Lamb racks from commercial carcasses were scanned using near-infrared spectroscopy. The prediction accuracies (R 2) for meat quality traits were assessed. Prediction accuracy ranged between 0.40 and 0.94. When predicted values were used to classify meat based on quality, 88.7%–95.2% of samples were correctly classified as quality guaranteed

Aalhus, Jennifer L.

Dugan, Mer

Galbraith, Jayson

Horcada Ibáñez, Alberto Luis

Hosford, Susan

Juarez, M.

Larsen, Ivy L.

López-Campos, Óscar

Prieto, Nuria

Roberts, Jordan Cyril

Uttaro, Bethany

English

idUS. Depósito de Investigación Universidad de Sevilla

For Review Only      Guaranteeing Canadian lamb meat quality using near infrared spectroscopy on intact rack   Journal: Canadian Journal of Animal Science Manuscript ID CJAS-2017-0106.R1 Manuscript Type: Short Communication Date Submitted by the Author: 15-Sep-2017 Complete List of Authors: Juarez, M.; Lacombe Research Centre, Agriculture and Agri-Food Canada HORCADA, Alberto; Universidad de Sevilla,  Prieto, Nuria Roberts, Jordan; Agriculture and Agri-Food Canada, Lacombe Research Centre Dugan, M.; Lacombe Research Centre, Agriculture and Agri-Food Canada Lopez-Campos, Oscar; Agriculture and Agri-Food Canada, Lacombe Research Centre Uttaro, Bethany; Lacombe Research Centre, Agriculture and Agri-Food Canada Larsen, Ivy; Lacombe Research Centre, Agriculture and Agri-Food Canada Hosford, Susan; Alberta Agriculture and Forestry, Sheep Industry Specialist Galbraith, Jayson; Alberta Agriculture, Food and Rural Development Aalhus, J.; Lacombe Research Centre, Agriculture and Agri-Food Canada Keywords: classification, guaranteed, lamb, NIRS, quality     https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only1  Short communication: Guaranteeing Canadian lamb meat quality using near infrared 1 spectroscopy on intact rack 2  3  4  5 Juárez, M.1*, Horcada, A.2, Prieto, N.1, Roberts, J.C.1, Dugan, M.E.R.1, López-Campos, O.1, 6 Uttaro, B.1, Larsen, I.L.1, Hosford, S.3, Galbraith, J.3 Aalhus, J.L.1 7  8  9  10 1Lacombe Research and Development Centre. Lacombe, AB T4L 1W1 Canada 11 2Departamento Ciencias Agroforestales. Universidad de Sevilla, Seville 41013 Spain 12 3Alberta Agriculture and Rural Development. Livestock and Farm Business Branch. Camrose, 13 AB T4V 0K1 Canada 14  15  16  17 *Corresponding author: manuel.juarez@agr.gc.ca; +1-403-392-5938 18 Page 1 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only2  Abstract 19 Lamb racks from commercial carcasses were scanned using near infrared spectroscopy (NIRS). 20 The predictions accuracies (R2) for meat quality traits were assessed. Prediction accuracy ranged 21 between 0.40 and 0.94. When predicted values were used to classify meat based on quality, 88.7- 22 95.2% of samples were correctly classified as quality-guaranteed.  23 Keywords: classification; guaranteed; lamb; NIRS; quality 24   25 Page 2 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only3  Current classification systems for lambs in Canada focus only on carcass traits. With further 26 processing becoming more important for Canadian packers, rapid and non-destructive tools for 27 guaranteeing minimum quality standards for lamb meat could be adopted. Near infrared 28 spectroscopy (NIRS) has shown ability to predict lamb meat quality, especially chemical traits, 29 such as moisture and intramuscular fat content (Kamruzzaman et al., 2012). For most meat 30 quality attributes, NIRS predictability from whole carcasses or primal cuts is usually lower than 31 those from ground meat (Prieto et al., 2017). However, NIRS technology could be used for on-32 line quality assurance purposes, even for those traits with limited prediction accuracy. The aim of 33 the present study was to evaluate the classification potential of portable NIRS on intact lamb 34 racks as a tool to guarantee the quality of lamb meat in the Canadian market. This technology 35 would allow packers to offer buyers meat with minimum quality attributes guaranteed, what 36 could be used to target niche markets with specific requirements and commercialized added-37 value meat and meat products. 38  39 MATERIAL AND METHODS 40 A total of 155 lamb carcasses, representative of the current variability in the Canadian lamb 41 industry (17-30 kg weight; 5-23 mm backfat thickness; 90-115 cm length), were selected from a 42 federally licensed plant (Innisfail, AB). At the Lacombe Research and Development Centre 43 (Lacombe, AB), intact lean (longissimus thoracis, LT) and backfat from lamb racks were 44 scanned at the level of the 13th rib using a portable LabSpec®4 Standard-Res spectrometer 45 (Analytical Spectral Device-ASD Inc., Boulder, CO, USA) from 350 to 2,500 nm (Vis-NIR 46 range) and a 20 mm ASD fibre-optic high intensity contact probe following 20 min bloom. 47 Page 3 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only4  Spectral data were stored as the logarithm of the reciprocal of reflectance [log (1/R)] in 1 nm 48 steps (2,151 data points).  