Estimation of freezing storage time and quality changes in hake (Merluccius merluccius, L.) by low field NMR

Food Chemistry 135 (2012) 1626–1634

Contents lists available at SciVerse ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Estimation of freezing storage time and quality changes in hake (Merluccius merluccius, L.) by low field NMR Isabel Sánchez-Alonso a, Iciar Martinez b,c, Javier Sánchez-Valencia d, Mercedes Careche d,⇑ a

Instituto de la Estructura de la Materia (IEM-CSIC), C/Serrano n-121, E-28006 Madrid, Spain Plentzia Marine Research Station, University of the Basque Country UPV-EHU, Leioa, Spain & IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Bizkaia, Spain c Norwegian College of Fishery Science, University of Tromsø, NO-9037 Tromsø, Norway d Instituto de Ciencia y Tecnología de los Alimentos y Nutrición (ICTAN-CSIC), C/José Antonio Novais n-10, E-28040 Madrid, Spain b

a r t i c l e

i n f o

Article history: Received 29 April 2011 Received in revised form 1 February 2012 Accepted 20 June 2012 Available online 28 June 2012 Keywords: Fish muscle Fish quality Hake Merluccius merluccius Freezing storage Authentication Fast methods Low field NMR

a b s t r a c t The potential of low field NMR (LF NMR) as a fast monitoring technique to estimate the quality of hake (Merluccius merluccius) frozen stored at 10 °C for up to 6 months was evaluated. LF NMR clearly detected three populations of water: water strongly bound to macromolecules (T2b), trapped water (T21) and free water (T22). As storage time increased, and concomitant with an increase in the T22 and a decrease in the T21 water populations, the water holding capacity (WHC) and apparent viscosity values decreased and the shear strength increased, reflecting the characteristic loss of juiciness and tougher texture developed by hake during frozen storage. Two mathematical models were constructed: a simple regression using the biexponential analysis of the relaxation times (T21, T22) and amplitudes (A21, A22) and a partial least square regression (PLS) of CONTIN analysis. Both models seemed suitable to estimate the quality of the product. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Freezing storage is not only one of the most efficient means of increasing the shelf life of a product, it also contributes to food safety and indeed it is obligatory by law in the EU that fishery products to be consumed raw or preserved by light treatments such as salting, smoking, drying or marinating, must be frozen at a core temperature of 20 °C or lower for at least 24 h in order to inactivate parasites that might contaminate the product (CD 91/493/EU of 22 July 1991). It is also obligatory by law to inform customers about the shelf-life of the products, the species, origin, method of production and whether the fish is fresh or thawed (RD 1380/2002 of 20 December 2002, CR EC No. 2065/2001 of 22 October 2001 laying down detailed rules for the application of CR EC No. 104/2000 as regards informing consumers about fishery and aquaculture products). Unfortunately, fraud is not uncommon and analytical methods have been developed to authenticate seafoods (Martinez et al., 2003). Most the efforts have been directed to authenticate the species, geographic origin and production method (farmed vs wild) of seafoods (Martinez, 2009; Martinez ⇑ Corresponding author. Tel.: +34 915445607; fax: +34 915493627. E-mail addresses: [email protected] (I. Sánchez-Alonso), iciarm@ iim.csic.es (I. Martinez), [email protected] (M. Careche). 0308-8146/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2012.06.038

et al., 2003) but there remains a clear need to develop methods to estimate freezing conditions and the shelf life of frozen products (Jørgensen et al., 2003). Freezing and frozen storage may alter the organoleptic properties of the product especially during prolonged storage times or high temperatures and involve a drip loss rich in bioactive compounds (Martinez et al., 2005). In gadiforms, as hake, frozen thawed products become tougher and dryer, and these sensory properties are reflected by lower water holding capacity (WHC), extractability and solubility of myofibrillar proteins, and apparent viscosity, together with an increase in the shear strength (Barroso, Careche, Barrios, & Borderias, 1998; Haard, 1992; Herrero, Carmona, & Careche, 2004). Morphological studies have shown changes in myofiber organisation: myofibers become more compact and break often, in addition to acquiring irregular forms due to the pressure exerted by ice crystals that induce the formation of spaces among the myofibers which in turn exert a gradual compression of the sarcoplasmic reticulum (Howgate, 1979; Shenouda, 1980). The changes in the functional properties of frozen fish muscle have been attributed to conformational transitions of muscle proteins, mainly actin an myosin, that lead to protein aggregation, involving hydrophobic interactions, hydrogen bridges and the formation of covalent, non-disulphide bonds as well as to changes in the structure of the water, and/or alterations in the protein–water

