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NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets
Journal of Food Engineering 116 (2013) 758–764
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Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng
VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets Takashi Kimiya a,⇑, Agnar H. Sivertsen b, Karsten Heia b a b
National Research Institute of Fisheries Science, Fisheries Research Agency, 2-12-4 Fukuura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan Nofima, P.O. Box 6122, N-9291 Tromsø, Norway
a r t i c l e
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Article history: Received 27 June 2012 Received in revised form 29 November 2012 Accepted 11 January 2013 Available online 30 January 2013 Keywords: Freshness Hyperspectral Imaging VIS/NIR spectroscopy Salmon
a b s t r a c t Visible/near-infrared spectroscopy has been evaluated for use in freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets, where fresh samples were stored as whole fish in ice. A handheld interactance probe for performing rapid measurements of single fillets and an imaging spectrometer for online analysis at an industrial speed of one fillet per second, have been used. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozenthawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previous results using the Quality Index Method with trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is appropriate for both frozen-thawed classification and freshness prediction of salmon fillets. The results indicate that the spectral changes are explained mainly by oxidation of heme proteins during the freeze–thaw cycle and during chilled storage in ice. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Freshness, especially when it comes to perishable products such as seafood, is an ambiguous term comprised of many factors, mainly related to a sensory impression which should not be used unless it is well defined (Bremner and Sakaguchi, 2000). Freshness is reduced during storage due to biochemical, chemical, physical and microbiological processes (Ólafsdóttir et al., 1997). These processes are mainly affected by time and temperature (Ashie et al., 1996), but other factors such as pre-harvest stress and handling can influence bleeding (Olsen et al., 2008) and the rate at which spoilage occurs (Richards and Hultin, 2002). It is a common conception among consumers that fresh fish is caught/harvested and then stored chilled for a brief period of time prior to use. The consumer considers freshness as important when buying fish (Brunsø, 2003; Kole, 2003) and demands documentation of freshness (Hansen and Fischer, 2003). Frozen fish usually has a lower market price than fresh fish. All fresh fish sold in Norway must be labeled with the date of slaughter for farmed fish or catch date for wild fish (Lovdata, 2010) and frozen-thawed fishery products sold in Japan must be labeled accordingly (MAFF, 2007). For fish processors, fish retailers and food control authorities, it is difficult to objectively verify the freshness and frozen-thawed sta-
⇑ Corresponding author. Tel.: +81 45 788 7664; fax: +81 45 788 5001. E-mail addresses: [email protected] (T. Kimiya), agnarhs@nofima.no (A.H. Sivertsen), karstenh@nofima.no (K. Heia). 0260-8774/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jfoodeng.2013.01.008
tus of every fish or fillet without rapid and reliable methods to evaluate these properties. For the authorities responsible for performing food controls a small, portable instrument is required. The quality of fishery products depends heavily on the freshness of the raw material (Ólafsdóttir et al., 1997) and a system capable of measuring and documenting the quality of every fillet in production would be of great value to fisheries industry (Jørgensen et al., 2003). Several sensory, microbial, physical and instrumental methods have been evaluated for freshness assessment of seafood (Alasalvar et al., 2010; Ólafsdóttir et al., 1997; Sivertsen et al., 2011). Applications of biosensors and chemical sensors, and micro- and nanotechnologies for the freshness assessment have recently attracted considerable attention. These methods can be implemented in inexpensive, robust and portable devices that can give chemical and biological information useful for the freshness assessment (Alasalvar et al., 2010; Baldwin et al., 2011). However, many of these methods are either time consuming, destructive or require trained personnel, and are therefore not suited for online or large scale operations in contrast to spectroscopic techniques. Visible/ near-infrared (VIS/NIR) spectroscopy has shown promising results for assessing the frozen-thawed status and predicting the freshness as storage days in ice for a range of different fish species. For cod, the method can separate fresh from frozen-thawed whole fish or fillets and predict the freshness as storage days in ice with an accuracy of 1.6 days (Sivertsen et al., 2011). This method provides accurate and objective results, requires little or no training
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and can be implemented using a small handheld device as well as in an online instrument for non-contact measurements of fish fillets on a conveyer belt during production. Previous work has shown that most of the spectral changes in the visible region observed in cod muscle during chilled storage and the freeze–thaw cycle, can be explained by changes in the heme proteins (Sivertsen et al., 2011). Farmed Atlantic salmon contains high levels of the carotenoids astaxanthin and canthaxanthin (Johnston et al., 2006). These pigments absorb light in the wavelength region below 600 nm (Christophersen et al., 1989) overlapping most of the absorption bands of heme proteins (Olsen and Elvevoll, 2011; Ottestad et al., 2011). It can therefore be problematic to directly apply findings from cod to salmon. Several studies have demonstrated the potential of spectroscopic techniques for rapid and non-destructive freshness assessment on salmonid fish. Nilsen et al. (2002) have reported a wellfitted model for predicting freshness of salmon fillets with spectral data in the NIR region (700–1100 nm). Furthermore, it has been suggested that VIS/NIR spectroscopy can detect spoilage in rainbow trout fillets (Lin et al., 2006) and autolytic changes in Atlantic salmon fillets (Sone et al., 2011). However, spectral and spatial understanding and interpretation remain poor for freshness assessment of salmon fillets using VIS/NIR spectroscopic techniques. In this study, we have examined VIS/NIR interactance spectroscopy as a tool for freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets using hyperspectral imaging (HSI) and a handheld probe. Throughout this paper freshness is defined as storage days in ice of gutted and cleaned whole fish, assuming correct handling with respect to minimum stress during harvest, proper bleeding in running water and rapid cooling using fresh ice. Sensory evaluation using the Quality Index Method (QIM) and microbiological measurements were used to document and validate the storage regime used in this work. 2. Materials and methods 2.1. Fish samples Farmed Atlantic salmon (gutted weight between 2.3 and 6.3 kg) provided by the Aquaculture station in Tromsø (Norway) were slaughtered by a blow to the head, bled in running water, gutted, cleaned, and then stored in boxes with ice for up to 17 days. The boxes had holes for drainage and were kept in a cold room (2– 4 °C). Fresh ice was added daily to completely cover the fish. On days 0, 2, 5, 8, 12, and 17, five fish were selected at random, evaluated using the QIM scheme, filleted and then measured using VIS/ NIR spectroscopy. For classification of fresh and frozen-thawed salmon, spectroscopic measurements were performed on fresh (N = 30) and on frozen-thawed fillets (N = 18). Of the 18 frozen-thawed fillets, 8 fillets were from 4 frozen and stored whole fish, whereas 10 fillets were frozen and stored as fillet with skin. The whole fish and fillets were covered with plastic to reduce drying and then frozen and stored at 40 °C. Half of the fillets and half of the whole fish were frozen after 0-days storage in ice and the other half after 2-days storage in ice. After 3 weeks of frozen storage the fillets were thawed over night at 2–4 °C and the whole fish were thawed overnight in running water, filleted and then measured using VIS/NIR spectroscopy. 2.2. Sensory and microbiological evaluations of fresh samples Sensory evaluation, using the QIM for farmed Atlantic salmon (Martinsdóttir et al., 2001; Sveinsdottir et al., 2002), was performed on whole fish. The QIM was performed by an expert panel
