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Near infrared reflectance spectroscopy predicts the content of
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polyunsaturated fatty acids and biohydrogenation products in
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the subcutaneous fat of beef cows fed flaxseed
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Running title Estimation of fatty acid composition in cow subcutaneous fat by NIR
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spectroscopy
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N. Prieto1, M.E.R. Dugan2, O. López-Campos2, T.A. McAllister3, J.L. Aalhus2, B.
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Uttaro2
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1Instituto
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Universidad de León). Finca Marzanas. E-24346 Grulleros, León, Spain.
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Lacombe, Alberta, T4L 1W1, Canada.
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5403, P.O. Box 3000, Lethbridge, Alberta T1J 4B1.
de Ganadería de Montaña (Consejo Superior de Investigaciones Científicas –
Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail,
Lethbridge Research Centre, Agriculture and Agri-Food Canada, 1st Avenue South
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*Corresponding
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ULE). Finca Marzanas. E-24346 Grulleros, León (Spain). Tel +34 987 317 064, Fax
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+34 987 317 161, E-mail: nuria.prieto@eae.csic.es
author: Nuria Prieto. Instituto de Ganadería de Montaña (CSIC–
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Abstract
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This study examined the ability of near infrared reflectance spectroscopy (NIRS) to
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estimate the concentration of polyunsaturated fatty acids and their biohydrogenation
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products in the subcutaneous fat of beef cows fed flaxseed. Subcutaneous fat samples at
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the 12th rib of 62 cows were stored at -80 ºC, thawed, scanned over a NIR spectral range
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from 400 to 2498 nm at 31 ºC and 2 ºC, and subsequently analyzed for fatty acid
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composition. Best NIRS calibrations were with samples at 31 ºC, showing high
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predictability for most of the n-3 (R2: 0.81-0.86; RMSECV: 0.11-1.56 mg. g-1 fat) and
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linolenic acid biohydrogenation products such as conjugated linolenic acids, conjugated
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linoleic acids (CLA), non-CLA dienes and trans-monounsaturated fatty acids with R2
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(RMSECV, mg. g-1 fat) of 0.85-0.85 (0.16-0.37), 0.84-0.90 (0.21-2.58), 0.90 (5.49) and
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0.84-0.90 (4.24-8.83), respectively. NIRS could discriminate 100 % of subcutaneous fat
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samples from beef cows fed diets with and without flaxseed.
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Keywords: near infrared reflectance spectroscopy, subcutaneous fat, fatty acid,
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flaxseed.
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1. Introduction
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Today’s health conscious consumers are interested in fat composition as scientific
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evidence suggests that diets high in saturated fat are associated with increased levels of
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blood total and low density lipoproteins, which are associated with increased risk of
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cardiovascular disease (Webb & O'Neill, 2008). Coronary heart disease is a major
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public health concern, as it accounts for more deaths than any other disease or group of
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diseases (British Heart Foundation, 2006). Thus, a lower saturated fatty acids (SFA) and
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a higher polyunsaturated fatty acids (PUFA) intake, especially of n-3 fatty acids (FA) to
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achieve an appropriate n-6/n-3 ratio (<5:1, World Health Organization, 2003), are
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recommended in order to avoid cardiovascular-type disease. Due to their importance in
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human health, Canadian regulatory authorities have recently approved a food labelling
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claim for foods enriched in n-3 fatty acids at ≥ 300 mg per 100 g serving (CFIA, 2003).
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Hence, development of value-added beef products with enhanced levels of n-3 fatty
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acids could substantially increase the n-3 FA intake of humans. The amount of
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subcutaneous fat and its fatty acid composition in beef are heavily influenced by diet
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(Wood et al., 2008), which also influences the quality of processed products such as
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sausages that are prepared with up to 30% subcutaneous fat. Feeding flaxseed is one
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approach known to increase levels of n-3 FA in pork, poultry, beef and dairy products
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and consumption of these enriched products increases erythrocyte n-3 FA levels in
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humans (Legrand et al., 2010). Flaxseed contains 40% oil and of this 50-60% is
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linolenic acid (18:3n-3, LNA) making flaxseed one of the richest plant sources of n-3
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FA. Furthermore, in ruminants, bacterial biohydrogenation in the rumen can result in
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accumulation of partial hydrogenation products including vaccenic acid (trans (t)11-
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18:1, VA) and rumenic acid (cis (c)9,t11-18:2, RA), both of which have purported
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health benefits (Field, Blewett, Proctor, & Vine, 2009; Park, 2009). Thus, feeding
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flaxseed to cattle may also present opportunities for producing beef products with
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enhanced levels of partial biohydrogenation products of linolenic acid as shown by
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Kronberg, Barcelo-Coblijn, Shin, Lee, & Murphy (2006), Montgomery, Drouillard,
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Nagaraja, Titgemeyer, & Sindt (2008) and Nassu et al. (In Press).
