Meat Science 96 (2014) 862–869

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Early on-line classification of beef carcasses based on ultimate pH by near infrared spectroscopy Marlon M. Reis ⁎, Katja Rosenvold 1 AgResearch Limited, Ruakura Research Centre, East Street, Private Bag 3123, Hamilton, New Zealand

a r t i c l e

i n f o

Article history: Received 16 June 2013 Received in revised form 13 September 2013 Accepted 10 October 2013 Keywords: Ultimate pH NIRS NIR On-line Hot carcass Beef

a b s t r a c t Prediction of ultimate pH (measured 48 h post mortem; pHu) in beef from Visible–near infrared (VIS–NIR) spectra collected 20 to 40 min post mortem was assessed. Spectra were collected from carcasses (cows: n = 86, bulls: n = 170, steers: n = 363, and heifers: n = 38) in a commercial hot boning abattoir under routine conditions. Partial Least Squares (PLS) models showed limited accuracy with RMSE for validation equal to 0.26, 0.20 and 0.36 for the All-animals, Non-bulls and Bulls models, respectively. The pHu–PLS-predicted values were used to segregate carcasses as normal (pHu b 5.8) or high (pHu ≥ 5.8) showing better performance, by correctly classifying at least 90% of high pHu carcasses. The Non-bulls model was equivalent to the current technology used in the abattoir to classify carcasses based on pHu. Thus near infrared spectroscopy (NIRS) could be used for on-line classification of beef carcasses based on pHu. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction The pH that the meat will reach once the glycolysis has been completed is termed ultimate pH (pHu). To be confident that all samples have reached ultimate pH it is normally measured at 48 h of slaughter in beef and is used as an indicator of meat quality attributes. Meat with pHu lower than 5.8 is considered as having normal pHu presenting desirable eating quality attributes (Viljoen, De Kock, & Webb, 2002). In contrast, meat with pHu higher than 5.8 has darker colour (Abril et al., 2001), has a shorter shelf life (Gill & Newton, 1981), can be inferior in flavour (Braggins, 1996) and shows inconsistent tenderness (Silva, Patarata, & Martins, 1999; Watanabe, Daly, & Devine, 1996). In New Zealand independent surveys have shown that just over 50% of bulls and 15% of prime attain pHu higher than 5.8 (Wiklund et al., 2009; Young, Thomson, Merhtens, & Loeffen, 2004). Thus pHu is assessed to prevent beef products below the quality standard (i.e. dark cutting) from being sold as premium beef. A common practice in the New Zealand meat industry is to process beef carcasses using hot boning, where removal of fat and lean muscle from the carcass takes place immediately after the grading station, and hence while the muscle are still warm and prior to rigor mortis. The advantages of hot boning include significant savings in refrigeration costs, space and shorter processing times (Kastner, 1977, 1982;

⁎ Corresponding author. Tel.: +64 7 838 5900. E-mail addresses: [email protected] (M.M. Reis), [email protected] (K. Rosenvold). 1 Present address: ANZCO Foods Limited, Food & Solutions Division, 1 Stafford St., PO Box 23, Waitara 4346, New Zealand. 0309-1740/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.meatsci.2013.10.016

Ockerman, Basu, & Werner Klinth, 2004). However measurement of pHu on hot boned muscles is complicated as cuts are already packed by the time the pHu is reached 48 h post mortem. After slaughter the muscle becomes anaerobic and glycogen is converted into lactate throughout the glycolytic pathway (Young, West, Hart, & Van Otterdijk, 2004). At the same time, the concentration of hydrogen cations increases, leading to a decline in pH. In carcasses, where muscle glycogen content at the time of slaughter is above the level required to achieve a pHu of 5.5, pH does not fall much lower than 5.4, possibly because the glycolytic enzymes are inactivated by the acidic conditions and residual glycogen remains in the meat (Young, West, et al., 2004). The meat buffer capacity also plays an important role preventing the pH from going below 5.4 (Puolanne & Immonen, 2000). However, in carcasses where the muscle glycogen levels at slaughter are insufficient for the meat to reach a pH of 5.5, this will lead to high pHu meat (Young, West, et al., 2004). Thus the muscle glycogen content in pre rigor meat can be used to predict pHu. Currently, a wet chemistry based method is used in some New Zealand hot boning abattoirs to estimate the pHu from samples excised from the carcass prior to boning. The method is based on the relationship between pre rigor glycogen content and pHu (Young, West, et al., 2004). This method is labour intensive being suitable for slower processing lines where only one person is required to operate the procedure (Young, West, et al., 2004). This has motivated the investigation of a non-invasive method to predict pHu from measurements collected prior to boning. Near infrared spectroscopy (NIRS) is a non-invasive technique which can be used in abattoirs providing a rapid assessment of the carcass (less than 30s) not requiring consumable or excision of samples.