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of three persons, where each person gave an individual Quality Index (QI) value for each sample (Nilsen and Esaiassen, 2005). The average value from the expert panel was then used as the QI value for each of the fresh samples. After filleting, microbial loads were measured in samples from left side fillets. Microbial load was determined as total viable count (TVC) and a selective count of H2S-producing bacteria on each measurement day, except for day 2, as described by Sveinsdottir et al. (2002) with some modifications. A 50 g portion of ordinary muscle was aseptically sampled from the thick loin region, located between neck and the anterior edge of the dorsal fin. Each sample was homogenized in sterile saline containing 0.9% (w/v) NaCl and 0.1% (w/v) peptone using a lab blender (the Stomacher 400, Seward, West Sussex, UK) for 2 min to obtain a 5-fold dilution. Further 10-fold dilutions were made as needed. After spread plating of the samples, plates were incubated at 12 °C for 4 days. Results were expressed as log10 of colony forming units per gram of fish muscle (log CFU/g). 2.3. Interactance imaging The HSI operated in interactance mode and data processing were performed as previously described (Sivertsen et al., 2011). The imaging spectrometer (VNIR-640, Norsk Elektro Optikk, Lørenskog Norway) has a field of view of 1 mm 300 mm and a spatial resolution, or pixel size, of 1.0 mm 0.5 mm. Each pixel is represented with a spectrum of variables, representing the recorded radiation in the region of 400–1000 nm, with a spectral resolution of approximately 10 nm. Each fillet was scanned line by line on a white diffuse conveyer belt at a speed of 400 mm per second. The imaging system was calibrated using a Teflon target (300 mm 300 mm 25 mm) before scanning the samples. 2.4. Handheld interactance spectroscopy Interactance spectroscopy with handheld probe system and its data processing were performed as previously described (Sivertsen et al., 2011). The system consisted of a spectrometer, XDS Optiprobe analyzer (FOSS NIRSystems, Laurel, USA), with the transmitting and receiving fiber bundles connected by an in-house made probe holder. Spectral data were recorded as absorbance units in the region of 400–2500 nm with a spectral resolution of 0.5 nm. Each spectrum was recorded as average of 32 spectra measured successively by holding the probe in contact with fillet, aligning the direction through both fiber bundles with the centerline of the fillet. The instrument was calibrated by transmission through air according to the manufacturer’s instruction. For spectral acquisition, two locations were selected: one in the middle of the loin region, as suggested by Nilsen et al. (2002) and the other in the tail region, adjacent to the centerline, as suggested by Sivertsen et al. (2011). 2.5. Data analysis Spectral pre-treatments and data analyses were performed according to Sivertsen et al. (2011). The Unscrambler software (ver. 9.8, CAMO, Oslo, Norway) was used for building and evaluating partial least square regression (PLSR) models between the spectral data, both for the handheld probe data and the HSI data, and freshness, quantified as storage days in ice. The optimal number of principal components (PCs) was determined by the software. The principal component analysis (PCA) of the spectral data, was also performed using the Unscrambler software. The IDL 7.1 and ENVI 4.7 (ITT Visual Information Solutions, Boulder, USA) were used for analyses of the HSI data to map the prediction and classification results.
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Interactance was calculated as I(k) = ln(Ri(k)/Ra(k)), where Ri(k) and Ra(k) are the spectral data recorded from the salmon fillet and the reference material, respectively. Segmentation of the fillet region from the hyperspectral images was achieved using the centerline as a reference system (Sivertsen et al., 2009), using the 585 nm band instead of 525 nm as the blood absorption band. The 585 nm band was used to avoid the absorption by the carotenoids in the salmon muscle. The belly flap area was included in the prediction and classification maps because the linings were trimmed from the salmon fillets in contrast to the cod fillets. The handheld data were down sampled by applying a 10 nm wide Gaussian filter and then selecting wavelengths to be comparable to the imaging data in the region 400–1000 nm before spectral pre-treatments and analyses. Standard normal variate (SNV) and Savitzky–Golay secondderivative were used as spectral pre-treatment methods. The PLSR and k-nearest neighbor (KNN) classifier with leave-one-out crossvalidation were used for freshness prediction and frozen-thawed classification, respectively. Storage days in ice (0, 2, 5, 8, 12, and 17 days) were used as reference value to develop PLSR models. The leave-one-out cross-validation were chosen for comparing the PLSR performance in this study with those in previous works (Nilsen et al., 2002; Sivertsen et al., 2011; Sveinsdottir et al., 2002). In addition, segmented cross-validation was applied in which all samples from one storage day were used as a validation set, while the model was constructed using the remaining data. We refer to this as leave-one-day-out cross-validation, and the procedure outlined above was repeated for all storage days. The PLSR performance was evaluated with the root mean square error of cross-validation (RMSECV) for the freshness predictions. The PCA was performed to investigate separability between fresh and frozen-thawed samples. The separating line between fresh and frozen-thawed samples was calculated using the Rosenblatt’s perceptron, as explained in Sivertsen et al. (2011).
3.2. Spectral characteristics A wide peak centered at 500 nm can be seen in interactance spectra from the salmon muscle (Fig. 2). This peak, which is due to absorption by carotenoids, such as astaxanthin and canthaxanthin (Christophersen et al., 1989), masks out most of the spectral characteristics of the heme proteins, previously found to be important for the freshness prediction and frozen-thawed classification of cod fillets (Sivertsen et al., 2011). The intensity of the carotenoid peak varied greatly among individual fish and the data in the region below 600 nm had a negative effect on the prediction and classification results (data not shown). Absorption by water is observed as a peak centered at 970 nm and as the weaker peak centered at 760 nm (Fig. 2) (Isaksson et al., 2002). A shoulder at 606 nm can also be related to absorption by water (Sone et al., 2012a), specifically the fourth overtone of the OH stretching band at 605 nm (Pope and Fry, 1997).
3.3. Freshness prediction The SNV pre-treated spectra recorded from the tail region, using both the imager and the handheld probe, resulted in freshness-predicting models with only two PCs and a root mean square error of leave-one-out cross-validation of 2.3 and 2.7 days, respectively (Table 1). The RMSECV values of the handheld probe models using four or more PCs increased by using the leave-one-day-out crossvalidation (Table 2), indicating that these three models were overfitted. For the imager model, using the wavelength region 605–
(a)
3. Results and discussion 3.1. Sensory and microbiological analysis The QI value of fresh fish increased linearly with storage days in ice from 1.0 ± 0.4 on day 0 to 14.5 ± 1.3 (mean ± SD) on day 17 (Fig. 1). The microbial loads on fillets increased from <1 log CFU/g on day 0 to 3.9 ± 0.5 log CFU/g (TVC) and 3.3 ± 0.4 log CFU/g (the count of H2S-producing bacteria) on day 17. These sensory and microbiological profiles are comparable to previous findings on farmed Atlantic salmon stored in ice (Sveinsdottir et al., 2002). This verifies that a proper storage regime has been used.
