Quality Index Method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar)

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1 Food Quality and Preference 14 (2003) Quality Index Method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar) Kolbrun Sveinsdottir a, Grethe Hyldig b, Emilia Martinsdottir a, Bo Jørgensen b, KristbergKristbergsson a, * a Icelandic Fisheries Laboratories and The University of Iceland, Department of Food Science at Icelandic Fisheries Laboratories, PO Box 1405, Skulagata 4, IS-121 Reykjavik, Iceland b Danish Institute for Fisheries Research, Technical University of Denmark, Building 221, DK-2800 Lyngby, Denmark Received 23 June 2001; received in revised form 30 June 2002; accepted 1 July 2002 Abstract The aim of the study was to develop a Quality Index Method (QIM) scheme for raw, farmed Atlantic salmon (Salmo salar) and to evaluate the scheme in a shelf life study. QIM is based on the evaluation of key parameters in the deterioration of seafood s. Demerit points are assigned to selected parameters according to their importance and a Quality Index (QI) is established by cumulating the resultingscores. The maximum storage time in ice was determined with Quantitative Descriptive Analysis (QDA) of the salmon after cookingand found to be days. This was used as a reference to enable prediction of the remainingstorage time of raw salmon in ice with QIM. The calculated QI evolved linearly with storage time in ice (QI=0.82x (days in ice)+0.18, R 2 =0.97). Individual salmon varied in QI within each storage day. However, the multivariate analysis (PLS1) demonstrated that storage time could be predicted with an accuracy of1. 5 days, assumingthat five salmon from each batch were included in the QIM assessment. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Salmon; Sensory evaluation; Quality Index Method; Shelf life; Freshness 1. Introduction The world wide production of farmed salmon has increased gradually between 1990 and 1999, going from approximately MT to more than MT per year (FAO, 1999), with Atlantic salmon (Salmo salar) comprisingapproximately 88% of the total. Norway is the leadingproducer of farmed salmon, with 53% of the total production in Sensory evaluation is an important method for the assessment of freshness and quality, and is commonly used in the fish sector and fish inspection services (Luten & Martinsdottir 1997; Martinsdottir, 1997). The Quality Index Method (QIM) is a grading system for estimating the freshness and quality of seafood, which has been demonstrated to be rapid for many fish species (Larsen, Heldbo, Jespersen, & Nielsen, 1991). The method is based upon a scheme developed by the Tasmanian Food * Correspondingauthor. Tel.: ; fax: address: kk@rf.is and kk@hi.is (K. Kristbergsson). Research Division (CSIRO) (Bremner, 1985) but needs to be developed separately for each fish species. QIM schemes have currently been developed for a number of fish species including: fresh herring, and cod (Jonsdottir, 1992; Larsen et al., 1991), red fish (Martinsdottir & Arnason, 1992), Atlantic mackerel, horse mackerel and European sardine (Andrade, Nunes, & Batista, 1997), brill, dab, haddock, pollock, sole, turbot and shrimp (Luten, 2000) and gilthead seabream (Huidobro, Pastor, & Tejada, 2000). QIM is composed of precise descriptions of quality parameters and may be used to predict the remaining shelf life of fish (Luten & Martinsdottir, 1997) and Hyldigand Nielsen (1997) found the method to be both rapid and easy to perform. The technique is based on selectinga number of quality attributes characteristic for a particular species and allocatingscores to each attribute dependingon the state of freshness or quality of the selected food item. The scores are assigned in whole numbers ranging from 0, for fresh, to 3 pending advancement of decay. A Quality Index (QI) is obtained by summarisingthe scores for all categories. The most /03/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved. PII: S (02)

