Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection

Trends in Food Science & Technology 34 (2013) 18e31

Review

Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection Jun-Hu Chenga, Qiong Daia, Da-Wen Suna,b,*, Xin-An Zenga, Dan Liua and Hong-Bin Pua

The traditional techniques and methods for evaluation and detection of fish quality and safety are tedious, laborious, expensive and time-consuming while spectroscopic techniques have successfully overcome some of these disadvantages and can supplement or replace them. There are growing interests in spectroscopic techniques due to high specificity, convenience, non-destructive, non-invasive, costeffective and quick response. Spectroscopic techniques have shown great potentials for the detection of pathogens, foreign contamination, protein structure changes, and lipid oxidation, and for spoilage monitoring in fish in order to confirm whether it is safe for consumption and international trades or not. This review focuses on several valuable spectroscopic techniques including visible (VIS) spectroscopy, near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and spectral imaging mainly related to hyperspectral imaging (HSI) and nuclear magnetic resonance imaging (NMRI). Moreover, the advantages and limitations of these techniques are noted and some perspectives about the current work are also presented.

a

Academy of Contemporary Food Engineering, College of Light Industry and Food Science, South China University of Technology, Guangzhou 510641, China b Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland (Tel.: D353 1 7167342; fax: D353 1 7167493; e-mails: dawen.sun@ ucd.ie)

Fish quality and safety is a scientific discipline describing handling, preparation, processing, transportation and storage condition in ways that prevent food-borne illness and provide fish and fish products with premium quality for human health and the acceptance of consumers. However, it is well-known that fish is one of the most vulnerable and perishable aquatic products, and it serves as a growth medium for microorganisms that can be pathogenic or cause fish spoilage. Therefore, it is imperative to pay close attention to fish quality and safety.

URL: http://www.ucd.ie/refrig, http://www.ucd.ie/sun * Corresponding author. 0924-2244/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tifs.2013.08.005

Introduction The market of fish and fish products is growing continuously as fish and fish products are commercially important for international trade as well as widely consumed muscle food. However, there are many problems and challenges associated with the evaluation of fish quality and safety at industrial level. It is widely known that fish and fish products are mainly composed of moisture, protein, fat and other compositions that contribute to fish quality and safety. Furthermore, fish quality and safety is also mainly influenced by diverse processes related to storage methods, time and temperature. In addition, changes in color, texture, juiciness, flavor and biochemical properties of fish are important factors that affect consumers’ sensory evaluation of fish quality and their decisions in making a second purchase. On the other hand, fish is considered as the best sources of good fats, vitamins, and minerals to promote good human health (Forn
e, Abi
an, & Cerda, 2010), thus it is a significant part of our daily diet, providing roughly 40% of the protein intake, and is consumed by nearly twothirds of the world’s population. Therefore, maintaining good quality and safety is of utmost importance in production and trade of fish and fishery products, hence it is

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necessary to develop fish quality evaluation and inspection techniques. On one hand, there are many well established traditional analytical techniques and methods available, including sensory evaluation based on quality index method (PonsSanchez-Cascado, Vidal-Carou, Nunes, & Veciana-Nogues, 2006), microbial inspection based on total viable counts (Song, Luo, You, Shen, & Hu, 2011), biochemical methods related to high-performance liquid chromatography (Mendes, Cardoso, & Pestana, 2009), solid-phase microextraction gas chromatographyemass spectrometry (Iglesias et al., 2009), two-dimensional difference gel electrophoresis (Addis et al., 2012), and proteome analysis (Carrera, Ca~ nas, & Gallardo, 2012). These techniques and methods play a pivotal role in current industrial fish quality and safety evaluation and inspection and some of them have been used as gold standards and regulation methods serving scientific researches due to their relative validity and accuracy. However these techniques and methods are normally expensive, timeconsuming, laborious, tedious, and require highly skilled operators and are not suitable for on/in-line monitoring. In order to surmount the aforementioned disadvantages, there is a need for complementary techniques to detect quality parameters and safety threats in the field of rapid screening. With recent technological progress in photonics and optics, many non-destructive, fast and cost-effective spectroscopic techniques have been developed for food quality and safety assessment and inspection and for online monitoring (van den Berg, Lyndgaard, Sørensen, & Engelsen, 2012). With regard to fish quality and safety, Uddin and Okazaki (2010) reviewed the applications of vibrational spectroscopy to the analysis of fish and other aquatic food products. In a recent study, another review published was intended to provide an overview of application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish,