Fig. 1. Quality Index of salmon tested.
(b)
Fig. 2. Mean interactance spectra with standard deviation collected at (a) the tail region of fresh samples for each day and (b) the thick loin region of the fresh and frozen-thawed (FT) samples, for both instruments.
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T. Kimiya et al. / Journal of Food Engineering 116 (2013) 758–764 Table 1 Parameters describing the PLSR models for the imager and the handheld probe data from the tail region of fresh samples based on the leave-one-out cross-validation. Instrument
Wavelength region (nm)
No pre-treatment a
Imager
c
Probec a b c
SNV # of PCsa
Correlation
RMSECVb
2.50 2.49
2 3
0.91 0.91
2.31 2.34
2.42 2.02
2 5
0.89 0.95
2.70 1.91
# of PCs
Correlation
RMSECV
605–735 605–965
4 4
0.90 0.90
605–735 605–965
4 6
0.91 0.94
b
Optimal number of principal components. Root mean square error of cross-validation estimated by leave-one-out cross-validation. One outlier was removed for each instrument.
Table 2 Parameters describing the PLSR models for the imager and the handheld probe data from the tail region of fresh samples based on the leave-one-day-out cross-validation. Instrument
a b c
Wavelength region (nm)
No pre-treatment
SNV
# of PCsa
Correlation
RMSECVb
# of PCsa
Correlation
RMSECVb
Imagerc
605–735 605–965
4 5
0.89 0.90
2.54 2.44
2 4
0.90 0.92
2.41 2.14
Probec
605–735 605–965
4 6
0.89 0.89
2.75 2.73
2 5
0.88 0.89
2.81 2.65
Optimal number of principal components. Root mean square error of cross-validation estimated by leave-one-day-out cross-validation. One outlier was removed for each instrument.
965 nm, one extra PC was selected when using the leave-one-dayout cross validation, indicating a more complex model. The other models, especially the models using the wavelength region 605– 735 nm and two PCs, showed only slight increase in the RMSECV values compared to those with the leave-one-out cross-validation, indicating that these models are simple and robust. Therefore, the models using two PCs in Table 1 are used for further analyses and for comparison with previous findings based on leave-one-out cross-validation. The SNV pre-treatment improved the models, reducing the number of PCs, RMSECV, or both for the two instruments (Tables 1 and 2). In contrast, second-derivative pre-treatment deteriorated the freshness prediction models for both instruments (data not shown). The effect of the spectral pre-treatment for freshness prediction of salmon fillets are consistent with previous work on cod fillets (Sivertsen et al., 2011). The fit, with respect to the RMSECV value, was worse for salmon fillets (Tables 1 and 2) than for cod fillets (Sivertsen et al., 2011). This is most likely due to the fact that wavelengths below 600 nm cannot be used for modeling freshness because of the carotenoids in the salmon muscle. This wavelength region has been found to be important for freshness prediction of cod (Sivertsen et al., 2011). Predicting the storage time from the QI values by linear regression have given an RMSECV value of 2.0 days in a previous study (Sveinsdottir et al., 2002), where the maximum storage time of farmed Atlantic salmon in ice as whole fish has been determined as 20 days. The RMSECV value of 2.0 days was better than those of the models based on the spectroscopic measurements in this study (Tables 1 and 2). It should be noted that twelve assessors performed the QIM evaluation on a batch of three whole fish (Sveinsdottir et al., 2002), while spectroscopic measurement was performed on individual fillets. Assuming measurements taken from successive fish are statistically independent and unbiased, it correp sponds to a RMSECV value of 2.0 3 = 3.5 per fish. The QIM scheme for salmon fillets has not been developed and sensory evaluations of fillets are more difficult than for whole fish due to lack of important features, such as eyes, gills and abdomen, which are incorporated in the QIM scheme for whole salmon (Sveinsdottir et al., 2003, 2002). Therefore, the prediction results found in
this study are comparable to previously reported results using QIM. Previous work by Nilsen et al. (2002) has reported a RMSECV value of 1.2 days for the PLSR model without any spectral pretreatments in the region between 700 and 1100 nm. Their experiment used the same fillets throughout the storage period hence reducing the individual variation. In addition, their use of 10 PCs involved a high risk of overfit. Storage as fillet with exposed muscle would accelerate post-mortem processes, such as biochemical, chemical, physical and microbiological processes (Ólafsdóttir et al., 1997), hence yielding a greater spectral change in filletstored samples than those in whole-stored samples. Previous work concluded that 700–1100 nm is the most important region for freshness prediction of salmon fillets (Nilsen et al., 2002). However, the region above 735 nm was not important for the freshness prediction in this study (Tables 1 and 2). Possible reasons for this inconsistency are that (1) inclusion of the data in the region between 600 and 700 nm in analyses, and (2) pre-treatment with SNV to extract underlying spectral changes with storage time were not considered in their study. Spectra in the region above 735 nm, from salmon muscle, include the absorption bands of water, fat and protein and their contents in salmon muscles vary among individuals (Isaksson et al., 2002). Therefore, we speculate that the data in the region above 735 nm adds noise to freshness prediction especially when using SNV as a pre-treatment. The first PC of the best PLSR model (the imager model with wavelength region of 605–735 nm and SNV pre-treatment in Table 1) explains 91% and 70% of the variance in the explanatory and response variables, respectively. The corresponding loading vector shows a high contribution at the wavelengths of 606 and 636 nm (Fig. 3). Similar results were found in the PLSR model using two PCs for the handheld probe data (data not shown) and also in the previous study on cod fillets (Sivertsen et al., 2011). The feature at 636 nm in the loading vector corresponds to increases in methemoglobin (Olsen and Elvevoll, 2011) and metmyoglobin (Ottestad et al., 2011) during storage. The feature at 606 nm in the loading
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3.4. Classification between fresh and frozen-thawed fillets
Fig. 3. Loading vectors for the best PLSR model (RMSECV = 2.3 days) for freshness prediction in salmon fillets.
vector corresponds to the spectral variation influenced by changes in the status of heme due to storage (Sone et al., 2012a). Mapping the RMSECV for the PLSR models, which used two PCs and a spectral range of 605–735 nm after removal of one outlier (same as in Table 1), showed that the tail region was best for spectroscopic freshness prediction of salmon fillets (Fig. 4). Good predictions in the tail region are consistent with previous findings for cod fillets (Sivertsen et al., 2011). As mentioned in Sivertsen et al. (2011), the high quality of the prediction is probably because the fillet is thin in this region and the light received from the sample has interacted with the dark muscle close to the skin. The dark muscle has a high content of heme proteins (Venugopal and Shahidi, 1996) and is found near the skin and along the centerline (Zhou et al., 1995). In contrast, other thin regions, such as the belly region, showed worse predictions (Fig. 4).