2 238 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) commonly used attributes for fish are the appearance of eyes, skin and gills together with odour and texture. The development of QIM for a particular seafood or fish species involves the selection of appropriate and best fittingattributes in order to observe a linear increase in the QI with storage time in ice. The maximum storage time of fish can be determined by the sensory evaluation of cooked samples using Quantitative Descriptive Analysis (QDA). The results from QDA may be used as a reference when developing QIM for fresh fish. When establishingoda profiles the panellists make a list of attributes describingthe product under the guidance of a panel leader. The panellists are then trained in usingan unstructured scale for each of the attributes, before participatingin a sensory analysis of the product to be tested. Applications of QDA are very wide and the methodology has been used for the resolution of complex data from both chemical and physical analysis and it has been used to organise information on various consumer preferences. It serves well for the identification of specific sensory characteristics of products pertinent to consumer preferences (Sidel, Stone, & Thomas, 1994). The method has been applied to the mappingof consumer preferences (Helgesen, Solheim, & Naes, 1997) and for relating sensory textural attributes to instrumental measurements (Reyes- Vega, Peralta-Rodriguez, Anzaldua-Morales, Figeurosa- Cardenas, & Martinez-Bustos, 1998). Other applications include its use in measuringshelf life of products without dependence on standards or control products. When determiningthe maximum shelf life of a product, the limits of consumption may be determined, when the panel or part of the panel detects spoilage attributes in the samples, or when the samples are increasingly described with words applyingto spoilage attributes. Accurate information on which quality characteristics are considered to be the most important to consumers is a valuable tool for quality control in food processing(stone & Sidel, 1998). The aim of this study was to develop a QIM scheme for raw farmed Atlantic salmon (S. salar) usinga trained sensory panel. Furthermore we wished to evaluate the scheme in a shelf life study of farmed salmon and to obtain information on the number of samples required per batch for the predictability of storage time. And finally we aimed to compare the obtained QI to storage time in ice estimated with QDA of salmon after cooking. 2. Materials and methods 2.1. Salmon A total of 195 farmed Atlantic salmon (S. salar) from Sekkingstad A/S (Skogsva g, Norway) were used; 12 for the initial observation and evaluation of the fish, 63 for trainingand 120 for the shelf life study. The salmon had been fed with pelleted diet (Vextra Omega from Ewos Aqua A/S, Bergen, Norway) and weighed between 3 and 4 kg. The salmon was slaughtered before onset of sexual maturity, in nine batches in March/May After slaughtering the salmon was gutted, gills cut through for bleedingand after rinsingin fresh water the fish was stored in ice in boxes at 0 2 C until analysed. Five or 10 salmon from each of the followingstorage day (15 from day 21) were assessed 3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 21, 22 and 23, respectively Sensory evaluation Quality Index Method (QIM) Nine people participated in the development and evaluation of the QIM scheme. They were employees of the Danish Institute for Fisheries Research, Department of Seafood Research (DIFRES). All members had prior trainingwith usingqim schemes for other fish species. Observations of the salmon were carried out under standardised conditions at room temperature using electric light and with as little distraction as possible. Salmon was collected from the iceboxes and placed on a table, 30 min before the evaluation. The side where gills had been cut through was placed face down. Each salmon was coded with a random three-digit number unrelated to storage time Preliminary observation. Three experts in sensory evaluation, experienced with QIM for fish, observed the salmon after 6, 7, 8, 11, 13, 14, 15, 18, 20, 21 and 22 days in ice, respectively. Changes observed in appearance, odour and texture duringstorage were listed in a preliminary scheme Development of the QIM scheme and training the sensory panel. Six 1-h sessions were used for the development of the scheme and trainingof the QIM sensory panel prior to the shelf life study. In each session, 9 12 salmon in 3 4 distinct groups, which had been stored between 4 and 24 days in ice, were observed. The preliminary scheme was explained to the panellists duringthe first 2 days while observingsalmon of varying freshness. All suggestions of improvements by the panel members, in the evaluation, were included in the final scheme. Then the panellists were trained for the following4 days and the scheme developed further. The storage time of each salmon was unknown to the panellists until after the session. The ultimate version of the scheme was finalized by the panel leader and presented to the panel on the last day of training Assessment procedure for QIM used in the shelf life study. The QIM scheme was used for assessingthe raw fish. Eight sessions were carried out lasting from 10 to 40 min each. The panellists (n=9), which were unaware of