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

interactions, together with a transfer of water to larger spatial domains (del Mazo, Torrejon, Careche, & Tejada, 1999; Haard, 1992; Herrero, Carmona, Garcia, Solas, & Careche, 2005; Herrero et al., 2004; Tejada, 2001). Herrero et al. (2005) studied the role of the water pools, their structure and mobility on the properties of frozen stored hake muscle by Raman spectroscopy: the behaviour of a strong band at 160 cm1 seemed to be related to conformational transitions of muscle proteins, to changes in the structure of the water and/or to alterations in protein–water interactions. In the same work these authors (Herrero et al., 2005) also noticed changes in the ms(OH) band, considered to be due to a transfer of water to larger spatial domains during the frozen storage period. It has been attempted to develop methods that may help to predict frozen storage time of fish products at a given storage temperature. One such method is the Quality Index Method (QIM) (Herrero, Huidobro, & Careche, 2003) based on sensory inspection. Other approaches use analytical techniques; for example Herrero and Careche (2006) constructed a model based on multiple linear regression analysis of combinations of apparent viscosity of muscle homogenates, puncture, and Kramer shear resistance tests to predict the storage time of hake at 20 °C. However, these and other procedures involve labour intensive and/or destructive methods. It would be highly desirable to develop a faster method, with the potential to be used on line, requiring minimal or no sample manipulation and no specialized personnel. Low field nuclear magnetic resonance (LF NMR) seemed a good candidate to fulfill this need because, as many spectroscopic techniques, it is non-destructive and non-invasive, it requires only minimal or no sample preparation and offers the possibility to produce characteristic fingerprints for a given sample. Moreover, it is known that water structure and water mobility are relevant parameters that vary during freezing, frozen storage, and thawing, and the T2 transversal relaxation times measured by LF NMR have been shown to detect the presence of several populations of water in muscle tissue (Finch, Harmon, & Muller, 1971). Thus, LF NMR, that already has been used to detect changes in the relaxation times of water molecules apparently related to frozen storage conditions in cod (Jepsen, Pedersen, & Engelsen, 1999; Lambelet, Renevey, Kaabi, & Raemy, 1995), offered good prospects in our intent to identify a technique suitable as a potential predictor of quality changes in muscle products, and indeed, multi-way chemometric analysis of LF NMR relaxation values allowed the identification of several water pools in herring muscle that varied according to the lipid content, season and year of the catch and spawning type, and also kept a relationship with the sensory quality of marinated products made from the analysed herring (Jensen, Jorgensen, Nielsen, & Nielsen, 2005). The aim of the present work was to establish whether LF NMR could be used to predict changes in the quality of frozen stored hake and/or estimate its shelf life. Since it is known that the rate of deterioration keeps a close relationship with the storage temperature, a temperature of 10 °C was chosen because it would permit to test our hypothesis in a relatively short time, i.e., about 6 months, rather than the 2 or more years that would have been necessary to observe similar quality changes at the commercially used temperature of 20 °C or 30 °C.

1627

visually inspected and their length and weight were measured (average 57.49 ± 2.02 cm and 1.75 ± 0.13 kg). The fish were then filleted and finally washed with iced water to remove blood, remains of viscera, etc. The temperature was always kept under 6 °C. The fillets were frozen in a blast freezer (Frigoscandia, Aga Frigoscandia, Freezer Division, Helsingborg, Sweden) for about 90 min, when the fish reached a core temperature of 40 °C. The right and left fillets were packed separately in Cryobac BB3050 plastic bags, labelled and sealed with no vacuum. A control group was also made with the fillets from 9 frozen fish that were stored at 80 °C and analysed after 2, 10 and 25 weeks of storage. The experimental group consisted of the fillets from the other 36 fish and were analysed every second week until week 23. For apparent viscosity measurements, the right fillets were thawed overnight in a cold room at 4 °C prior to analysis and the left fillets were kept frozen until use in the rest of the experimental techniques. Three individual fish were analysed each sampling date. 2.2. Elemental composition analysis The water, ash and protein contents were determined according to the AOAC (1995) methods. The water content was measured by drying 5 g of sample at 100 °C for 24 h until a constant weigh of dry matter was reached. Protein determination was done by the Dumas combustion method in a Leco CNS 2000 instrument (St. Joseph, MI, USA). For the protein content a conversion factor of 6.25 (nitrogen to protein) was used. The fat content was measured by the Bligh and Dyer method (1959). The ash content was measured by first incinerating 5 g of sample and then drying them at 500 °C for further 24 h. The pH was measured using a Thermo Orion 3star (Thermo Scientific, Environmental Instruments, Beverly, USA). The results are expressed as gram of water, ash, protein or fat, respectively, per 100 g wet muscle. All analyses were performed in n = 6 for each fish. 2.3. Water holding capacity (WHC) The method of Roussel and Cheftel (1990) was used with the following modifications: 3 g of frozen sample from the dorsal part of fillet were introduced in 50 mL Falcon tubes along with enough filter paper (2 filter Whatman No. 1, 110 mm diameter) and allowed to thaw. After centrifugation for 15 min at 3000g and 20 °C the filters were weighted and the water absorbed by the paper (the water lost from the sample) was calculated. The WHC was expressed as % of water retained by the sample after centrifugation. Analyses were performed in n = 3 per fish. 2.4. Apparent viscosity

2. Materials and methods

It was determined according to Barroso, Careche, Barrios, et al. (1998) in a homogenate of thawed muscle in 50 mM phosphate buffer, pH 7.0, containing 5% NaCl. The whole procedure was performed in an ice bath. Measurements were made at 12 rpm with an RV3 or RV4 spindle (depending on the viscosity of the homogenate), using a Brookfield model DV-III rotary viscometer (Stoughton, MA) and the Rheocalc V 1.2 software system. Measurements were carried out in n = 2 per fish and results were expressed in centipoises (cP).