Fresh and frozen-thawed fillets were linearly separable in PCA space using the non-pre-treated and pre-treated spectra from the loin region for both instruments (data not shown). Second-derivative pre-treatment (polynomial order, second; smoothing window, 40 nm-wide) seemed to be most effective on the separation for both instruments. The separation was achieved mainly along the first PC using the spectral range of 605–735 nm (Fig. 5). The explained variances are 87% (first PC) and 11% (second PC) for the hyperspectral imager and 85% (first PC) and 13% (second PC) for the handheld probe. The loading vector plots indicate that the variation at around 606 and 636 nm explains most of the classification ability along the first PC (Fig. 6). These important wavelengths for frozen-thawed classification are consistent with previous results for cod fillets (Sivertsen et al., 2011) and with those for freshness prediction in this study (Fig. 3). This consistency indicates that VIS/NIR spectroscopy detects common spectral changes in fish muscle during the freeze–thaw cycle and during chilled storage in ice. These changes are probably due to the changes in heme proteins as discussed above for freshness prediction. The frozen-thawed samples, stored as fillet, are more clearly separated from fresh samples than the frozen-thawed samples, stored as whole fish (Fig. 5). This is reasonable because the muscle in fillets is more exposed to air than that in whole fish, resulting in an increase in interactance at 636 nm during air storage, probably
(a)
(b)
Fig. 4. Root mean square error of cross-validation (RMSECV) for the PLSR models between SNV pre-treated interactance spectra and days in ice as a function of standard position on the fillet.
Fig. 5. PCA score plots for second-derivative pre-treated spectra from the thick loin region of fresh and frozen-thawed (FT) samples using (a) the hyperspectral imager and (b) the handheld probe.
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(a)
(b) Fig. 7. Classification rate (CR) using the KNN classifier to discriminate between fresh and frozen-thawed as a function of standard position on the fillet.
Fig. 6. PCA loading vectors for (a) imager and (b) handheld probe data from thick loin region.
due to the oxidation of heme proteins (Ottestad et al., 2011; Sone et al., 2012b). By mapping the classification rates, using KNN and the two wavelength bands at 606 nm and 636 nm after applying secondderivative pre-treatment, the highest classification rate of 100% was achieved in the thick loin region (Fig. 7). This region is consistent with previous findings for cod (Sivertsen et al., 2011). Despite the similar spectral changes during the freezing-thawing and chilled storage in ice, the best measurement regions were different; the thick loin region for frozen-thawed classification (Fig. 7) and the tail region for freshness prediction (Fig. 4). The thick loin region showed high errors in freshness prediction (Fig. 4), indicating that the spectral changes caused by chilled storage were not enough to predict storage time using the spectra from the thick loin region. This was probably because the muscle thickness in the loin region was sufficient to avoid the recorded light interacting with the dark muscle, which seemed to contribute to chilledstorage-dependent changes in the spectra from the tail region (Fig. 4). The freeze–thaw cycle caused sufficient spectral changes in the loin region (Fig. 2b) making it possible to separate frozenthawed fillets from 17-day-stored fresh fillets. It has been shown that the freeze–thaw cycle affects the muscle structure of Atlantic salmon (Sigurgisladottir et al., 2000) and that the muscle structure affects light scattering (Xia et al., 2008). Therefore, the change in scattering may also contribute to the frozen-thawed classification. The SNV can reduce the multiplicative
effect of scattering more efficiently compared to the second-derivative (Blanco et al., 2000). These may explain why the secondderivative improves the frozen-thawed classification, but not freshness prediction. In addition, storage-dependent spectral changes indicate increases in oxidized heme proteins in salmon muscles. This is supported by the previous findings for cod (Sivertsen et al., 2011), indicating that similar mechanisms affect spectral changes occurring in fish muscle during storage. Light attenuation in biological tissues, like fish muscles, arises from the combination of light absorption and scattering (Delpy and Cope, 1997; Pétursson, 1991). Considering changes in absorption and scattering separately will provide further understanding of storage-dependent spectral changes and thereby provide basic information for the spectroscopic quality assessment of fish fillets. Further work should also be conducted to clarify how the spectral changes are influenced by seasonal variations, handling methods and different storage conditions. 4. Conclusion The VIS/NIR interactance spectroscopy can detect spectral changes that allow freshness prediction and frozen-thawed classification of salmon. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozen-thawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previously reported results using sensory evaluation by the QIM with a group of trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is well suited to both freshness prediction and frozen-thawed classification of salmon fillets. The results indicate that the spectral changes, in this region, are explained mainly by oxidation of heme proteins during the freeze– thaw cycle and storage in ice. Acknowledgements This work was supported by the Fisheries Research Agency of Japan and the Norwegian Seafood Research Fund. The authors would like to thank Kjell Ø. Midling, Leif Akse, Mats Carlehög, Mette S. Wesmajervi Breiland, Sjurdur Joensen and Torbjørn Tobi-
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assen at Nofima for their contributions to sample preparation and the sensory and microbiological evaluations.
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Martinsdóttir, E., Sveinsdóttir, K., Luten, J.B., Schelvis-Smit, R., Hyldig, G., 2001. Reference manual for the fish sector: sensory evaluation of fish freshness. QIM Eurofish, IJmuiden, The Netherlands. Nilsen, H., Esaiassen, M., 2005. Predicting sensory score of cod (Gadus morhua) from visible spectroscopy. Lebensmittel-Wissenschaft Und-Technologie – Food Science and Technology 38 (1), 95–99. Nilsen, H., Esaiassen, M., Heia, K., Sigernes, F., 2002. Visible/near-infrared spectroscopy: a new tool for the evaluation of fish freshness? Journal of Food Science 67 (5), 1821–1826. Ólafsdóttir, G., Martinsdóttir, E., Oehlenschläger, J., Dalgaard, P., Jensen, B., Undeland, I., Mackie, I.M., Henehan, G., Nielsen, J., Nilsen, H., 1997. Methods to evaluate fish freshness in research and industry. Trends in Food Science & Technology 8 (8), 258–265. Olsen, S.H., Elvevoll, E.O., 2011. PH-induced shift in hemoglobin spectra: a spectrophotometeric comparison of Atlantic cod (Gadus morhua) and mammalian hemoglobin. Journal of Agricultural and Food Chemistry 59 (4), 1415–1422. Olsen, S.H., Sørensen, N.K., Larsen, R., Elvevoll, E.O., Nilsen, H., 2008. Impact of preslaughter stress on residual blood in fillet portions of farmed Atlantic cod (Gadus morhua) – measured chemically and by visible and near-infrared spectroscopy. Aquaculture 284 (1–4), 90–97. Ottestad, S., Sørheim, O., Heia, K., Skaret, J., Wold, J.P., 2011. Effects of storage atmosphere and heme state on the color and visible reflectance spectra of salmon (Salmo salar) fillets. Journal of Agricultural and Food Chemistry 59 (14), 7825–7831. Pétursson, J., 1991. Optical spectra of fish flesh and quality defects in fish. In: Pau, L.F., Olafsson, R. (Eds.), Fish Quality Control by Computer Vision. Marcel Dekker, New York, pp. 45–69. Pope, R.M., Fry, E.S., 1997. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Applied Optics 36 (33), 8710–8723. Richards, M.P., Hultin, H.O., 2002. Contributions of blood and blood components to lipid oxidation in fish muscle. Journal of Agricultural and Food Chemistry 50 (3), 555–564. Sigurgisladottir, S., Ingvarsdottir, H., Torrissen, O.J., Cardinal, M., Hafsteinsson, H., 2000. Effects of freezing/thawing on the microstructure and the texture of smoked Atlantic salmon (Salmo salar). Food Research International 33 (10), 857–865. Sivertsen, A.H., Chu, C.