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On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy
Meat Science 100 (2015) 156–163
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On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy Reddy R. Pullanagari a,⁎, Ian J. Yule a, M. Agnew b a b
Department of Soil and Earth Sciences, Institute of Agriculture and Environment (IAE), Massey University, Palmerston North Private Bag 11-222, New Zealand AgResearch, Ruakura, Private Bag 3123 Hamilton, New Zealand
a r t i c l e
i n f o
Article history: Received 2 May 2014 Received in revised form 3 October 2014 Accepted 7 October 2014 Available online 16 October 2014 Keywords: Vis-NIRS Lamb Fatty acid Chemometrics
a b s t r a c t This study investigated the potential of visible near infrared spectroscopy (Vis-NIRS) to quantify the fatty acid (FA) composition of lamb meat under commercial abattoir conditions. Genetic algorithm based partial least squares (PLS) were used to develop regression models for predicting individual FA and FA groups such as saturated FA (SFA), monounsaturated FA (MUFA) and polyunsaturated FA (PUFA). Overall, the majority of the FA (C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n−6, C18:2 c9 t11 and C18:1 t11), intramuscular fat (IMF) and all FA groups were predicted with an R2CV, the squared correlation between observed and cross validated predicted values, which ranged between 0.60 and 0.74 and ratio prediction to deviation (RPD) values between 1.60 and 2.24. However the results for the remaining FA (C17:1, C18:0, C18:3 n−3, C20:4, C20:5, C22:5, C22:6) were unsatisfactory (R2 = 0.35–0.57, RPD = 0.76–1.49). This indicates that Vis-NIRS could be used as an on-line tool to predict a number of FA. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Lamb meat quality is an important attribute that significantly influences consumer decision to purchase. Lamb meat is considered a healthy component of the human diet because of its nutritional quality with essential vitamins, protein, mineral content and fatty acids (FA). Among the nutritional values, FA composition of lamb meat is a critical trait in determining the perception of meat quality. Moreover, fat tissue firmness and meat shelf life and sensory (tenderness, juiciness and flavour) properties of meat are considerably influenced by FA composition (Wood, Richardson, Nute, et al., 2003; Wood, Enser, Fisher, et al., 2008). Excessive consumption of fat, particularly saturated FA, gives rise to health risks such as cardiovascular diseases (CVDs) (Kontogianni, Panagiotakos, Pitsavos, Chrysohoou, & Stefanadis, 2007), high cholesterol levels and colorectal cancer (Chao, Thun, Connell, et al., 2005). On the other hand, some unsaturated FA are associated with numerous health benefits. For instance, long chain (fatty acids with 13–21 carbons) v n −3 polyunsaturated FA (PUFA) such as eicosapentaenoic acid (EPA, C20:5), docosapentaenoic acid (DPA, C22:5), and docosahexaenoic acid (DHA, C22:6) are recognised for their benefits for heart health, central nervous system, platelet aggregation and thrombotic tendency (McAfee, McSorley, Cuskelly, et al., 2010). Another group of beneficial FA, conjugated linoleic acid (CLA), has anti-carcinogenic properties and induces body fat loss (Hargrave-Barnes, Azain, & Miner, 2008). ⁎ Corresponding author. Tel.: +64 6 356 9099x83240. E-mail addresses: [email protected] (R.R. Pullanagari), [email protected] (I.J. Yule).
http://dx.doi.org/10.1016/j.meatsci.2014.10.008 0309-1740/© 2014 Elsevier Ltd. All rights reserved.
Following the increasing awareness of the importance of a healthy and balanced diet, consumers and human nutritionists need accurate nutritional information particularly on FA composition of meat. This information is not commonly measured or available on individual cuts of lamb in a commercial setting, largely due to the lack of a suitable measurement tool. Under laboratory conditions, FA composition is routinely measured by gas–liquid chromatography which involves time consuming, high labour costs and destructive nature of expensive sampling procedures which can release harmful reagents unsuitable for commercial conditions. Furthermore, the method is unsuited to an on-line application in the meat processing plant. The ability to measure the FA composition (or other desirable properties) of lamb meat on-line would allow the product to be labelled with nutritional/quality information, differentiating it from competitors. Such products could be sold at a premium price adding commercial value. Furthermore, if FA composition was readily measureable on-line, this would complement research addressing manipulation of FA through breeding programme (Prieto, Ross, Navajas, et al., 2011) and diet. NIR (near infrared) is the region in which absorptions corresponding to overtones and combinations of the fundamental vibrational transitions occur (Osborne, 2006). Vis-NIRS has been proven to be a potential non-destructive tool for predicting various lamb meat attributes: tenderness (Chandraratne, Samarasinghe, Kulasiri, & Bickerstaffe, 2006), pH (Kamruzzaman, ElMasry, Sun, & Allen, 2012b), fat, protein and water content (Kamruzzaman, ElMasry, Sun, & Allen, 2012a) and FA composition (Guy, Prache, Thomas, Bauchart, & Andueza, 2011). VisNIRS can also be set up for on-line monitoring of meat attributes which enables industrial applications. Prieto et al. (2011) conducted
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an experiment to evaluate the on-line application of Vis-NIRS to estimate FA composition in crossbred Limousin and Aberdeen Angus beef cattle and their results showed reasonable accuracy (R2 = 0.81–0.87) for predicting FA composition. Few researchers have attempted to predict FA composition of whole meat cuts using Vis-NIRS, and the majority of them used ground meat in a laboratory environment. Thus the performance of Vis-NIRS to predict the FA composition of lamb meat under commercial operating conditions is not well characterized. The objective of this study was to evaluate the use of Vis-NIRS as an on-line tool to predict FA composition of fresh intact lamb meat muscle under commercial operating conditions encountered in a lamb processing plant. 2. Materials and methods 2.1. Lambs A total of 500 lambs sourced from two commercially operated lamb processing plants (250 lambs from each plant), across New Zealand were included in this experiment. All lambs were pasture-fed on commercial farms prior to slaughter and the average carcass weight was 19.5 kg. No measurements were taken on the live animals so ethical approval for this experiment was not deemed necessary. The lambs were of different breeds, sex and age which ensured the wide variability of FA composition. The lambs were electrically stunned, exsanguinated and eviscerated according to the standard commercial dressing protocol (New Zealand food safety authority, 2004). The carcasses were chilled for 24 h at 4 °C immediately after dressing. Then after being moved to the boning room, each carcass was cut into three primals, including forequarter, middle and hindquarter, using robotic cutters and then sent to the cutting lines via conveyor belt. 2.2. Spectral data collection The middle primal was taken for spectral scanning directly and immediately after primal cutting. The posterior surface of the right m. longissimus lumborum was selected for spectral scanning using a high intensity contact probe (ASD Inc., Boulder, CO) connected to a Vis-NIR spectroradiometer (ASD FieldSpec® Pro) via a fibre optic cable. The probe was internally illuminated with a halogen bulb for consistent broad-band illumination, and scans a spot of 10 mm in diameter. The spectral range of the instrument is 350 nm to 2500 nm with a spectral resolution of 3 nm for the region 350–1000 nm and 10 nm for the region 1000–2500 nm. The probe was held perpendicular to a muscle fibre on the fresh cut surface. For each lamb sample, three spectral measurements were taken at one position on the cut surface muscle and then averaged for further analysis. 2.3. Fatty acid composition analysis After the meat sample was scanned, the middle of the carcass was left on the conveyor belt for cuts with its barcode. At the tail end of the boning room, the spectral measured caudal portion of the m. longissimus lumborum muscle (150 g) was collected, vacuum packed and stored for 30 days at −25 °C for FA analysis. This study used a one-step method FA analysis in which extraction and esterification of lipids were combined (Knight, Knowles, Death, et al., 2003). Lipids were extracted from the freeze dried samples by a modified Folch method (Folch, Lees, & Sloane-Stanley, 1957). The freeze-dried lamb samples, with a weight of 200 mg, were ground to a powder using a coffee grinder. The ground samples were placed into Kimax 15 ml tubes. 3 ml of toluene and 300 μl of internal standard (C11 and C23 TAG in chloroform) were added with a volumetric pipette under fume hood. Then 5 ml of 5% methanolic sulphuric acid was added. After adding the solutions the tubes were tightly fitted with caps and incubated at 70 °C for 2 h, the samples were mixed properly at least twice