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NIRS has been be used to predict meat chemical composition and meat physical chemical attributes (e.g. pH), with the large majority of work done after the muscle has reached rigor (Prevolnik et al., 2004; Prietro et al., 2009; Weeranantanaphan et al., 2011). Of interest to the present study is the ability of NIRS to predict components in the pre rigor meat that are associated with pHu (e.g. glycogen, lactate as described above). The detection of these components by NIRS is feasible as they are active in the near infrared spectral range, 1100–2500 nm (Davies, 2005; Tamburini, Vaccari, Tosi, & Trilli, 2003). Indeed, NIRS has been found to predict glycogen content in abalone (Fluckiger, Brown, Ward, & Moltschaniwskyj, 2011) and in beef, NIRS was found to predict glycogen content in pre rigor meat with reasonable accuracy (R2 = 0.70) for spectra collected in a labouratory environment (Rosenvold et al., 2009). In pork, NIRS was found to predict pHu (measured 24 h post mortem) from spectra collected 30 min post mortem (n = 96), with a correlation between predicted and measured pHu of 0.57 (Josell, Martinsson, Borggaard, Andersen, & Tornberg, 2000). The ability of NIRS to predict glycogen and pHu from spectra collected on-line of pre rigor muscle was investigated in beef in a pilot study (n = 90) (Lomiwes, Reis, Wiklund, Young, & North, 2010). Quantitative and qualitative models found a low correlation between NIRS-predicted and measured values of glycogen and pHu. However, the study also found that bulls inconsistently exhibited different spectral trends from those of cows and steers, particularly in their pHu profile, suggesting that predictive models for individual animal classes could be more accurate for pHu predictions. This hypothesis is investigated in this study involving data collected from 657 carcasses including cows, bulls, steers and heifers. To our knowledge, a successful on-line classification according to pHu of hot boned beef carcasses from VIS–NIR spectra collected pre rigor has not yet been reported. 2. Material and methods 2.1. Animals A total of 657 carcasses including cows (n = 86), bulls (n = 170), steers (n = 363) and heifers (n = 38) slaughtered following typical practices in a hot boning commercial abattoir on two consecutive days, in three different weeks, were scanned. Each day, VIS–NIR spectra were collected during the day and night shifts. Slaughter chain speeds varied depending on the animals being slaughtered and on the shift (day or night).

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the 11th and 12th rib). This split was further opened with a knife and three spectra were collected, each at a different location of a transverse section of the M. longissimus dorsi on this freshly cut surface, as illustrated in Fig. 1. 2.3. Prediction of pHu from pre rigor glycogen The concentration of pre rigor glycogen was determined using a commercial system (RapidpH™, AgResearch Ltd) in use at the plant. This system is based on an enzymatic conversion of glycogen to glucose (Young, West, et al., 2004). The concentration of glucose is related to pHu using a mathematical equation, which is then used to predict the pHu. This system is currently used as an in-plant classification tool, where carcasses are classified as having pHu below or above 5.8 from samples collected 5 min post mortem. 2.4. colour and pH measurements After the VIS–NIR spectra had been collected and the carcasses had entered the boning room, the muscle was excised from the carcass and a transverse section of the NIRS-scanned area of the muscle (approximately 10 cm thick) was collected, vacuum packaged and chilled for 48 h under the normal plant chilling conditions. At 48 h post mortem pH and colour were measured in the boning room operating at 10 °C. colour was measured using a HunterLab MiniScan XE Plus colour Meter (Illuminant D65, 2.5 cm diameter aperture, 10° standard observer; Hunter Associates Laboratory, Inc., Reston, VA). Calibration of the colour meter was performed by using standard black and white tiles prior to the colour measurement. Five colour measurements were taken across a fresh cut surface bloomed for 30 min. The blooming time was rigorously controlled to ensure that all samples were bloomed for exactly the same length of time. The blooming time was chosen to be consistent with commercial practice used at plants to evaluate meat colour. The pH of the loins was measured immediately following the colour measurement using a calibrated pH probe (Testo 205 pH meter; Lenzkirch, Germany) inserted directly into the meat. Three pH measurements were obtained in three different locations resembling the three scan positions where VIS–NIR spectra were collected for each loin. These three pH values were averaged. The calibration of pH electrode was performed with standardized buffers (pH 4.0 and 7.0) every hour. 2.5. Data analysis

2.2. VIS–NIR spectra collection of pre rigor M. longissimus dorsi Reflectance spectra were collected using a LabSpec 5000 (ASD Inc., USA) with a customised probe. The spectral range of the spectrophotometer was 350–2500 nm, with spectral resolution (Hatchell, 1999) of 3 nm at 700 nm, 10 nm at 1400 nm and 2100 nm. The scanning time was 10 ms and a sampling interval (Hatchell, 1999) of 1.4 nm at the 350–1000 nm range and 2 nm in the 1000–2500 nm range. The wavelength accuracy was ± 1 nm. The probe resembles the optics and internal design of the standard high intensity probe from ASD, but built in stainless steel. It has a holder also built in stainless steel and a 10 mm stand-off to prevent the meat from touching the glass window (Fig. 1). The area illuminated by the probe has a diameter of 25 mm and the area viewed has a diameter of 15 mm (Fig. 1). This probe was specifically developed to withstand the environment in the slaughter plant. The spectrophotometer and probe were warmed-up for 1 h before starting. Each spectrum corresponds to the accumulation of 40 VIS–NIR scans. The NIRS spectrophotometer was positioned just after the carcass grading station, where the carcasses arrived approximately 20 min post mortem during the day shift and up to 40 min post mortem during the night shift. Each carcass was split for quartering following the normal routine of the plant (between

Partial least square models (PLS) (Wold, Sjöström, & Eriksson, 2001) were fitted to predict pHu. The original data were split into a calibration and a validation data set. The calibration data set was used to develop the best calibration models, while the validation data set was used to assess the accuracy of these models. The carcasses were distributed between the calibration and validation data sets so that both contained the full variability of the original data set with respect to pHu, animal categories (bulls, steers, heifers and cows) and days of data collection. Approximately 50% of animals in each category were put in the calibration data set and the other 50% in the validation data set. Three different models were fitted: A model including the spectra from all four categories of animals (All-animals model); a model including the spectra from heifers, cows and steers but excluding the spectra from bulls (Non-bulls model); and finally a model including only the spectra from bulls (Bulls model). Three spectra were collected for each carcass with the probe placed at specific positions to capture the muscle variability (Fig. 1). The first position is the most external, and is preferable, for a commercial calibration due to ease of access. Thus calibrations were developed using data from the first position only (scan 1). These calibrations were then applied on spectra collected from positions 2 and 3 (scans 2 and 3). As a result, for each calibration model there were six sets of