49 Objective colour measurements from the posterior end of the LT were obtained in duplicate 50 using a Minolta Spectrophotometer Model CM 700D with SpectraMagic™ NX Lite Color Data 51 Software (CM-S100w Version 2; Minolta Canada Inc., Mississauga, ON, Canada) and averaged. 52 A chop removed from the posterior of the LT was ground and the intramuscular fat content was 53 analysed using NMR technology (SMART-Trac System, CEM Corporation Ltd., Matthews, NC, 54 USA). The adjacent chop was grilled to a final internal temperature of 71°C and two 1.9 cm 55 diameter cores were then used to determine peak shear force values (TA-XT Plus Texture 56 Analyzer, Texture Technologies Corp., Scarsdale, NY, USA). Backfat free fatty acid methyl 57 esters were prepared and analyzed as described by Dugan et al. (2007). 58 All statistical analyses were conducted with SAS (v 9.4). Several mathematical transformations 59 were applied to NIR spectra prior to analysis, so the most accurate models could be developed. 60 Partial least squares regressions (PLSR) were run using the transformed and untransformed 61 spectra to determine their relationship with meat quality attributes and fatty acid groups. Internal 62 full leave-one-out cross-validations were performed on the models in order to avoid over-fitting 63 the PLSR equations. The coefficient of determination (R2) from the resulting PLSR models was 64 used to determine the NIRS prediction accuracy. In order to develop a guaranteed quality system,   65 minimum quality thresholds were defined at the 20th percentile of the actual values for each 66 quality trait and fatty acid group (i.e. the value where 80% of samples would meet the minimum 67 quality threshold). For L*, shear force, saturated fatty acids (SFA), and n-6 lower values 68 indicated higher quality, therefore thresholds were reversed so the 20th percentile was defined 69 according to the value where 80% of samples had lower values. The percent of NIRS predicted 70 Page 4 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only5  values correctly classified within (non-minimum quality guaranteed) and outside the 20th 71 percentile (minimum quality guaranteed) were reported for all traits. Increasing the percentage of 72 correctly classified samples meeting the minimum quality threshold can be achieved by using 73 more stringent criteria to classify the predicted values. Therefore, NIRS predicted values were 74 reclassified and retested using two additional thresholds (25th and 30th percentile of actual 75 values). 76  77 RESULTS AND DISCUSSION 78 Average values for quality attributes from lamb meat (Table 1) are within those previously 79 reported for commercial Canadian lamb (Pouliot et al., 2009). The NIRS prediction models for 80 quality attributes on intact lamb rack ranged in accuracy (R2) between 0.40 for shear force and 81 0.94 for hue. These results are in agreement with those from other studies shown in the review 82 by Prieto et al. (2009), where high NIRS predictability was reported for meat chemical 83 composition and colour, but lower for tenderness in several species.  84 In commercial settings, being able to classify primals based on minimum quality requirements 85 might be more useful than predicting specific values. This approach may be especially useful for 86 attributes with lower R2, as it could allow accurate meat quality classifications based on any trait 87 using specific market requirements. When samples were classified according to the predicted 88 values using the 20th percentile quality threshold rank, 88.7-95.2% of samples were correctly 89 classified outside the 20th percentile (minimum quality guaranteed). The percentage of samples 90 correctly classified within the 20th percentile (non-minimum quality guaranteed) ranged between 91 54.8 and 80.6%. Although these thresholds were not based on industry standards, they allowed 92 for assessment of the classification protocols in the present study. In the case of shear force, with 93 Page 5 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only6  a low accuracy for predicting actual values, the percentage of correctly classified samples was 94 still lower than for other quality traits (<90%). In order to successfully guarantee minimum 95 quality, the proportion of correctly identified samples meeting the minimum quality standards 96 needs to be high, with very few samples wrongly classified outside the 20th percentile. Increasing 97 the threshold rank for predicted values up to the 25th and 30th percentiles increased the 98 percentage of samples correctly classified outside the 20th percentile (91.8-98.1%). However, 99 correct classification within the 20th percentile decreased (46.8-61.7%). Therefore, as expected, 100 higher thresholds resulted in fewer samples wrongly classified outside the 20th percentile (high 101 quality) and more samples wrongly classified as within the 20th percentile (low quality).  