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during incubation time. The tubes were cooled down to room temperature using cold water bath before 5 ml of saturated NaCl was added and then again centrifuged at 2500 rpm for 10 min to separate solvent layers. The top FAME layer was transferred into 1.5 ml GLC (Gas Liquid Chromatography, Hewlett Packard, model 6890) auto sampler vials. GLC equipped with a SGE BPX70 column of 120 m length, 0.25 mm i.d., and 0.25 μm film thickness and a flame ionisation detector (FID). The peaks were identified and quantified by using internal standard (C23:0) and theoretical FID response factors. The equations for generating the response and conversion factors to quantify individual FA from FAME were obtained from American Oil Chemists' Society (AOAC, 2009). The precision of a laboratory method is usually determined by calculating standard error of the laboratory (SEL) where the samples were analysed in duplicates using standard laboratory procedures. The SEL is calculated according to the following equation (Rosenvold, Micklander, Hansen, et al., 2009): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n X r 2 X u 1 xi j −xi SEL ¼ t nðr−1Þ i¼1 j¼1 where n and r represent the number of samples and number of replicates, respectively, xij denotes the value of ith sample and jth replicate and xi is the average value of ith sample. A lower SEL value is good for model development. 2.4. Data analysis A series of pre-processing steps were carried out on the raw reflectance spectra prior to modelling to improve spectral absorptions and minimise the random noise generated from sampling and instrumental effects. Pre-processing steps include log transformation, smoothing using a Savitzky–Golay filter, derivative transformation and auto scaling. There is no standard pre-processing approach available, hence we considered them based on trial and error. A high level of noise was noticed at the extreme ends of the spectral range which may interfere with the analysis of original spectra and, thus removed from the analysis, finally resulting in a working spectral range of 400–2450 nm. In order to quantify the FA composition using the pre-processed VisNIR spectra, a regression model was developed using the genetic algorithm (GA) based partial least squares (GA-PLS) method. GA-PLS is a sophisticated hybrid approach in which GA is an evolutionary algorithm. It computes to find optimization solutions which can be used for informative wavelength selection (Leardi & Lupiáñez González, 1998) sensitive to individual FA whereas PLS links the GA selected wavelengths with reference values of individual FA. GA-PLS comprises a five step procedure: coding of variables, creation of initial population, evaluation of the responses, cross-over and mutation. GA creates a number of random initial populations of chromosomes from the full dataset that contain a sequence of genes. Chromosomes represent a set of wavelengths while a gene denotes a wavelength. Each chromosome is a binary string in that genes encode the chosen input spectral variables into binary data as zeros and ones. The gene value “1” means the wavelength being selected is informative while “0” implies that the wavelength is not important. All possible best chromosomes were selected and their performance was evaluated with a predefined fitness function generated from the internal productivity of the PLS model between observed and predicted values. GA uses this approach iteratively to search the chromosomes with the highest fitness value (Leardi, 2000). The population of the best fit chromosomes were reproduced in the next generation by the use of cross-over and mutation. In the cross-over, new offspring are produced through the random selection of chromosomes, divided individually, mutually exchanged and merged. Mutation follows the cross-over which allows for the chance of adding or removing the variables. The fitness function of the GA-PLS is determined by root mean square error via leave-one-out cross-validation (RMSECV)
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criterion. Fig. 1 illustrates the steps involved in the GA-PLS model development. The calculation RMSECV formula is described as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðy ^i −yi Þ2 RMSECV ¼ t n i¼1
RMSEP for cross-validation and validation, respectively) and ratio prediction to deviation for cross-validation (RPDCV) and prediction (RPDP). The R2 and RPD are computed using the following equations. Xn
2
2
R ¼ 1− Xi¼1 n
^i Þ ðyi −y
i¼1
where ŷi is the predicted value of the ith sample, yi is the reference measurement value of ith sample, and n is the number of samples in the calibration set. GA selects the important subset of variables (wavelengths) which produces the lowest RMSECV. The best subset of wavelengths was selected for individual FA and used as an input for developing the PLS (partial least squares) model. PLS is a widely accepted multivariate regression modelling approach and effectively deals with multi-collinear data (Wold, Sjöström, & Eriksson, 2001). To avoid over-fitting and optimising the number of factors, leave-one-out cross-validation was performed, and the optimal number of latent variables was determined when the RMSECV was lowest. The analysis was performed using an inhouse programme in MATLAB (R2012a Mathworks Inc., Natick, MA). The total dataset was randomly divided into calibration and validation sets in order to test the prediction performance of the calibration models. In each set, a similar level of range and variation was conserved as it is important to evaluate the models' performance. Half of the samples (n = 250) were used for developing GA-PLS calibration model and the remaining half (n = 250) for validating the calibration model. In the case of IMF content, only 400 samples have been analysed hence n = 200 for calibration and validation were considered. During the PLS model development, the samples that had a standard residual greater than 2.5 were removed as outliers in calibration and validation datasets. The number of outliers was different in each dataset. The model performance was evaluated using coefficient of determination; R2CV for crossvalidation and R2P for validation, root mean square error (RMSECV and
Vis-NIR data
2
ðyi −yÞ
The RPD is the ratio of the standard deviation in reference values of the samples and root mean square error between observed and predicted values. SD ðyÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RPD ¼ sX n ^ −yi Þ2 ðy i¼1 i n where n is number of samples and ŷi and yi are the predicted and measured values of ith sample, respectively. SD(y) is the standard deviation of reference measurement values (y) and y is the average of reference measurement. 3. Results Descriptive statistics values including the mean, minimum, maximum, standard deviation and coefficient of variation of individual FA of corresponding data sets are presented in Table 1. Overall, a wide range of fatty acid values (CV = 9%–48%) were observed in the lamb meat samples (Table 1). Among SFA, myristic acid (C14:0) showed the highest variation (CV = 44%) with a range of 9–142 mg/100 g meat, whilst stearic acid (C18:0) has the lowest variation (CV = 27%). In MUFA group, palmitoleic acid (C16:1) showed high variability (CV = 37%) and vaccenic acid (C18:1 c11) showed lower variability
Reference data
Encoding the Vis-NIR data
Generating initial random population
Making a PLS model and evaluating with fitness function
Mutation and crossover
Update population and evaluate with fitness function
GA selected bands
Reference data
GA-PLS model
Fig. 1. Flow chart for GA-PLS model development.
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Table 1 Statistical summary of individual fatty acids and fatty acid groups (mg/100 g meat) and IMF content (%/100 g) of lamb meat m. longissimus lumborum samples for calibration (n = 250) and validation data (n = 250) sets. Fatty acid
C14:0 (myristic) C16:0 (palmitic) C16:1 (palmitoleic) C17:0 (margaric) C17:1 (heptadecenoic) C18:0 (stearic) C18:1 c9 (oleic) C18:1 c11 (vaccenic) C18:2 n−6 (linoleic) C18:2 c9, t11 (conjugated linoleic) C18:1 t11 (trans vaccenic) C18:3 n−3 (linolenic) C20:4 n−6 (arachidonic) C20:5 n−3 (eicosapentaenoic) C22:5 n−3 (docosapentaenoic) C22:6 n−3 (docosahexaenoic) IMFd SFAe MUFAf PUFAg a b c d e f g
Calibration data
SELc
Validation data
Mean
Range
Stda
CVb (%)
Mean
Range
Std
CV (%)
60.64 482.8 23.70 23.54 11.51 387.4 741.8 17.54 73.89 27.55 75.57 47.52 21.16 22.57 21.01 6.29 8.90 964.7 798.2 297.0
9.1–142.4 165.1–1036 7.2–56.1 7.8–52.9 4.1–24.7 163.3–794.2 264–1570 8.2–28.9 44.5–105.6 5.1–79.7 19.6–197.6 27.7–80.1 14.0–31.5 14.9–31.5 15.63–27.74 3.24–10.87 2.78–18.56 385.39–2031 288.5–1648.4 190.8–547.2
26.73 171.9 8.92 7.66 3.54 107.4 245.3 3.94 11.35 13.43 35.40 9.11 3.00 3.42 2.04 1.35 2.95 306.4 261.7 59.96
44.07 35.60 37.63 32.54 30.75 27.77 33.06 22.46 15.36 48.74 46.84 19.17 14.17 15.15 9.74 21.46 33.14 31.76 32.78 20.19
60.95 484.41 23.82 23.65 11.49 387.03 740.98 17.66 74.02 27.64 75.91 47.49 21.15 22.56 22.34 6.10 8.98 968.45 801.0 296.74
10.2–154.84 170.52–1055 7.8–56.7 9.7–56.9 4.4–23.1 173.6–761.2 269.4–1503.4 8.42–30.7 45.2–107.9 5.70–81.0 20.5–197.09 27.91–79.13 14.39–30.92 15.19–31.51 16.23–26.89 3.38–10.54 3.49–18.54 393.13–2065 289.3–1678.5 191–533.9
26.78 172.74 9.05 7.82 3.46 105.60 244.52 3.95 11.41 13.69 35.64 8.94 2.95 3.38 1.95 1.30 2.84 309.78 263.82 58.92
44.26 35.65 37.99 33.06 30.11 27.28 32.99 22.36 15.41 49.52 46.95 18.82 13.94 14.98 8.81 21.31 31.60 31.98 32.93 19.86
3.8 22.1 1.12 1.48 0.52 19.6 36.8 0.84 2.47 1.76 7.51 1.56 0.50 0.38 0.39 0.17 – 63.2 32.1 8.55
Std: standard deviation. CV: coefficient of variation. SEL: standard error of laboratory. IMF: intramuscular fat (note: n = 200 for calibration and n = 200 for validation). SFA: saturated fatty acids (C14:0, C16:0, C17:0, C18:0). MUFA: monounsaturated fatty acids (C16:1, C17:1, C18:1 c9, C18:1 c11). PUFA: polyunsaturated fatty acids (CLA, C18:1 t11, C18:2 n−6, C18:3 n−3, C20:4, C20:5, C22:5, C22:6).