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Fig. 1. Design used for spectra collection at the plant. (a) shows the position where VIS–NIR spectra were collected in the carcass and (b), the position across a transverse section of the M. longissimus dorsi on the fresh cut surface. (c) presents the areas in the sample illuminated and viewed by the probe and an illustration of internal design of the probe where (1) refers to fiber optic for collection of reflectance signal and (2) the light source.

predictions: one corresponding to scan 1 for the calibration data set; two corresponding to scans 2 and 3 for the calibration data set; and three for each of the three scans of the validation data set. Extended multiplicative scatter correction (EMSC) and orthogonal signal correction (OSC) were used as pre-processing methods applied to the spectral range between wavelengths of 450–1850 nm (Rinnan, Berg, & Engelsen, 2009). The number of latent variables for each of these three models was chosen using cross validation with n segments of samples randomly selected. Briefly, spectra of samples from the calibration data set were randomly split into n segments, each with approximately the same number of samples (n, Non-bulls: 20, Bulls: 9, and All animals: 10). The spectra in each segment were excluded from the data set and a series of PLS models were fitted, differing in the number of latent variables. Each of these models was applied to the excluded spectra. This procedure was repeated until all samples from the calibration data set had been excluded from the model fitting once. The resulting predictions for each model were used to evaluate the best number of latent variables for the model. Hotelling T2 and Q statistics were estimated during the development of the calibration and used to identify whether a new spectrum (i.e. not used in the fit of the model) is similar to those used to fit the calibration or otherwise (Reis, Araújo, Sayer, & Giudici, 2007). These parameters allow the operator to identify bad scans (for example, if the NIRS operator does not position the probe correctly over the carcass) avoiding inappropriate predictions. These parameters are used in this work to evaluate if spectra used to validate the model are similar to those used in the fit of the model or if they are distinct from calibration data set and if there are any outlier in the calibration data set that should not be used. A limit of 95% of confidence is used for Hotelling T2 and Q statistics.

The most important variables (i.e. wavelengths) for prediction of pHu were identified using the method proposed by Martens and Martens (1999). Briefly, the cross validation procedure generates a series of sub-models, each having a set of model parameters (i.e. regression coefficients, loadings and loading weights). In addition to the sub-models from the cross validation, a total model is also fitted with all samples in the calibration data set. For each variable, the difference between its corresponding value in the regression coefficient for a sub-model and for the total model is calculated. The sum of the squares of the differences in all sub-models is calculated to estimate the variance of the regression coefficient for each variable, which is tested statistically for significance (Martens, & Martens, 1999). It is important to observe that this procedure is applied postmodeling and is not used for variable selection. A classification approach was also considered. In this case, the predictions of the fitted PLS models were evaluated regarding its ability of classify carcasses as below or above the pHu threshold of 5.8. In this case, each value predicted using a PLS model (All-animal model, Non-bull model and Bull model) is compared against the threshold of 5.8: if it is below 5.8, the carcass is considered as normal pHu; otherwise the carcass is considered as high pHu. The performance for this approach was evaluated using the rate of carcasses correctly classified in the expected classes (i.e. normal pHu and high pHu). A generalized additive model (GAM) was applied to compare if the relationship between glucose concentration and pHu was statistically different between bulls and prime (Hastie & Tibshirani, 1990). The model used for GAM was: ‘pHu ~ s(Glucose, by = Animal) + Animal’, where ‘s(.)’ is a non-linear function, fitted from thin plate regression splines and ‘Animal’ the term describing the animal effect, i.e. bulls vs prime (steers and heifers) (Wood, 2001).

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The R2 and root mean square error (RMSE) were used to assess the model performance. Data analysis was carried using R v.2.9.1(R-Development, 2009), Unscrambler X (v. 10.2, CAMO software AS), PLS-Toolbox (v.4.1.1 Eigenvector Research Inc.) running on Matlab® 7.4 (The MathWorks, Inc.). 3. Results & discussion The summary of measured attributes is presented in Table 1. The models and their performances are presented and discussed in the following section. 3.1. Modelling approach The best models produced R2 values for internal cross validation of 0.50 (RMSE: 0.26; number of latent variables: 3, SDy = 0.36), 0.41 (RMSE: 0.24; number of latent variables: 12, SDy = 0.31) and 0.43 (RMSE: 0.33; number of latent variables: 3, SDy = 0.44) for the Allanimal model, Non-bull model and Bull model, respectively (‘SDy’ refers to the standard deviation of pHu values used to fit the model). Corresponding values for the validation data set are present in Table 2. RMSE values are similar to standard deviation of pHu (SDy) showing that these models have limited prediction accuracy, more suitable for the classification approach. The results from the classification approach (i.e. PLS-predicted values are classified either as high or normal pHu) are showed in Table 3, which presents six sets of predictions, each with two diagonal and two off-diagonal values. The diagonal values represented correct classification, for example, for the All-animal model 93% of the samples with a pHu ≥ 5.8 were correctly classified, while 7% of the samples with pHu ≥ 5.8 were classified as having a pHu b 5.8. Using this classification approach an improved performance was achieved (Table 4), especially for classification using the Non-bull model, having the overall rates of correctly classified samples of 90% for high pHu and 89% for normal pHu in the validation data set, as shown in Tables 3 and 4. Thus in the following sections only results for classification are discussed.