2.1. Fish samples

2.5. Shear resistance

Forty-five hake (Merluccius merluccius) captured in the Northeast Atlantic (FAO zone 27) were transported in perforated polystyrene boxes with ice from the port of Celeiro (Galicia, Spain) to our laboratory in Madrid. Upon arrival to our lab, the fish were

Shear resistance was determined according to Kramer, Burkardt, and Rogers (1951). 5  1  1 cm portions were cut from frozen fillets, wrapped in aluminium paper and let to thaw in an ice bath. Two parallelepipeds were placed inside the Kramer cell,

1628

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

centred and few mm apart from each other. Determinations were performed with a Kramer shear cell of five blades (model HDP/ KS5) connected to a TA-X T2i Texture Analyzer (Stable Micro Systems, Ltd., Godalming, Surrey, UK). A load cell of 25 kg was used and the crosshead speed was 2.0 mm/s. The software Texture Expert, Stable Micro Systems Ltd., version 1.22 was used. Results were expressed as maximum load per gram of sample (N/g) and n = 5 determinations were performed per individual.

measured as a function of the storage time. Linear regression analysis was performed on the data obtained by biexponential fitting, and principal component analysis (PCA) and partial least regression (PLS) analyses on the CONTIN data, where storage time was chosen as Y-variable. Correlations between LF NMR parameters, and between these and WHC, shear resistance or apparent viscosity were performed. The software used were Unscrambler v 9.6 (CAMO Software AS, Oslo, Norway) and/or IBM SPSS statistics v 19 (IBM Corporation, NY).

2.6. Low field NMR analysis (LF NMR) It was performed according to Aursand, Gallart-Jornet, Erikson, Axelson, and Rustad (2008) as follows. Portions of 1  1  3 cm (about 3 g) were cut from the dorsal part of frozen fillets. The samples were placed in NMR tubes (1.8 cm diameter and 18 cm high) in an ice bath and let to thaw. Prior to analysis the samples were placed in a thermostated water bath for about 10 min until they were equilibrated to 4 °C. T2 relaxation measurements were performed using a LF NMR analyzer minispec mq 20 (Bruker Optik GmbH, Ettlingen, Germany) with a magnetic field strength of 0.47 T corresponding to a proton resonance frequency of 20 MHz. T2 was measured using the Carr-Purcell-Meiboom-Gill pulse sequence (CPMG) (Carr & Purcell, 1954; Meiboom & Gill, 1958). The T2 measurements were performed with a time delay between the 90° and 180° pulses (s) of 150 ls. For each measurement 16 scans were performed with 3000 echoes. The repetition time between two succeeding scans was set to 2 s. At least three measurements per fish were performed. In general, a T2 relaxation curve from a sample containing N relaxation populations can be mathematically described by a sum of some (N) discrete exponential functions as follows:

NMR signal ¼

N X

An et=T2;n

ð1Þ

n¼1

Where N is the number of exponential components, An is the amplitude of the n-th relaxation component present in the curve and T2,n is its corresponding relaxation time (Andersen & Rinnan, 2002). Different approaches can be applied to analyse the NMR relaxation times, the most common being the direct analytical approach (exponential decay functions) and/or the inverse Laplace transformation (ILT, distribution of relaxation times). By the first method the transverse relaxation curves were fitted to a bi-exponential mathematical model as shown in Eq. (2) (Erikson, Veliyulin, Singstad, & Aursand, 2004; Lambelet et al., 1995), using the software Bruker-the minispec v 1.2.

NMR signal ¼ A21 et=T 21 þ A22 et=T 22

ð2Þ

Where T21 and T22 were, respectively, the shorter (10–100 ms) and longer (100–400 ms) transversal relaxation components; A21 and A22 were the corresponding amplitudes and t is the acquisition time. In the second method the relaxation data are treated with an ILT algorithm, the CONTIN analysis, resulting in the corresponding distributions of the relaxation times from the multiexponential decay curve (Provencher, 1982a, 1982b). The CONTIN analysis is also part of the software provided with the equipment (CONTIN – the minispec – v 1.2). 2.7. Data analysis The average and standard deviation of the data corresponding to the elemental composition analysis, WHC, apparent viscosity, and shear resistance were calculated and one-factor analysis of variance (ANOVA) was performed for each of the parameters

2.8. Certification UNE-EN ISO 9001 The Institute of Food Science and Nutrition (ICTAN) is certified since 2008 under UNE-EN ISO 9001 with scope ‘‘Management and execution of research projects and contracts in the area of Food Science and Technology and Nutrition’’ (Certificate No. ER-0366/ 2008).

3. Results and discussion The elemental compositional analysis was similar to that previously reported in hake (Careche & Tejada, 1990), i.e. 79.8 ± 0.2% water, 20.0 ± 0.4% protein; 0.8 ± 0.2% fat and 1.28 ± 0.1 ash. These values did not suffer significant alterations during the frozen storage period (p > 0.05). 3.1. Functional properties and shear resistance values No significant differences (p > 0.05) were detected in any of the parameters measured (i.e., WHC, apparent viscosity, Kramer shear stress or proton relaxation by LF NMR) (1) between the fish of the control group stored at 80 °C and the fish stored at 10 °C after 1 week of storage nor (2) among the fish stored at 80 °C after 1, 10 or 25 weeks. The data of these controls are not shown since they were similar to those of the experimental group after 1 week of storage described below. In accordance with Herrero et al. (2005), the WHC decreased with storage time at 10 °C. It dropped from approximately 60% after 1 week of frozen storage to about 43% at the end of the experiment (Fig. 1a). The WHC of a sample keeps a relationship with its sensory quality (Jensen & Jorgensen, 1997) and its loss has been related to changes in the water–protein interactions (Offer & Knight, 1988). We have previously reported changes in the secondary structure of proteins and exposure of aliphatic groups in frozen hake muscle (Careche, Herrero, Rodríguez-Casado, del Mazo, & Carmona, 1999; Herrero et al., 2004), as well as changes in the 160 cm1 band of the Raman spectrum attributed also to changes in the water–protein interactions. Transmission electron microscopy (TEM) results and evolution of the ms(OH) Raman band during storage time further suggested changes in the structure and mobility of water (Herrero et al., 2005). A decrease in apparent viscosity has been considered to be a consequence of the denaturation and aggregation of myofibrillar proteins (Herrero et al., 2005). The present results were, as expected, consistent with previous works (Careche, del Mazo, & Fernandez-Martin, 2002; Herrero et al., 2004) and show a significant decrease (p < 0.05) in this parameter throughout frozen storage at 10 °C (Fig. 1b) from approximately 8000 cp in after 1 week of frozen storage to about 1000 cp whereas no significant differences were observed with storage time at 80 °C (total average 8853 cP, standard deviation 1165 cP). The decrease in apparent viscosity was more pronounced during the first 14 weeks when it reached values below to 1000 cP which is considered the threshold for acceptance in frozen hake.