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fish products, and seafood (Cozzolino & Murray, 2012). However, no review is available to specifically address the application of several spectroscopic techniques including visible (VIS) spectroscopy (Abdel-Nour, Ngadi, Prasher, & Karimi, 2011; Antonucci et al., 2011; Zhu, Cheng, Wu, & He, 2011), near-infrared (NIR) spectroscopy (Alexandrakis, Downey, & Scannell, 2012; Magwaza et al., 2012; Poji
c & Mastilovi
c, 2013), mid-infrared (MIR) spectroscopy (Boubellouta & Dufour, 2012; Woodcock, Fagan, O’Donnell, & Downey, 2008; Wu, Nie, He, & Bao, 2012), Raman spectroscopy (G€unaydın, S¸ir, Kavlak, G€uner, & Mutlu, 2010; Liu, et al., 2013; Lu, Al-Qadiri, Lin, & Rasco, 2011; Sowoidnich, Schmidt, Maiwald, Sumpf, & Kronfeldt, 2010), nuclear magnetic resonance (NMR) spectroscopy (Ko, Cheng, Chen, & Hsieh, 2012; Rodr
ıguez, Eim, Simal, Femenia, & Rossell
o, 2013; Santagapita et al., 2012; Shao & Li, 2011; Sivam, Waterhouse, Zujovic, Perera, & Sun-Waterhouse, 2013), and spectral imaging (ElMasry, Sun, & Allen, 2011, 2012; Kamruzzaman, ElMasry, Sun, & Allen, 2011) for assessing, measuring and predicting the quality of fish and for quantitative and qualitative analysis of fish and related products. Therefore, the objective of this paper is to review applications of these spectroscopic techniques for fish quality and safety evaluation and inspection. Fig. 1 compares the spectral ranges used in these spectroscopic techniques. Applications The objective of determining fish quality is to provide the consumer wholesome, tasty and safe fish muscle at a reasonable price. Evaluation and inspection of fish quality is also critical for preparation of consistent quality fish products. In the past few years, the potential of using spectroscopic techniques for objective and non-contact fish quality measurements has been proved in the industry. However, due to the complex and enormous amount of hidden information

Fig. 1. Schematic representation of the electromagnetic spectrum (Santos et al., 2010).

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in spectral data, particular attention should be paid to data mining with chemometrics for both qualitative and quantitative analysis. Fig. 2 shows the application of chemometric methods for spectroscopic analysis. With the developments of chemometric methods, many spectroscopic techniques including visible spectroscopy, near-infrared spectroscopy, mid-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy and spectral imaging have been successfully developed for fish quality and safety evaluation and inspection. Visible (VIS) spectroscopy VIS spectroscopy is a kind of electromagnetic spectrum that can be perceived by the human eyes. The range of the VIS spectrum wavelength is generally in 380e780 nm, and different wavelength of VIS indicates different perceived color. Such as, 770e622 nm (red), 622e597 nm (orange), 597e577 nm (yellow), 577e492 nm (green), 492e455 nm (indigo), 455e380 nm (purple). In this wavelength region, absorption spectra are original from the transition of electrons from their ground state to higher electronic states. The maximum absorption in this spectral region for some particular compounds corresponds with the structure, geometry and symmetry of the material. The most known expression of this principle is the development of compounds related to color in many analytical methods, which can be captured by spectroscopy in the visible region (Scotter, 1997). Thus, in fish analysis, color measurement that can make a general response to the sensory evaluation of fish freshness quality is probably the most important use of the visible region, and is beneficial to an extensive variety of color-sorting tasks implemented in the fish industry.