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Table 2 Statistics describing the performance of models according to predictions in the validation data set (n refers to number of carcasses evaluated after removing outliers according Hotelling T2 and Q statistics). Model

All (n = 361) Non (n = 277) Bull (n = 84)

Scan 1

Scan 2 2

n

R

RMSE

n

R

RMSE

n

R2

RMSE

322 248 72

0.38 0.36 0.27

0.26 0.20 0.36

261 209 56

0.39 0.36 0.52

0.26 0.20 0.32

185 146 44

0.39 0.48 0.35

0.28 0.22 0.40

three for the three scans of the validation data set. The outlier spectra, identified according to Hotelling T2 and Q statistics, were eliminated from the evaluation of predictions for the calibration and validation data sets. In Table 3, for example, the first row shows the predictions for the All-animal model applied on spectra used to fit the calibration model. In this case, the results are presented as the number of carcasses predicted. Thus the value in row 1 column 1 corresponds to the number of carcasses with pHu ≥ 5.8 that were correctly classified as pHu ≥ 5.8 from spectra collected in scan 1 while the value in row 1 column 2 corresponds to the number of carcasses with pHu b5.8 that were classified as pHu ≥ 5.8 from spectra collected in scan 1. The interpretation of columns 3 and 4, 5 and 6 is equivalent to columns 1 and 2, respectively. The number of carcasses presented in Table 3 differs for scans 2 and 3 compared to scan 1 used to fit the model due to elimination of outliers according to Hotelling T2 and Q statistics. For example, for the calibration data set used in the All-animal model there were 257 animals in scan 1,

Table 3 Classification results distributed according to: scanning position (scans 1, 2, and 3); data set (calibration and validation data sets); pHu classes (high: pHu ≥ 5.8; normal: pHu b 5.8); model type (All-animals, Non-bulls (cows, heifers and steers) and Bulls). The distribution of predictions is presented as number of animals (n) and percentage. These results exclude outlier spectra. NIRS predicted pHu

Measured pHu Scan 1

3.2. Best scanning position

High

Table 3 presents the results for the 6 types of predictions according to the position in which the probe was placed in the muscle (see also Section 2.5): one corresponding to scan 1 for the calibration data set; two corresponding to scans 2 and 3 for the calibration data set; and

Table 1 Summary of attributes (pHu, glucose — RapidpH™, colour) for the evaluated carcasses. Attribute

Animal

Mean

Min.

Max

SD

CV

pHu

Bull Cow Heifer Steer Bull Heifer Steer Bull Cow Heifer Steer Bull Cow Heifer Steer Bull Cow Heifer Steer

5.79 5.55 5.55 5.65 6.8 8.0 6.9 29.2 30.4 34.5 32.5 12.0 13.5 11.9 12.5 8.9 11.6 9.8 10.5

5.36 5.39 5.42 5.30 0.9 1.0 0.9 18.7 22.4 21.6 20.8 6.0 7.0 6.7 3.4 3.4 5.1 5.3 2.3

7.10 5.87 6.75 6.78 19.7 17.6 18.3 37.9 37.8 40.7 45.2 16.9 20.8 16.3 21.4 14.8 18.2 15.0 20.6

0.44 0.11 0.22 0.31 3.60 3.50 3.98 3.62 3.19 3.25 4.03 2.61 2.31 2.08 3.34 2.72 2.30 1.96 3.42

0.076 0.019 0.039 0.055 0.53 0.44 0.57 0.12 0.11 0.09 0.12 0.22 0.17 0.18 0.27 0.30 0.20 0.20 0.33

Glucose

L*

a*

b*

Min: minimum measured value. Max: maximum measured value. SD: standard deviation. CV: coefficient of variation defined as the ratio of the standard deviation to the mean.

Scan 3 2

All-animal model Calibration data set High Normal High Normal Validation data set High Normal High Normal Non-bull model Calibration data set High Normal High Normal Validation data set High Normal High Normal Bull model Calibration data set High Normal High Normal Validation data set High Normal High Normal

Scan 2

Scan 3

Normal

High

Normal

High

Normal

n n % %

57 4 93 7

23 173 12 88

51 3 94 6

32 131 20 80

46 6 88 12

23 98 19 81

n n % %

48 4 92 8

52 218 19 81

38 5 88 12

49 169 22 78

28 10 74 26

29 118 20 80

n n % %

37 0 100 0

16 148 10 90

32 1 97 3

7 113 6 94

27 1 96 4

8 66 11 89

n n % %

26 3 90 10

24 195 11 89

25 3 89 11

26 155 14 86

24 2 92 8

19 101 16 84

n n % %

24 2 92 8

11 39 22 78

20 3 87 13

14 30 32 68

17 7 71 29

10 25 29 71

n n % %

16 1 94 6

17 38 31 69

16 2 89 11

15 23 39 61

13 5 72 28

7 19 27 73

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M.M. Reis, K. Rosenvold / Meat Science 96 (2014) 862–869

Table 4 Rate of correctly classified and misclassified carcasses with respect to pHu class (normal: pHu b 5.8 and high: pHu ≥ 5.8) by the NIRS prediction when compared with pHu measured 48 h post mortem for the All-animal (ncal = 257, nval = 322), the Non-bull (cows, heifer and steers; ncal = 201, nval = 248) and Bull (bulls only; ncal = 76, nval = 72) models combined. Measured pHu

NIRS predicted pHu

Calibration data set High (%) Normal (%) Validation data set High (%) Normal (%)

All-animals

Non-bulls

Bulls

High

Normal

High

Normal

High

Normal

93 7

12 88

100 0

10 90

92 8

22 78

92 8

19 81

90 10

11 89

94 6

31 69

217 for scan 2 and 173 for scan 3. The increase in the number of outliers as per Hotelling T2 and Q statistics could have resulted from the muscle variability and misplacement of the probe in the carcass. In a commercial application it is not a problem if a spectrum is distinct from those used in the calibration as software can be designed to apply Hotelling T2 and Q statistics and show immediately on the computer screen that the carcass needs to be re-scanned. The classification rates (showed in Table 3 as percentage) were marginally different between scans 1, 2 and 3, although scan 1 produced slightly better results and a lower number of outliers. The results from scan 1 are those of most interest as they are preferable for commercial application due to ease of access with the NIRS equipment. The analysis which follows is focused on classifications for scan 1 of the validation data set.