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

1629

3.2. Study of the water populations by LF NMR analysis

Fig. 1. Variation in the mean ± standard error values of (a) water holding capacity, (b) apparent viscosity and (c) Kramer shear stress of hake fillets during the 23 weeks’ storage at 10 °C.

Also the changes of Kramer shear resistance were similar to those previously reported by Herrero et al. (2005), showing an increase with storage time from about 3.2 to 11.1 N/g after 23 weeks of frozen storage at 10 °C (Fig. 1c). In general higher values are usually obtained after frozen storage, particularly if frozen stored at high temperatures such as 10 °C (Barroso, Careche, & Borderías, 1998; Careche & Barroso, 2009). In species such as hake and cod the increase in shear resistance has been attributed to an observed reduction in intermyofibrillar space (Herrero et al., 2005) and a combination of the values of apparent viscosity and shear resistance has already been used by our group to elaborate a model to estimate shelf life of frozen hake (Herrero & Careche, 2006).

Biexponential analysis of the T2 NMR curve forces the data to be adjusted to two relaxation times. The software provided with the equipment did not allow the adjustment to three exponentials, which would be necessary to detect an additional water population. CONTIN analysis, on the other hand, and in agreement with previous works (Aursand et al., 2008; Belton, Jackson, & Packer, 1972) did reveal the presence of three water populations (Fig. 2a). The population detected only by CONTIN analysis corresponded to that with the shortest relaxation time, T2b, between 1 and 10 ms (Fig. 2a). It represents about 1–4% of the total water in the system and has been considered to be water tightly bound to macromolecules (Aursand et al., 2008; Belton et al., 1972). The population with an intermediate relaxation time, 30–60 ms (generally 10–100 ms) called T21 (Fig. 2a), is considered to be water associated to, or trapped within, highly organised structures, such as water bound to the tertiary or quaternary protein structures or located in spaces with high density of myofibrils, including actin and myosin filaments. Finally the third population, T22, with the longest relaxation time, 100–400 ms (Fig. 2a), corresponds to extra-myofibrillar water (Aursand et al., 2008; Belton et al., 1972). With increasing storage time, both the value and amplitude of T21 decreased while T22 and its amplitude increased (Fig. 2a). This is more evident in Fig. 2b, which represents the differences in the CONTIN spectra calculated by subtracting to the spectra obtained after each sampling week the spectrum obtained after 1 week of frozen storage: it is possible to observe a progressive decrease in the water population with a relaxation time close to 55 ms and an increase in the amount of water populations with both lower (25–32 ms) and higher (115–253 ms) relaxation times, with a widening in the profiles of the latter pool. The changes in the amplitudes of both water populations (A21 and A22) were more pronounced during the first 14 weeks of storage (Figs. 2 and 3). The changes in A21 were more prominent than A22 and even more so when estimated by CONTIN analysis (Fig. 3b). There was a high (0.91 6 R2 6 0.95) and significant correlation (p < 0.01) between the values of T21, A21 and A22 estimated by biexponential and CONTIN methods, but not between the T22 values (R2 = 0.03, n.s.). Although both data treatment analyses showed similar evolution of the parameters A22 and T21, there were differences in the values of A21 detected by both methods: CONTIN analysis revealed a larger decrease in A21 while biexponential analysis only detected a slight but continuous decrease in this parameter (Fig. 3b). Nevertheless, both methods are included since both of them can help to interpret the distribution of water in the muscle, and the time constants of each population (T values) as well as their relative abundance (A values) are considered in both cases to be apparent. Although CONTIN provides a more intuitive interpretation of the changes in the water structure, biexponential analysis is, on the hand, useful to compare our results with other works where the authors choose to use this data treatment. We have not detected free water, with a well known relaxation time close to 2 s, but we have occasionally detected a small population with relaxation times close to 1000 ms (data not shown) that presumably correspond to water less bound than intermyofibrillar or extracellular water. This may explain in part the differences in the evolution between the T22 measured by both methods since biexponential fitting would have forced this additional water population to be part of the T22 population. The values of T21 and T22 as well as their respective populations A21 and A22, are within the range observed in frozen cod mince by Steen and Lambelet (1997), even though the present work showed less variation during the storage period. More drastic variation can be attributed to the fact that minced muscle deteriorates faster

1630

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

Fig. 2. Panel (a) shows the distributions of the relaxation times after CONTIN analysis of LF NMR spectra of hake muscle upon frozen storage from up to 23 weeks. The numbers to the right represent the number of weeks stored at 10 °C. T2b, T21 and T22 are relaxation times referred to in the text. Panel (b) shows the difference LF NMR spectra calculated by substracting the 1 week average spectrum to those spectra corresponding to fillets stored for 3 (solid line), 5 (stripped line), 14 (dotted line), and 20 (stripped and doted line) weeks. Each line represents the average spectrum of the 9 spectra per sampling time (three measurements per fish on three fish).