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Quantitative chemical techniques for the comprehensive determination of FA
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involves solvent extraction of total lipids, followed by conversion of fatty acids to their
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methyl esters and then analysis by GC and Ag+-HPLC (Kramer, Hernandez, Cruz-
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Hernandez, Kraft, & Dugan, 2008). This procedure is costly and time-consuming and
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does not lend itself to rapid on-line analysis of fatty acid profiles in meat. On the
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contrary, near infrared reflectance (NIR) spectroscopy is a rapid and non destructive
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method, neither requiring reagents nor producing waste (Osborne, Fearn, & Hindle,
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1993; Prieto, Roehe, Lavín, Batten, & Andrés, 2009a). Because of these advantages,
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this technology is being used for large-scale meat quality evaluation to predict chemical
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composition (Alomar, Gallo, Castañeda, & Fuchslocher, 2003; Prieto, Andrés, Giráldez,
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Mantecón, & Lavín, 2006) as well as physical and sensory characteristics of meat
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(Shackelford, Wheeler, & Koohmaraie, 2005; Andrés et al., 2007; Prieto, Andrés,
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Giráldez, Mantecón, & Lavín., 2008; Prieto et al., 2009b). Regarding FA, their structure
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can produce individual spectral characteristics and therefore are very suitable for
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detection and identification by NIR spectroscopy (González-Martín, González-Pérez,
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Hernández-Méndez, Alvarez-García,
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spectroscopy has been applied to study the FA composition in intact pork (González-
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Martín, González-Pérez, Alvarez-García, & Gónzalez-Cabrera, 2005) and beef loins
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(Prieto et al., 2011), ground beef (Realini, Duckett, & Windham, 2004; Sierra, Aldai,
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Castro, Osoro, Coto-Montes, & Oliván, 2008) and Iberian pig fat (González-Martín,
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González-Pérez, Hernández-Méndez, & Álvarez-García, 2003). Nevertheless, to our
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knowledge, there are no studies testing the ability of this technology to estimate the FA
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composition in the subcutaneous fat of cows, particularly those enriched with linolenic
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acid biohydrogenation products. Hence, this study was conducted to examine the
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potential of NIR spectroscopy to predict the FA composition in intact subcutaneous fat
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samples of beef cows following frozen storage. This work focused on those FA with
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potential health effects, whose content was increased in the subcutaneous fat of beef
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cows when flaxseed was included in the diet.
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2. Material and methods
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2.1. Animals and diets
& Merino
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Lázaro, 2002).
Hence,
NIR
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Sixty-four crossbred (>30 months of age) non-lactating, non-pregnant beef cows
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with body weight averaging 620 ± 62 kg were used. Cows were cared for according to
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Canadian Council on Animal Care guidelines (CCAC, 1993) and fed at the Lethbridge
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Research Centre. Cows were randomly assigned to one of four diets, with four pens of
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four cows per diet. Cows had ad libitum access to feed and water. Diets were designed
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to meet or exceed nutrient requirements for mature cows (Nassu et al., In Press; NRC,
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2000) and consisted of 50:50 forage to concentrate (dry matter basis) and were fed as
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total mixed rations. Diets included hay control, barley silage control, hay plus flaxseed
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and barley silage plus flaxseed. Flaxseed was ground together with barley in a 7:3 ratio
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and flaxseed diets contained 15% flax substituted for dry rolled barley (dry matter
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basis). Diets were fed for 20 weeks. Duringthe study two animals were withdrawn due
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to lameness, one each from the silage and the silage plus flaxseed treatments.
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2.2. Slaughter and sample collection
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Animals were slaughtered at the Lacombe Research Centre. At 24 h post mortem,
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approximately 200 g of subcutaneous fat was removed from the 12th rib and stored at -
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80 ºC for subsequent fatty acid determinations and NIR spectral analysis.
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2.3. Fatty acid analysis
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From the subcutaneous fat collected, five grams were freeze dried and subsampled
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for fatty acid analysis according to Aldai, Dugan, Rolland, and Kramer (2009).
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2.4. Spectra collection
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Subcutaneous fat for NIRS analysis was thawed overnight at +2 ºC. Duplicate intact
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circular fat cores were obtained with the help of a custom-constructed stainless steel
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device (Figure 1a) to enable consolidation of fat and produce fat discs of an appropriate
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diameter (38 mm) and thickness (7 mm) to fit the ring cups of the NIRS machine
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(Figure 1b). Each cold fat disc was placed in a ring cup, all visible air bubbles removed
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by squeezing, and the cup backed with thin black foam (Figure 1c). NIR spectra were
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collected when the subcutaneous fat samples were at 2 ºC, hereafter referred to as “cold
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samples”. Subsequently, the cold samples were placed in open plastic bags and heated
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in a water bath at 35º C. A DuaLogR model 600-1050 (Barnant Company Barrington,
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USA) thermocouple was inserted into the center of each fat sample for temperature
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monitoring during warming. As soon as the core sample reached the target endpoint
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temperature (31º C), samples were immediately removed from the water bath and NIR
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spectra were collected from these “warm samples”. The aim of using two temperatures
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was to know at which point in the slaughter chain NIR could be used on-line. The
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temperature of the warm samples approximates the temperature of subcutaneous fat
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immediately after skinning, and the temperature of the cold sample mirrored that which
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would be obtained after carcasses were stored in a cooler for 24 h. Subcutaneous fat
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sample was scanned 32 times over the range (400-2498 nm) using a NIRSystems
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Versatile Agri Analyzer (SY-3665-II Model 6500, FOSS, Sweden), and spectra
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averaged by the equipment software. Two fat samples per animal were scanned using
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two different cells, and each sample was scanned twice (resulting in four average
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spectra per cow). This approach increased the area of the subcutaneous fat scanned and
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reduced the sampling error (Downey & Hildrum, 2004). The four reflectance spectra of
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each sample were visually examined for consistency and then averaged, with the mean
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spectrum being used to predict the fatty acid content of each subcutaneous fat sample.
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The spectrometer interpolated the data to produce measurements in 2 nm steps, resulting
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in a diffuse reflectance spectrum of 1050 data points. Absorbance data were stored as
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log (1/R), where R is the reflectance. Instrument control and initial spectral
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manipulation were performed with WinISI II software (v1.04a; Infrasoft International,
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Port Matilda, MD).