(CV = 22%). The majority of the PUFA had a variation (CV) between 9% and 21% except for CLA and C18:1 t11 which had 46% ≤ CV. Intramuscular fat content (IMF), groups of FA (SFA and MUFA) have very similar variability (CV = 31%–33%) but PUFA showed lower variability (CV = 20%). GA-PLS based calibration models were constructed to predict individual FA and the results are tabulated in Table 2. The numbers of outliers detected in each calibration and validation models were different. After removing the outliers the remaining number of samples in the model development was presented in Table 2. Unlike a regular PLS model, GA-PLS significantly reduces the number of wavebands by including the most important wavebands that are relevant to individual FA. In this study the relative model performance for individual FA prediction was improved significantly with GA-PLS (R2 = 0.04–0.21) compared to a PLS model alone (Fig. 2). The performance of cross-validated calibration models was externally validated, and similar predictive performance was shown with all models (Table 2). With the essence of testing Vis-NIRS as an on-line tool to predict FA composition, this experiment was conducted under commercial abattoir conditions by direct application on the intact muscle of lamb meat. The majority of the FA (C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n−6, C18:2 c9 t11, C18:1 t11), groups of FA (SFA, MUFA and PUFA) and IMF were predicted with an R2 of 0.60–0.74 and the remaining FA were predicted poorly (R2 = 0.42–0.57). In SFA group, myristic, palmitic and margaric acid models yielded with an R2CV, RMSECV and relative RMSECV (the error after considering the laboratory error) of 0.71, 11.90 and 8.1, 0.72, 85.11 and 63.01, 0.63, 4.60 and 3.12, respectively (RPDCV = 1.66–2.24), whereas stearic acid was poorly determined (R2CV = 0.54, RMSECV = 72.74, relative RMSECV = 53.14, RPDCV = 1.47). The majority of MUFA were predicted with moderate accuracy (R2 = 0.63– 0.74, RPDCV = 1.63–2.00) except for heptadecenoic acid (R2 = 0.57, RPDCV = 1.53). The prediction models for PUFA were relatively poor as compared to other groups of FA but the proportion of linoleic acid and CLA predicted with reasonable accuracy (R2CV = 0.64 and R2CV = 0.70; RMSECV = 5.82 and RMSECV = 6.95, respectively). The
Table 2 Prediction statistics of FA profile (n = 250) and IMF content (n = 200) of lamb meat m. longissimus lumborum samples using genetic algorithm based partial least squares (GA-PLS). Fatty acid lipid number
Number of PLS factors
ncala R2CVb RMSECVc RPDCV nvald R2e P
C14:0 C16:0 C16:1 C17:0 C17:1 C18:0 C18:1 c9 C18:1 c11 C18:2 n−6 C18:2 c9 t11 C18:1 t11 C18:3 n−3 C20:4 C20:5 C22:5 C22:6 IMFg SFAh MUFAi PUFAj
2 5 3 5 4 3 5 2 2
235 242 243 247 248 247 235 240 243
0.71 11.90 0.72 85.11 0.63 5.44 0.63 4.60 0.57 2.30 0.54 72.74 0.74 126.49 0.73 1.97 0.64 5.82
2.24 2.00 1.63 1.66 1.53 1.47 1.93 2.00 1.95
243 244 244 247 247 246 238 239 244
0.70 11.98 0.70 87.01 0.63 5.43 0.60 4.69 0.55 2.32 0.53 73.09 0.69 128.31 0.73 2.01 0.62 5.88
2.23 1.99 1.66 1.66 1.49 1.44 1.90 1.96 1.94
4
241
0.70
6.95
1.93
240
0.68
7.10
1.92
4 2
243 242
0.60 0.55
21.42 6.00
1.65 1.51
243 243
0.61 0.53
21.10 6.11
1.68 1.47
3 3 1 2 5 3 4 5
245 248 241 244 195 245 243 242
0.42 2.22 0.51 2.39 0.45 1.56 0.35 1.61 0.71 1.55 0.61 191.23 0.62 162.74 0.71 28.09
1.35 1.43 1.56 0.83 1.90 1.60 1.61 2.13
246 247 239 245 190 246 242 243
0.40 2.30 0.50 2.41 0.47 1.57 0.32 1.69 0.69 1.60 0.60 192.21 0.60 168.72 0.67 27.86
1.29 1.40 1.24 0.76 1.77 1.61 1.56 2.11
a
RMSEPf RPDP
ncal: number of samples in calibration set. R2CV: coefficient of determination of cross-validated calibration. c RMSECV: root mean square error of cross validated calibration. d nval: number of samples in validation set. e R2P: coefficient of determination of validation. f RMSEP: root mean square error of validation. g IMF: intramuscular fat. h SFA: saturated fatty acids (C14:0, C16:0, C17:0, C18:0). i MUFA: monounsaturated fatty acids (C16:1, C17:1, C18:1 c9, C18:1 c11). j PUFA: polyunsaturated fatty acids (CLA, C18:1 t11, C18:2 n−6, C18:3 n−3, C20:4, C20:5, C22:5, C22:6). b
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0.8 0.7 0.6
R-square
0.5 0.4 0.3 0.2 0.1
PLS GA-PLS
0
Fatty acids Fig. 2. Comparison of prediction results of fatty acids using partial least squares (PLS) and genetic algorithm based partial least squares (GA-PLS).