3 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) the storage time in ice, assessed 5 fish from each of the 2 3 different slaughter occasions in each session. The fish were served in a random order and evaluated individually Quantitative Descriptive Analysis (QDA) of cooked salmon QDA as described by Stone and Sidel (1992) was used to assess the cooked salmon. Four samples with skin were collected from each fish, all from the loin part, collected by cuttingfrom the spine to 2 cm below the lateral line. The samples were placed in porcelain jars and coded with three random digit numbers. The samples were cooked at 100 C in a pre-warmed oven (RATIONAL Combi-Da mpfer CCM, Landsberg, Germany) with air circulation for 30 min. Ten panellists from a trained external panel at DIFRES, participated in the QDA of the cooked salmon. They were selected and trained accordingto International Standards (ISO, 1993). Prior to the shelf life study they were trained accordingto International Standards (ISO, 1994) for the QDA assessment. Six 1 2 h sessions were used for trainingthe panel usingsalmon of different freshness. The panel developed a vocabulary to describe odour, flavour, appearance and texture and then trained usinga 15 cm unstructured scale for the selected attributes. The assessments were carried out in a sensory laboratory equipped with separate booths and a FIZZ sensory registration system (FIZZ Network, Version 1,1 b, Biosystemes, Couternon, France). The QDA on the cooked salmon was carried out parallel to the QIM assessment. Each panellist evaluated samples in duplicate but one by one, in a randomised order from 3 storage days Data analysis The equation of best fit and the correlation coefficient (r) between the QI and storage time in ice were calculated. Multivariate analysis of scores for different attributes assessed with QIM and QDA was carried out with the statistical programme Unscrambler 1, (version 7.5, CAMO ASA, Trondheim, Norway), with principal component analysis (PCA) and partial least-squares regression (PLS). The variables were scaled prior to the statistical analysis: Each element in the matrix was multiplied with the inverse of the standard deviation of the correspondingvariable when variables had different ranges. An estimate of the uncertainty of prediction of days in ice from the Quality Index (QI) was calculated by the PLS-software. For this purpose a calibration model was calculated usingthe average QI for each storage day, including assessment of five salmon, as the X matrix (one column) and the storage time in ice as the y vector. A full cross validation method was used, i.e. as many sub-models were calculated as there were samples, where the samples, one at a time, were kept out of the calculation. The squared difference between the predicted and real y-values for each omitted sample was averaged, thus giving an estimate of the prediction variance (Esbensen, Scho nkopf, Midtgaard, & Guyot, 1998). The root mean square error of prediction (RMSEP) was calculated providinga prediction error in original units (days). Bias was calculated and presented the averaged difference between predicted and measured Y-values for all samples in the validation set. The standard error of performance (SEP) was used to estimate the precision of the results, corrected for bias. Table 1 The QIM scheme developed for farmed salmon Quality parameters Description Points Skin Colour/appearance Pearl-shiny all over the skin 0 The head is still pearl-shiny, but the rest less, perhaps yellow 1 Mucus Clear and not clotted 0 Milky and clotted 1 Yellow and clotted 2 Odour a Fresh seaweedy, cucumber 0 Neutral to metal, dry grass, corn 1 Sour 2 Rotten 3 Eyes Pupils Clear and black, metal shiny 0 Dark grey 1 Mat, grey 2 Form Flat 0 Little sunken 1 Sunken 2 Abdomen Blood in abdomen Blood light red/not present 0 Blood more brown 1 Odour Neutral 0 Corn 1 Sour 2 Rotten/rotten kale 3 Gills b Colour/appearance Red/dark brown 0 Light red/brown 1 Grey-brown, grey, green 2 Mucus Transparent 0 Yellow, clotted 1 Brown 2 Odour Fresh, seaweed 0 Metal 1 Sour 2 Rotten 3 Texture Elasticity Finger mark disappears immediately 0 Finger leaves mark over 3 s 1 Quality Index Total 0 22 a Turn the salmon and smell the skin on the other side. b Examine the side that has not been cut through.

4 240 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) The individual assessor performance of each attribute was studied by discriminant partial component analysis (DPLSR) as described in Thybo and Martens (2000). The significance level was set at 5% if not stated elsewhere. 3. Results and discussion 3.1. Quality Index Method Initial changes in the following parameters were listed in a preliminary scheme: Colour/appearance of skin folds, mucus and smell of skin; colour and form of eyes; cut surface and blood in abdomen, membrane on the inside of abdomen and odour of abdomen; colour/ appearance of mucus and odour of gills; and texture (elasticity) of flesh. The maximum sum of the points was 33. Duringthe development of the scheme, the parameters; scale, skin-folds and abdomen coat (membrane on the inside of the abdomen) were removed from the scheme since the evaluation was destructive to the sample. The parameter abdomen cut-surface was removed since it was difficult to evaluate. Scores were omitted or added and some changes in the selection of words were made to describe the changes more precisely in skin colour/appearance, abdomen odour, abdomen blood, gills colour/appearance, gills mucus, texture elasticity and eyes form. After the development of the scheme, the total sum of points was 22 (Table 1) describing11 sensory attributes for appearance and odour of skin, eyes, abdomen, gills and texture. The Fig. 1. Average QI of each storage day analysed versus days in ice. Fig. 3. Average QI given by each QIM panellist in the shelf life study for each day of analysis versus days in ice. Fig. 2. PLS modelling of QIM data from salmon stored in ice using full cross validation: Measured versus predicted Y values. Average QI for each storage day based on assessment of five salmon used to predict storage time in days.