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2.5. Data analysis
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Calibration and validation of the NIRS data were performed using The
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Unscrambler® program (version 8.5.0, Camo, Trondheim, Norway). The detection of
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anomalous spectra was accomplished using the Mahalanobis distance (H-statistic) to the
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centre of the population, which indicates how different a sample spectrum is from the
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average spectrum of the set (Williams & Norris, 2001). A sample with an H statistic of
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≥ 3.0 standardized units from the mean spectrum was defined as a global H outlier and
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was eliminated from the population. In addition, some samples were removed from the
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initial data set as concentration outliers (T-statistic), which measures how closely the
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reference value matches the predicted value. Hence, the samples whose predicted values
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exceed 2.5 times the standard error of estimation were considered as T statistic outliers
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and excluded from the population. Spectral data were subjected to multiplicative scatter
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correction (MSC; Dhanoa, Lister, Sanderson, & Barnes, 1994) to reduce
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multicolinearity and the effects of baseline shift and curvature on spectra arising from
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scattering effects due to physical effects. First or second order derivatives (Shenk,
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Westerhaus, & Workman, 1992) were applied to the spectra to increase the resolution of
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spectral peaks, and heighten signals related to the chemical composition of
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subcutaneous fat samples (Davies & Grant, 1987). Partial least square regression type I
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(PLSR1) was used for predicting FA concentration using NIR spectra as independent
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variables. Internal full cross-validation was performed to avoid over-fitting the PLSR
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equations. Thus, the optimal number of factors in each equation was determined as the
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number of factors after which the standard error of cross-validation no longer decreased.
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The predictive ability of the PLS calibration models was evaluated in terms of
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coefficient of determination (R2), root mean square error of cross-validation (RMSECV)
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(Westerhaus, Workman, Reeves III, & Mark, 2004) and ratio performance deviation
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(RPD) (Williams, 2001 & 2008). RMSECV and RPD are regarded as measures of
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precision and accuracy of prediction and are defined by:
RMSECV 
1 n  cv
( yi  yi ) 2

n i 1
RPD 
SD
RMSECV
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where n is the number of samples in the calibration set, the yi represents the real
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(measured) responses, the y icv represents the estimated responses obtained via cross-
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validation and SD is the standard deviation of the reference values of the calibration set.
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Williams (2001 & 2008) suggested that the RPD statistic should be equal or larger than
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2, since lower RPD values could be attributed either to a narrow range of the reference
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values (giving a small SD) or to a large error in the estimation (RMSECV) compared to
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SD (Tøgersen, Arnesen, Nielsen, & Hildrum, 2003).
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In order to discriminate among subcutaneous fat samples from beef cows fed
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different diets (hay/barley silage with or without flaxseed supplementation) by NIR
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spectra, discriminant analysis was performed using the dummy regression technique on
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the absorbance data with The Unscrambler® software (version 8.5.0, Camo, Trondheim,
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Norway) (Cozzolino, De Mattos, & Martins, 2002; Cozzolino, & Murray, 2004). The
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subcutaneous fat samples were identified with dummy variables (hay/barley silage = 1,
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hay/barley silage with flax = 2) and PLSR was used to generate a mathematical model
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that was cross-validated (leave one-out) to select the most relevant PLS components.
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According to this equation, a sample was classified as subcutaneous fat belonging to a
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specific category (hay/barley silage or hay/barley silage with flax) if the predicted value
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was within ±0.5 of the dummy value.
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3. Results and discussion
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3.1. Chemical data
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Ranges, means, standard deviations (SD) and coefficients of variation (CV) of
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PUFAs and their biohydrogenation intermediates from subcutaneous fat are summarized
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in Table 1. In general terms, the concentrations of FA in the subcutaneous fat were
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within the normal range of variation reported by other authors in the subcutaneous
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adipose tissue of beef (Noci, Monahan, French, & Moloney, 2005; Dugan, Rolland,
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Aalhus, Aldai, & Kramer, 2008). The results revealed wide variability, which is
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important when searching for calibration equations to be used for predictions. The
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causes of such variability resulted from the different feeding regimes used in the study.
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Hence, the CV were higher than 50% for most of the FA and even higher than 100% for
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C20:3n-3, total conjugated linolenic acids (CLNA), c9,t11,t15-18:3 and c9,t11,c15-
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18:3.
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The n-6:n-3 FA ratio is often used to evaluate the nutritional quality of fat. In this
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study, the n-6:n-3 ratio was 2.6 (Table 1), a value considered suitable according to the
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recommendation of the World Health Organization (<5; 2003).
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Additionally, FA values expressed as mg n-3 FA per 100 g subcutaneous fat were
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calculated to verify if the subcutaneous fat from cows fed the four diets achieved the
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regulatory label claim status for meat products in Canada (≥ 300 mg omega-3 per 100 g
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serving; CFIA, 2003). The n-3 FA content of the subcutaneous fat was 2x (i.e. 600 mg
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per 100 g-1 fat) that required for a label claim and thus would be suitable for producing
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meat products such as sausages and ground beef that satisfy the source claim.