One of the most common applications of VIS spectroscopy in fish quality evaluation is the measurement of fish freshness. It has been well-known that different storage methods affect the quality of fish, as during storage, the quality of fish will gradually change until spoilage occurs, thus influencing the freshness. Based on the storage conditions, the freshness as affected with storage time can be estimated by VIS spectroscopy. Nilsen, Esaiassen, Heia, and Sigernes (2002) used VIS spectroscopy to evaluate the freshness quality of cod in ice, and the correlation between spectral data and storage time was modeled by multivariate statistics. The best-fit model was found by using the visible wavelength range, giving correlation of prediction of 0.97 with an error value of 1.04 days. A similar work was undertaken by Heia et al. (2003, pp. 201e209) to investigate the suitability of VIS spectroscopy with a transmission mode as a tool to estimate the freshness of cod and hake. The best-fit model for iced cod and frozen stored hake showed correlation of prediction of 0.98, 0.96 with an error value of 0.97 days, 48 days, respectively. These two studies illustrate that VIS spectroscopy enables rapid, non-instructive and low-cost measurements for the evaluation of fish freshness and is capable of satisfying the needs in the fish section for development of industrial methods. Moreover, as reported by Nilsen and Esaiassen (2005), VIS spectroscopy was used to predict the QIM (quality index method) score of cod, and it was shown that a relatively narrow band in the visible spectral range was sufficient for measuring fish freshness. In a recent study, VIS spectroscopy for differentiating between fresh and frozen-thawed cod fillets and for assessing the freshness as days on ice has been evaluated. Results showed that frozen-thawed

Calibration set

Spectral extraction

Baseline calibration

Smoothing

Data preprocessing

Normalization

MSC

SNV

FT, WT

MLR

PCR

PLSR

ANN

Spectrum selecting

Calibration

Model building Spectral analysis Model Validation set

Model validation

Validation result

Fig. 2. Flow chart of the routine for NIR spectroscopy processing. MSC: Multiplicative scatter correction; SNV: Standard normal variation; FT: Fourier transform, WT: Wavelet transform; MLR: Multiple linear regression; PCR: Principal component regression; PLSR: Partial least squares regression; ANN: Artificial neural network.

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cod fillets could be fully separated from fresh fillets using a small subset of wavelengths in the visible region (Sivertsen, Heia, et al., 2011; Sivertsen, Kimiya, et al., 2011). VIS spectroscopy can also be used to discriminate European sea bass quality cultured under different conditions. The results of this analysis demonstrated that spectral measurements could better discriminate individual animals at 48 h postmortem (87% in the independent test) with respect to 96 h postmortem (66.7% in the independent test) (Costa et al., 2011). Results of these studies proved that VIS spectroscopy is suitable for measuring the quality of fish and fish fillets and is also a direct extension of the human sensory assessment with great value for the fish processing industry. Near-infrared (NIR) spectroscopy According to American Society for Testing and Materials (ASTM), the wavelength region of NIR spectrum is about 780e2526 nm. NIR spectrum instrument wavelength range is usually divided into two sections, namely, short wave near infrared spectral region (SW-NIR) of 780e1100 nm and long wave near infrared spectral region (LW-NIR) of 1100e2526 nm. In the NIR spectral regions, the overtones and combinations of fundamental vibration responses occur, which contains feature information from the chemical bonds (such as OeH, NeH, CeH and CeO, etc.) of organic molecules (Tan et al., 2012), and NIR instrument works on the principle of absorption, reflection, transmission and/or scattering of light in or through a food material following the Beer Lambert law. Generally absorption or reflectance of light in known range of wavelengths is used to measure and correlate with various quality parameters of food material, therefore NIR spectroscopy has established itself as a useful analytical technique in the food industry with data mining and data processing based on chemometric methods. In recent years, qualitative and quantitative applications of NIR spectroscopy have been developed in the fish sector. These applications include detection of microbial behavior and spoilage, measurements of chemical compositions and evaluation of fish freshness. Microbial detection NIR spectroscopy is a useful and reliable technique for detecting microbial behavior and spoilage in fish. It is widely believed that the occurrence of microorganisms especially bacteria and their active or passive dissemination to some extent can affect fish quality during storage and processing, therefore microbiological safety criteria must be met (Sone, Olsen, Dahl, et al., 2011; Sone, Olsen, Sivertsen, et al., 2011), as it is mostly important to safeguard against food-borne diseases, to ensure public health, and to maintain the high quality of fish and fish products. Quantitative or qualitative bacterial measurements in fish often lead to perceptions and decisions on its acceptability for consumption and trade. However, conventional

