value of the predicted residual error sum of squares (PRESS) that shows the sum of squares of deviation between predicted and reference values of quality parameters. The accuracy and the predictive capabilities of the model were evaluated based on coefficient of determination in calibration (R2C ), coefficient of determination in cross-validation (R2CV ), the root mean square error of calibration (RMSEC) and the root mean square error by cross-validation (RMSECV). These statistical parameters are defined as follows:

PRESS ¼

X ðycal  yact Þ2

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðycal  yact Þ2 RMSEC ¼ n sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðypred  yact Þ2 RMSECV ¼ n

ð2Þ

ð3Þ

ð4Þ

where n is the number of spectra (samples), yact the actual value, ycal the calculated value from calibration model and ypred the predicted value of colour, pH or tenderness in fresh beef samples estimated by cross validation. All computations and multivariate data analyses were performed with the aid of chemometric software Unscrambler 7.9 (CAMO, Trondheim, Norway) and Matlab 7.7 (The Mathworks Inc., Natick, MA, USA). Because the hyperspectral image data are characterized by its very high dimensionality with redundancy among contiguous wavelengths, image dimension reduction needs to be conducted to speed up the subsequent analysis process. In practice, model accuracy for high dimension data such as hyperspectral images could be improved by reducing dimensionality or by increasing the number of training samples. The important wavelengths could be chosen in the spectral regions at which a great difference in the spectral patterns of the tested samples exists. In addition to significantly reduce processing time, selected wavelengths enhance the predictive ability of the model (Osborne et al., 1997). In most reliable applications, it is recommended to use the wavelengths/variables that carry the most useful information since some of the variables may have irrelevant information or noise (Keskin et al., 2004). By this way, the model having fewer variables will be easier to interpret, and usually the prediction error will be lower (Indahl and Næs, 2004). Different interpretations of PLSR models exist, either according to the PLSR factors or regression beta-coefficients. In this study, the interpretation of the beta-coefficients is used to determine the relevant wavelengths regions of the models. An important value of the beta-coefficient is interpreted as a relevant absorption region for the considered predictive model. In this study, the weighted regression coefficients (Bw) resulting from the best PLSR prediction model are considered as an indication of the most important wavelengths that do not suffer from redundancy and contribute most in predicting quality attributes of the beef samples. In fact, the normal Beta-coefficients cannot be used directly to choose which wavelengths are the most important for modelling. Indeed, a large Beta-coefficient may indicate a significant variable, but also a variable with small absolute value and a large variance. This problem can be avoided by standardizing the data. Therefore, the spectral data were first standardized by dividing each spectrum by its standard deviation to ensure that each variable had the same variance (Frenich et al., 1995). The PLSR model was then developed

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between the weighted data and the measured values of the attribute under study and the resulting regression coefficient is called the weighted regression coefficients (Bw). The weighted regression coefficients (Bw) and the normal Beta-coefficients (B) are identical if no weights are experienced on the spectral data. Since all variables are adjusted to the same scale, the resulting coefficients show the relative importance of the X-variables in the model. Variables (wavelengths) having large weighted regression coefficient values (irrespective of sign) were considered as good candidates for effective prediction. In this study, the PLSR model that uses the full spectral range (237 wavelengths) for predicting beef quality traits will be called Model 1; meanwhile the PLSR model utilizing only the important wavelengths for predicting the same attributes will be called Model 2 throughout this paper. 2.4.3. Distribution maps of beef quality attributes To observe the difference in beef quality attributes from sample to sample and even within the same sample, a reduced image space was first formed by selecting spectral bands at the most important wavelengths in order to reduce the amount of time for image analysis. Predicting and visualizing quality parameters in all pixels of the examined samples was executed by calculating the dot product between spectrum of each pixel in the image and the regression coefficients obtained from the PLSR model (Model 2). The result of this multiplication extrapolates quality values in all spots of the sample which facilitates the discovering of the difference in this property within one sample as well as among the samples of different sources. The resulting images were called the ‘distribution maps’. The whole steps involved in building such distribution maps are depicted in the flowchart shown in Fig. 2. Only the distribution maps of colour lightness (L⁄) and pH are presented in this paper.