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3.2. Spectral information
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Figure 2a shows the raw spectrum [log (1/R)], averaged over warm and cold
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subcutaneous fat samples. Although the overall absorbance represented by these spectra
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was different for warm and cold samples as a consequence of the temperature, they
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followed the same pattern. In both samples the spectral information showed a series of
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characteristic absorption bands at 1130-1250, 1350-1450, 1720-1760 and 2200-2400
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nm, which are known wavelengths where the C-H bond (fundamental constituent of
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fatty acid molecules) causes different forms of vibration (Murray, 1986; Murray &
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Williams, 1987; Shenk et al., 1992). In addition, there was a peak at 1940 nm which
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corresponds to the absorption of the O-H bond that is related to water content.
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The application of the second-order derivative to the NIR spectra resulted in a
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spectral pattern display of absorption peaks both above and below the baseline (Shenk
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et al., 1992), with enhanced resolution of those signals related to the fatty acid
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composition of the fat (Figure 2b). The derivative decreased the spectral difference due
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to temperature between warm and cold samples, showing a spectral pattern very similar
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for both. Nevertheless, the peaks at 1215, 1725, 1765 and 2310 nm in the second-order
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derivative spectrum, which were located in the same wavelength as in the raw spectra of
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both fat samples but with better definition and inverted, were different in intensity for
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both warm and cold samples. The inverted peaks can be attributed to the absorption by
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the C-H bonds present in fatty acids. In this way, the absorption produced at 1215 and
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2310 nm is attributed at the second overtone of the C-H bond and that at 1725 and 1765
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nm corresponds to the first overtone of this bond (Murray, 1986; Murray & Williams,
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1987; Shenk et al., 1992). Hence, it is possible to predict the FA profiles of
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subcutaneous fat samples based on absorbance of C-H bonds and their different forms
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and degrees of vibration at different wavelengths of NIRS measurements. Thus, all
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information of C-H bond absorbance was combined and equations to estimate the
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content of polyunsaturated fatty acid and biohydrogenation products in subcutaneous fat
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were developed separately for cold and warm samples.
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3.3. Prediction of the fatty acid composition
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After eliminating outliers (which were different for each estimated FA and ranged
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from 0 to 2) and testing different mathematical treatments, the best calibration equations
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for the FA composition of subcutaneous fat samples, using the criteria of maximising
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the coefficient of determination (R2) and minimising the RMSECV, are shown in Tables
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2 and 3, respectively. In relation to mathematical treatments, all the FA were more
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successfully predicted when derivatives with or without previous MSC were applied to
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the spectra, which reduced noise and light scattering effects. This is in agreement with
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the results of others (González-Martín et al., 2002, 2003, 2005; Sierra et al., 2008;
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Prieto et al., 2011) who observed that the use of the MSC or standard normal variance
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and de-trend (SNVD) treatment and/or derivatives generated the NIRS calibrations that
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most accurately predicted the FA content in pig subcutaneous fat, and pork and beef
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meat samples.
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As presented in Table 2 and 3, the prediction equations for total n-6, C18:2n-6 and
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C20:4n-6 in subcutaneous fat samples showed R2 from 0.03 to 0.11 when NIR spectra
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were collected on both warm and cold fat samples, indicating low NIRS predictability.
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Furthermore, the RMSECV (0.09-1.88 mg. g-1 fat) were high when compared to SD,
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thus the RPD were lower than 1.00, deviating substantially from that considered as
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suitable for screening purposes (RPD ≥ 2; Williams, 2001 & 2008). Only for the
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C20:3n-6 was the percentage of variance explained by the model over 59% on both
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warm and cold fat samples (R2 = 0.62 and 0.59, respectively). Nevertheless, the
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RMSECV for C20:3n-6 in warm and cold samples (RMSECV = 0.17 and 0.18 mg. g-1
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fat, respectively) were still high when compared to SD (SD = 0.22 mg. g-1 fat);
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generating RPD values that were not high enough (RPD = 1.29 and 1.22, respectively)
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to suitably predict it. It is well known that the success of this procedure relies partially
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on the variability present in the samples analyzed, which was relatively low among
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samples for these FA (Table 1); limiting prediction via NIRS.
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On the other hand, when the content of total n-3, C18:3n-3 (linolenic acid, LNA)
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and C20:3n-3 were estimated for warm fat samples, the predictability was higher than
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found for n-6 content. In this sense, the R2 (RMSECV) ranged from 0.81 (0.11 mg. g-1
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fat) to 0.86 (1.56 mg. g-1 fat) and the RPD statistics from 1.90 to 2.01, indicating that
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NIRS was more suitable for predicting the presence of these FA. NIRS was less suitable
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for predicting C22:5n-3 as the variance explained by the model was very low (5 %) and
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the RMSECV (0.20 mg. g-1 fat) was higher than the SD (0.18 mg. g-1 fat), generating a
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RPD lower than 1.0. Again, a narrower range of variability for this FA together with a
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low concentration could have negatively influenced the NIRS prediction. When looking
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at the equation predictions performed with the NIR spectra collected on cold samples,
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the accuracy of prediction was lower for n-3, C18:3n-3 and C20:3n-3 (R2 = 0.77-0.80;
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RMSECV = 0.12-1.75 mg. g-1 fat; RPD = 1.76-1.83). During the trial it was observed
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that when the samples were warmed to 31 ºC, the fat which occasionally showed small
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and unremovable air bubbles became free of these bubbles and also became slightly
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translucent. A less homogeneous distribution of fat throughout the cells and more air
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bubbles or reduced molecular vibration due to the cooler temperature could have been
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the reasons for the poorer predictions when using cold samples. Thus, NIR spectroscopy
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showed a higher predictability of estimation for n-3 FA content on intact warm than on
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cold samples. This could be useful for early in-plant identification of beef fat that is
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enriched with these FA. Regarding the n-6/n-3 ratio, the NIRS predictability was low
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when both warm and cold samples were scanned (R2 = 0.71 and 0.74; RMSECV = 0.98
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and 1.07 mg. g-1 fat; RPD = 1.51 and 1.44; respectively).