important wavebands for quantifying individual FA which have an RPD of more than 2 are illustrated in Fig. 3. 4. Discussion Absolute concentration (mg/100 g meat) of individual FA values was used in modelling as it produces more accurate results than expressing as a percentage of total FA (Riovanto, De Marchi, Cassandro, & Penasa, 2012). Overall, wide ranges of FA were noticed (Table 1). This may have emanated from including different breeds of lamb, with different ages, raised in a diverse range of farms, which is useful for obtaining better prediction accuracy and for developing parsimonious and stable models (Kamruzzaman et al., 2012a). The concentration of SFA and MUFA is high compared to PUFA. However, the mean concentrations and variation in individual FA, FA groups and IMF were lower than reported by Guy et al. (2011) as they used pasture-fed, concentrate and hay-fed lambs to establish wide variability. The concentrations of CLA (C18:2 c9 t11) present in a wide range (5.1 to 79.7 mg/100 g meat, CV = 48%) compared to other studies (Fisher, Enser, Richardson, et al., 2000). It is well known that feeding grass based diets significantly increases the content of CLA compared to a concentrate based diet (Daniel, Wynn, Salter, & Buttery, 2004). Similar to the findings of Santos-Silva, Bessa, and Santos-Silva (2002) the meat samples had higher proportions of linolenic acid (C18:3 n − 3) compared to other n − 3 PUFA (eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid) as grass lipids are rich in linolenic acid. The variation in FA composition is likely to have appeared in every stage of the production process. Moreover, it is influenced by genetics (Belo, Almeida, Ribeiro, & Belo, 2005), age, gender, within meat cut, feed type (Rowe, Macedo, Visentainer, Souza, & Matsushita, 1999), and slaughter weight (Tejeda, Peña, & Andrés, 2008). Little research has been conducted to assess the potential of NIRS as an on-line tool to predict the FA profile in lamb. Overall, the prediction performance for individual FA, FA groups and IMF is slightly better (R2CV = 0.35–0.74) than the results of Guy et al. (2011) where they determined FA composition using NIRS performed on slices of intact lamb m. longissimus lumborum and obtained R2CV between 0.05 and 0.53. The myristic, palmitic, oleic, and vaccenic acids were estimated accurately; this is in agreement with the findings of Prieto et al. (2011). CLA was predicted accurately with an R2 of 0.70 (RPDCV = 1.93). A previous study of Prieto et al. (2011) also has reported similar predictions (R2 = 0.71) in Limousin crossbreed beef. The importance of CLA as a FA has been growing
because of its numerous health benefits (Hargrave-Barnes et al., 2008). The PUFA models particularly linolenic, eicosapentaenoic, docosapentaenoic, and docosahexaenoic showed poor predictions. This is probably due to a limited range and low variability (CV = 9– 21%) of FA contents. In addition to the variability, this is also attributed to the inability of NIRS to detect the higher double bonds present in PUFA (Zomeño, Juste, & Hernández, 2012). These results are in accordance with the findings of Sierra, Aldai, Castro, Osoro, Coto-Montes, and Oliván (2008) in beef. GA-PLS based prediction models significantly improved accuracy (Fig. 2) by including the important wavebands and eliminated the irrelevant bands for model building. This might be related to the fact that important bands are less influenced by a variance of unnecessary factors such as temperature and moisture content (Ferrand, Huquet, Barbey, et al., 2011). From these results, Vis-NIRS can be used as an on-line tool for monitoring FA, but there are some key factors that might limit the prediction performance. Below are possible causes that are thought to impact on the predictions: • Fatty acid, C22:6 n−3 mean concentration is relatively low (6.29 mg/ 100 g meat) and this makes it challenging for Vis-NIRS to detect the relevant signals, and negatively impacts final predictions (Prieto et al., 2011). The FA with large contribution can be easily predicted with NIRS compared to those with small contribution (De Marchi, Riovanto, Penasa, & Cassandro, 2012). In a study by Zhou, Wu, Li, Wang, & Zhang (2012) satisfactory prediction performance was obtained for the FA which have a mean concentration over 10 mg/100 g sample. • Meat muscle, in general, is highly heterogeneous in terms of FA composition and the concentration varies spatially across the muscle. VisNIRS scans a small portion of muscle that may not be representative of total FA variation of that muscle. Guy et al. (2011) clearly showed evidence that the predictability of NIRS in ground (homogenised) muscle samples is very high while prediction is low when the predictions are made by direct contact on non-ground samples. Similarly, Kamruzzaman et al. (2012b) stressed that the heterogeneity of the meat samples makes it more difficult to obtain accurate calibration models for predicting chemical parameters. • FA content may vary with breed type (Fisher et al., 2000). In beef, for example, Prieto et al. (2011) estimated FA composition of the m. longissimus lumborum using on-line NIRS under abattoir conditions in two breeds (crossbred Limousin and Aberdeen Angus beef cattle).
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Fig. 3. Important wavebands (bars) selected using genetic algorithm-partial least squares (GA-PLS) across the meat spectrum (background shape). (a) Wavebands for C14:0. (b) Wavebands for C16:0. (c) Wavebands for C18:1 c11. (d) Wavebands for PUFA (poly unsaturated fatty acid).
The prediction accuracy was lower in Aberdeen Angus beef than Limousin; this might be due to genetic differences that create different FA composition concentrations across the muscle. Guy et al. (2011)
attempted to predict CLA of meat from male Limousine lambs using NIRS, but only achieved an R2 of 0.39, RPD = 1.24, compared to R2 of 0.70, RPD = 1.93 in the current study. This indicates that
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Table 3 Fundamental absorption bands in the near infrared region for fats and oils (Hourant et al., 2000). Wavelength (nm)
Molecule
Vibration
1090–1180 1100–1200 1150–1260 1208 1700–1760 2100–2200 2240–2360 2290–2470
\CH2 \CH3 \CH_CH\ C\H \CH2,\CH3, C\H \CH_CH\ \CH3 \CH2
2nd overtone 2nd overtone 2nd overtone Combination 1st overtone Combination Combination Combination
Fig. 4, the 3D scatter plots of the first three PLS component scores corresponding to FA concentrations of C14:0 and PUFA were shown to have high RPD values. As we see in Fig. 4, the lamb samples with respect to the high and low FA concentrations were efficiently discriminated (Fig. 4a and b). Based on the findings of this study and by considering the aforementioned issues, further research needs to be conducted in order to improve the prediction performance for FA profile in lamb using Vis-NIRS. Accounting for meat heterogeneity by accounting for spatial variation within a sample could provide a greater level of detail regarding chemical composition, and distribution of intramuscular fat in lamb meat (Barbin, ElMasry, Sun, & Allen, 2013). 5. Conclusion
genetic difference is likely to limit the adoption of Vis-NIR as an online tool. • The accuracy of reference measures is a known limiting factor to the development of high-accuracy prediction equations (Prieto et al., 2011). These errors may be associated with the efficiency of fat extraction from the freeze-dried powder. In Table 1 the standard error of laboratory (SEL) of individual FA was tabulated which contributes approximately 10–36% of model error (Table 2), and the values are larger than those reported by Guy et al. (2011). NIRS prediction ability may be further improved by improving the efficiency of fat extraction for reference samples. • Most of the FA have similar biochemical structures which make it more demanding to configure individual FA with Vis-NIRS. In this sense, Windham and Morrison (1998) reported that some individual FA have the same CH2 group absorption that leads to analogous spectra and thus limits the prediction accuracy.
Given the limitations which have been identified and the lack of studies in this area, the results of this study explained that the VisNIRS can be used as an on-line tool for predicting FA composition and IMF in lamb meat. The prediction accuracy with an R2CV of 0.60–0.74, (RPDCV = 1.60–2.24) was found for C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n − 6, C18:2 c9 t11, C18:1 t11, SFA, MUFA, PUFA and IMF in lamb meat and less accurate (R2 = 0.35–0.57) prediction model found for C17:1, C18:0, C18:3 n − 3, C20:4, C20:5, C22:5, and C22:6. Even though the prediction accuracies of individual FA were low, Vis-NIRS could still be used as a screening tool. However, there is potential shown by this data that warrants further research to be conducted in order to improve the prediction accuracy of individual FA. Conflict of interest The authors have no conflict of interest to declare.
The spectral absorptions between 1600 and 1800, 2120 and 2170 and 2200 and 2400 nm were strongly determined by fatty acids (Sato, 2011). The important wavebands responsible for individual FA absorptions (Fig. 3) closely corresponded to the fundamental absorptions (Table 3) (Hourant, Baeten, Morales, Meurens, & Aparicio, 2000). The absorptions at around 2100–2200 nm related to the absorptions of FA having cis double bonds (Hourant et al., 2000). Although the prediction models for quantifying individual FA were not adequate, they could be adequate for screening purposes (Williams, 2001). For instance, in
Acknowledgements The authors wish to express their gratitude to the Silver Fern Farms, New Zealand for providing financial assistance and their support for conducting experiments in their processing plants. In addition, we wish to acknowledge the contributions of Wendy Bain, John McEwan and Cameron Craigie from AgResearch for support during the experiments and for comments on the manuscript.