102 Average fatty acid groups (Table 2) were in the range of those reported for commercial lamb 103 meat from different origins and countries (Juárez et al., 2008). NIRS prediction accuracies (R2) 104 ranged between 0.57 for total monounsaturated (MUFA), and 0.94 for total polyunsaturated 105 (PUFA). In the review by Prieto et al. (2017), a wide range in R2 for NIRS predictions of fatty 106 acid composition in lamb meat was also reported. The percentages of samples correctly classified 107 as meeting the guaranteed quality threshold (outside the 20th percentile) were 88.7% for n-6 and 108 95.2% for MUFA. The percentage of samples not meeting this threshold (within the 20th 109 percentile) ranged between 54.8 for n-6, and 80.6% for MUFA. As observed for meat quality 110 traits, higher thresholds (25th and 30th percentiles) resulted in higher percentages of samples 111 correctly classified as meeting the guaranteed quality threshold (outside the 20th percentile) 112 (90.7-95.2%) and lower percentages of samples correctly classified within the 20th percentile 113 (44.7-61.7%). This suggests that, in order to develop a system that can guarantee minimum 114 quality with a very high level of accuracy, a significant number of samples that do meet the 115 minimum quality threshold would be misclassified. This approach would be acceptable in cases 116 Page 6 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only7  where a specific market demands a minimum quality at a premium price. This approach has been 117 used previously for meat quality assurance systems with other technologies, such as 118 hyperspectral imaging (Naganathan et al. 2015). In the case of Canadian lamb, fatty acid 119 composition is not a common trait for quality differentiation. However, the ability to classify 120 lamb meat with a guaranteed minimum content in fatty acid groups of interest would allow 121 added-value differentiation using current claim regulations (such as "source of n-3": 300 mg of 122 n-3 per serving; CFIA and Health Canada, 2009). 123 In the current study, NIRS has shown potential to guarantee minimum quality in traits such as 124 intramuscular fat, colour, tenderness and fatty acid composition. Results from the proposed 125 NIRS-based classification system show the possibility of manipulating thresholds for meat 126 quality traits and fat composition in Canadian lamb meat to achieve high levels of guaranteed 127 quality required by the industry. However, the thresholds used to classify lamb racks must be 128 balanced between client satisfaction and financial needs to maintain accuracy necessary to 129 guarantee quality but limit high quality samples that are incorrectly classified as not meeting the 130 threshold. Quality classification using NIRS could potentially be developed for other lamb 131 carcass primals; however, further research is required to establish scanning areas and suitable 132 quality traits. 133  134 Acknowledgements 135 This study was supported by funds from the Alberta Livestock and Meat Agency (ALMA). 136  137 Page 7 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only8  CFIA and Health Canada. (2009). Guide to Food Labelling and Advertising. Canadian Food 138 Inspection Agency, http://www.inspection.gc.ca/english/fssa/labeti/guide/toce.shtml. [Last 139 Accessed: July 11, 2017] 140 Dugan, M.E.R., Kramer, J.K.G., Robertson, W.M., Meadus, W.J., Aldai, N., and Rolland, 141 D.C. (2007). Comparing subcutaneous fat in beef and muskox with emphasis on trans-18:1 and 142 conjugated linoleic acids. Lipids 42, 509–518. 143 Juárez, M., Horcada, A., Alcalde, M.J., Valera, M., Mullen, A.M., and Molina, A. (2008). 144 Estimation of factors influencing fatty acid profiles in light lambs. Meat Science, 79, 203-210. 145 Kamruzzaman, M., ElMasry, G., Sun, D.W., and Allen, P. (2012). Non-destructive prediction 146 and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and 147 multivariate regression. Innovative Food Science & Emerging Technologies, 16, 218–226. 148 Naganathan, G.K., Cluff, K., Samal, A., Calkins, C.R., Jones, D.D., Lorenzen, C.L., 149 Subbiah, J. (2015). Hyperspectral imaging of ribeye muscle on hanging beef carcasses for 150 tenderness assessment. Computers and Electronics in Agriculture, 116, 55-64. 151 Pouliot, E., Gariepy, C., Theriault, M., Avezard, C., Fortin, J. and Castonguay, F. W. 152 (2009). Growth performance, carcass traits and meat quality of heavy lambs reared in a warm or 153 cold environment during winter. Can. J. Anim. Sci. 89: 229-239. 154 Prieto, N., Roehe, R., Lavin, P., Batten, G. and Andrés, S. (2009). Application of near 155 infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat 156 Science, 83, 175-186. 157 Page 8 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal ScienceFor Review Only9  Prieto, N., Pawluczyk, O., Dugan, M.E.R. Aalhus, J.L. (2017). A review on the principles and 158 applications of near infrared spectroscopy to characterize meat, fat and meat products. Applied 159 Spectroscopy (In Press) 160 Page 9 of 9https://mc.manuscriptcentral.com/cjas-pubsCanadian Journal of Animal Science

Guaranteeing Canadian lamb meat quality using near-infrared spectroscopy on intact rack

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Guaranteeing Canadian lamb meat quality using near-infrared spectroscopy on intact rack

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