Fig. 3. Evolution during 10 °C frozen storage of (a) relaxation times T21 (N) and T22 (d) and b) amplitudes A21 (N) and A22 (d). Dotted lines correspond to the biexponential analysis and continuous lines to the CONTIN analysis of the proton relaxation times. Each point represents average values of at least 9 values (three measurements per fish on three fish).

than intact fillet but also to species and experimental differences between both works. TEM of frozen hake fillets stored for up to 40 weeks at 10 °C by Herrero et al. (2005) showed a progressive reduction in the intermyofibrillar space that ended up with the disappearance of the sarcoplasmic reticulum at the end of the storage period while there was an increase in the heterogeneity (both in size and shape) of the interfiber space, which was taken up by large ice crystals. According to the interpretation for the origin of the T2 signals from muscle proposed by Bertram et al. (2001), the translocation of water to larger spatial domains shown by TEM would contribute to T22, but LF NMR fails to give the subtle information regarding the physical location of these two water populations that TEM provides. Since we have used here the same experimental conditions described by Herrero et al. (2005), a comparison between both works is feasible. The signal of the population with the longest relaxation time (A22) not only increases with storage, but also shifts to higher values (T22), in accordance with the literature (Steen and Lambelet 1997). This is clearly shown by biexponential analysis (Figs. 2 and 3) and it is compatible with a larger proportion of free water within the reservoir of intermyofibrillar or extracellular water. It is therefore possible to infer the presence of a larger proportion of extracellular water with respect to intermyofibrilar water. This is also supported by the CONTIN profile of hake fillets stored for 23 weeks (Fig. 2), where it is possible to detect a maximum and a shoulder in the T22 region, suggesting two

subpopulations within the same band. In addition, the results from the TEM study (Herrero et al., 2005) showed a gradual decrease of the intramyofibrilar space with fusion of myosin and actin filaments, observation that is compatible and would help to explain the decrease in the T21 population observed in this work. 3.3. Relationship between LF NMR analysis, WHC and shear stress Renou, Monin, and Sellier (1985) were the first to show that there was a relationship between T1 and T2 and traditional quality characteristics of pork meat, and later studies have confirmed a relationship between WHC, drip loss and LF NMR measurements. In particular biexponential values kept a significant correlation (p < 0.10, see Table 1) with WHC measurements and the values are in accordance with those (0.6 6 R 6 0.8) reported by other authors (Brown et al., 2000; Tornberg, Andersson, Goransson, & von Seth, 1993). These low correlation values are considered to be acceptable given the high variability known to exist in the WHC measurements (Bertram & Andersen, 2006). Bertram, Dønstrup, Karlsson, and Andersen (2002) found an increase in the water population with larger relaxation times (i.e. higher A22 values) with increasing drip loss in pork, and suggested that the reason for the correlation between WHC and T2 relaxation values was due to the fact that the water lost from the muscle is directly related to the amplitude of the water component that has a longer relaxation time (A22). In order to test this hypothesis

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634 Table 1 Linear regression analyses between the relaxation times and amplitudes of the populations of water determined by both biexponential and CONTIN analyses and WHC, apparent viscosity and Kramer shear stress. R, regression coefficient; p, significance level; ns, no significant. R

R2

p

Correlation with WHC Biexponential T21 A21 Biexponential T22 Biexponential A22 Biexponential T21 CONTIN A21 CONTIN T22 CONTIN A22 CONTIN

0.58 0.57 0.60 0.52 0.49 0.50 0.17 0.60

0.34 0.32 0.37 0.28 0.24 0.25 0.03 0.36

p < 0.10 p < 0.10 p < 0.05 p < 0.10 ns ns ns p < 0.05

Correlation with apparent viscosity Biexponential T21 A21 Biexponential T22 Biexponential A22 Biexponential T21 CONTIN A21 CONTIN T22 CONTIN A22 CONTIN

0.90 0.92 0.73 0.89 0.83 0.89 0.27 0.83

0.80 0.85 0.54 0.79 0.69 0.80 0.07 0.69

p < 0.01 p < 0.01 p < 0.05 p < 0.01 p < 0.01 p < 0.01 ns p < 0.01

Correlation with Kramer shear stress T21 Biexponential A21 Biexponential T22 Biexponential A22 Biexponential T21 CONTIN A21 CONTIN T22 CONTIN A22 CONTIN

0.94 0.93 0.89 0.82 0.89 0.83 0.89 0.73

0.88 0.87 0.79 0.67 0.80 0.69 0.79 0.54

p < 0.01 p < 0.01 p < 0.01 p < 0.05 p < 0.01 p < 0.01 p < 0.01 p < 0.05

T2 parameters

Analysis

with frozen hake we decided to perform an additional test not included in the initial experimental design: a sample of hake that had been frozen at 10 °C for 40 weeks was measured by LF NMR before and after being centrifuged under the same conditions used to measure the WHC (3000 g for 15 min). Fig. 4 shows that after centrifugation, the T22 population almost disappeared, thus supporting Bertram et al. (2002) hypothesis. Nevertheless it cannot be discarded that some losses of the water population corresponding to T21 in the centrifuged samples may occur. Additional works are necessary to elucidate this point.

Fig. 4. Distributions of the relaxation times after CONTIN analysis of LF NMR spectra of hake muscle stored for 40 weeks. Spectra obtained before (dotted line) and after (solid line) centrifugation at 3000g  15 min.