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microbiological measurement methods related to sample collection, preparation and testing are really timeconsuming and tedious, which might be critical or unsatisfactory for the needs of real-time monitoring. In order to overcome the disadvantages of traditional methods, NIR spectroscopy as a valuable and non-destructive technique is of significance for bacterial detection and has been proved to be a promising technology for rapid and accurate detection of fish quality. In recent studies, the possibility of using SW-NIR spectroscopy to detect the onset of spoilage and to quantify microbial loads in rainbow trout was investigated. The results indicated that SW-NIR spectroscopy in combination with multivariate statistical methods based upon PCA and PLS models could be used to detect and monitor the spoilage process in rainbow trout and to rapidly and accurately quantify microbial loads (Lin, Mousavi, AlHoly, Cavinato, & Rasco, 2006). Meanwhile, as reported by Sone, Olsen, Dahl, et al. (2011) and Sone, Olsen, Sivertsen, et al. (2011), NIR spectroscopy was used to investigate spectroscopic changes occurring during storage of Atlantic salmon fillets with and without bacterial growth. It was observed that NIR spectroscopy had the ability of detecting autolytic changes occurring in salmon muscle during the early stage of storage, independent of microbial growth. In addition, most recently, Tito, Rodemann, and Powell (2012) used NIR spectroscopy to predict microbial spoilage on Atlantic salmon. PCA and PLSR models were developed and clear separation was shown between the fresh salmon fillets and those stored for 9 days at 4
C, indicating that NIR spectroscopy could be capable of detecting fish spoilage. In view of these studies, it can be clearly indicated that the application of NIR spectroscopy is valuable and promising in bacteriology and fish safety inspection. Protein structure and protein content Protein is one of the most important nutritional components of fish. The protein structural changes in different handling and storage conditions have an effect on fish quality. NIR spectroscopy has been proved to be a valuable tool for the investigation of molecular mechanism of protein structure reactions and of protein folding, unfolding and misfolding. In addition, some relevant studies have also been reported for predicting and monitoring the secondary structure of proteins by NIR or FT-NIR spectroscopy. For example, as studied by Carton, B€ocker, Ofstad, Sørheim, and Kohler (2009), FT-NIR microspectroscopy was used to investigate the changes in the myofibrillar proteins in salmon muscle due to dry salting and smoking. It was shown that salting time mostly contributed to the amide I region, revealing that secondary structural changes of proteins were primarily affected by salting. The main variation in the amide II region was caused by smoking. In a recent work, Masoum, Alishahi, Farahmand, Shekarchi, and Prieto (2012) focused on the prediction of the crude protein in fish using NIR spectroscopy by scanning spectra in the range of 1000e2500 nm with PLS multivariate regression model. This study proved