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3. Results and discussion 3.1. Spectral profiles The acquired hyperspectral image (hypercube) consists of a series of 237 contiguous sub-images; each one represents the intensity and spatial distribution of the tested beef sample from 910 to 1700 nm. All individual sub-images could be easily picked up from the ‘hypercube’ at any wavelength(s) to display the sample at this wavelength. Generally speaking, the hyperspectral image described as I(x, y, k) can be viewed either as a separate spatial sub-image I(x, y) at each wavelength (k) as shown in Fig. 3, or as a spectrum I(k) at any pixel (x, y) as shown in Fig. 4. Each pixel in a hyperspectral image contains the spectrum of that specific position. The resulting spectrum of certain location in the image acts as a fingerprint which can be used to characterize the physicochemical features of that particular location. Since hyperspectral imaging acquires spatially distributed spectral responses at pixel levels, this allows flexible selection of any regions of interest (ROI) on the target sample, e.g., the lean parts of the beef sample as shown in Fig. 3. The typical spectral signatures of the tested beef samples originated either from different muscles (LD, PM and ST), from samples having different pH, or from samples having different colour are shown in Fig. 4. The coincidence between spectral profiles (Fig. 4) and reflectance images at different wavelengths (Fig. 3) is very obvious which reflects the importance of displaying the hidden information within the hyperspectral image. In other words, the sample with low absorbance (high reflectance) at certain wavelength looks lighter in the sub-image at this particular wavelength. On the contrary, the sample with high absorbance (low reflectance) at certain wavelength(s) looks darker in the sub-image at this

Fig. 2. Flowchart of the algorithm developed for analyzing hyperspectral images and for displaying difference in colour and pH in beef samples.

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Fig. 3. Assortment of some NIR sub-images at wavelengths indicated. (a) Full mask marking all pixels belonging to the sample, (b) fat mask marking only fat pixels, (c) lean mask marking only the lean part of the sample (ROI) from which the spectral signature was extracted and (d) pseudo-colour image constructed from concatenating three NIR sub-image altogether. The gray bar in the right-hand side indicates the magnitude of the relative reflectance in the spectral images.

particular wavelength. For instance, the sample at very low absorbance in its spectrum (e.g. at 950 nm or 1050 nm) is lighter compared to the other bands in the spectrum. Similarly, the sample at very high absorbance in the spectrum (e.g. at 1450 nm) is very dark compared to the other bands in the spectrum. Although the samples originated from different muscles (LD, ST and PM) are sharing the same spectral patterns over the entire wavelength region (910–1700), the three muscles had some differences in the magnitudes of absorbance intensities as shown in Fig. 4a. This is to be expected because different anatomic locations in the carcass are different in their major chemical composition such as fat, protein and/or dry matter (Alomar et al., 2003). The PM muscle had the highest absorbance intensities throughout the whole spectral region followed by LD muscles; meanwhile the ST muscle had the lowest absorbance (highest reflectance) compared to the other two muscles. When all examined beef samples classified into two classes (high and low pH), beef samples with high pH values (pH P 5.8) exhibited higher absorbance intensities than those samples of low pH values (pH < 5.8) as depicted in Fig. 4b. Moreover, absorbance magnitudes of samples having low L⁄ value (L⁄ < 30) are greater than those of samples having high L⁄ value (L⁄ P 30) especially in the spectral region from 910 to 1400 as illustrated in Fig. 4c. This could be elucidated to the fact that dissimilar samples in terms of their physicochemical properties cause variations in the recorded spectra due to difference in

Fig. 4. Differences in spectral profiles of beef samples. Spectral profiles of beef samples different in (a) muscle anatomical location, (b) pH value, and (c) colour lightness (L⁄ value). (d) Discriminant analysis among samples categorized to two classes (low and high L⁄ value). (PM = M. psoas major, LD = M. longissimus dorsi, and ST = M. semitendinosus).