3.3. Best classification model Table 5 shows the classification results according to pHu levels, where models are evaluated regarding its ability to classify carcasses into normal and high pHu. The analysis of these results was performed with stratification of pHu samples into two groups: pHu b 5.6 called “low”; 5.6 ≤ pHu b 5.8 called “intermediate”. This stratification of the data was applied to identify whether the rate of misclassification for

normal pHu was concentrated at the boundary between the two classes used in the classification approach, in this case 5.6 ≤ pHu b 5.8. Thus: i. The number of correctly classified carcasses in the high pHu group corresponds to the percentage of all high pHu carcasses that were predicted correctly as high pHu; ii. The number of correctly classified carcasses for the low pHu group corresponds to the percentage of carcasses with pHu b 5.6 that were correctly classified as normal pHu; iii. While the number of correctly classified carcasses in the intermediate group means carcasses that had pHu between 5.6 and 5.8 and were classified as normal pHu (pHu b 5.8). The results presented in Table 5 indicate that the highest number of misclassified carcasses was found for the intermediate group, which is at the boundary between the other two pHu classes. The best models were those fitted specifically for each category, Non-bull and Bull models, although predictions of bulls are not as good as for the Nonbull model (Table 5). The difference between the two approaches (a single model for Bulls and Non-bulls versus specific models for each animal category) was investigated by identifying the most important variables for each model (Non-bulls and Bulls) using the method proposed by Martens, & Martens, (1999). Martens' method identifies the variables statistically significant to predict a given attribute (Martens, & Martens, 1999). This investigation showed that the most significant difference between the two specific models (Non-bulls and Bulls) were in the spectral range between 1400 and 1850 nm, which is important for the Bull models, but not for the Non-bull model, as shown in Fig. 2. The original VIS– NIR spectra of bulls and steers do show some difference in the spectral range between 1400 and 1850 nm, as shown in Fig. 3, especially for animals with pHu N 6. In this case, spectra from bulls show higher absorption on average compared to steers. Indeed, the relationship between pHu and glucose (measure of pre rigor glycogen/RapidpH™) also showed a different pattern for bulls when compared to the steers (p b 0.001, GAM: R2 = 0.62, Deviance explained = 62%), Fig. 4, which has also been previously reported (Lomiwes et al., 2010). Rødbotten et al. (2000) observed a discrimination between bulls and cows for spectra obtained from samples collected 4 h post mortem, but not for samples collected 26 h post mortem from the same animals (Rødbotten, Nilsen, & Hildrum, 2000). Rødbotten et al. (2000) suggested

Table 5 Distribution of correctly classified (Corr.) and misclassified (Miss.) carcasses into pHu classes (low: pHu b 5.6; intermediate: 5.6 ≤ pHu b 5.8; high: pHu ≥ 5.8) by NIRS when compared with pHu measured at 48 h post mortem for the All-animal (ncal = 257, nval = 322) and the Non-bull (cows, heifer and steers; ncal = 201, nval = 248) & Bull (bulls only; ncal = 76, nval = 72) models combined.

Bulls High Low Intermediate Cows High Low Intermediate Heifers High Low Intermediate Steers High Low Intermediate a

All-animal model (%)

Bull or Non-bull model (%)

Calibration

Validation

Calibration

Miss.

Corr.

Miss.

Corr.

Miss.

Corr.

Miss.

Corr.

17 7 16 42 3

83 93 84 58 97 100 96 100 100 100 100 100 90 94 96 53

35 10 44 50 6 100a 4

65 90 56 50 94

17 8 16 38 5

25 6 35 17 8

96 100 92 100 91 100 86 97 90 59

4

83 92 84 62 95 100 96 88 100

75 94 65 83 92 100 92 100 100

4

10 6 4 47

8 9 14 3 10 41

9 7 35

100 100 91 100 93 65

There was only one cow with high pHu in the validation data set.

Validation

8

13 7 8 41

100 100 87 93 92 59

Fig. 2. Important variables (in gray) on the specific PLS models for (a) the Non-bull (cows, heifers and steers; ncal = 201) and (b) Bull (bulls only; ncal = 76) models as estimated by the uncertainty test.

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Fig. 3. VIS–NIR spectra of bulls and steers for two pHu ranges: ‘Low’ pHu b 5.5 (41 steers; 40 bulls) and ‘High’ pHu N 6 (52 steers; 52 bulls).

that discrimination between bulls and cows on spectra collected at 4 h post mortem was due to indirect effect of different fat contents in cows and bulls while the lack of discrimination on spectra collected at 26 h