Fig. 4. 3-D PLS score scatter plot of the first three PLS components. (a) The distribution of lamb samples with respect to the concentration of myristic acid (C14:0). (b) The distribution of lamb samples with respect to the concentration of polyunsaturated fatty acids (PUFA). The concentration of the corresponding FA is represented by a colour bar. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Contents lists available at ScienceDirect
Meat Science journal homepage: www.elsevier.com/locate/meatsci
On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy Reddy R. Pullanagari a,⁎, Ian J. Yule a, M. Agnew b a b
Department of Soil and Earth Sciences, Institute of Agriculture and Environment (IAE), Massey University, Palmerston North Private Bag 11-222, New Zealand AgResearch, Ruakura, Private Bag 3123 Hamilton, New Zealand
a r t i c l e
i n f o
Article history: Received 2 May 2014 Received in revised form 3 October 2014 Accepted 7 October 2014 Available online 16 October 2014 Keywords: Vis-NIRS Lamb Fatty acid Chemometrics
a b s t r a c t This study investigated the potential of visible near infrared spectroscopy (Vis-NIRS) to quantify the fatty acid (FA) composition of lamb meat under commercial abattoir conditions. Genetic algorithm based partial least squares (PLS) were used to develop regression models for predicting individual FA and FA groups such as saturated FA (SFA), monounsaturated FA (MUFA) and polyunsaturated FA (PUFA). Overall, the majority of the FA (C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n−6, C18:2 c9 t11 and C18:1 t11), intramuscular fat (IMF) and all FA groups were predicted with an R2CV, the squared correlation between observed and cross validated predicted values, which ranged between 0.60 and 0.74 and ratio prediction to deviation (RPD) values between 1.60 and 2.24. However the results for the remaining FA (C17:1, C18:0, C18:3 n−3, C20:4, C20:5, C22:5, C22:6) were unsatisfactory (R2 = 0.35–0.57, RPD = 0.76–1.49). This indicates that Vis-NIRS could be used as an on-line tool to predict a number of FA. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Lamb meat quality is an important attribute that significantly influences consumer decision to purchase. Lamb meat is considered a healthy component of the human diet because of its nutritional quality with essential vitamins, protein, mineral content and fatty acids (FA). Among the nutritional values, FA composition of lamb meat is a critical trait in determining the perception of meat quality. Moreover, fat tissue firmness and meat shelf life and sensory (tenderness, juiciness and flavour) properties of meat are considerably influenced by FA composition (Wood, Richardson, Nute, et al., 2003; Wood, Enser, Fisher, et al., 2008). Excessive consumption of fat, particularly saturated FA, gives rise to health risks such as cardiovascular diseases (CVDs) (Kontogianni, Panagiotakos, Pitsavos, Chrysohoou, & Stefanadis, 2007), high cholesterol levels and colorectal cancer (Chao, Thun, Connell, et al., 2005). On the other hand, some unsaturated FA are associated with numerous health benefits. For instance, long chain (fatty acids with 13–21 carbons) v n −3 polyunsaturated FA (PUFA) such as eicosapentaenoic acid (EPA, C20:5), docosapentaenoic acid (DPA, C22:5), and docosahexaenoic acid (DHA, C22:6) are recognised for their benefits for heart health, central nervous system, platelet aggregation and thrombotic tendency (McAfee, McSorley, Cuskelly, et al., 2010). Another group of beneficial FA, conjugated linoleic acid (CLA), has anti-carcinogenic properties and induces body fat loss (Hargrave-Barnes, Azain, & Miner, 2008). ⁎ Corresponding author. Tel.: +64 6 356 9099x83240. E-mail addresses: [email protected] (R.R. Pullanagari), [email protected] (I.J. Yule).
http://dx.doi.org/10.1016/j.meatsci.2014.10.008 0309-1740/© 2014 Elsevier Ltd. All rights reserved.
Following the increasing awareness of the importance of a healthy and balanced diet, consumers and human nutritionists need accurate nutritional information particularly on FA composition of meat. This information is not commonly measured or available on individual cuts of lamb in a commercial setting, largely due to the lack of a suitable measurement tool. Under laboratory conditions, FA composition is routinely measured by gas–liquid chromatography which involves time consuming, high labour costs and destructive nature of expensive sampling procedures which can release harmful reagents unsuitable for commercial conditions. Furthermore, the method is unsuited to an on-line application in the meat processing plant. The ability to measure the FA composition (or other desirable properties) of lamb meat on-line would allow the product to be labelled with nutritional/quality information, differentiating it from competitors. Such products could be sold at a premium price adding commercial value. Furthermore, if FA composition was readily measureable on-line, this would complement research addressing manipulation of FA through breeding programme (Prieto, Ross, Navajas, et al., 2011) and diet. NIR (near infrared) is the region in which absorptions corresponding to overtones and combinations of the fundamental vibrational transitions occur (Osborne, 2006). Vis-NIRS has been proven to be a potential non-destructive tool for predicting various lamb meat attributes: tenderness (Chandraratne, Samarasinghe, Kulasiri, & Bickerstaffe, 2006), pH (Kamruzzaman, ElMasry, Sun, & Allen, 2012b), fat, protein and water content (Kamruzzaman, ElMasry, Sun, & Allen, 2012a) and FA composition (Guy, Prache, Thomas, Bauchart, & Andueza, 2011). VisNIRS can also be set up for on-line monitoring of meat attributes which enables industrial applications. Prieto et al. (2011) conducted
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an experiment to evaluate the on-line application of Vis-NIRS to estimate FA composition in crossbred Limousin and Aberdeen Angus beef cattle and their results showed reasonable accuracy (R2 = 0.81–0.87) for predicting FA composition. Few researchers have attempted to predict FA composition of whole meat cuts using Vis-NIRS, and the majority of them used ground meat in a laboratory environment. Thus the performance of Vis-NIRS to predict the FA composition of lamb meat under commercial operating conditions is not well characterized. The objective of this study was to evaluate the use of Vis-NIRS as an on-line tool to predict FA composition of fresh intact lamb meat muscle under commercial operating conditions encountered in a lamb processing plant. 2. Materials and methods 2.1. Lambs A total of 500 lambs sourced from two commercially operated lamb processing plants (250 lambs from each plant), across New Zealand were included in this experiment. All lambs were pasture-fed on commercial farms prior to slaughter and the average carcass weight was 19.5 kg. No measurements were taken on the live animals so ethical approval for this experiment was not deemed necessary. The lambs were of different breeds, sex and age which ensured the wide variability of FA composition. The lambs were electrically stunned, exsanguinated and eviscerated according to the standard commercial dressing protocol (New Zealand food safety authority, 2004). The carcasses were chilled for 24 h at 4 °C immediately after dressing. Then after being moved to the boning room, each carcass was cut into three primals, including forequarter, middle and hindquarter, using robotic cutters and then sent to the cutting lines via conveyor belt. 2.2. Spectral data collection The middle primal was taken for spectral scanning directly and immediately after primal cutting. The posterior surface of the right m. longissimus lumborum was selected for spectral scanning using a high intensity contact probe (ASD Inc., Boulder, CO) connected to a Vis-NIR spectroradiometer (ASD FieldSpec® Pro) via a fibre optic cable. The probe was internally illuminated with a halogen bulb for consistent broad-band illumination, and scans a spot of 10 mm in diameter. The spectral range of the instrument is 350 nm to 2500 nm with a spectral resolution of 3 nm for the region 350–1000 nm and 10 nm for the region 1000–2500 nm. The probe was held perpendicular to a muscle fibre on the fresh cut surface. For each lamb sample, three spectral measurements were taken at one position on the cut surface muscle and then averaged for further analysis. 2.3. Fatty acid composition analysis After the meat sample was scanned, the middle of the carcass was left on the conveyor belt for cuts with its barcode. At the tail end of the boning room, the spectral measured caudal portion of the m. longissimus lumborum muscle (150 g) was collected, vacuum packed and stored for 30 days at −25 °C for FA analysis. This study used a one-step method FA analysis in which extraction and esterification of lipids were combined (Knight, Knowles, Death, et al., 2003). Lipids were extracted from the freeze dried samples by a modified Folch method (Folch, Lees, & Sloane-Stanley, 1957). The freeze-dried lamb samples, with a weight of 200 mg, were ground to a powder using a coffee grinder. The ground samples were placed into Kimax 15 ml tubes. 3 ml of toluene and 300 μl of internal standard (C11 and C23 TAG in chloroform) were added with a volumetric pipette under fume hood. Then 5 ml of 5% methanolic sulphuric acid was added. After adding the solutions the tubes were tightly fitted with caps and incubated at 70 °C for 2 h, the samples were mixed properly at least twice