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that application of NIR spectroscopy with PLS was a suitable technique for assessing the protein content of fish. Fat content Fat content is another one of the most important quality criteria of fish, not only from a nutritional point of view, but also due to its sensory and functional properties. However, conventional chemical analytical methods for fat content measurements are highly destructive and time-consuming, which requires the use of hazardous chemicals that may be harmful to analysts and the environment. NIR spectroscopy offers many possibilities for determination of fat content in fish. For instance, Nielsen, Hyldig, Nielsen, and Nielsen (2005) studied the fat content in herring based on three analytical methods of fat-meter, solvent extraction and NIR spectroscopy measurements. It was found that NIR spectroscopy measurements had high correlation with solvent extraction measurements but low correlation with measurements made by the fat-meter. A similar study was reported by Khodabux, L’Omelette, Jhaumeer-Laulloo, Ramasami, and Rondeau (2007) on tuna fish and yellow fin and a calibration model based on NIR spectra was established with the reference methods, namely acid-hydrolysis method for total fat and Soxhlet method for free fat, with good correlations being obtained. Moreover, a NIR spectrometer equipped with surface interactance optical fiber probe (780e1100 nm) was used to determine the fat content in intact sardine. PLSR model was developed and relatively good performance was shown with regression coefficients higher than 0.9 and errors below 1% on a fresh weight basis (Uddin, Turza, & Okazaki, 2007). Experiments have also been carried out to use NIR spectroscopy for predicting fat concentration in livefish and the result had a good correlation with chemical reference values of R ¼ 0.94 (Folkestad et al., 2008). In addition, the use of FT-NIR spectroscopy to monitor lipids extracted from hake fillets during frozen storage was reported. Kramer shear resistance was used as a marker of texture changes and lipid damage was also investigated by following the development of conjugated dienes and free fatty acids using spectrophotometric methods. Results showed that the intensity of the free fatty acid carboxylic n(C]O) band measured by attenuated total reflectance FT-NIR spectroscopy could be used for monitoring the development of lipid hydrolysis in hakelipids (S
anchez-Alonso, Carmona, & Careche, 2011). The above investigations proved that NIR spectroscopy has the potential for rapid, accurate and non-destructive determination of fat contents in fishes. Salt content Like protein and fat contents, salt content also plays a significant role in fish storage quality and NIR spectroscopy has been investigated for measuring salt content in fish. For example, Lin, Cavinato, Huang, and Rasco (2003) developed SW-NIR spectroscopy technique for predicting salt content in the hot smoked Pacific king and chum salmon.

Spectra were collected in the diffuse reflectance mode, and the results indicated that the salt content varied by more than 1% between the dorsal and ventral sections of a single sample. SW-NIR spectroscopy was also used to determine the salt content in smoked fish products with back-propagation neural network method, and it was proved that this technique had the potential for non-destructive determination of salt content and was feasible to adopt this technology in the aquatic foods processing industry (Huang, Cavinato, Mayes, Bledsoe, & Rasco, 2006). Freshness Generally speaking, frozen fish has a much lower market price than fresh fish. Therefore, the substitution of frozenthawed for fresh fish is a significant authenticity issue. Differentiating fresh and frozen-thawed fish using NIR spectroscopy has been carried out and promising performance was achieved compared with the traditionally quality methods (Bøknæs, Jensen, Andersen, & Martens, 2002). The quality of frozen minced red hake was assessed by FTNIR spectroscopy in transmission mode. The region of 1530e1866 nm was best correlated to dimethylamine concentration, which was an accepted quality index for frozen gadoid fish. PLS analysis showed that this technique predicted the dimethylamine content sufficiently well for quality assurance (Pink et al., 1998). In addition, Nilsen et al. (2002) used NIR spectroscopy to estimate the fish freshness as storage time in ice of salmon. The correlation between spectral data and storage time was modeled by multivariate statistics. The best-fit model was obtained with data from the NIR range giving correlation of prediction of 0.98 and an error value of 1.20 days. In a recent report, NIR spectroscopy successfully classified fillets according to sea bass size in both fresh-minced and freeze-dried samples with correct classification of 90% (Trocino et al., 2012). Besides, the performance of NIR spectroscopy has also been evaluated and reported to predict moisture content in cured Atlantic salmon (Huang et al., 2003), residual blood in intact cod muscle (Olsen, Sørensen, Larsen, Elvevoll, & Nilsen, 2008), glycogen concentrations in the foot muscle of cultured abalone (Fluckiger, Brown, Ward, & Moltschaniwskyj, 2011), and has the ability of discriminating between wild and farmed sea bass (Ottavian et al., 2012). These studies show that NIR spectroscopy is a rapid measurement technique and can be a valuable tool for the assessment of the quality of fish. On the basis of the studies mentioned above, it has been demonstrated that NIR spectroscopy is a valuable and useful analytical technique that can be capable of successfully monitoring and evaluating the quality of fish. On the other hand, NIR spectroscopy cannot completely replace all reference analytical methods and it is thus important to maintain skills in reference analysis by lab staff. There is a need for the fish industry to acquire specific chemical information related to the quality of fish sample periodically, and installation of NIR instrument can lead to release of lab staff time