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scattering profiles among these samples. The difference between absorbance spectra of different samples in the 910–1700 nm region is mainly due to difference in scattering properties besides samples’ quality attributes. The uneven physical structure of the beef samples as well as unfixed scatter of the surface leads to various spectral motifs. In general, there were well-defined signatures with remarkable absorption bands that can be observed in all samples throughout the spectrum. The absorbance intensities above 1450 nm are clearly high and the samples look very dark at these particular range. However, at 1650 nm the absorption started to decrease sharply up to the end of the spectrum. The most prominent absorption bands occurring in the NIR region are related to overtones and combinations of fundamental vibrations of C–H, N–H, O–H and S– H functional groups (Garini et al., 2006; Cen and He, 2007). However, this is actually advantageous, since absorption bands that have sufficient intensity to be observed in the NIR region arise primarily from functional groups that have a hydrogen atom attached to oxygen, carbon or nitrogen which are common groups in the major constituents of meat samples such as water, fat and proteins. Also, bands in the 1300–1400 nm region are ascribed to combination bands of the C–H vibrations. Broad bands in the 1400– 1600 nm region are due to the overlaps of the first overtones of the O–H/N–H stretching modes of self-associated and waterbonded OH/NH functional groups in beef compositions (Liu et al., 2000). Strong absorption bands can be observed at a number of wavelengths, as described by Osborne et al. (1997) especially those associated with water (970 and 1440 nm) because it is the main component of fresh beef samples (ranging from 73% to 78%). Strictly speaking, local absorption maxima appeared at 970 nm and 1440 (O–H stretching second and first overtones, respectively) are due to presence of water in the sample and the absorption peak at 1215 nm (C–H stretching second overtone) is due to fat content. Apart from the observed absorption bands, other can be seen below the 1100 nm, with differences attributed fat (930 nm) and protein (910 nm) (Alomar et al., 2003) although it is difficult to be discerned from the spectra shown in Fig. 4. Protein information is usually found around 1500 nm (N–H stretching first overtone) but this information in the spectra of Fig. 4 seems to be immersed in water information. For the respiratory pigment in beef samples, the different forms of myoglobin in the meat samples (oxymyoglobin, deoxymyoglobin and metmyoglobin) have no distinct absorption bands in the NIR region (1100–2500 nm) and the correlation between NIR spectra and myoglobin content is very low (Prieto et al., 2006). In practice, the examined beef samples should have a reasonable range of variation in their quality attributes to build a robust prediction model. As shown in Table 1, the tested beef samples had a broad range of colour, pH and tenderness to establish realistic calibration models. Prior to the establishment of a high-throughput quantitative regression model, the extracted spectral data from all samples were evaluated to ensure how these spectra were classified in respect to any quality parameter. To examine this, samples were pre-classified into well defined groups or categories based on their L⁄ values and their spectral data were analysed by linear discriminant analysis. The ultimate aim of discriminant analysis is to unambiguously determine the identity of each sample. The welldefined discriminant analysis learns to recognize the spectra of beef sample based entirely on the spectral data themselves without any other external information other than the logical grouping of the spectra. It is aimed to know whether the sample falls within a defined category. It should be noted that a wide range of variability present in the reference data are beneficial to produce stable calibration models, whereas a narrow range can negatively affect predictability of any parameter. The results of discriminant analysis test (Fig. 4d) indicated that the examined beef samples could be

classified into two distinct categories based on their colour which facilitates building a robust PLSR model for predicting the exact magnitude of their physicochemical properties.

3.2. Prediction of meat properties using full spectral range (Model 1) 3.2.1. Prediction of colour The prediction of meat quality attributes was performed by using partial least squares regressions in which a dependent variable (colour, pH or tenderness) was predicted from a very large set of independent variables (237 wavelengths). Specifically, PLSR model searches for a set of components called ‘latent factors’ that perform a simultaneous decomposition of both spectral data (Xmatrix) and measured value of quality traits (Y-matrix) with the constraint that these components explain as much as possible of the covariance between X and Y followed by a regression step where the decomposition of X is used to predict Y. Table 2 shows the main statistics used to evaluate the performance of the best developed calibration and cross-validation models for predicting colour, pH and tenderness of the examined beef samples. The key step in building a robust PLSR model was to find the correct number of latent factors (lf) needed to obtain the best prediction. First, calibrations were attempted by paying particular attention to choosing a relative small number of latent factors to be introduced to the models. Limiting this number is necessary in order to perform a reliable model and to avoid overfitting and underfitting of the models (Næs et al., 2002). The optimum number of latent factors (lf) could be chosen in many different ways. In this study, the optimal number of latent factors corresponds to either the lowest value of the prediction residual error sum of squares (PRESS) or the highest value of the explained variance. Fig. 5 shows how the correct number of latent factors was chosen in the calibration model of L⁄ parameter (seven latent factors in this case). A similar procedure was executed to determine the correct number of latent factors of all PLSR models. As shown in Table 2, it is obvious that the PLSR models for colour components (L⁄ and b⁄) are reasonably good; meanwhile prediction of colour component (a⁄) was not satisfactory (data not shown). This could be attributed to the narrow range of a⁄ values involved in building its PLSR calibration model. This result agrees with that reported by other authors attempting to estimate a⁄ in beef (Leroy et al., 2003 and Prieto et al., 2008) and pork (Geesink et al., 2003). However, the model of a⁄ could be enhanced by involving more samples from different breeds with various initial quality traits. The calibration correlation expressed as coefficient of determination (R2C ) between samples’ spectra and their colour lightness (L⁄) was as high as 0.89 with root mean square error of calibration (RMSEC) of 1.16 (Table 2). In addition, the model has a cross-validation correlation (R2CV ) of 0.88 and a root mean square error by cross validation (RMSECV) of 1.21 indicating a brilliant PLSR for further prediction of future samples. Similarly the PLSR model for predicting colour component b⁄ was rationally good with R2C of 0.83 and RMSEC of 0.55 in calibration and R2CV of 0.81 and Table 2 PLSR models for predicting colour (L⁄ and b⁄ values), pH and slice shear force (SSF) of fresh beef using the full spectral range (237 wavelengths). PLSR Model 1