1631

Good correlations between apparent viscosity and LF NMR parameters were registered (0.54 P R2 P 0.85) that were significant except in the case of T22 estimated by CONTIN, while Kramer shear stress values showed similar correlation values (0.54 P R2 P 0.88) that were always significant regardless of whether biexponential or CONTIN analysis was used (Table 1). Shear stress is considered to be a good indicator for frozen storage quality loss and good correlations have been found between T22 and sensory assessed toughness and shear stress in minced frozen stored cod (Steen & Lambelet, 1997). Our results support that work, and even better correlations were obtained with the T21 parameters, possibly due to a larger dispersion in the T22 data as opposed to T21. Thus, it seems easier to follow the fate of the water assumed to be intramyofibrillar than changes in the intermyofibrillar and extracellular water. The total water content in the samples did not suffer variations during the storage period, therefore, we consider that the loss of the T21 population implies an increase in the T22, which is intrinsically more heterogeneous than T21, and possibly also of other water populations with even larger relaxation times closer to that of free water. The heterogeneity in the T22 population may also be the cause for poorer correlations between shear stress and T22. The progressive decrease in the intermyofibrilar space has been proposed as the main responsible for the changes in shear stress that take place in frozen stored hake muscle (Herrero et al., 2005), although intramyofibrillar spaces also decrease. Accordingly, the shear stress may be directly related to T22, although as already mentioned T22 may encompass several water subpopulations that vary with time. The correlation values shown in Table 1 suggest that it might be possible to substitute the instrumental methods of WHC apparent viscosity and shear stress by LF NMR. 3.4. LF NMR as predictor of freezing storage time We were also interested in exploring the potential value of LF NMR as predictor of freezing storage time, which is one of the most relevant parameters determining quality loss in frozen products. The results of the linear regression analysis performed between LF NMR parameters estimated by biexponential analysis and the storage time at 10 °C (Fig. 5a–d) indicate the possibility of building up a model to predict storage time using LF NMR. Interestingly, while both T21 and T22 constants suffer a linear evolution along the entire experimental period, A21 and A22 have the potential to serve as indicators of the shelf life of this product. A21 and A22 were only linear during the first 14 weeks, which is the estimated shelf life period for frozen hake, since longer frozen storage time provoked a fall in apparent viscosity values below 1000 and hake muscle lost its positive quality attributes. The potential of LF NMR as predictor of freezing storage time improved when the data input were not individual measurements but ‘‘batch’’ values, (i.e., the average for all the samples measured at each sampling period) (Fig. 5e–h). Taking into account the large individual differences known to exist in biological material and the fact that for a commercial application input of ‘‘batch’’ data and conclusions about the whole batch are of high relevance, these results are considered very encouraging. One of the advantages of these data is their higher discriminatory power during the first weeks of storage as compared by other spectroscopy methods such as Raman (Herrero et al., 2004). Accordingly, we consider that the parameters A21 and A22 may provide relevant information during the intervals of storage time and quality that are of highest commercial value for the product. PCA of the CONTIN data revealed that 4 PCs explained 95% of the variability in the data (66%, 16%, 8% and 5%, respectively). The first two components seemed to keep a clear relationship with storage time (Fig. 6a–d) and, as in the case of linear regression

1632

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

Fig. 5. Linear regressions estimated by biexponential analysis to predict storage time at 10 °C, and where the x variable correspond to individual measurements per fish (a– d), and average values per sampling time (e–h) with relaxation times (a) T21 (y = 0.50x + 43.71; R2 = 0.50), (b) T22 ((y = 1.62x + 135.79; R2 = 0.22); and their corresponding amplitudes c) A21 (first 14 days y = 0.014x + 0.613; R2 = 0.88) and (d) A22 (first 14 days) (y = 0.0164x + 0.1648; R2 = 0.93); (e) T21 (y = 0.4922x + 43.461; R2 = 0.7129); (f) T22 (y = 1.6241x + 136.61; R2 = 0.4594); (g) A21 (y = 0.0136x + 0.6124; R2 = 0.906) for the first 14 days and (h) A22 (y = 0.0161x + 0.1656; R2 = 0.9273 for the first 14 days).

estimations, a linear trend was followed only during the first 14 weeks of frozen storage, therefore the PLS regression of the CONTIN profiles was performed for the first 14 weeks of frozen storage. It resulted in a calibration model with 3 PCs, R2 = 0.98 and an error of ±1 week. The R2 and error of the validation model were 0.92 and 1.8, respectively and the slope was close to 1 in both calibration and validation models (Fig. 6e–f). These results suggest that LF NMR is a clear candidate to evaluate the quality of frozen stored hake and predictor of its shelf life. We intend to further validate this model with additional fish samples and conditions.