5 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) total sum of the scores in the QIM was designated as the Quality Index (QI). There was a linear relationship with high correlation (r 2 =0.969) between the average QI for each storage day and storage time in ice (Fig. 1). Because of the biological variation between individual fish, an average QI was used based on the assessment of five fish. The inverse regression model was calculated as described in Section 2, and the predicted versus known days in ice (Fig. 2). The standard error of prediction was 1.4. Since the QI is a sum of 11 parameter values, the measurement error may be assumed to be normally distributed. The predictions may then be considered as t-distributed, and from the 95% confidence interval (estimated by SEPt (df=13)= =3.02) it was estimated that the QI for five salmon from the same batch could predict the storage time with an accuracy of 1.5 days. The individual panellists participatingin the QIM evaluation of salmon performed differently, where some Fig. 4. Average scores of each quality attribute assessed in the shelf life study with QIM scheme for salmon stored in ice versus days in ice.

6 242 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) gave consistently higher or lower QI scores throughout the storage time (Fig. 3). Panellist number 4 and 9 scored highest while panellist number 3 and 6 scored lowest. The variation was greatest early in the storage period (Fig. 3) but decreased with storage time in ice. This indicates that the panellists became more unanimous in their evaluation of salmon as decay progressed which may be because changes became more prominent with time. It was assumed in the Quality Index Method that the scores for all quality attributes increased with storage time in ice. This was indeed observed but to a different extent for different quality attributes (Fig. 4). There was a high variability in the scores for skin colour. The average scores given for storage days 3 and 5 were considerably higher than for storage day 6 and those of storage day 12 were low compared to days 11 and 13. However, the average scores reached a maximum after 20 days of storage. The scores for skin mucus and odour increased consistently throughout the storage time, from approximately 0.5 to almost the maximum score. At the beginning of the storage time when the salmon was very fresh, the odour was described as fresh seaweed or cucumber and then the odour became neutral. Duringthe later stages, the odour was described as sour and finally as rotten. Freshly caught fish contains low levels of volatile compounds, which contribute to the fresh like odours where 2,6 nonadienal has been characterised to have a cucumber like odour and a low odour threshold (0.001 ppb). The sour and rotten odour may have originated from short chain fatty acids, alcohols, sulphur compounds and amines generated by microbial activity (Olafsdottir & Fleurence, 1997). The scores for the quality attributes of eyes, abdomen and gills increase rather constantly throughout the storage time in ice from approximately even though the scores varied somewhat with storage time, especially for form of eyes. At the end of the storage time, the scores reached values close to the maximum score. The assessment of texture and skin colour appeared to be difficult since the average scores fluctuated considerably throughout the storage time. The average scores for the individual quality attributes increased with storage in ice (Table 2). The correlation coefficient between the quality attributes and days in ice was very high for most of the attributes (r > 0.95). However, a regression model based on one individual parameter would be less robust and probably less precise than one based on the QI, because the available values for each parameter in the QIM scheme range from 0 to 3, whereas the full quality index ranges from 0 to 22. In order to obtain a better understandingof how the different quality parameters of salmon change with storage time, the results were analysed with principal component analysis (PCA) (Fig. 5). The first Principal Component axis (PC1) defined the variation between samples observed with storage time in ice, where the parameter days in ice at the right end of the PC-axis, explained approximately 80% of the variation between the samples. All variables in the QIM scheme received high scores with storage time in ice and were located on the right side of PC1. The quality parameters for each group assessed (skin, eyes, gills and abdomen), displayed a certain grouping, and therefore it may be assumed that the spoilage rate estimated by the quality parameters within each group may have been related Quantitative descriptive analysis The criteria for the selection of attributes, which might be used to discriminate between days of storage, had to be relevant to salmon and discriminate clearly between the samples and furthermore be non-redundant, and unambiguous to the panellists. To see if these demands were fulfilled, the sensory data was analysed with respect to signal to noise for each panellists and attribute. The signal to noise analysis of panellists and sensory attributes showed that salmon from different storage days could be distinguished and described with varyinglevels of precision. Specific words were designated to describe the quality parameters appearance, odour, flavour and texture of cooked salmon in the QDA (Table 3). Table 2 Average scores for each quality attribute assessed with the QIM scheme for salmon stored in and the correlation to days in ice Days in ice Correlation (r) Skin colour Skin mucus Skin odour Eyes pupils Eyes form Abdomen blood Abdomen odour Gills colour Gills mucus Gills odour Texture