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post mortem resulted in the solidification of the intramuscular fat, which would alter light absorbance (Rødbotten et al., 2000). Prieto, Andrés, Giráldez, Mantecón, and Lavín (2008) compared NIR spectra of adult steers (over 4 years old) and young cattle (under 14 months old), respectively, observing that the key difference was associated to bands related to the absorption by O\H (first and second overtones of the O\H bonds) and C\H bonds (1174 nm) (Prieto et al., 2008). Prieto et al. (2008) suggested that this difference could be associated to intramuscular fat fraction. While bulls are recognized to be leaner than steers (Conroy et al., 2012; Pike, Ringkob, Beekman, Koh, & Gerthoffer, 1993; Purchas & Zou, 2008; Restle, Grässi, & Feijó, 1996), the difference in fat content between bulls and prime alone does not explain the fact that the main difference in NIR spectra between the two animal categories is associated with high pHu (Fig. 3). Alomar, Gallo, Castañeda, and Fuchslocher (2003) observed a similar effect as detected in this work (additive effect in the spectral range between 1400 nm and 1850 nm, Fig. 3) when comparing two breeds (i.e. Friesian and Hereford males) and different muscle types (i.e. M. longissimus, semitendinosus and supraspinous) (Alomar et al., 2003). This additive effect has also been found when comparing four different types of muscle in beef (M. longissimus dorsi, extensor capri, semimembranosus and semitendinosus) (Reis, Mercer, Wiklund, Rosenvold, & North, 2012). Xia, Berg, Lee, and Yao (2007) observed that the difference among muscle types in beef is due to an additive effect associated with optical scattering (Xia et al., 2007). Ranasinghesagara and Yao (2007) investigated the optical reflectance from fresh pre-rigor skeletal muscles observing that muscle sarcomere structures played important roles in modulating light propagation in whole muscle (Ranasinghesagara & Yao, 2007). Ranasinghesagara et al. (2010) observed that the scattering intensity was generally greater in muscles obtained from Bos indicus than Bos taurus steers (Ranasinghesagara et al., 2010). Overall these studies indicate that the structural part of the muscle plays an important role on the optical scattering characteristics of the muscle, which is affected by factors such as muscle type and breed. Hence the difference in spectra of bulls and steers observed in Fig. 3 might also be associated with the structure of muscle. Indeed, Zerouala and Stickland (1991) observed that animals exhibiting dark-cutting 48 h post mortem had a significantly greater proportion of oxidative fibers in M. longissimus dorsi than normal animal and that dark-cutting bulls contained fewer and smaller fast glycolytic fibers than the dark-cutting steers (Zerouala & Stickland, 1991). The difference in abundance of fiber types between bulls and steers has also been observed in other studies (Schreurs et al., 2008; Young & Bass, 1984). Hence the difference between spectra from bulls and steers might also be associated with fiber type and fiber abundance. However, this is speculative and further investigation is required to better understand the key factors driving this difference between bulls and steers shown in Fig. 3. The Bull and Non-bull models are discussed separately in the following sections. 3.4. The Non-bull model

Fig. 4. Relationship between glucose (mmol/L) at 5 min post mortem as determined using the RapidpH™ system and ultimate pH (pHu) in M. longissimus dorsi measured 48 h post mortem. (b) corresponds to the enlargement of regions highlighted in (a) with a rectangle, where the highest difference between bulls (open circle) and prime (dots) is observed. (c) GAM model fitted to describe the pHu as a function of glucose (p b 0.001, GAM: R2 = 0.62, Deviance explained = 62%).

The Non-bull model correctly classified 90% of the high pHu carcasses, and hence had a rate of misclassification of 10% in the validation data set (Table 4). For the normal pHu carcasses, the model classified 89% of the carcasses correctly (Table 4). Table 5 shows the distribution of misclassified and correctly classified carcasses for normal pHu where the highest rate of misclassification is found for carcasses in intermediate pHu range. The chance of a carcass with pHu below 5.6 being predicted as high pHu is below 7% (steers/validation data set). For carcasses in the intermediate range this corresponds to 35% (steers/validation data set). This means

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that a carcass with pHu in the range 5.6≤pHu b5.8 has a 35% of chance of being predicted as a high pHu carcass. Overall this model identifies high and low pHu carcasses well, but still classifies a significant number of carcasses within the range 5.6 ≤ pHu b 5.8 as having pHu higher than 5.8. As a filtering tool to prevent high pHu carcasses from being sold as normal pHu carcasses, this model achieved a 10% chance of a high pHu carcass being sold as normal pHu.

3.5. The Bull model For high pHu carcasses this model classified 94% of the carcasses in the validation data set correctly. This means that there is a small chance (6%) of the model classifying a high pHu carcass as one of normal pHu (Table 5). For classification of the low pHu samples, the model was inferior to that for the high pHu samples with a misclassification rate of 35%. This means that there is a 35% chance of a carcass with pHu below 5.6 being predicted as a high pHu animal (Table 5). The model was superior for samples found in the intermediate range compared to the low pHu range, but still presented a high rate of misclassification in the validation data set (17%). Overall this model identifies high pHu carcasses, but still classifies a significant number of carcasses with normal pHu as having pHu higher than 5.8. As a commercial filtering tool to prevent high pHu carcasses from being sold as normal pHu carcasses, this model shows potential.

3.6. Comparison with RapidpH™ The NIRS classification was compared with the classification by RapidpH™ as presented in Table 6. The predictions of NIRS for Nonbulls (cow, heifers and steers) is compared to RapidpH™ applied to prime (steers and heifers) showing similar performance for high pHu and slightly better for normal pHu. While for bulls, NIRS outperforms RapidpH™ for high pHu, but not for normal pHu animals. In both technologies a future prediction of ultimate pH is made from measurement taken up to 1 h post mortem when rigor has not yet been reached. Thus it is expected that these measurements are affected by a series of upstream conditions affecting the rigor process such as variations in the stimulation, temperature as well natural biological variation from animal to animal (Hwang, Devine, & Hopkins, 2003). Therefore variation between the prediction of ultimate pH and its measured value is not only due to instrumental and sampling sources but also due to variation associated with the carcass processing.

3.7. Colour measurements The relationship between pHu and colour parameters L*-value (lightness), a*-value (redness) and b*-value (yellowness) show that the best correlations are found for the a*-value and b*-value; correlations for bulls: L*-value = −0.67 (p b 0.001), a*-value = −0.80 (p b 0.001), b*-value = − 0.78 (p b 0.001); correlations for Nonbulls: L*-value = − 0.29 (p b 0.001), a*-value = − 0.67 (p b 0.001), b*-value = − 0.65 (p b 0.001) (Fig. 5). The a*-value and b*-value showed a threshold separating samples with pHu below and above 5.8. In this case, samples with pHu higher than 5.8 showed a*-values below 11 and 10, for Bulls and Non-bulls respectively, and for b*values below 8, for both Bulls and Non-bulls. The decrease in a* and b* values corresponding with an increase in pHu indicates a less intense red, while the decrease in L*-values indicates darker colour (Goñi, Indurain, Hernandez, & Beriain, 2008).