-K., Wang, L.-C., Godtliebsen, F., Heia, K., Nilsen, H., 2009. Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering 90 (3), 317–324. Sivertsen, A.H., Kimiya, T., Heia, K., 2011. Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy. Journal of Food Engineering 103 (3), 317–323. Sone, I., Olsen, R.L., Dahl, R., Heia, K., 2011. Visible/near-infrared spectroscopy detects autolytic changes during storage of Atlantic salmon (Salmo salar L.). Journal of Food Science 76 (3), S203–S209. Sone, I., Olsen, R.L., Heia, K., 2012a. Spectral changes of Atlantic salmon (Salmo salar L.) muscles during cold storage as affected by the oxidation state of heme. Journal of Agricultural and Food Chemistry 60 (38), 9719–9726. Sone, I., Olsen, R.L., Sivertsen, A.H., Eilertsen, G., Heia, K., 2012b. Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. Journal of Food Engineering 109 (3), 482–489. Sveinsdottir, K., Martinsdottir, E., Hyldig, G., Jørgensen, B., Kristbergsson, K., 2002. Application of Quality Index Method (QIM) scheme in shelf-life study of farmed Atlantic salmon (Salmo salar). Journal of Food Science 67 (4), 1570–1579. Sveinsdottir, K., Hyldig, G., Martinsdottir, E., Jørgensen, B., Kristbergsson, K., 2003. Quality Index Method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar). Food Quality and Preference 14 (3), 237–245. Venugopal, V., Shahidi, F., 1996. Structure and composition of fish muscle. Food Reviews International 12 (2), 175–197. Xia, J., Weaver, A., Gerrard, D.E., Yao, G., 2008. Heating induced optical property changes in beef muscle. Journal of Food Engineering 84 (1), 75–81. Zhou, S., Ackman, R.G., Morrison, C., 1995. Storage of lipids in the myosepta of Atlantic salmon (Salmo salar). Fish Physiology and Biochemistry 14 (2), 171– 178.
Contents lists available at SciVerse ScienceDirect
Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng
VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets Takashi Kimiya a,⇑, Agnar H. Sivertsen b, Karsten Heia b a b
National Research Institute of Fisheries Science, Fisheries Research Agency, 2-12-4 Fukuura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan Nofima, P.O. Box 6122, N-9291 Tromsø, Norway
a r t i c l e
i n f o
Article history: Received 27 June 2012 Received in revised form 29 November 2012 Accepted 11 January 2013 Available online 30 January 2013 Keywords: Freshness Hyperspectral Imaging VIS/NIR spectroscopy Salmon
a b s t r a c t Visible/near-infrared spectroscopy has been evaluated for use in freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets, where fresh samples were stored as whole fish in ice. A handheld interactance probe for performing rapid measurements of single fillets and an imaging spectrometer for online analysis at an industrial speed of one fillet per second, have been used. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozenthawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previous results using the Quality Index Method with trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is appropriate for both frozen-thawed classification and freshness prediction of salmon fillets. The results indicate that the spectral changes are explained mainly by oxidation of heme proteins during the freeze–thaw cycle and during chilled storage in ice. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Freshness, especially when it comes to perishable products such as seafood, is an ambiguous term comprised of many factors, mainly related to a sensory impression which should not be used unless it is well defined (Bremner and Sakaguchi, 2000). Freshness is reduced during storage due to biochemical, chemical, physical and microbiological processes (Ólafsdóttir et al., 1997). These processes are mainly affected by time and temperature (Ashie et al., 1996), but other factors such as pre-harvest stress and handling can influence bleeding (Olsen et al., 2008) and the rate at which spoilage occurs (Richards and Hultin, 2002). It is a common conception among consumers that fresh fish is caught/harvested and then stored chilled for a brief period of time prior to use. The consumer considers freshness as important when buying fish (Brunsø, 2003; Kole, 2003) and demands documentation of freshness (Hansen and Fischer, 2003). Frozen fish usually has a lower market price than fresh fish. All fresh fish sold in Norway must be labeled with the date of slaughter for farmed fish or catch date for wild fish (Lovdata, 2010) and frozen-thawed fishery products sold in Japan must be labeled accordingly (MAFF, 2007). For fish processors, fish retailers and food control authorities, it is difficult to objectively verify the freshness and frozen-thawed sta-
⇑ Corresponding author. Tel.: +81 45 788 7664; fax: +81 45 788 5001. E-mail addresses: [email protected] (T. Kimiya), agnarhs@nofima.no (A.H. Sivertsen), karstenh@nofima.no (K. Heia). 0260-8774/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jfoodeng.2013.01.008
tus of every fish or fillet without rapid and reliable methods to evaluate these properties. For the authorities responsible for performing food controls a small, portable instrument is required. The quality of fishery products depends heavily on the freshness of the raw material (Ólafsdóttir et al., 1997) and a system capable of measuring and documenting the quality of every fillet in production would be of great value to fisheries industry (Jørgensen et al., 2003). Several sensory, microbial, physical and instrumental methods have been evaluated for freshness assessment of seafood (Alasalvar et al., 2010; Ólafsdóttir et al., 1997; Sivertsen et al., 2011). Applications of biosensors and chemical sensors, and micro- and nanotechnologies for the freshness assessment have recently attracted considerable attention. These methods can be implemented in inexpensive, robust and portable devices that can give chemical and biological information useful for the freshness assessment (Alasalvar et al., 2010; Baldwin et al., 2011). However, many of these methods are either time consuming, destructive or require trained personnel, and are therefore not suited for online or large scale operations in contrast to spectroscopic techniques. Visible/ near-infrared (VIS/NIR) spectroscopy has shown promising results for assessing the frozen-thawed status and predicting the freshness as storage days in ice for a range of different fish species. For cod, the method can separate fresh from frozen-thawed whole fish or fillets and predict the freshness as storage days in ice with an accuracy of 1.6 days (Sivertsen et al., 2011). This method provides accurate and objective results, requires little or no training
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and can be implemented using a small handheld device as well as in an online instrument for non-contact measurements of fish fillets on a conveyer belt during production. Previous work has shown that most of the spectral changes in the visible region observed in cod muscle during chilled storage and the freeze–thaw cycle, can be explained by changes in the heme proteins (Sivertsen et al., 2011). Farmed Atlantic salmon contains high levels of the carotenoids astaxanthin and canthaxanthin (Johnston et al., 2006). These pigments absorb light in the wavelength region below 600 nm (Christophersen et al., 1989) overlapping most of the absorption bands of heme proteins (Olsen and Elvevoll, 2011; Ottestad et al., 2011). It can therefore be problematic to directly apply findings from cod to salmon. Several studies have demonstrated the potential of spectroscopic techniques for rapid and non-destructive freshness assessment on salmonid fish. Nilsen et al. (2002) have reported a wellfitted model for predicting freshness of salmon fillets with spectral data in the NIR region (700–1100 nm). Furthermore, it has been suggested that VIS/NIR spectroscopy can detect spoilage in rainbow trout