⁄

L b⁄ pH SSF

No. of wavelengths

237 237 237 237

No. of latent factors

7 8 20 13

Calibration

Crossvalidation

R2C

RMSEC

R2CV

RMSECV

0.89 0.83 0.83 0.91

1.16 0.55 0.05 29.42

0.88 0.81 0.73 0.83

1.21 0.58 0.06 40.75

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Fig. 5. Determination of the optimal number of latent factors for the prediction of L⁄ value. The optimum number of latent factors (7 in case of L⁄ model) is defined at the maximum explained variance (solid markers) or at the minimum PRESS (open markers). The solid curves correspond to calibration and the dashed curves correspond to cross-validation.

RMSECV of 0.57 in cross validation. In addition, the PLSR models using the NIR spectra appeared to be robust in predicting the colour of fresh beef since small number of latent factors were used in developing such models. These results are in accordance with those reported by Prieto et al. (2008) in cattle meat for estimating L⁄ (R2 = 0.869, SEC = 1.31, SECV = 1.56) and b⁄ (R2 = 0.901, SEC = 1.31, SECV = 0.91) by NIR spectroscopy. In the present study, a single PLSR model was developed to effectively predict the colour of beef samples from three different muscles originated from three different breeds. Colour expressed as lightness (L⁄) or as yellowness (b⁄) were reliably predicted regardless of the breeds or the anatomical locations on the carcasses. Different PLSR models could also be developed for each individual muscle for each breed or even for each muscle. PLSR prediction results for colour (L⁄ and b⁄ values) are presented in the scatter plots shown in Fig. 6. In all figures, the ordinate and abscissa axes respectively represent the predicted and measured fitted

values of the corresponding parameters. The correlation between them for each characteristic in addition to the number of latent factors (lf) and RMSECV were embedded inside each graph to indicate the prediction performances of the models. 3.2.2. Prediction of pH The calibration correlation between the NIR spectra at the entire spectra range (237 wavelengths) and the pH of beef samples was R2C = 0.83 with RMSEC = 0.05 (Table 2). In cross-validation, the correlation was less R2CV = 0.73 and RMSECV = 0.06. The PLSR model appeared to be not as good as that obtained in predicting colour since correlation coefficients are less besides a large number of latent factors (twenty latent factors) being used in the model. This could be elucidated to the narrow range of pH values of the tested samples (5.37–6.13) involved in developing such models. Enlarging this range by including more samples from different anatomical locations of the carcasses as well as practising different

Fig. 6. PLSR model (Model 1) for predicting colour of beef samples. Plots present the actual vs. predicted values of (a) L⁄ value and (b) b⁄ value (All muscles from the three different breeds were gathered in one dataset). The dotted line is the PLS regression model line.