4. Conclusions In conclusion, during frozen storage of hake there were changes in the water pools that could be measured by LF NMR and that were consistent with a decrease in the relative amount of intramyofibrillar water simultaneous with an increase in the amount of extramyofibrillar and extracellular water. The spectroscopic changes seemed to keep a relationship with the WHC, apparent viscosity and shear stress of the product, and in particular the population of water with higher relaxation times seemed to be directly

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

1633

Fig. 6. (a–d) PCA of the CONTIN profiles obtained during 23 weeks of storage at 10 °C: (a and c) scores versus storage time and (b and d) loading plots for PC1 (a and b) and PC2 (c and d) that explained 66% and 16%, respectively, of the variability of the model. (e and f) PLS regression model with the CONTIN spectra as X-variable and storage time at 10 °C as the Y.

related to the WHC. Finally, both mathematical models used in this work, i.e. simple regression of biexponential fitting and the PLS of CONTIN data, suggest that it is possible to use LF NMR to evaluate the quality of frozen hake. Acknowledgements This work was financed by the Spanish Ministry of Science and Innovation (AGL2007-65661). Isabel Sánchez-Alonso was funded by a Juan de la Cierva postdoctoral contract from the Spanish Ministry of Science and Innovation and Javier Sánchez-Valencia by a FPI predoctoral fellowship from the same Ministry. Thanks are due to Ms. Pilar Moreno for her excellent technical assistance. References Andersen, C. M., & Rinnan, A. (2002). Distribution of water in fresh cod. Lebensmittel-Wissenschaft Und-Technologie – Food Science and Technology, 35(8), 687–696.

AOAC (1995). Official methods of analysis of the association of official agricultural chemists. Gaithersburg, Maryland, USA: AOAC International. Aursand, I. G., Gallart-Jornet, L., Erikson, U., Axelson, D. E., & Rustad, T. (2008). Water distribution in brine salted cod (Gadus morhua) and Salmon (Salmo salar): A low-field 1H NMR study. Journal of Agricultural and Food Chemistry, 56(15), 6252–6260. Barroso, M., Careche, M., Barrios, L., & Borderias, A. J. (1998). Frozen hake fillets quality as related to texture and viscosity by mechanical methods. Journal of Food Science, 63(5), 793–796. Barroso, M., Careche, M., & Borderías, A. J. (1998). Quality control of frozen fish using rheological techniques. Trends in Food Science & Technology, 9(6), 223–229. Belton, P. S., Jackson, R. R., & Packer, K. J. (1972). Pulsed NMR studies of water in striated muscle: I transverse nuclear spin relaxation-times and freezing effects. Biochimica Et Biophysica Acta, 286(1), 16. Bertram, H. C., & Andersen, H. J. (2006). Proton NMR relaxometry in meat science. In G. A. Webb (Ed.), Modern magnetic resonance (pp. 1729–1733). Netherlands: Springer. Bertram, H. C., Dønstrup, S., Karlsson, A. H., & Andersen, H. J. (2002). Continuous distribution analysis of T2 relaxation in meat – An approach in the determination of water-holding capacity. Meat Science, 60(3), 279–285. Bertram, H. C., Karlsson, A. H., Rasmussen, M., Pedersen, O. D., Donstrup, S., & Andersen, H. J. (2001). Origin of multiexponential T-2 relaxation in muscle myowater. Journal of Agricultural and Food Chemistry, 49(6), 3092–3100. Bligh, E. G., & Dyer, W. J. (1959). A rapid method of total lipid extraction and purification. Journal of Biochemistry and Physiology, 37(8), 911–917.

1634

I. Sánchez-Alonso et al. / Food Chemistry 135 (2012) 1626–1634

Brown, R. J. S., Capozzi, F., Cavani, C., Cremonini, M. A., Petracci, M., & Placucci, G. (2000). Relationships between 1H NMR relaxation data and some technological parameters of meat: A chemometric approach. Journal of Magnetic Resonance, 147(1), 89–94. Careche, M., & Barroso, M. (2009). Texture in seafood. In H. Rebbein & J. Ochlenschlager (Eds.), Fishery products: Quality safety and authenticity (pp. 214–231). Oxford, UK: Wiley Blackwell. Careche, M., del Mazo, M. L., & Fernandez-Martin, F. (2002). Extractability and thermal stability of frozen hake (Merluccius merluccius) fillets stored at 10 and 30 °C. Journal of the Science of Food and Agriculture, 82(15), 1791–1799. Careche, M., Herrero, A. M., Rodríguez-Casado, A., del Mazo, M. L., & Carmona, P. (1999). Structural changes of hake (Merluccius merluccius L.) fillets: Effects of freezing and frozen storage. Journal of Agricultural and Food Chemistry, 47(3), 952–959. Careche, M., & Tejada, M. (1990). The effect of neutral and oxidized lipids on functionality in hake (Merluccius merluccius L.): A dimethylamine- and formaldehyde-forming species during frozen storage. Food Chemistry, 36(2), 113–128. Carr, H. Y., & Purcell, E. M. (1954). Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 94(3), 630. del Mazo, M. L., Torrejon, P., Careche, M., & Tejada, M. (1999). Characteristics of the salt-soluble fraction of hake (Merluccius merluccius) fillets stored at 20 and 30 °C. Journal of Agricultural and Food Chemistry, 47(4), 1372–1377. Erikson, U., Veliyulin, E., Singstad, T. E., & Aursand, M. (2004). Salting and desalting of fresh and frozen-thawed cod (Gadus morhua) fillets: A comparative study using 23Na NMR, 23Na MRI, low-field 1H NMR, and physicochemical analytical methods. Journal of Food Science, 69(3), FEP107–FEP114. Finch, E. D., Harmon, J. F., & Muller, B. H. (1971). Pulsed NMR measurements of diffusion constant of water in muscle. Archives of Biochemistry and Biophysics, 147(1), 299. Haard, N. F. (1992). Biochemical reactions in fish muscle during frozen storage. In E. G. Bligh (Ed.), Seafood science and technology. Fishing news books (pp. 176–209). Canada: Blackwell Scientific Publications. Herrero, A. M., & Careche, M. (2006). Prediction of frozen storage time of Cape hake (Merluccius capensis and Merluccius paradoxus) by instrumental methods. Journal of the Science of Food and Agriculture, 86(13), 2128–2133. Herrero, A. M., Carmona, P., & Careche, M. (2004). Raman spectroscopic study of structural changes in hake (Merluccius merluccius L.) muscle proteins during frozen storage. Journal of Agricultural and Food Chemistry, 52(8), 2147–2153. Herrero, A. M., Carmona, P., Garcia, M. L., Solas, M. T., & Careche, M. (2005). Ultrastructural changes and structure and mobility of myowater in frozenstored hake (Merluccius merluccius L.) muscle: Relationship with functionality and texture. Journal of Agricultural and Food Chemistry, 53(7), 2558–2566. Herrero, A. M., Huidobro, A., & Careche, M. (2003). Development of a quality index method for frozen hake (M-capensis and M-paradoxus). Journal of Food Science, 68(3), 1086–1092. Howgate, P. (1979). Fish. In J. G. Vaughan (Ed.), Food microscopy (pp. 343–392). London UK: Academic Press. Jensen, H. S., & Jorgensen, B. M. (1997). A sensometric approach to cod-quality measurement. Food Quality and Preference, 8(5–6), 403–407. Jensen, K. N., Jorgensen, B. M., Nielsen, H. H., & Nielsen, J. (2005). Water distribution and mobility in herring muscle in relation to lipid content, season, fishing ground and biological parameters. Journal of the Science of Food and Agriculture, 85(8), 1259–1267.