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during incubation time. The tubes were cooled down to room temperature using cold water bath before 5 ml of saturated NaCl was added and then again centrifuged at 2500 rpm for 10 min to separate solvent layers. The top FAME layer was transferred into 1.5 ml GLC (Gas Liquid Chromatography, Hewlett Packard, model 6890) auto sampler vials. GLC equipped with a SGE BPX70 column of 120 m length, 0.25 mm i.d., and 0.25 μm film thickness and a flame ionisation detector (FID). The peaks were identified and quantified by using internal standard (C23:0) and theoretical FID response factors. The equations for generating the response and conversion factors to quantify individual FA from FAME were obtained from American Oil Chemists' Society (AOAC, 2009). The precision of a laboratory method is usually determined by calculating standard error of the laboratory (SEL) where the samples were analysed in duplicates using standard laboratory procedures. The SEL is calculated according to the following equation (Rosenvold, Micklander, Hansen, et al., 2009): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n X r 2 X u 1 xi j −xi SEL ¼ t nðr−1Þ i¼1 j¼1 where n and r represent the number of samples and number of replicates, respectively, xij denotes the value of ith sample and jth replicate and xi is the average value of ith sample. A lower SEL value is good for model development. 2.4. Data analysis A series of pre-processing steps were carried out on the raw reflectance spectra prior to modelling to improve spectral absorptions and minimise the random noise generated from sampling and instrumental effects. Pre-processing steps include log transformation, smoothing using a Savitzky–Golay filter, derivative transformation and auto scaling. There is no standard pre-processing approach available, hence we considered them based on trial and error. A high level of noise was noticed at the extreme ends of the spectral range which may interfere with the analysis of original spectra and, thus removed from the analysis, finally resulting in a working spectral range of 400–2450 nm. In order to quantify the FA composition using the pre-processed VisNIR spectra, a regression model was developed using the genetic algorithm (GA) based partial least squares (GA-PLS) method. GA-PLS is a sophisticated hybrid approach in which GA is an evolutionary algorithm. It computes to find optimization solutions which can be used for informative wavelength selection (Leardi & Lupiáñez González, 1998) sensitive to individual FA whereas PLS links the GA selected wavelengths with reference values of individual FA. GA-PLS comprises a five step procedure: coding of variables, creation of initial population, evaluation of the responses, cross-over and mutation. GA creates a number of random initial populations of chromosomes from the full dataset that contain a sequence of genes. Chromosomes represent a set of wavelengths while a gene denotes a wavelength. Each chromosome is a binary string in that genes encode the chosen input spectral variables into binary data as zeros and ones. The gene value “1” means the wavelength being selected is informative while “0” implies that the wavelength is not important. All possible best chromosomes were selected and their performance was evaluated with a predefined fitness function generated from the internal productivity of the PLS model between observed and predicted values. GA uses this approach iteratively to search the chromosomes with the highest fitness value (Leardi, 2000). The population of the best fit chromosomes were reproduced in the next generation by the use of cross-over and mutation. In the cross-over, new offspring are produced through the random selection of chromosomes, divided individually, mutually exchanged and merged. Mutation follows the cross-over which allows for the chance of adding or removing the variables. The fitness function of the GA-PLS is determined by root mean square error via leave-one-out cross-validation (RMSECV)
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criterion. Fig. 1 illustrates the steps involved in the GA-PLS model development. The calculation RMSECV formula is described as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðy ^i −yi Þ2 RMSECV ¼ t n i¼1
RMSEP for cross-validation and validation, respectively) and ratio prediction to deviation for cross-validation (RPDCV) and prediction (RPDP). The R2 and RPD are computed using the following equations. Xn
2
2
R ¼ 1− Xi¼1 n
^i Þ ðyi −y
i¼1
where ŷi is the predicted value of the ith sample, yi is the reference measurement value of ith sample, and n is the number of samples in the calibration set. GA selects the important subset of variables (wavelengths) which produces the lowest RMSECV. The best subset of wavelengths was selected for individual FA and used as an input for developing the PLS (partial least squares) model. PLS is a widely accepted multivariate regression modelling approach and effectively deals with multi-collinear data (Wold, Sjöström, & Eriksson, 2001). To avoid over-fitting and optimising the number of factors, leave-one-out cross-validation was performed, and the optimal number of latent variables was determined when the RMSECV was lowest. The analysis was performed using an inhouse programme in MATLAB (R2012a Mathworks Inc., Natick, MA). The total dataset was randomly divided into calibration and validation sets in order to test the prediction performance of the calibration models. In each set, a similar level of range and variation was conserved as it is important to evaluate the models' performance. Half of the samples (n = 250) were used for developing GA-PLS calibration model and the remaining half (n = 250) for validating the calibration model. In the case of IMF content, only 400 samples have been analysed hence n = 200 for calibration and validation were considered. During the PLS model development, the samples that had a standard residual greater than 2.5 were removed as outliers in calibration and validation datasets. The number of outliers was different in each dataset. The model performance was evaluated using coefficient of determination; R2CV for crossvalidation and R2P for validation, root mean square error (RMSECV and
Vis-NIR data
2
ðyi −yÞ
The RPD is the ratio of the standard deviation in reference values of the samples and root mean square error between observed and predicted values. SD ðyÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RPD ¼ sX n ^ −yi Þ2 ðy i¼1 i n where n is number of samples and ŷi and yi are the predicted and measured values of ith sample, respectively. SD(y) is the standard deviation of reference measurement values (y) and y is the average of reference measurement. 3. Results Descriptive statistics values including the mean, minimum, maximum, standard deviation and coefficient of variation of individual FA of corresponding data sets are presented in Table 1. Overall, a wide range of fatty acid values (CV = 9%–48%) were observed in the lamb meat samples (Table 1). Among SFA, myristic acid (C14:0) showed the highest variation (CV = 44%) with a range of 9–142 mg/100 g meat, whilst stearic acid (C18:0) has the lowest variation (CV = 27%). In MUFA group, palmitoleic acid (C16:1) showed high variability (CV = 37%) and vaccenic acid (C18:1 c11) showed lower variability
Reference data
Encoding the Vis-NIR data
Generating initial random population
Making a PLS model and evaluating with fitness function
Mutation and crossover
Update population and evaluate with fitness function
GA selected bands
Reference data
GA-PLS model
Fig. 1. Flow chart for GA-PLS model development.
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Table 1 Statistical summary of individual fatty acids and fatty acid groups (mg/100 g meat) and IMF content (%/100 g) of lamb meat m. longissimus lumborum samples for calibration (n = 250) and validation data (n = 250) sets. Fatty acid
C14:0 (myristic) C16:0 (palmitic) C16:1 (palmitoleic) C17:0 (margaric) C17:1 (heptadecenoic) C18:0 (stearic) C18:1 c9 (oleic) C18:1 c11 (vaccenic) C18:2 n−6 (linoleic) C18:2 c9, t11 (conjugated linoleic) C18:1 t11 (trans vaccenic) C18:3 n−3 (linolenic) C20:4 n−6 (arachidonic) C20:5 n−3 (eicosapentaenoic) C22:5 n−3 (docosapentaenoic) C22:6 n−3 (docosahexaenoic) IMFd SFAe MUFAf PUFAg a b c d e f g
Calibration data
SELc
Validation data
Mean
Range
Stda
CVb (%)
Mean
Range
Std
CV (%)
60.64 482.8 23.70 23.54 11.51 387.4 741.8 17.54 73.89 27.55 75.57 47.52 21.16 22.57 21.01 6.29 8.90 964.7 798.2 297.0
9.1–142.4 165.1–1036 7.2–56.1 7.8–52.9 4.1–24.7 163.3–794.2 264–1570 8.2–28.9 44.5–105.6 5.1–79.7 19.6–197.6 27.7–80.1 14.0–31.5 14.9–31.5 15.63–27.74 3.24–10.87 2.78–18.56 385.39–2031 288.5–1648.4 190.8–547.2
26.73 171.9 8.92 7.66 3.54 107.4 245.3 3.94 11.35 13.43 35.40 9.11 3.00 3.42 2.04 1.35 2.95 306.4 261.7 59.96
44.07 35.60 37.63 32.54 30.75 27.77 33.06 22.46 15.36 48.74 46.84 19.17 14.17 15.15 9.74 21.46 33.14 31.76 32.78 20.19
60.95 484.41 23.82 23.65 11.49 387.03 740.98 17.66 74.02 27.64 75.91 47.49 21.15 22.56 22.34 6.10 8.98 968.45 801.0 296.74
10.2–154.84 170.52–1055 7.8–56.7 9.7–56.9 4.4–23.1 173.6–761.2 269.4–1503.4 8.42–30.7 45.2–107.9 5.70–81.0 20.5–197.09 27.91–79.13 14.39–30.92 15.19–31.51 16.23–26.89 3.38–10.54 3.49–18.54 393.13–2065 289.3–1678.5 191–533.9
26.78 172.74 9.05 7.82 3.46 105.60 244.52 3.95 11.41 13.69 35.64 8.94 2.95 3.38 1.95 1.30 2.84 309.78 263.82 58.92
44.26 35.65 37.99 33.06 30.11 27.28 32.99 22.36 15.41 49.52 46.95 18.82 13.94 14.98 8.81 21.31 31.60 31.98 32.93 19.86
3.8 22.1 1.12 1.48 0.52 19.6 36.8 0.84 2.47 1.76 7.51 1.56 0.50 0.38 0.39 0.17 – 63.2 32.1 8.55
Std: standard deviation. CV: coefficient of variation. SEL: standard error of laboratory. IMF: intramuscular fat (note: n = 200 for calibration and n = 200 for validation). SFA: saturated fatty acids (C14:0, C16:0, C17:0, C18:0). MUFA: monounsaturated fatty acids (C16:1, C17:1, C18:1 c9, C18:1 c11). PUFA: polyunsaturated fatty acids (CLA, C18:1 t11, C18:2 n−6, C18:3 n−3, C20:4, C20:5, C22:5, C22:6).