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Fig. 7. Comparison of measured and predicted values of beef samples using PLSR model (Model 1) with the full spectral range (237 wavelengths). (a) pH and (b) slice shear force (SSF).

pre- and post-mortem treatments that modify the pH values would play a great role for enhancing the PLSR calibration model. Furthermore, it is important to emphasize that hyperspectral imaging system does not measure pH directly as the case with H+ sensitive electrodes. The NIR hyperspectral system registers the differences in absorbance patterns due to changes in e.g. inter-molecular forces or structural changes in the meat at different pH levels. Since meat is a very complex medium the proposed system must be calibrated on a large number of samples representing a wide variation in meat quality. Also, a method that has been calibrated on some muscles may not be necessarily applicable for measurements on different muscles. Moreover, sample temperature and time that has passed from exsanguinations are also factors that have to be taken into account for the calibration (Andersen, 1999). In fact, accurate prediction of pH is extremely important because it gives a reasonably good indication of the final meat quality. For example, protein denaturation occurs if pH falls to a too low level or if a relatively low pH sets in at a time after slaughter where the carcass temperature is still high. However, performances of the developed models for predicting colour and pH in this study were much better to those obtained by Qiao et al. (2007) in pork with r = 0.86 and 0.55 respectively. The scatter plot for the measured pH values vs. predicted values from the NIR spectra using the above PLSR model (pH Model 1 shown in Table 2) is exemplified in Fig. 7a. 3.2.3. Prediction of SSF As shown in Table 2, the PLSR model for beef tenderness yields R2C = 0.91, RMSEC = 29.42 N, R2CV = 0.83 and RMSECV = 40.75 N with 13 latent factors. The large number of latent factors as well as the high values of error in both calibration and cross validation indicates that this model is not as robust as colour model. In general, models with fewer factors will be more robust than models with many factors – in particular in small sample sets (Rødbotten et al., 2000). More samples should be evaluated to guarantee more powerful model of tenderness prediction. Indeed, the predictive information of sample tenderness is not extracted from specific chemical moieties in the samples, but it is most related to a certain aspect of the light scatter in the sample (Hildrum et al., 1994). The performance of the model in terms of measured vs. predicted values of shear force is plotted in Fig. 7b. It is very remarkable to notice how the tender samples (with very low shear force values) are aggregated together at the beginning of the trend line compared to

the rest of the other samples. These particular points represent the PM (tenderloin) samples characterized by their high tenderness characteristics. In fact, discriminating tender samples (e.g. M. psoas major-PM muscle) from the intermediate or very tough samples (M. longissimus dorsi – LD or M. semitendinosus – ST) is a straightforward task that can be easily performed because PM muscle has the highest tenderness in the carcass by its own. However, the challenge in the meat industry is to distinguish samples of different tenderness categories because it is a crucial sensory quality attribute associated with consumer satisfaction. In fact, direct evaluation of beef tenderness is still absent in the meat industry because there is currently no reliable method available for predicting tenderness in a non-destructive way. In practice today, tenderness of meat is measured either by sensory profiling or by mechanical techniques. Both are useful, but they are time-consuming, destructive and quite cumbersome in practice. In this sense hyperspectral imaging could be incorporated into the quality evaluation chain for a direct measurement of beef tenderness as a rapid and non-destructive method which enables correct labelling and pricing of meat cuts on the basis of their tenderness. Typical manufacturers of beef will usually not be interested in the exact tenderness of a sample, but they will be interested in whether a sample belongs to one of a number of categories or subgroups (e.g., very tough, acceptable or very tender). If reference values from different tenderness levels are available, such problems can be solved by standard multivariate calibration (Næs and Hildrum, 1997). Therefore, more samples from different muscles of the whole carcass should be considered to cover the whole range of tenderness and to help in establishing more precise calibration model. 3.3. Selection of sensitive wavelengths Hyperspectral images, as high-dimensional data, exhibit a high degree of inter-band correlation, leading to data redundancy that can cause convergence instability in the multivariate prediction models. Therefore, the use of fewer wavebands is preferable for more stable model and easier implementation in the subsequent multispectral imaging system. In addition to its high dimensionality, hyperspectral data suffer from the well-known problem of multi-colinearity, which means that some congruent variables (wavelengths) exhibit similar spectral information. In this aspect, wavelength selection is advantageous in the sense that it can not