fillets (Lin et al., 2006) and autolytic changes in Atlantic salmon fillets (Sone et al., 2011). However, spectral and spatial understanding and interpretation remain poor for freshness assessment of salmon fillets using VIS/NIR spectroscopic techniques. In this study, we have examined VIS/NIR interactance spectroscopy as a tool for freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets using hyperspectral imaging (HSI) and a handheld probe. Throughout this paper freshness is defined as storage days in ice of gutted and cleaned whole fish, assuming correct handling with respect to minimum stress during harvest, proper bleeding in running water and rapid cooling using fresh ice. Sensory evaluation using the Quality Index Method (QIM) and microbiological measurements were used to document and validate the storage regime used in this work. 2. Materials and methods 2.1. Fish samples Farmed Atlantic salmon (gutted weight between 2.3 and 6.3 kg) provided by the Aquaculture station in Tromsø (Norway) were slaughtered by a blow to the head, bled in running water, gutted, cleaned, and then stored in boxes with ice for up to 17 days. The boxes had holes for drainage and were kept in a cold room (2– 4 °C). Fresh ice was added daily to completely cover the fish. On days 0, 2, 5, 8, 12, and 17, five fish were selected at random, evaluated using the QIM scheme, filleted and then measured using VIS/ NIR spectroscopy. For classification of fresh and frozen-thawed salmon, spectroscopic measurements were performed on fresh (N = 30) and on frozen-thawed fillets (N = 18). Of the 18 frozen-thawed fillets, 8 fillets were from 4 frozen and stored whole fish, whereas 10 fillets were frozen and stored as fillet with skin. The whole fish and fillets were covered with plastic to reduce drying and then frozen and stored at 40 °C. Half of the fillets and half of the whole fish were frozen after 0-days storage in ice and the other half after 2-days storage in ice. After 3 weeks of frozen storage the fillets were thawed over night at 2–4 °C and the whole fish were thawed overnight in running water, filleted and then measured using VIS/NIR spectroscopy. 2.2. Sensory and microbiological evaluations of fresh samples Sensory evaluation, using the QIM for farmed Atlantic salmon (Martinsdóttir et al., 2001; Sveinsdottir et al., 2002), was performed on whole fish. The QIM was performed by an expert panel
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of three persons, where each person gave an individual Quality Index (QI) value for each sample (Nilsen and Esaiassen, 2005). The average value from the expert panel was then used as the QI value for each of the fresh samples. After filleting, microbial loads were measured in samples from left side fillets. Microbial load was determined as total viable count (TVC) and a selective count of H2S-producing bacteria on each measurement day, except for day 2, as described by Sveinsdottir et al. (2002) with some modifications. A 50 g portion of ordinary muscle was aseptically sampled from the thick loin region, located between neck and the anterior edge of the dorsal fin. Each sample was homogenized in sterile saline containing 0.9% (w/v) NaCl and 0.1% (w/v) peptone using a lab blender (the Stomacher 400, Seward, West Sussex, UK) for 2 min to obtain a 5-fold dilution. Further 10-fold dilutions were made as needed. After spread plating of the samples, plates were incubated at 12 °C for 4 days. Results were expressed as log10 of colony forming units per gram of fish muscle (log CFU/g). 2.3. Interactance imaging The HSI operated in interactance mode and data processing were performed as previously described (Sivertsen et al., 2011). The imaging spectrometer (VNIR-640, Norsk Elektro Optikk, Lørenskog Norway) has a field of view of 1 mm 300 mm and a spatial resolution, or pixel size, of 1.0 mm 0.5 mm. Each pixel is represented with a spectrum of variables, representing the recorded radiation in the region of 400–1000 nm, with a spectral resolution of approximately 10 nm. Each fillet was scanned line by line on a white diffuse conveyer belt at a speed of 400 mm per second. The imaging system was calibrated using a Teflon target (300 mm 300 mm 25 mm) before scanning the samples. 2.4. Handheld interactance spectroscopy Interactance spectroscopy with handheld probe system and its data processing were performed as previously described (Sivertsen et al., 2011). The system consisted of a spectrometer, XDS Optiprobe analyzer (FOSS NIRSystems, Laurel, USA), with the transmitting and receiving fiber bundles connected by an in-house made probe holder. Spectral data were recorded as absorbance units in the region of 400–2500 nm with a spectral resolution of 0.5 nm. Each spectrum was recorded as average of 32 spectra measured successively by holding the probe in contact with fillet, aligning the direction through both fiber bundles with the centerline of the fillet. The instrument was calibrated by transmission through air according to the manufacturer’s instruction. For spectral acquisition, two locations were selected: one in the middle of the loin region, as suggested by Nilsen et al. (2002) and the other in the tail region, adjacent to the centerline, as suggested by Sivertsen et al. (2011). 2.5. Data analysis Spectral pre-treatments and data analyses were performed according to Sivertsen et al. (2011). The Unscrambler software (ver. 9.8, CAMO, Oslo, Norway) was used for building and evaluating partial least square regression (PLSR) models between the spectral data, both for the handheld probe data and the HSI data, and freshness, quantified as storage days in ice. The optimal number of principal components (PCs) was determined by the software. The principal component analysis (PCA) of the spectral data, was also performed using the Unscrambler software. The IDL 7.1 and ENVI 4.7 (ITT Visual Information Solutions, Boulder, USA) were used for analyses of the HSI data to map the prediction and classification results.
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Interactance was calculated as I(k) = ln(Ri(k)/Ra(k)), where Ri(k) and Ra(k) are the spectral data recorded from the salmon fillet and the reference material, respectively. Segmentation of the fillet region from the hyperspectral images was achieved using the centerline as a reference system (Sivertsen et al., 2009), using the 585 nm band instead of 525 nm as the blood absorption band. The 585 nm band was used to avoid the absorption by the carotenoids in the salmon muscle. The belly flap area was included in the prediction and classification maps because the linings were trimmed from the salmon fillets in contrast to the cod fillets. The handheld data were down sampled by applying a 10 nm wide Gaussian filter and then selecting wavelengths to be comparable to the imaging data in the region 400–1000 nm before spectral pre-treatments and analyses. Standard normal variate (SNV) and Savitzky–Golay secondderivative were used as spectral pre-treatment methods. The PLSR and k-nearest neighbor (KNN) classifier with leave-one-out crossvalidation were used for freshness prediction and frozen-thawed classification, respectively. Storage days in ice (0, 2, 5, 8, 12, and 17 days) were used as reference value to develop PLSR models. The leave-one-out cross-validation were chosen for comparing the PLSR performance in this study with those in previous works (Nilsen et al., 2002; Sivertsen et al., 2011; Sveinsdottir et al., 2002). In addition, segmented cross-validation was applied in which all samples from one storage day were used as a validation set, while the model was constructed using the remaining data. We refer to this as leave-one-day-out cross-validation, and the procedure outlined above was repeated for all storage days. The PLSR performance was evaluated with the root mean square error of cross-validation (RMSECV) for the freshness predictions. The PCA was performed to investigate separability between fresh and frozen-thawed samples. The separating line between fresh and frozen-thawed samples was calculated using the Rosenblatt’s perceptron, as explained in Sivertsen et al. (2011).