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Accurate NIRS predictions were found for the total conjugated linolenic acids
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(CLNA) and its two isomers c9,t11,t15-18:3 and c9,t11,c15-18:3, when the NIR spectra
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were collected on both warm and cold fat samples. The coefficients of determination
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were over 0.83 (reaching up to 0.87) and the standard errors of cross-validation were
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low (RMSECV = 0.16-0.37 mg. g-1 fat) compared to SD for these FA. Consequently,
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RPD statistics ranged from 1.90 to 2.05, making them suitable for screening purposes
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(Williams 2001 & 2008). In the same way, total conjugated linoleic acids (CLA) and
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total t,t-CLA and total c,t-CLA could be accurately predicted by NIR spectroscopy
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when spectra from warm fat samples were collected (R2 = 0.87, 0.90 and 0.86;
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RMSECV = 2.58, 0.21 and 2.39 mg. g-1 fat; RPD = 2.12, 2.71 and 2.02; respectively).
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When the NIR spectra were collected on cold samples, the predictability was slightly
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lower (R2 = 0.82, 0.83 and 0.84; RMSECV = 2.79, 0.27 and 2.58 mg. g-1 fat; RPD =
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1.96, 2.11 and 1.90; respectively) although the prediction equations were accurate
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enough to be used for screening purposes. According to De la Torre et al. (2006) and
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Nassu et al. (In Press), these products coming from the LNA biohydrogenation
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preferentially accumulate in intramuscular and back fat when flaxseed combined with
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hay has been fed. In this sense, in the current study NIR spectroscopy was demonstrated
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to be a rapid and accurate approach to estimate their content. Within c,t-CLA isomers,
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c9,t11-CLA (rumenic acid, RA) is typically the most concentrated isomer and widely
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studied. Considered to have beneficial effects on human health (Field et al., 2009), the
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levels of RA were increased in back fat and Longissimus thoracis muscle when feeding
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flaxseed together with hay, in comparison with feeding flaxseed plus silage in those
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tissues (Nassu et al., In Press). The NIRS predictability for the RA content was slightly
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lower than that for total CLA, total t,t- and total c,t-CLA, but the corresponding
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calibration equations still showed high R2 and low RMSECV (R2 = 0.84 and 0.82;
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RMSECV = 2.24 and 2.26 mg. g-1 fat; RPD = 1.90 and 1.89; warm and cold fat spectra,
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respectively) and were deemed appropriate for prediction.
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Regarding the non-CLA dienes, successful prediction byNIR spectroscopy was
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observed when spectra were collected on both warm and cold fat samples (R2 = 0.90
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and 0.90, RMSECV = 5.49 and 5.46 mg. g-1 fat, RPD = 2.39 and 2.40, respectively).
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Nassu et al. (In Press) observed a forage type by flaxseed level interaction indicating a
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preferential accumulation of LNA biohydrogenation products such as the non-CLA
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dienes in backfat when feeding flaxseed combined with hay. The potential health effects
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of many non-CLA dienes are not known, but if flaxseed is to be fed to ruminants at
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elevated levels, it will be important to ascertain if non-CLA dienes have any positive or
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negative effects on human or animal health (Chilliard et al., 2007). NIR spectroscopy
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could provide a rapid estimate of the dienes content of fat.
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In the case of monounsaturated FA (MUFA), content of total trans-MUFA was
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predicted with accuracy when NIR spectra of both warm and cold fat samples were
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collected (R2 = 0.90 and 0.90, RMSECV = 8.83 and 9.13 mg. g-1 fat, RPD = 2.52 and
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2.43; respectively). In contrast, the NIRS predictability for total cis-MUFA content was
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less reliable (R2 = 0.71 and 0.76, RMSECV = 29.84 and 27.15 mg. g-1 fat, RPD = 1.51
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and 1.66; respectively), probably due to lower variability in the sample population (CV
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= 8.6 % vs. 64.4 %, Table 1). Furthermore, NIR spectroscopy was shown to be an
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accurate method to predict the content of (t)11-18:1 (vaccenic acid, VA) (R2 = 0.84 and
337
0.84; RMSECV = 4.24 and 4.42 mg. g-1 fat, RPD = 2.02 and 1.95; warm and cold fat
338
spectra, respectively). As with RA, bacterial biohydrogenation of PUFAs in the rumen
339
can result in accumulation of partial biohydrogenation products among which VA has
340
purported health benefits (Field et al., 2009). Feeding flaxseed may present
14
341
opportunities for producing beef products with enhanced levels of VA and NIR
342
spectroscopy shows good potential to accurately predict VA content.