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only improve the predictive power of the calibration model, but also simplify the model by avoiding repetition of information or redundancies. The problem caused by the huge amount of data generated in the hyperspectral imaging can be overcome by using data reduction schemes, in which only those wavelengths that are of special interest are selected. Selecting those sensitive wavelengths would reduce the time required for acquiring the spectral and spatial information from the entire sample (Xing et al., 2006). For fast quantitative analysis of samples, it is important to conjugate multivariate prediction model with some sensitive wavelengths instead of using the full spectra (237 wavelengths). Indeed, inclusion of unuseful wavelengths in the calibration model makes the model inefficient for predicting future samples. In the PLSR models described in Table 2 (Model 1), no consideration was given to the contributions of the individual wavelengths to the prediction results because all wavelengths were delivered to the final calibration model. It is often desired to ascertain how individual wavelengths are directly related to the quantities to be predicted. The sensitive wavelengths reflecting the characteristics of spectra for predicting colour, pH and tenderness were obtained based on weighted regression coefficients (Bw). Variables (wavelengths) with a large regression coefficient (despite its sign) play an important role in the regression model. The high positive or negative peaks indicate that the wavelengths at these points contain important information about quality attribute under analysis. Wavelengths with a small coefficient are negligible and have little contribution on the productivity of the model. The weighted regression coefficients shown in Fig. 8 highlight some strong peaks (positive relationship with the attributes) and valleys (negative

relationship with the attributes) at certain wavelengths. From Fig. 8a, six wavelengths at 947, 1078, 1151, 1215, 1376 and 1645 nm were identified as sensitive wavelengths for predicting L⁄ values. The sensitive wavelengths for predicting b⁄ values were 934, 1074, 1138, 1399 and 1665 nm (Fig. 8b). It can be observed that both colour components (L⁄ and b⁄) are much related to C–H second overtone and C–H combination bands, and C–H first overtone, respectively. These wavelengths could be related to the absorbance of long C–H chains of fatty acids. This is reasonable since intramuscular fat is highly correlated to the lightness (L⁄) in meat samples (Ruiz de Huidobro et al., 2003). Also, wavelengths related to the absorbance of O–H bonds also showed good correlations with L⁄ as a result of the negative correlation between the intramuscular fat content and the water of the meat samples (Prieto et al., 2006). As shown in Fig. 8c, 24 sensitive wavelengths (924, 937, 951, 961, 984, 1044, 1091, 1111, 1117, 1158, 1245, 1251, 1285, 1316, 1342, 1363, 1376, 1406, 1413, 1443, 1476, 1500, 1524 and 1541 nm) had particular importance for pH calibration model. Similarly, the sensitive wavelengths identified from Bw plot in case of shear force model were 927, 941, 974, 1034, 1084, 1105, 1135, 1175, 1218, 1249, 1285, 1309, 1571, 1658, 1682 nm as declared in Fig. 8d. Although it was possible to choose wavelengths coinciding with known absorption peaks for the different bond groups to quantify chemical compositions of the tested samples, it was not possible to perform this routine in several intrinsic attributes such as pH and tenderness. That is due to the fact that hyperspectral imaging employed in the NIR spectral region is particularly sensitive to the presence of molecules containing certain functional

Fig. 8. Selection of sensitive wavelengths. Weighted Beta-coefficient (Bw) values of PLSR models of (a) L⁄ value, (b) b⁄ value, (c) pH and (d) tenderness of beef samples (all beef breeds were gathered in a same data set).

G. ElMasry et al. / Journal of Food Engineering 110 (2012) 127–140 Table 3 Performance of PLSR models at sensitive wavelengths (Model 2) for predicting colour, pH and slice shear force (SSF) of beef samples. PLSR Model 2

No. of wavelengths

No. of latent factors

R2C

R2CV

RMSEC

RMSECV

L⁄ b⁄ pH SSF

6 5 24 15

6 5 15 9

0.88 0.81 0.75 0.84

0.88 0.80 0.71 0.77

1.22 0.59 0.06 39.64

1.24 0.60 0.07 47.45

groups. Also, structural changes in the meat, variations in muscle geometry and surrounding environment have a great influence on the spectra. Therefore, it was expected that a larger number of wavelengths and PLSR latent factors were needed in comparison to the common chemical attributes (Osborne et al., 1997). 3.4. PLSR models using the sensitive wavelengths (Model 2) As a consequence of the previous analyses, spectral data were then reduced by selecting only those spectral data at the identified sensitive wavelengths. The reduced form of the spectral data were then used instead of the entire spectral data (237 wavelengths) to

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develop new PLSR models (Model 2) to predict the same quality traits. The performances of these reduced models (Model 2) as shown in Table 3 and Fig. 9, which are very close to those of Model 1 developed by using the whole wavelengths (Table 2). Table 3 shows the main statistics achieved in the calibration and cross validation prediction of the PLSR models developed by using the abovementioned sensitive wavelengths. In brief, the new reduced models resulted in R2CV of 0.88, 0.8, 0.71 and 0.77 with RMSECV of 1.24, 0.6, 0.07 and 47.45 for L⁄, b⁄, pH and SSF, respectively, which reflects the reasonable accuracy of these models. However, to augment the stability and practicability of the models, it is important to make some improvements to enhance the models by investigating wider ranges of the quality traits.