3.2. Spectral characteristics A wide peak centered at 500 nm can be seen in interactance spectra from the salmon muscle (Fig. 2). This peak, which is due to absorption by carotenoids, such as astaxanthin and canthaxanthin (Christophersen et al., 1989), masks out most of the spectral characteristics of the heme proteins, previously found to be important for the freshness prediction and frozen-thawed classification of cod fillets (Sivertsen et al., 2011). The intensity of the carotenoid peak varied greatly among individual fish and the data in the region below 600 nm had a negative effect on the prediction and classification results (data not shown). Absorption by water is observed as a peak centered at 970 nm and as the weaker peak centered at 760 nm (Fig. 2) (Isaksson et al., 2002). A shoulder at 606 nm can also be related to absorption by water (Sone et al., 2012a), specifically the fourth overtone of the OH stretching band at 605 nm (Pope and Fry, 1997).
3.3. Freshness prediction The SNV pre-treated spectra recorded from the tail region, using both the imager and the handheld probe, resulted in freshness-predicting models with only two PCs and a root mean square error of leave-one-out cross-validation of 2.3 and 2.7 days, respectively (Table 1). The RMSECV values of the handheld probe models using four or more PCs increased by using the leave-one-day-out crossvalidation (Table 2), indicating that these three models were overfitted. For the imager model, using the wavelength region 605–
(a)
3. Results and discussion 3.1. Sensory and microbiological analysis The QI value of fresh fish increased linearly with storage days in ice from 1.0 ± 0.4 on day 0 to 14.5 ± 1.3 (mean ± SD) on day 17 (Fig. 1). The microbial loads on fillets increased from <1 log CFU/g on day 0 to 3.9 ± 0.5 log CFU/g (TVC) and 3.3 ± 0.4 log CFU/g (the count of H2S-producing bacteria) on day 17. These sensory and microbiological profiles are comparable to previous findings on farmed Atlantic salmon stored in ice (Sveinsdottir et al., 2002). This verifies that a proper storage regime has been used.
Fig. 1. Quality Index of salmon tested.
(b)
Fig. 2. Mean interactance spectra with standard deviation collected at (a) the tail region of fresh samples for each day and (b) the thick loin region of the fresh and frozen-thawed (FT) samples, for both instruments.
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T. Kimiya et al. / Journal of Food Engineering 116 (2013) 758–764 Table 1 Parameters describing the PLSR models for the imager and the handheld probe data from the tail region of fresh samples based on the leave-one-out cross-validation. Instrument
Wavelength region (nm)
No pre-treatment a
Imager
c
Probec a b c
SNV # of PCsa
Correlation
RMSECVb
2.50 2.49
2 3
0.91 0.91
2.31 2.34
2.42 2.02
2 5
0.89 0.95
2.70 1.91
# of PCs
Correlation
RMSECV
605–735 605–965
4 4
0.90 0.90
605–735 605–965
4 6
0.91 0.94
b
Optimal number of principal components. Root mean square error of cross-validation estimated by leave-one-out cross-validation. One outlier was removed for each instrument.
Table 2 Parameters describing the PLSR models for the imager and the handheld probe data from the tail region of fresh samples based on the leave-one-day-out cross-validation. Instrument
a b c
Wavelength region (nm)
No pre-treatment
SNV
# of PCsa
Correlation
RMSECVb
# of PCsa
Correlation
RMSECVb
Imagerc
605–735 605–965
4 5
0.89 0.90
2.54 2.44
2 4
0.90 0.92
2.41 2.14
Probec
605–735 605–965
4 6
0.89 0.89
2.75 2.73
2 5
0.88 0.89
2.81 2.65
Optimal number of principal components. Root mean square error of cross-validation estimated by leave-one-day-out cross-validation. One outlier was removed for each instrument.
965 nm, one extra PC was selected when using the leave-one-dayout cross validation, indicating a more complex model. The other models, especially the models using the wavelength region 605– 735 nm and two PCs, showed only slight increase in the RMSECV values compared to those with the leave-one-out cross-validation, indicating that these models are simple and robust. Therefore, the models using two PCs in Table 1 are used for further analyses and for comparison with previous findings based on leave-one-out cross-validation. The SNV pre-treatment improved the models, reducing the number of PCs, RMSECV, or both for the two instruments (Tables 1 and 2). In contrast, second-derivative pre-treatment deteriorated the freshness prediction models for both instruments (data not shown). The effect of the spectral pre-treatment for freshness prediction of salmon fillets are consistent with previous work on cod fillets (Sivertsen et al., 2011). The fit, with respect to the RMSECV value, was worse for salmon fillets (Tables 1 and 2) than for cod fillets (Sivertsen et al., 2011). This is most likely due to the fact that wavelengths below 600 nm cannot be used for modeling freshness because of the carotenoids in the salmon muscle. This wavelength region has been found to be important for freshness prediction of cod (Sivertsen et al., 2011). Predicting the storage time from the QI values by linear regression have given an RMSECV value of 2.0 days in a previous study (Sveinsdottir et al., 2002), where the maximum storage time of farmed Atlantic salmon in ice as whole fish has been determined as 20 days. The RMSECV value of 2.0 days was better than those of the models based on the spectroscopic measurements in this study (Tables 1 and 2). It should be noted that twelve assessors performed the QIM evaluation on a batch of three whole fish (Sveinsdottir et al., 2002), while spectroscopic measurement was performed on individual fillets. Assuming measurements taken from successive fish are statistically independent and unbiased, it correp sponds to a RMSECV value of 2.0 3 = 3.5 per fish. The QIM scheme for salmon fillets has not been developed and sensory evaluations of fillets are more difficult than for whole fish due to lack of important features, such as eyes, gills and abdomen, which are incorporated in the QIM scheme for whole salmon (Sveinsdottir et al., 2003, 2002). Therefore, the prediction results found in
this study are comparable to previously reported results using QIM. Previous work by Nilsen et al. (2002) has reported a RMSECV value of 1.2 days for the PLSR model without any spectral pretreatments in the region between 700 and 1100 nm. Their experiment used the same fillets throughout the storage period hence reducing the individual variation. In addition, their use of 10 PCs involved a high risk of overfit. Storage as fillet with exposed muscle would accelerate post-mortem processes, such as biochemical, chemical, physical and microbiological processes (Ólafsdóttir et al., 1997), hence yielding a greater spectral change in filletstored samples than those in whole-stored samples. Previous work concluded that 700–1100 nm is the most important region for freshness prediction of salmon fillets (Nilsen et al., 2002). However, the region above 735 nm was not important for the freshness prediction in this study (Tables 1 and 2). Possible reasons for this inconsistency are that (1) inclusion of the data in the region between 600 and 700 nm in analyses, and (2) pre-treatment with SNV to extract underlying spectral changes with storage time were not considered in their study. Spectra in the region above 735 nm, from salmon muscle, include the absorption bands of water, fat and protein and their contents in salmon muscles vary among individuals (Isaksson et al., 2002). Therefore, we speculate that the data in the region above 735 nm adds noise to freshness prediction especially when using SNV as a pre-treatment. The first PC of the best PLSR model (the imager model with wavelength region of 605–735 nm and SNV pre-treatment in Table 1) explains 91% and 70% of the variance in the explanatory and response variables, respectively. The corresponding loading vector shows a high contribution at the wavelengths of 606 and 636 nm (Fig. 3). Similar results were found in the PLSR model using two PCs for the handheld probe data (data not shown) and also in the previous study on cod fillets (Sivertsen et al., 2011). The feature at 636 nm in the loading vector corresponds to increases in methemoglobin (Olsen and Elvevoll, 2011) and metmyoglobin (Ottestad et al., 2011) during storage. The feature at 606 nm in the loading
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3.4. Classification between fresh and frozen-thawed fillets
Fig. 3. Loading vectors for the best PLSR model (RMSECV = 2.3 days) for freshness prediction in salmon fillets.