4. Conclusions Calibration models showed a limited ability to predict the nominal pHu value. The best approach to evaluate pHu was the classification of carcasses between high (pHu ≥ 5.8) and normal (pHu b 5.8) using two separate models, one for bulls and the other for Non-bulls (steers, heifers and cows). NIRS can be used as a filtering tool for both Bulls and Non-bulls as it is able to detect at least 90% of high pHu carcasses. The main risk of using the model to detect normal pHu carcasses is that carcasses with normal pHu can be incorrectly classified as high pHu. For Non-bulls this risk is higher for carcasses with pHu between 5.6 ≤ pHu b 5.8, since carcasses with pHu below 5.6 have a 92% chance of being correctly classified In this case, normal carcasses could be downgraded and hence sold for a lower price. However the number of carcasses found below 5.6 was much higher than the number of

Table 6 Summary of the rate of correctly and misclassified carcasses with respect to pHu class (high: pHu ≥ 5.8; normal: pHu b 5.8 and normal pHu stratified classes intermediate: pHu 5.6 ≤ pHu b 5.8 and low: pHu b 5.6) for the Non-bulls (cows, heifers and steers), steers (steers only) and Bulls (bulls only). pH class

Non-bulls High Normal Steers High Low Intermediate Bulls High Low Intermediate

Correct classification (%)

Correct classification (n)

RapidpH

NIRS

RapidpH

NIRS

92 80

90 89

23/25 162/203

26/29 195/219

92 85 53

93 92 59

23/25 145/171 17/32

25/27 120/130 17/29

67 86 63

94 65 83

18/27 38/44 5/8

16/17 28/43 10/12

Fig. 5. Relationship between colour measurements (L*-, a*- and b*-values) and pHu measured 48 h post mortem in M. longissimus dorsi. The vertical lines indicate pHu = 5.8.

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carcasses found between 5.6 ≤ pHu b 5.8, which would reduce the costs associated with the misclassification of normal carcasses. When compared with the current pHu technology used in plant (RapidpH™) to sort carcasses with a threshold of 5.8, NIRS matched the performance for Non-bull animals. RapidpH™ is measured just after slaughter and is less affected by variation in the chain speed compared to NIRS, however NIRS is able to match performance. This suggests that NIRS has the potential to replace wet chemistry based evaluation of pHu for prime animals (steers + heifers). Further work will be dedicated to evaluate whether the effect of seasonal variation influences NIRS predictions. Finally, this work confirms our initial hypothesis that specific calibrations centered on animal type perform better in terms of pHu classification. Acknowledgments The authors acknowledge Silver Fern Farms for funding the project and providing the sites for data collection. The New Zealand Ministry for Primary Industries contributed through the Primary Growth Partnership — PGP program. Mr. Kevin Taukiri assisted with the NIRS data collection. Mr. Daniel Lousich and Silver Fern Farms staff provided valuable support for the trials at the plant. References Abril, M., Campo, M. M., Onenc, A., Sanudo, C., Alberti, P., & Negueruela, A. I. (2001). Beef colour evolution as a function of ultimate pH. Meat Science, 58(1), 69–78. 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(4), 441–450. Braggins, T. J. (1996). Effect of stress-related changes in sheep meat ultimate pH on cooked odor and flavour. Journal of Agricultural and Food Chemistry, 44(8), 2352–2360. Conroy, S. B., Drennan, M. J., McGee, M., Keane, M. G., Kenny, D. A., & Berry, D. P. (2012). Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection. Animals, 4, 234–241. Davies, A.M. C. (2005). Back to basics: Application of principal component analysis. Spectroscopy Europe, 25, 30–31. Fluckiger, M., Brown, M. R., Ward, L. R., & Moltschaniwskyj, N. A. (2011). Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). Food Chemistry, 126(4), 1817–1820. Gill, C. O., & Newton, K. G. (1981). Microbiology of DFD beef. The Problem of Dark-cutting in Beef (pp. 305–327). Goñi, V., Indurain, G., Hernandez, B., & Beriain, M. J. (2008). Measuring muscle colour in beef using an instrumental method versus visual colour scales. Journal of Muscle Foods, 19, 209–221. Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. London. Great Britain: Chapman and Hall. Hatchell, D. C. (1999). ASD Technical Guide. Hwang, I. H., Devine, C. E., & Hopkins, D. L. (2003). The biochemical and physical effects of electrical stimulation on beef and sheep meat tenderness. Meat Science, 65, 677–691. Josell, Å., Martinsson, L., Borggaard, C., Andersen, J. R., & Tornberg, E. (2000). Determination of RN− phenotype in pigs at slaughter-line using visual and near-infrared spectroscopy. Meat Science, 55(3), 273–278. Kastner, C. L. (1977). Hot processing: Update on potential energy and related economics. Proceedings of the Meat Industry Research Conference, 43–51. Kastner, C. L. (1982). Hot processing—Overview. Proceedings Int. Symposium Meat Sci. Technol., Lincoln, USA (pp. 149–168). Lomiwes, D., Reis, M. M., Wiklund, E., Young, O. A., & North, M. (2010). Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi. Meat Science, 86(4), 999–1004. Martens, H., & Martens, M. (1999). Modified jack-knife estimation of parameter uncertainty in bilinear modelling (PLSR). Food Quality and Preference, 11, 5–16. Ockerman, H. W., Basu, L., & Werner Klinth, J. (2004). Carcass chilling and boning. Encyclopedia of Meat Sciences, 144–149. Pike, M. M., Ringkob, T. P., Beekman, D.D., Koh, Y. O., & Gerthoffer, W. T. (1993). Quadratic relationship between early-post-mortem glycolytic rate and beef tenderness. Meat Science, 34(1), 13–26.