Jepsen, S. M., Pedersen, H. T., & Engelsen, S. B. (1999). Application of chemometrics to low-field 1H NMR relaxation data of intact fish flesh. Journal of the Science of Food and Agriculture, 79(13), 1793–1802. Jørgensen, B., Oehlenschläger, J., Olafsdottir, G., Tryggvadóttir, S. V., Careche, M., Heia, K., et al. (2003). A study of the attitudes of the European fish sector towards quality monitoring and labelling. In J. Luten, J. Oehlenschläger, & G. Olafsdottir (Eds.), Quality of fish from catch to consumer: Labelling, monitoring and traceability (pp. 57–71). The Netherlands: Wageningen Academic Publishers. Kramer, A., Burkardt, G. J., & Rogers, H. P. (1951). The shear press, a device for measuring food quality. Canner, 112, 34–36. Lambelet, P., Renevey, F., Kaabi, C., & Raemy, A. (1995). Low-field nuclear magnetic resonance relaxation study of stored or processed cod. Journal of Agricultural and Food Chemistry, 43(6), 1462–1466. Martinez, I. (2009). Other principles: Analysis of lipids, stable isotopes and trace elements. In J. Oehlenschläger & H. Rehbein (Eds.), Fishery products: Quality, safety and authenticity (pp. 388–410). United Kingdom: Blackwell Publishing. Martinez, I., Aursand, M., Erikson, U., Singstad, T. E., Veliyulin, E., & van der Zwaag, C. (2003). Destructive and non-destructive analytical techniques for authentication and composition analyses of foodstuffs. Trends in Food Science & Technology, 14(12), 489–498. Martinez, I., Bathen, T., Standal, I. B., Halvorsen, J., Aursand, M., Gribbestad, I. S., et al. (2005). Bioactive compounds in cod (Gadus morhua) products and suitability of 1 H NMR metabolite profiling for classification of the products using multivariate data analyses. Journal of Agricultural and Food Chemistry, 53(17), 6889–6895. Meiboom, S., & Gill, D. (1958). Modified spin-echo method for measuring nuclear relaxation times. Review of Scientific Instruments, 29(8), 688–691. Offer, G., & Knight, P. (1988). The structural basis of water-holding in meat. In R. Lawrie (Ed.). Developments in meat science (vol. 4, pp. 53–173). New York: Elsevier Applied Science. Provencher, S. W. (1982a). A constrained regularization method for inverting data represented by linear algebraic or integral-equations. Computer Physics Communications, 27(3), 213–227. Provencher, S. W. (1982b). CONTIN: A general-purpose constrained regularization program for inverting noisy linear algebraic and integral-equations. Computer Physics Communications, 27(3), 229–242. Renou, J. P., Monin, G., & Sellier, P. (1985). Nuclear magnetic resonance measurements on pork of various qualities. Meat Science, 15(4), 225–233. Roussel, H., & Cheftel, J. C. (1990). Mechanisms of gelation of sardine proteins: Influence of thermal processing and of various additives on the texture and protein solubility of kamaboko gels. International Journal of Food Science and Technology, 25(3), 260–280. Shenouda, S. Y. K. (1980). Theories of protein denaturation during frozen storage of fish flesh. In E. M. M. C. O. Chichesters & G. F. Stewart (Eds.). Advances in food research (Vol. 26, pp. 275–311). Academic Press. Steen, C., & Lambelet, P. (1997). Texture changes in frozen cod mince measured by low-field nuclear magnetic resonance spectroscopy. Journal of the Science of Food and Agriculture, 75(2), 268–272. Tejada, M. (2001). Aggregation of myofibrillar proteins during frozen storage of fish. In F. Bozoglu, T. Deak, & B. Ray (Eds.). Novel processes and control technologies in the food industry (Vol. 338, pp. 212–226). Amsterdam: IOS Press. Tornberg, E., Andersson, A., Goransson, A., & von Seth, G. (1993). Water and fat distribution in pork relation to sensory properties. In E. Puolanne, D. I. Demeyer, M. Ruusunen, & S. Ellis (Eds.), Pork quality: Genetic and metabolic factors. Wallingford, UK: CAB International.