7 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) Fig. 5. Loadings in PCA of salmon data including all quality parameters assessed in the QIM scheme for salmon and storage time in ice. All variables are weighed (1/SD). Only the right side of the graph is displayed for PC1. Table 3 Attributes of cooked salmon samples assessed by the QDA method Appearance Odour Flavour Texture discoloration ( ) other seaweed (+) seaweed (+) FLAKINESS CUCUMBER (+) SWEETISH a (+) FIRMNESS SOURISH b (+) SOURISH b (+) juiciness RANCID ( ) FISH OIL (+) other sour ( ) MUSHROOM (+) AMINE ( ) RANCID ( ) other Mushy-sweet ( ) sour ( ) amine ( ) Other Positive attributes are marked with (+), negative with ( ) and the best sensory terms are designated in capital letters. a The term sweetish was used in QDA to describe sweet flavour (positive) to distinguish from the term mushy-sweet (negative). b The term sourish was used in QDA to describe odour and flavour that could best be described as fresh (positive)-sour to distinguish from the term sour. The words used to describe odour and flavour of the salmon were grouped into positive sensory parameters and negative sensory parameters, depending on whether they described fresh salmon or salmon at the end of the respective storage period (Fig. 6). A clear grouping was evident on each side of the PC1-axis, indicatingthat fresh samples were easily distinguished from spoiled samples. Storage time in ice was located to the right of the PC1-axis with all the negative QDA attributes while positive attributes were all on the left side. Most of the negative attributes in the QDA were equally useable for identifyingspoilage in the cooked samples. The same was observed for the positive attributes when identifyingfresh samples. This, together with the signal to noise analysis, indicated that the number of attributes for determiningthe quality of cooked samples could be reduced for simplification and a more rapid analysis. The most reliable sensory attributes were cucumber, sourish, rancid and amine for odour. Sweetish, sourish, fish oil, mushroom and rancid were most reliable for flavour and flakiness and firmness for texture. The salmon appeared to be more firm and juicy at the beginning of storage as these attributes were grouped with the positive attributes on the left side of the PC1-axis. At the beginning of the storage time, the salmon was characterised by all the positive attributes, but after days of storage these were hardly detectable. Changes in sensory attributes indicated that the salmon was approachingthe end of shelf life when the salmon was increasingly characterised by sour, amine and rancid odour and flavour (Fig. 7). This strongly indicated that after days of storage in ice, the salmon was reaching marginal acceptability since the negative attributes dominated. Thereafter the salmon was no longer fit for human consumption. Other studies have come to similar conclusions, accordingto Magnussen, Johansen, and Nordtvedt (1996), the maximum storage time of salmon was assumed to be 21 days under the best storage conditions.

8 244 K. Sveinsdottir et al. / Food Quality and Preference 14(2003) Fig. 6. Loadings in PCA of salmon data including all quality parameters assessed with QDA of cooked salmon in the shelf life study and storage time in ice. f=flavour, o=odour, t=texture. Fig. 7. Changes in odour and flavour with storage time. Attributes assessed with QDA using a 15-cm unstructured scale. 4. Conclusions A QIM scheme for farmed Atlantic salmon (S. salar) was developed in this study. The total scores for quality attributes (QI) gave a linear relationship with storage time in ice. The maximum storage life of salmon determined with descriptive sensory evaluation was days in ice. The QI could therefore be used to estimate the remainingstorage time in ice. The storage life in ice was predictable with the QIM method with an accuracy of 1.5 days when five salmon from each batch were used for a particular assessment. The method offers a fast and reliable procedure to evaluate the freshness of farmed salmon, providing information about its quality and the remainingshelf life in ice. However, differences amongassessors indicated that the freshness assessment with the QIM scheme should preferably be based upon the assessment of more than one assessor. Acknowledgements This work was a part of an on-going project at the Danish Institute for Fisheries Research, Department of Seafood Research (DIFRES) coveringquality control

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