3.5. Visual representation of meat properties The automatic grouping of pixels having a ‘‘similar characteristic’’ in a hyperspectral image is an important problem that should be highlighted (Tran et al., 2005). One of these methods is to transfer the multivariate calibration model in a pixel-wise manner, and all pixels having similar spectral motifs would exhibits similar visual representation. Once the sensitive wavelengths for certain

Fig. 9. Prediction of L⁄, b⁄, pH and slice shear force values using sensitive wavelengths by PLSR model (Model 2). Plots present the actual vs. predicted values of (a) L⁄, (b) b⁄, (c) pH and (d) slice shear force.

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Fig. 10. Distribution maps of colour and pH in beef samples. (a) Pseudo-colour image of the sample constructed by concatenating three spectral sub-images at 950 nm, 1200 nm and 1300 nm, (b) distribution maps of L⁄ resulting from Model 2 and (c) distribution maps of pH resulting from Model 2. The number below each sample is the overall average of L⁄ and pH of this sample (ST = M. semitendinosus, LD = M. longissimus dorsi, and PM = M. psoas major).

attribute are identified, a reduced image space is first formed by selecting only spectral bands at those wavelengths followed by multiplication with PLSR coefficients. PLSR models were transferred to all pixels of the reduced spectral image by calculating the dot product between spectrum of each pixel in the image and the regression coefficients obtained from PLSR model (Model 2). The resulting image is called the ‘distribution map’ of this attribute, which demonstrates how the magnitude of this attribute varies from sample to sample or from spot to spot within the same sample. Pixels having similar spectral features (at the sensitive wavelengths) would produce the same predicted value of this attribute, which were then visualized in a similar scale in the resulting distribution map. Fig. 10 shows distribution maps of L⁄ and pH on the tested muscles (LD, PM and ST). Fig. 10b and c (the second and the third rows of Fig. 10) represent some distribution maps of L⁄ and pH value of the tested muscles, respectively. As expected in Fig. 10, the M. semitendinosus muscles (ST) showed the highest L⁄ values indicating that this muscle is very light in colour compared to the other two muscles. Colour of LD and PM muscles is very close to each other. In spite of muscle type, the difference among samples having different L⁄ values could be easily discerned as scrutinized in Fig. 10b (the second row of Fig. 10). Although it was far difficult to distinguish the difference in colour lightness between LD and PM samples (as seen in the pseudo-colour images shown Fig. 10a), it was possible to make this discrimination by using distribution maps of these samples. In reality, the colour of fresh red meat is of the utmost importance in meat marketing since it is the first quality attribute seen by the consumer who uses it as an indication of freshness and wholesomeness as consumers will discriminate negatively against meat that does not appear to match expectations or that is discoloured (Troy and Kerry, 2010). Producing such colour distribution maps will facilitate the truthful grouping of meat products as well as accurate labelling and pricing of the products. In general, distinguishing muscle colour is a straightforward process that could be performed by normal com-

puter vision systems employed in the visible spectral range. However, under certain lightening conditions, the conventional colour camera operated in the visible range would fail to render the colours of the sample properly as the visible spectrum is either faintly observable in the scene or the presence of glare corrupts the colours sensed (Shin et al., 2010). Therefore, the challenge is to accurately predict meat colour in addition to other attributes simultaneously from the same NIR spectral region. However, this problem becomes very complex in case of prediction of pH and the other physicochemical properties in all pixels of the sample. Therefore, simultaneous prediction of several attributes is very advantageous using the proposed hyperspectral imaging system, which cannot be achieved with either conventional imaging or spectroscopy alone (ElMasry et al., 2008). Similar to those obtained in L⁄ prediction, the distribution maps shown in Fig. 10c (the third row of Fig. 10) demonstrate how the pH values vary from sample to sample and from location to location in the same sample. It was also noticeable that samples having high L⁄ values (Fig. 10b) possess low pH values (Fig. 10c) and vice versa. For instance, PM muscle with a low L⁄ value of 26.89 and ST muscle with a high L⁄ value of 41.20 have pH value of 6.05 and 5.35, respectively. If associated with drip loss prediction (ElMasry et al., 2011) the examined samples could be easily classified into different quality grades which is very important in the current highly competitive markets.