869

Prevolnik, M., Potokar, M.Č., & Škorjanc, D. (2004). Ability of NIR spectroscopy to predict meat chemical composition and meat quality. Czech Journal of Animal Science, 49, 500–510. Prieto, N., Andrés, S., Giráldez, F. J., Mantecón, A.R., & Lavín, P. (2008). Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS). Meat Science, 79(1), 198–201. Prieto, N., Roehe, R., Lavín, P., Batten, G., & Andrés, S. (2009). Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Science, 83, 175–186. Puolanne, E., & Immonen, K. (2000). Variation of residual glycogen–glucose concentration at ultimate pH values below 5.75. Meat Science, 55, 279–283. Purchas, R. W., & Zou, M. (2008). Composition and quality differences between the longissimus and infraspinatus muscles for several groups of pasture-finished cattle. Meat Science, 80(2), 470–479. Ranasinghesagara, J., Nath, T. M., Wells, S. J., Weaver, A.D., Gerrard, D. E., & Yao, G. (2010). Imaging optical diffuse reflectance in beef muscles for tenderness prediction. Meat Science, 84(3), 413–421. Ranasinghesagara, J., & Yao, G. (2007). Imaging 2D optical diffuse reflectance in skeletal muscle. Optics Express, 15, 3998–4007. R-Development, C. T. (2009). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing3-900051-07-0. Reis, M. M., Araújo, P. H. H., Sayer, C., & Giudici, R. (2007). Spectroscopic on-line monitoring of reactions in dispersed medium: Chemometric challenges. Analytica Chimica Acta, 595(1-2 SPEC. ISS), 257–265. Reis, M. M., Mercer, G. J. K., Wiklund, E., Rosenvold, K., & North, M. F. (2012). The ability of NIR to detect simultaneous changes in meat collagen and shear force. 15th Australian NIR Spectroscopy Conference (ANISG), Queenstown, New Zealand. Restle, J., Grässi, C., & Feijó, G. L. D. (1996). Carcass characteristics of beef cattle bulls or steers castrated with burdizzo or knife, under pasture conditions. Características das carcaças e da carne de bovinos inteiros ou submetidos a duas formas de castração, em condições de pastagem, 25(2). (pp. 334–344). Rinnan, Å., Berg, F. v. d, & Engelsen, S. B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC — Trends in Analytical Chemistry, 28(10), 1201–1222. Rødbotten, R., Nilsen, B. N., & Hildrum, K. I. (2000). Prediction of beef quality attributes from early post mortem near infrared reflectance spectra. Food Chemistry, 69(4), 427–436. Rosenvold, K., Micklander, E., Hansen, P. W., Burling-Claridge, R., Challies, M., Devine, C., & North, M. (2009). Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy. Meat Science, 82(3), 379–388. Schreurs, N. M., Garcia, F., Jurie, C., Agabriel, J., Micol, D., Bauchart, D., Listrat, A., & Picard, B. (2008). Meta-analysis of the effect of animal maturity on muscle characteristics in different muscles, breeds, and sexes of cattle. Journal of Animal Science, 86(11), 2872–2887. Silva, J. A., Patarata, L., & Martins, C. (1999). Influence of ultimate pH on bovine meat tenderness during ageing. Meat Science, 52(4), 453–459. Tamburini, E., Vaccari, G., Tosi, S., & Trilli, A. (2003). Near-infrared spectroscopy: A tool for monitoring submerged fermentation processes using an immersion optical-fiber probe. Applied Spectroscopy, 57, 132–138. Viljoen, H. F., De Kock, H. L., & Webb, E. C. (2002). Consumer acceptability of dark, firm and dry (DFD) and normal pH beef steaks. Meat Science, 61(2), 181–185. Watanabe, A., Daly, C. C., & Devine, C. E. (1996). The effects of the ultimate pH of meat on tenderness changes during ageing. Meat Science, 42(1), 67–78. Weeranantanaphan, J., Downey, G., Allen, P., & Sun, D. -W. (2011). A review of near infrared spectroscopy in muscle food analysis:2005–2010. Journal of Near Infrared Spectroscopy, 19, 61–104. Wiklund, E., Farouk, M., Stuart, A., Dobbie, P., Lomiwes, D., & Frost, D. (2009). Survey of current pH status in New Zealand bull beef. Proceedings 55th International Congress of Meat Science and Technology, Copenhagen (pp. 221–223). Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. Wood, S. N. (2001). mgcv: GAMs and generalized ridge regression for R. R News, 1, 20–25. Xia, J. J., Berg, E. P., Lee, J. W., & Yao, G. (2007). Characterizing beef muscles with optical scattering and absorption coefficients in VIS–NIR region. Meat Science, 75(1), 78–83. Young, O. A., & Bass, J. J. (1984). Effect of castration on bovine muscle composition. Meat Science, 11(2), 139–156. Young, O. A., Thomson, R. D., Merhtens, V. G., & Loeffen, M. P. F. (2004). Industrial application to cattle of a method for the early determination of meat ultimate pH. Meat Science, 67(1), 107–112. Young, O. A., West, J., Hart, A. L., & Van Otterdijk, F. F. H. (2004). A method for early determination of meat ultimate pH. Meat Science, 66(2), 493–498. Zerouala, A.C., & Stickland, N. C. (1991). Cattle at risk for dark-cutting beef have a higher proportion of oxidative muscle fibres. Meat Science, 29(3), 263–270.