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from routine quality control analysis so that more efforts can be directed towards the establishment of more sophisticated chemical and physical information on the raw fish, which in future may be also analyzed by NIR spectroscopy. Mid-infrared (MIR) spectroscopy The MIR region of the electromagnetic spectrum locates between 4000 and 400 cm1 and can be divided into four wide regions: the XeH stretching region (4000e 2500 cm1), the triple bond region (2500e2000 cm1), the double bond region (2000e1500 cm1), and the fingerprint region (1500e400 cm1). MIR absorbance derives from only one type of vibrational response, corresponding to a transition between the ground and fundamental states. Thus, MIR absorbance peaks in a spectrum are exclusive for a particular type of organic bond (Scotter, 1997). MIR spectroscopy analysis has already been developed for many years for the elucidation of organic compound structures such as secondary structure of food proteins (Carbonaro & Nucara, 2010) and for rapid and precise detection of microorganisms (Lu et al., 2011). And the application of MIR spectroscopy coupled with multivariate analytical methods represents an alternative approach for the estimation of the quality of fish and fish products. In the following sections, a range of such applications are described on the basis of the nature of the fish product. Application examples of MIR spectroscopy are available in the study of structural features of fish proteins critical of nutritional and technological performance. One of the examples is that FT-MIR spectroscopy was shown as a powerful tool for analyzing chemical composition of fish, indicating the fraction of peptide bonds in a-helical, bpleated sheet, turns and aperiodic conformations by analysis of the amide I band (1600e1700 cm1) in the MIR region (Carbonaro & Nucara, 2010). The potential of MIR spectroscopy combined with PCA and FDA methods for the determination of frozen-thawed fish was also demonstrated (Karoui et al., 2007). Within the 1500e900 cm1 spectral region, correct classification rates of 100% and 75% were observed for the calibration and validation spectra, respectively. Improved classification was obtained from the 3000e2800 cm1 spectral region, with correct classification of 100% and 87.5% of the calibration and validation spectra, respectively. This study confirmed that the 3000e2800 cm1 and 1500e900 cm1 spectral region should provide useful fingerprints allowing the differentiation between fresh and frozen-thawed fish, thus these regions could be considered as a reliable indicator of fish freshness. In addition, Rodriguez-Casado et al. (2007) investigated the potential of diffuse reflectance and transmission FT-MIR spectroscopy for the illumination of postmortem changes in sardine during iced storage condition. The FT-MIR spectra showed that the most sensitive bands to iced storage were sited near 3055 and 3015 cm1 representing lipid unsaturations, 1745 cm1 indicating lipid carbonyl, 1525 cm1 indicating b-sheet protein structure