343
Comparisons among the current study and those in the literature for the prediction of
344
FA in subcutaneous fat by NIR spectroscopy are complicated because of the use of
345
different NIRS equipment, measurement modes, wavelength ranges, sample preparation
346
and FA chemical analysis. Furthermore, it must be emphasised this work was focused
347
only on those FA with potential health effects whose content was increased in the
348
subcutaneous fat of beef cows when flaxseed was included in the diet. Additionally,
349
most researchers test the ability of NIR spectroscopy to predict the FA composition in
350
intramuscular fat, not in subcutaneous fat. A few researchers have used NIR
351
spectroscopy to predict the FA composition in the subcutaneous fat in pigs (González-
352
Martín et al., 2002 & 2003; Pérez-Marín, De Pedro Sanz, Guerrero-Ginel, & Garrido-
353
Varo, 2009; Pérez-Juan et al., 2010), but to our knowledge there are no studies that have
354
evaluated the ability of NIRS to estimate the FA composition of subcutaneous fat in
355
beef. In comparison with pork, the current study shows stronger predictions than those
356
obtained by González-Martín et al. (2002) for C18:1, C18:2 and C18:3 content in the
357
subcutaneous fat of swine when NIR spectra were collected on fat extracted with
358
solvents (R2 = 0.83, 0.77 and 0.59; respectively) or when melted using microwaves (R2
359
= 0.81, 0.69 and 0.40; respectively). In the present study the spectra were collected on
360
intact frozen-thawed subcutaneous fat whereas in the study by González-Martín et al.
361
(2002) the fat underwent significant treatment before spectral collection, which could
362
have negatively influenced the strength of the predictions. Indeed, González-Martín et
363
al. (2003) showed better results when scanning intact the subcutaneous fat of swine for
364
C18:2 (R2 = 0.91), which was similar to the accuracy of the predictions in the current
365
study. Pérez-Juan et al. (2010) found similar results for c9,t11-CLA in subcutaneous fat
15
366
from pigs (R2 = 0.92, RMSECV = 2 mg. g-1 fat) compared to beef subcutaneous fat in
367
the present study. In contrast, in two separate studies Pérez-Juan et al. (2010; R2 = 0.68,
368
RMSECV = 11 mg. g-1 fat, RPD = 1.67) and Pérez-Marín et al. (2009; R2 = 0.39,
369
RMSECV = 4.70 mg. g-1 fat, RPD = 1.3) reported that NIRS more reliably predicted the
370
C18:2n-6 content of subcutaneous fat from pigs than found in the present study for beef.
371
However, these were still not accurate enough to be used for screening purposes. This
372
lack of agreement between studies could be due to differences in the variability of the
373
samples. Indeed, the FA studied in the present work showed a wider range of variation
374
than that found in the previous studies (Pérez-Marín et al., 2009; Pérez-Juan et al.,
375
2010) with subcutaneous fat from swine which likely arose from either the different
376
feeding regimes used in this study or different levels between species (pig vs. cattle, that
377
is monogastric vs. ruminant due to complexity of the rumen environment).
378
In general, the prediction equations for FA composition were more accurate when
379
NIR spectra were collected on intact warm than cold subcutaneous fat samples. This
380
approach would potentially allow NIR spectra to be collected immediately after
381
slaughter when fat is still warm, a very important aspect when considering on-line use
382
of this technology in the abattoir. The NIRS equipment used in this study was a
383
benchtop unit not configured for on-line testing; hence, further studies with equipment
384
provided with a fibre-optic probe are required to assess the on-line implementation of
385
NIR spectroscopy in the abattoir. Under practical conditions where fat samples are
386
scanned fresh the predictability of NIRS predictions are expected to be higher than
387
those using fat whose structure and cell walls may have been affected by the formation
388
of ice crystals of varying sizes during freezing and thawing, since the possible effects
389
arising from the frozen storage would be eliminated.
390
3.4. Discrimination of subcutaneous fat samples from beef cows fed different diets by
16
391
NIR spectroscopy.
392
In order to ascertain whether the NIR spectra collected on warm fat samples could
393
provide useful information to discriminate subcutaneous fat samples from beef cows fed
394
diets with or without flaxseed, the absorbance data matrix (MSC+2D, mathematical
395
treatment that provided better predictions for most FA) was reduced to a coordinate axis
396
system, so each sample was defined by the corresponding scores for each PLS
397
component. When the whole sample set was represented on a XY plane according to the
398
scores for PLS component 1 and PLS component 2, two different clusters were
399
observed (Figure 3) with one cluster on the left representing subcutaneous fat samples
400
derived from beef cows fed hay or silage (hay / barley silage) and the other on the right
401
from cows that were fed these forages along with flaxseed (hay / barley silage flax).
402
Thus, most of the samples belonging to the hay / barley silage group showed negative
403
scores in relation to PLS component 1 whereas those for the samples included in the
404
hay/ barley silage flax group were positive, with sample groupings being related to the
405
degree of similarity in their spectra.
406
With regard to the dummy regression, 5 PLS components were retained in the model
407
since after that the standard error of cross validation no longer meaningfully decreased.
408
The scores corresponding to 5 PLS components could successfully discriminate 100 %
409
of the subcutaneous fat samples according to the diet that the beef cows were fed (hay
410
or barley silage alone or combined with flaxseed) (Figure 4). Statistically significant
411
differences (p < 0.001) in some of the studied FA between the subcutaneous fat samples
412
from beef cows fed diets with and without flaxseed (Nassu et al., In Press) could have
413
provided the basis for successfully classifying the whole sample set according to the
414
spectral data.
415
4. Conclusion
17
416
This study shows that the content of n-3 FA and linolenic biohydrogenation
417
products such as CLNA, CLA, non-CLA dienes and trans-MUFA were predicted with
418
accuracy by means of NIR spectroscopy in the subcutaneous fat of beef cows fed
419
flaxseed. These predictions were better from warm than from cold subcutaneous fat
420
samples what would potentially allow NIR spectra to be collected immediately after
421
slaughter. Additionally, accurate NIRS predictions were found for individual
422
biohydrogenation intermediates including rumenic and vaccenic acids, which have
423
purported health benefits. Furthermore, NIR spectroscopy could discriminate 100 % of
424
subcutaneous fat samples from beef cows fed different diets (hay/ barley silage with or
425
without flaxseed supplementation). Hence, this technology has the potential to quickly
426
and accurately estimate the content of FA of subcutaneous fat from beef cows,
427
particularly when feeding diets with large differences in polyunsaturated fatty acids.