vector corresponds to the spectral variation influenced by changes in the status of heme due to storage (Sone et al., 2012a). Mapping the RMSECV for the PLSR models, which used two PCs and a spectral range of 605–735 nm after removal of one outlier (same as in Table 1), showed that the tail region was best for spectroscopic freshness prediction of salmon fillets (Fig. 4). Good predictions in the tail region are consistent with previous findings for cod fillets (Sivertsen et al., 2011). As mentioned in Sivertsen et al. (2011), the high quality of the prediction is probably because the fillet is thin in this region and the light received from the sample has interacted with the dark muscle close to the skin. The dark muscle has a high content of heme proteins (Venugopal and Shahidi, 1996) and is found near the skin and along the centerline (Zhou et al., 1995). In contrast, other thin regions, such as the belly region, showed worse predictions (Fig. 4).
Fresh and frozen-thawed fillets were linearly separable in PCA space using the non-pre-treated and pre-treated spectra from the loin region for both instruments (data not shown). Second-derivative pre-treatment (polynomial order, second; smoothing window, 40 nm-wide) seemed to be most effective on the separation for both instruments. The separation was achieved mainly along the first PC using the spectral range of 605–735 nm (Fig. 5). The explained variances are 87% (first PC) and 11% (second PC) for the hyperspectral imager and 85% (first PC) and 13% (second PC) for the handheld probe. The loading vector plots indicate that the variation at around 606 and 636 nm explains most of the classification ability along the first PC (Fig. 6). These important wavelengths for frozen-thawed classification are consistent with previous results for cod fillets (Sivertsen et al., 2011) and with those for freshness prediction in this study (Fig. 3). This consistency indicates that VIS/NIR spectroscopy detects common spectral changes in fish muscle during the freeze–thaw cycle and during chilled storage in ice. These changes are probably due to the changes in heme proteins as discussed above for freshness prediction. The frozen-thawed samples, stored as fillet, are more clearly separated from fresh samples than the frozen-thawed samples, stored as whole fish (Fig. 5). This is reasonable because the muscle in fillets is more exposed to air than that in whole fish, resulting in an increase in interactance at 636 nm during air storage, probably
(a)
(b)
Fig. 4. Root mean square error of cross-validation (RMSECV) for the PLSR models between SNV pre-treated interactance spectra and days in ice as a function of standard position on the fillet.
Fig. 5. PCA score plots for second-derivative pre-treated spectra from the thick loin region of fresh and frozen-thawed (FT) samples using (a) the hyperspectral imager and (b) the handheld probe.
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(a)
(b) Fig. 7. Classification rate (CR) using the KNN classifier to discriminate between fresh and frozen-thawed as a function of standard position on the fillet.
Fig. 6. PCA loading vectors for (a) imager and (b) handheld probe data from thick loin region.
due to the oxidation of heme proteins (Ottestad et al., 2011; Sone et al., 2012b). By mapping the classification rates, using KNN and the two wavelength bands at 606 nm and 636 nm after applying secondderivative pre-treatment, the highest classification rate of 100% was achieved in the thick loin region (Fig. 7). This region is consistent with previous findings for cod (Sivertsen et al., 2011). Despite the similar spectral changes during the freezing-thawing and chilled storage in ice, the best measurement regions were different; the thick loin region for frozen-thawed classification (Fig. 7) and the tail region for freshness prediction (Fig. 4). The thick loin region showed high errors in freshness prediction (Fig. 4), indicating that the spectral changes caused by chilled storage were not enough to predict storage time using the spectra from the thick loin region. This was probably because the muscle thickness in the loin region was sufficient to avoid the recorded light interacting with the dark muscle, which seemed to contribute to chilledstorage-dependent changes in the spectra from the tail region (Fig. 4). The freeze–thaw cycle caused sufficient spectral changes in the loin region (Fig. 2b) making it possible to separate frozenthawed fillets from 17-day-stored fresh fillets. It has been shown that the freeze–thaw cycle affects the muscle structure of Atlantic salmon (Sigurgisladottir et al., 2000) and that the muscle structure affects light scattering (Xia et al., 2008). Therefore, the change in scattering may also contribute to the frozen-thawed classification. The SNV can reduce the multiplicative
effect of scattering more efficiently compared to the second-derivative (Blanco et al., 2000). These may explain why the secondderivative improves the frozen-thawed classification, but not freshness prediction. In addition, storage-dependent spectral changes indicate increases in oxidized heme proteins in salmon muscles. This is supported by the previous findings for cod (Sivertsen et al., 2011), indicating that similar mechanisms affect spectral changes occurring in fish muscle during storage. Light attenuation in biological tissues, like fish muscles, arises from the combination of light absorption and scattering (Delpy and Cope, 1997; Pétursson, 1991). Considering changes in absorption and scattering separately will provide further understanding of storage-dependent spectral changes and thereby provide basic information for the spectroscopic quality assessment of fish fillets. Further work should also be conducted to clarify how the spectral changes are influenced by seasonal variations, handling methods and different storage conditions. 4. Conclusion The VIS/NIR interactance spectroscopy can detect spectral changes that allow freshness prediction and frozen-thawed classification of salmon. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozen-thawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previously reported results using sensory evaluation by the QIM with a group of trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is well suited to both freshness prediction and frozen-thawed classification of salmon fillets. The results indicate that the spectral changes, in this region, are explained mainly by oxidation of heme proteins during the freeze– thaw cycle and storage in ice. Acknowledgements This work was supported by the Fisheries Research Agency of Japan and the Norwegian Seafood Research Fund. The authors would like to thank Kjell Ø. Midling, Leif Akse, Mats Carlehög, Mette S. Wesmajervi Breiland, Sjurdur Joensen and Torbjørn Tobi-
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assen at Nofima for their contributions to sample preparation and the sensory and microbiological evaluations.
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