(CV = 22%). The majority of the PUFA had a variation (CV) between 9% and 21% except for CLA and C18:1 t11 which had 46% ≤ CV. Intramuscular fat content (IMF), groups of FA (SFA and MUFA) have very similar variability (CV = 31%–33%) but PUFA showed lower variability (CV = 20%). GA-PLS based calibration models were constructed to predict individual FA and the results are tabulated in Table 2. The numbers of outliers detected in each calibration and validation models were different. After removing the outliers the remaining number of samples in the model development was presented in Table 2. Unlike a regular PLS model, GA-PLS significantly reduces the number of wavebands by including the most important wavebands that are relevant to individual FA. In this study the relative model performance for individual FA prediction was improved significantly with GA-PLS (R2 = 0.04–0.21) compared to a PLS model alone (Fig. 2). The performance of cross-validated calibration models was externally validated, and similar predictive performance was shown with all models (Table 2). With the essence of testing Vis-NIRS as an on-line tool to predict FA composition, this experiment was conducted under commercial abattoir conditions by direct application on the intact muscle of lamb meat. The majority of the FA (C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n−6, C18:2 c9 t11, C18:1 t11), groups of FA (SFA, MUFA and PUFA) and IMF were predicted with an R2 of 0.60–0.74 and the remaining FA were predicted poorly (R2 = 0.42–0.57). In SFA group, myristic, palmitic and margaric acid models yielded with an R2CV, RMSECV and relative RMSECV (the error after considering the laboratory error) of 0.71, 11.90 and 8.1, 0.72, 85.11 and 63.01, 0.63, 4.60 and 3.12, respectively (RPDCV = 1.66–2.24), whereas stearic acid was poorly determined (R2CV = 0.54, RMSECV = 72.74, relative RMSECV = 53.14, RPDCV = 1.47). The majority of MUFA were predicted with moderate accuracy (R2 = 0.63– 0.74, RPDCV = 1.63–2.00) except for heptadecenoic acid (R2 = 0.57, RPDCV = 1.53). The prediction models for PUFA were relatively poor as compared to other groups of FA but the proportion of linoleic acid and CLA predicted with reasonable accuracy (R2CV = 0.64 and R2CV = 0.70; RMSECV = 5.82 and RMSECV = 6.95, respectively). The
Table 2 Prediction statistics of FA profile (n = 250) and IMF content (n = 200) of lamb meat m. longissimus lumborum samples using genetic algorithm based partial least squares (GA-PLS). Fatty acid lipid number
Number of PLS factors
ncala R2CVb RMSECVc RPDCV nvald R2e P
C14:0 C16:0 C16:1 C17:0 C17:1 C18:0 C18:1 c9 C18:1 c11 C18:2 n−6 C18:2 c9 t11 C18:1 t11 C18:3 n−3 C20:4 C20:5 C22:5 C22:6 IMFg SFAh MUFAi PUFAj
2 5 3 5 4 3 5 2 2
235 242 243 247 248 247 235 240 243
0.71 11.90 0.72 85.11 0.63 5.44 0.63 4.60 0.57 2.30 0.54 72.74 0.74 126.49 0.73 1.97 0.64 5.82
2.24 2.00 1.63 1.66 1.53 1.47 1.93 2.00 1.95
243 244 244 247 247 246 238 239 244
0.70 11.98 0.70 87.01 0.63 5.43 0.60 4.69 0.55 2.32 0.53 73.09 0.69 128.31 0.73 2.01 0.62 5.88
2.23 1.99 1.66 1.66 1.49 1.44 1.90 1.96 1.94
4
241
0.70
6.95
1.93
240
0.68
7.10
1.92
4 2
243 242
0.60 0.55
21.42 6.00
1.65 1.51
243 243
0.61 0.53
21.10 6.11
1.68 1.47
3 3 1 2 5 3 4 5
245 248 241 244 195 245 243 242
0.42 2.22 0.51 2.39 0.45 1.56 0.35 1.61 0.71 1.55 0.61 191.23 0.62 162.74 0.71 28.09
1.35 1.43 1.56 0.83 1.90 1.60 1.61 2.13
246 247 239 245 190 246 242 243
0.40 2.30 0.50 2.41 0.47 1.57 0.32 1.69 0.69 1.60 0.60 192.21 0.60 168.72 0.67 27.86
1.29 1.40 1.24 0.76 1.77 1.61 1.56 2.11
a
RMSEPf RPDP
ncal: number of samples in calibration set. R2CV: coefficient of determination of cross-validated calibration. c RMSECV: root mean square error of cross validated calibration. d nval: number of samples in validation set. e R2P: coefficient of determination of validation. f RMSEP: root mean square error of validation. g IMF: intramuscular fat. h SFA: saturated fatty acids (C14:0, C16:0, C17:0, C18:0). i MUFA: monounsaturated fatty acids (C16:1, C17:1, C18:1 c9, C18:1 c11). j PUFA: polyunsaturated fatty acids (CLA, C18:1 t11, C18:2 n−6, C18:3 n−3, C20:4, C20:5, C22:5, C22:6). b
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0.8 0.7 0.6
R-square
0.5 0.4 0.3 0.2 0.1
PLS GA-PLS
0
Fatty acids Fig. 2. Comparison of prediction results of fatty acids using partial least squares (PLS) and genetic algorithm based partial least squares (GA-PLS).