428
Further research will now be required to further validate NIR spectroscopy for fatty acid
429
analyses on-line in the abattoir.
430
5. Acknowledgements
431
The authors wish to thank Lacombe Research Centre operational, processing and
432
technical staff for their dedication and expert assistance. Nuria Prieto has a JAE-Doc
433
contract from the Spanish National Research Council (CSIC) under the programme
434
“Junta para la Ampliación de Estudios”.
435
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25
1
Table 1. Descriptive statistics for fatty acids (mg. g-1 fat tissue) in subcutaneous fat of
2
beef cows (n = 62).
Range
Mean
SD10
CV11 (%)
7.2-15.4
11.5
1.68
14.6
C18:2n-6
6.4-14.4
10.8
1.61
14.9
C20:3n-6
0.1-1.1
0.5
0.22
45.5
C20:4n-6
0.1-0.6
0.2
0.09
39.8
1.3-14.5
6.0
3.09
51.6
C18:3n-3
0.8-13.2
5.4
2.86
53.4
C20:3n-3
0.0-0.7
0.2
0.21
110.0
C22:5n-3
0.1-1.3
0.4
0.18
40.2
0.0-2.4
0.6
0.75
128.0
c9,t11,t15-18:3
0.0-1.5
0.3
0.45
137.6
c9,t11,c15-18:3
0.0-1.1
0.3
0.32
122.8
1.8-24.3
9.6
5.46
56.6
t,t-CLA4
0.2-2.7
0.8
0.57
70.08
c,t-CLA5
1.7-21.3
8.7
4.83
55.3
c9,t11-CLA
1.3-18.5
7.0
4.17
59.9
Non-CLA dienes6
3.7-55.6
17.1
13.10
76.5
cis-MUFA8
411.6-616.4
521.6
44.99
8.6
trans-MUFA9
10.2-103.1
34.5
22.22
64.4
2.8-35.7
12.7
8.54
67.1
1.0-6.8
2.6
1.57
60.5
Fatty acid
PUFA1
n-6
n-3
CLNA2
CLA3
MUFA7
t11-18:1
Ratios
n-6/n-3
3
4
5
6
7
8
9
10
11
12
1
PUFA: polyunsaturated fatty acids; 2CLNA: conjugated linolenic acids; 3CLA: conjugated
linoleic acids; 4t,t-CLA: t12,t14 + t11,t13 + t10,t12 + t9,t11+ t8,t10 + t7,t9 + t6,t8-CLA; 5c,tCLA: t12,c14 + c12,t14+ t11,c13 + c11,t13 + t10,c12 + t8,c10 + t7,c9 + c9,t11 + t9,c11-CLA;
6
Non-CLA dienes: t11,t15-18:2 + c9,t13-/t8,c12-18:2 + t8,c13-18:2 + c9t12-18:2/c16-18:1 +
t9c12-18:2 + t11c15-18:2 + c9c15-18:2 + c12c15-18:2; 7MUFA: monounsaturated fatty acids;
8
cis-MUFA: c9-14:1 + c9-15:1 + c7-16:1 + c9-16:1 + c10-16:1 + c11-16:1 + c13-16:1 + c917:1 + c9-c10-18:1 + c11-18:1 + c12-18:1 + c13-18:1 + c14-18:1 + c15-18:1 + c9-20:1 + c1120:1; 9trans-MUFA: t9-16:1 + t11/t12-16:1 + t6-t8-18:1 + t9-18:1 + t10-18:1 + t11-18:1 + t1218:1 + t13-t14-18:1 + t15-18:1 + t16-18:1; 10SD: standard deviation; 11CV: coefficient of
variation.
26
1
Table 2. Prediction of fatty acid profile in subcutaneous fat of beef cows from NIR
2
spectra collected on warm fat samples (31 ºC).
Mathematical
treatment
T1
R2 2
RMSEC3
RMSECV4
RPD5
MSC6+1D
1
0.07
1.61
1.88
0.89
C18:2n-6
1D7
1
0.06
1.54
1.78
0.91
C20:3n-6
MSC+2D8
6
0.62
0.14
0.17
1.29
C20:4n-6
1D
1
0.11
0.09
0.09
1.00
MSC+2D
5
0.86
1.36
1.56
2.01
C18:3n-3
MSC+2D
6
0.83
1.24
1.50
1.92
C20:3n-3
MSC+2D
4
0.81
0.10
0.11
1.90
C22:5n-3
1D
1
0.05
0.17
0.20
0.90
MSC+2D
6
0.85
0.29
0.37
2.03
c9,t11,t15-18:3
MSC+2D
5
0.85
0.17
0.23
1.96
c9,t11,c15-18:3
MSC+2D
6
0.85
0.12
0.16
2.00
MSC+2D
5
0.87
1.85
2.58
2.12
t,t-CLA
MSC+2D
6
0.90
0.16
0.21
2.71
c,t-CLA
MSC+2D
6
0.86
1.73
2.39
2.02
c9,t11-CLA
MSC+2D
6
0.84
1.67
2.24
1.90
Non-CLA dienes
MSC+2D
5
0.90
4.10
5.49
2.39
cis-MUFA
MSC+2D
6
0.71
23.64
29.84
1.51
trans-MUFA
MSC+2D
5
0.90
6.81
8.83
2.52
t11-18:1
MSC+2D
6
0.84
3.35
4.24
2.02
1D
6
0.71
0.79
0.98
1.51
PUFA
n-6
n-3
CLNA
CLA
MUFA
Ratios
n-6/n-3
3
1
T: number of PLS terms utilized in the calibration equation, 2R2: coefficient of determination of
4
calibration, 3RMSEC: root mean square error of calibration,4RMSECV: root mean square error
5
of cross-validation, 5RPD: ratio performance deviation calculated as SD/RMSECV, 6MSC:
6
multiplicative scatter correction, 71D: first-order derivative, 82D: second-order derivative.