4. Conclusions This study was carried out to evaluate the feasibility of using a hyperspectral imaging system in the NIR spectral region (900– 1700 nm) for rapid prediction of some quality attributes in intact fresh beef. The results discussed in this paper indicated that it was possible to utilize this non-destructive technique to simultaneously predict both intrinsic quality (tenderness and pH) and extrinsic quality (colour). By means of PLSR, correlations were

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established between the NIR absorbance spectra and the quality parameters of beef samples. All results showed good performances of the established PLSR models for predicting beef quality parameters. However, such PLSR model remains less robust in case of pH and tenderness prediction which requires further studies including external validations and analyses of a bigger number of samples. In the light of the present results it seems feasible to use hyperspectral imaging technique as a reliable and rapid alternative to traditional colorimeter, standard pH electrodes and universal testing machines for measuring colour, pH and tenderness, respectively. In addition to predicting the overall average of quality parameters in beef samples, hyperspectral imaging offers additional merit to display the distribution of these parameters on the tested beef samples. The realistic application of this trend in the meat industry comes from the fact that light from the NIR wavelength bands interact with surfaces in the same way as visible light, so machine vision systems could be set up for these wavelengths in the same way that they are set up for visible-responding systems. Once optimized, this technique is anticipated to provide several advantages over other traditional techniques to solve challenging quality control problems since it does not require any consumables or supporting equipment. Moving the implementation from near-line application to on-line approach needs more efforts in order to realise it. Acknowledgements The authors gratefully acknowledge the financial support provided by the Irish Government Department of Agriculture, Fisheries and Food under the Food Institutional Research Measure (FIRM) programme. References Alomar, D., Gallo, C., Castañeda, M., Fuchslocher, R., 2003. Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Science 63, 441–450. Andrés, S., Murray, I., Navajas, E.A., Fisher, A.V., Lambe, N.R., Bünger, L., 2007. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Science 76 (3), 509–516. Andersen, J.R., Borggaard, C., Rasmussen, A.J., Houmøller, L.P., 1999. Optical measurements of pH in meat Meat Science 53 (2), 135–141. Belk, K.E., Scanga, J.A., Wyle, A.M., Wulf, D.M., Tatum, J.D., Reagan, J.O., Smith, G.C., 2000. The use of video image analysis and instrumentation to predict beef palatability. Proceeding Reciprocal Meat Conference 53, 10–15. Bowling, M.B., Vote, D.J., Belk, K.E., Scanga, J.A., Tatum, J.D., Smith, G.C., 2009. Using reflectance spectroscopy to predict beef tenderness. Meat Science 82, 1–5. Brøndum, J., Munck, L., Henckel, P., Karlsson, A., Tornberg, E., Engelsen, S.B., 2000. Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Science 55 (2), 177–185. Byrne, C.E., Downey, G., Troy, D.J., Buckley, D.J., 1998. Non-destructive prediction of selected quality attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm. Meat Science 49 (4), 399–409. Cen, H., He, Y., 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 18, 72–83. Cluff, K., Naganathan, G.K., Subbiah, J., Lu, R., Calkins, C.R., Samal, A., 2008. Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region. Sensing and Instrumentation for Food Quality and Safety 2, 189–196. Cozzolino, D., Murray, I., 2004. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT-Food Science and Technology 37 (4), 447–452. Cozzolino, D., De Mattos, D., Vaz Martins, D., 2002. Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle. Animal Science 74, 477–484. Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., Blasco, J., 2011. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology 4 (4), 487–504. Damez, J.L., Clerjon, S., 2008. Meat quality assessment using biophysical methods related to meat structure. Meat Science 80 (1), 132–149. Desmond, E.M., Kenny, T.A., Ward, P., Sun, D.-W., 2000. Effect of rapid and conventional cooling methods on the quality of cooked ham joints. Meat Science 56 (3), 271–277. ElMasry, G., Sun, D.-W., 2010. Meat quality assessment using a hyperspectral imaging system. In: Sun, Da-Wen (Ed.), Hyperspectral Imaging for Food Quality

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