important wavebands for quantifying individual FA which have an RPD of more than 2 are illustrated in Fig. 3. 4. Discussion Absolute concentration (mg/100 g meat) of individual FA values was used in modelling as it produces more accurate results than expressing as a percentage of total FA (Riovanto, De Marchi, Cassandro, & Penasa, 2012). Overall, wide ranges of FA were noticed (Table 1). This may have emanated from including different breeds of lamb, with different ages, raised in a diverse range of farms, which is useful for obtaining better prediction accuracy and for developing parsimonious and stable models (Kamruzzaman et al., 2012a). The concentration of SFA and MUFA is high compared to PUFA. However, the mean concentrations and variation in individual FA, FA groups and IMF were lower than reported by Guy et al. (2011) as they used pasture-fed, concentrate and hay-fed lambs to establish wide variability. The concentrations of CLA (C18:2 c9 t11) present in a wide range (5.1 to 79.7 mg/100 g meat, CV = 48%) compared to other studies (Fisher, Enser, Richardson, et al., 2000). It is well known that feeding grass based diets significantly increases the content of CLA compared to a concentrate based diet (Daniel, Wynn, Salter, & Buttery, 2004). Similar to the findings of Santos-Silva, Bessa, and Santos-Silva (2002) the meat samples had higher proportions of linolenic acid (C18:3 n − 3) compared to other n − 3 PUFA (eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid) as grass lipids are rich in linolenic acid. The variation in FA composition is likely to have appeared in every stage of the production process. Moreover, it is influenced by genetics (Belo, Almeida, Ribeiro, & Belo, 2005), age, gender, within meat cut, feed type (Rowe, Macedo, Visentainer, Souza, & Matsushita, 1999), and slaughter weight (Tejeda, Peña, & Andrés, 2008). Little research has been conducted to assess the potential of NIRS as an on-line tool to predict the FA profile in lamb. Overall, the prediction performance for individual FA, FA groups and IMF is slightly better (R2CV = 0.35–0.74) than the results of Guy et al. (2011) where they determined FA composition using NIRS performed on slices of intact lamb m. longissimus lumborum and obtained R2CV between 0.05 and 0.53. The myristic, palmitic, oleic, and vaccenic acids were estimated accurately; this is in agreement with the findings of Prieto et al. (2011). CLA was predicted accurately with an R2 of 0.70 (RPDCV = 1.93). A previous study of Prieto et al. (2011) also has reported similar predictions (R2 = 0.71) in Limousin crossbreed beef. The importance of CLA as a FA has been growing
because of its numerous health benefits (Hargrave-Barnes et al., 2008). The PUFA models particularly linolenic, eicosapentaenoic, docosapentaenoic, and docosahexaenoic showed poor predictions. This is probably due to a limited range and low variability (CV = 9– 21%) of FA contents. In addition to the variability, this is also attributed to the inability of NIRS to detect the higher double bonds present in PUFA (Zomeño, Juste, & Hernández, 2012). These results are in accordance with the findings of Sierra, Aldai, Castro, Osoro, Coto-Montes, and Oliván (2008) in beef. GA-PLS based prediction models significantly improved accuracy (Fig. 2) by including the important wavebands and eliminated the irrelevant bands for model building. This might be related to the fact that important bands are less influenced by a variance of unnecessary factors such as temperature and moisture content (Ferrand, Huquet, Barbey, et al., 2011). From these results, Vis-NIRS can be used as an on-line tool for monitoring FA, but there are some key factors that might limit the prediction performance. Below are possible causes that are thought to impact on the predictions: • Fatty acid, C22:6 n−3 mean concentration is relatively low (6.29 mg/ 100 g meat) and this makes it challenging for Vis-NIRS to detect the relevant signals, and negatively impacts final predictions (Prieto et al., 2011). The FA with large contribution can be easily predicted with NIRS compared to those with small contribution (De Marchi, Riovanto, Penasa, & Cassandro, 2012). In a study by Zhou, Wu, Li, Wang, & Zhang (2012) satisfactory prediction performance was obtained for the FA which have a mean concentration over 10 mg/100 g sample. • Meat muscle, in general, is highly heterogeneous in terms of FA composition and the concentration varies spatially across the muscle. VisNIRS scans a small portion of muscle that may not be representative of total FA variation of that muscle. Guy et al. (2011) clearly showed evidence that the predictability of NIRS in ground (homogenised) muscle samples is very high while prediction is low when the predictions are made by direct contact on non-ground samples. Similarly, Kamruzzaman et al. (2012b) stressed that the heterogeneity of the meat samples makes it more difficult to obtain accurate calibration models for predicting chemical parameters. • FA content may vary with breed type (Fisher et al., 2000). In beef, for example, Prieto et al. (2011) estimated FA composition of the m. longissimus lumborum using on-line NIRS under abattoir conditions in two breeds (crossbred Limousin and Aberdeen Angus beef cattle).
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Fig. 3. Important wavebands (bars) selected using genetic algorithm-partial least squares (GA-PLS) across the meat spectrum (background shape). (a) Wavebands for C14:0. (b) Wavebands for C16:0. (c) Wavebands for C18:1 c11. (d) Wavebands for PUFA (poly unsaturated fatty acid).
The prediction accuracy was lower in Aberdeen Angus beef than Limousin; this might be due to genetic differences that create different FA composition concentrations across the muscle. Guy et al. (2011)
attempted to predict CLA of meat from male Limousine lambs using NIRS, but only achieved an R2 of 0.39, RPD = 1.24, compared to R2 of 0.70, RPD = 1.93 in the current study. This indicates that
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Table 3 Fundamental absorption bands in the near infrared region for fats and oils (Hourant et al., 2000). Wavelength (nm)
Molecule
Vibration
1090–1180 1100–1200 1150–1260 1208 1700–1760 2100–2200 2240–2360 2290–2470
\CH2 \CH3 \CH_CH\ C\H \CH2,\CH3, C\H \CH_CH\ \CH3 \CH2
2nd overtone 2nd overtone 2nd overtone Combination 1st overtone Combination Combination Combination
Fig. 4, the 3D scatter plots of the first three PLS component scores corresponding to FA concentrations of C14:0 and PUFA were shown to have high RPD values. As we see in Fig. 4, the lamb samples with respect to the high and low FA concentrations were efficiently discriminated (Fig. 4a and b). Based on the findings of this study and by considering the aforementioned issues, further research needs to be conducted in order to improve the prediction performance for FA profile in lamb using Vis-NIRS. Accounting for meat heterogeneity by accounting for spatial variation within a sample could provide a greater level of detail regarding chemical composition, and distribution of intramuscular fat in lamb meat (Barbin, ElMasry, Sun, & Allen, 2013). 5. Conclusion
genetic difference is likely to limit the adoption of Vis-NIR as an online tool. • The accuracy of reference measures is a known limiting factor to the development of high-accuracy prediction equations (Prieto et al., 2011). These errors may be associated with the efficiency of fat extraction from the freeze-dried powder. In Table 1 the standard error of laboratory (SEL) of individual FA was tabulated which contributes approximately 10–36% of model error (Table 2), and the values are larger than those reported by Guy et al. (2011). NIRS prediction ability may be further improved by improving the efficiency of fat extraction for reference samples. • Most of the FA have similar biochemical structures which make it more demanding to configure individual FA with Vis-NIRS. In this sense, Windham and Morrison (1998) reported that some individual FA have the same CH2 group absorption that leads to analogous spectra and thus limits the prediction accuracy.
Given the limitations which have been identified and the lack of studies in this area, the results of this study explained that the VisNIRS can be used as an on-line tool for predicting FA composition and IMF in lamb meat. The prediction accuracy with an R2CV of 0.60–0.74, (RPDCV = 1.60–2.24) was found for C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n − 6, C18:2 c9 t11, C18:1 t11, SFA, MUFA, PUFA and IMF in lamb meat and less accurate (R2 = 0.35–0.57) prediction model found for C17:1, C18:0, C18:3 n − 3, C20:4, C20:5, C22:5, and C22:6. Even though the prediction accuracies of individual FA were low, Vis-NIRS could still be used as a screening tool. However, there is potential shown by this data that warrants further research to be conducted in order to improve the prediction accuracy of individual FA. Conflict of interest The authors have no conflict of interest to declare.
The spectral absorptions between 1600 and 1800, 2120 and 2170 and 2200 and 2400 nm were strongly determined by fatty acids (Sato, 2011). The important wavebands responsible for individual FA absorptions (Fig. 3) closely corresponded to the fundamental absorptions (Table 3) (Hourant, Baeten, Morales, Meurens, & Aparicio, 2000). The absorptions at around 2100–2200 nm related to the absorptions of FA having cis double bonds (Hourant et al., 2000). Although the prediction models for quantifying individual FA were not adequate, they could be adequate for screening purposes (Williams, 2001). For instance, in
Acknowledgements The authors wish to express their gratitude to the Silver Fern Farms, New Zealand for providing financial assistance and their support for conducting experiments in their processing plants. In addition, we wish to acknowledge the contributions of Wendy Bain, John McEwan and Cameron Craigie from AgResearch for support during the experiments and for comments on the manuscript.
Fig. 4. 3-D PLS score scatter plot of the first three PLS components. (a) The distribution of lamb samples with respect to the concentration of myristic acid (C14:0). (b) The distribution of lamb samples with respect to the concentration of polyunsaturated fatty acids (PUFA). The concentration of the corresponding FA is represented by a colour bar. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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