27
1
Table 3. Prediction of fatty acid profile in subcutaneous fat of beef cows from NIR
2
spectra collected on cold fat samples (2 ºC).
Mathematical
treatment
T1
R2 2
RMSEC3
RMSECV4
RPD5
MSC6+2D
1
0.04
1.63
1.71
0.98
C18:2n-6
MSC+2D
1
0.03
1.57
1.64
0.98
C20:3n-6
MSC+2D
4
0.59
0.16
0.18
1.22
C20:4n-6
MSC+2D
1
0.10
0.09
0.09
1.00
MSC+2D
6
0.77
1.54
1.75
1.76
C18:3n-3
1D7
6
0.80
1.29
1.56
1.83
C20:3n-3
1D
6
0.79
0.11
0.12
1.79
C22:5n-3
MSC+2D8
1
0.08
0.17
0.20
0.90
2D
6
0.87
0.27
0.37
2.05
c9,t11,t15-18:3
2D
5
0.83
0.18
0.24
1.90
c9,t11,c15-18:3
2D
6
0.87
0.12
0.16
2.00
MSC+2D
5
0.82
2.26
2.79
1.96
t,t-CLA
MSC+2D
6
0.83
0.21
0.27
2.11
c,t-CLA
MSC+2D
6
0.84
1.92
2.58
1.90
c9,t11-CLA
MSC+2D
5
0.82
1.79
2.26
1.89
Non-CLA dienes
MSC+2D
6
0.90
4.20
5.46
2.40
2D
6
0.76
21.66
27.15
1.66
MSC+2D
5
0.90
7.06
9.13
2.43
2D
6
0.84
3.42
4.42
1.95
6
0.74
0.78
1.07
1.44
PUFA
n-6
n-3
CLNA
CLA
MUFA
cis-MUFA
trans-MUFA
t11-18:1
Ratios
n-6/n-3
1D
3
1
T: number of PLS terms utilized in the calibration equation, 2R2: coefficient of determination of
4
calibration, 3RMSEC: root mean square error of calibration, 4RMSECV: root mean square error
5
of cross-validation, 5RPD: ratio performance deviation calculated as SD/RMSECV, 6MSC:
6
multiplicative scatter correction, 71D: first-order derivative, 82D: second-order derivative.
28
Figure 1.
a) Custom-built device to obtain uniform circular cores of intact subcutaneous fat.
b) i: Backfat is cored, and corer is fitted with a fat-advancement device. ii: The corer is
clamped into the sampling device. Fat is advanced slightly into the sizing chamber to
trim the end of the sample flat. Trimmed material is removed, and fat is fully advanced
into the sizing chamber before sample is cut to the correct thickness (7 mm). iii: The
end of the sizing chamber is opened and fat is further advanced to load it directly into a
ring cup.
i
ii
iii
c) Filled ring cup used for measurement with the NIR apparatus.
29
Figure 2. Average NIR spectra of warm (31 ºC) and cold (2 ºC) subcutaneous fat
samples collected from cows (a) prior to mathematical treatment [Log (1/R)] and (b)
second-order derivative.
a)
2.5
Warm subcutaneous fat
Cold subcutaneous fat
Log (1/R )
2
1.5
1
0.5
Wavelength (nm)
b)
Warm subcutaneous fat
Cold subcutaneous fat
0.3
0.1
-0.2
-0.3
-0.4
-0.5
Wavelength (nm)
30
2414
2308
2202
2096
1672
1566
1460
1354
1248
1142
1036
930
824
718
612
-0.1
506
0
400
Second-order derivative
0.2
2458
2360
2262
1968
1990
2164
1870
1884
2066
1772
1778
1674
1576
1478
1380
1282
1184
1086
988
890
792
694
596
498
400
0
1
Figure 3. Scores corresponding to PLS component 1 and PLS component 2 calculated
2
using the MSC+2D spectra of warm subcutaneous fat samples from beef cows fed
3
different diets (hay/silage with or without flaxseed supplementation).
4
31
5
Figure 4. PLS discriminant analysis using the 5 PLS components of the MSC+2D
6
spectra of warm subcutaneous fat samples from beef cows fed different diets (hay / barley
7
silage with or without flaxseed supplementation).
Discriminant analysis
Predicted dummy value
3
2.5
2
1.5
1
0.5
0
Hay/Silage (dummy value = 1)
32
Hay/SilageFlax (dummy value = 2)
Highlights
> NIR spectra were collected on subcutaneous fat samples at the 12th rib of 62 cows. >
Then, polyunsaturated fatty acids and biohydrogenation products were analysed. > We
found high predictability for most of the n-3, conjugated linolenic acids and CLA. >
Non-CLA dienes and trans-monounsaturated fatty acids were successfully predicted. >
NIR discriminated 100% of subcutaneous fats from cows fed with and without flaxseed.
33