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and 1395 cm1 indicating salt bridges. The fact that some of the changes were consistent with the evolution of wellknown physicochemical indicators of fish quality loss in early stages of iced storage. MIR spectroscopy was also used for the prediction of salt content in desalting cod solutions and cod muscle. It has been proved that this technique is a convenient alternative for a real-time salt analytical determination with a demonstrated minimization of environmental impact and time saving when compared with the traditional analytical determination (GalvisS
anchez, T
oth, Portela, Delgadillo, & Rangel, 2011). Based on the studies published, it has been demonstrated that MIR spectroscopy could support conventional techniques and has the potential to reduce considerably the analytical time and cost when comparing to enzymatic and biochemical measurements. Raman spectroscopy Raman scattering spectroscopy has similar wavelength regions with MIR spectroscopy, but there are different operating principles between them. Raman spectroscopy focuses on the polarizability response of the molecular vibrations (Scotter, 1997), and is also one of vibrational spectroscopy based upon the interaction of a laser radiation with molecular vibrations in order to obtain relative information about the material (Celedon & Aguilera, 2002). Raman spectroscopy as an emerging non-invasive technique has great potential for biochemical and chemical structural analysis, which can be used in situ without the need of sample pretreatment. One major advantage of this technique is its ability to provide information about concentration, structure, and interaction of biochemical molecules within intact cells and tissues (Marquardt & Wold, 2004), and therefore its applications in fish quality evaluation mainly focus on fat content, lipid oxidation and protein structures. Fat content and lipid oxidation Lipid content and some specifical fatty acids in fish are valuable to human health. Their composition and degree of unsaturation can affect the intensity and location of chemical bonds in the Raman spectra, thus further influencing the quality of fish. Fish is easily subjected to oxidative deterioration that can generate off-flavor development. The iodine value is the generally accepted parameter for expressing the degree of unsaturation of fats or their derivative. Raman spectroscopy has been successfully proved to be suitable for predicting the total level of unsaturation in the form of the iodine value (Afseth, Segtnan, Marquardt, & Wold, 2005). Another exploratory study was conducted to elucidate the potential of using Raman spectroscopy (785 nm excitation) for quantitative measurements of fat in fish muscle. Raman spectra were collected and analyzed to correlate with relative concentration information about fat content. The result suggested that Raman spectroscopy might be a useful tool for rapid and non-destructive analysis of fat content in fish (Marquardt & Wold, 2004).

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Protein structure Fish proteins are highly susceptible to biochemical changes during frozen storage, leading to changes in protein solubility, functionality, structure, nutritional quality and formation of aggregates. These changes can affect the rheological properties, which alter the texture and eating quality of fish causing wastage of a scarce and rich protein source (Badii & Howell, 2002). Raman spectroscopy can provide information on the secondary and tertiary structure of proteins related to a-helix, b-sheet, turns and random coil structures (Herrero, 2008), and has also been used to investigate the proteineprotein and proteinelipid interactions. Liu, Zhao, Xie, and Xiong (2011) used Raman spectroscopy to measure changes in protein conformation and intermolecular interactions during gelling in order to explore the basis for gel properties of fish after heat treatment. The analysis revealed that hydrophobic interaction increased and then decreased with increasing temperature, reaching a maximum value at 60e70
C, and the formation of disulfide bonds mainly occurred at 70e80
C for fish. Similar experiments were also performed, showing a significant decrease (P < 0.05) in a-helix content accompanied by a significant increase (P < 0.05) in b-sheet structure after heating (Shao, Zou, Xu, Wu, & Zhou, 2011). In addition, Raman spectroscopic study of protein structural changes in hake muscle during frozen storage (10 and 30
C) has been conducted by Herrero, Carmona, and Careche (2004), who revealed that some structural changes occurred in secondary and tertiary protein structures. The quantification of changes in secondary structure showed an increase of b-sheet at the expense of a-helix structure and an increased in intensity at the n(CeH) stretching band near 2935 cm1, indicating denaturation of the muscle proteins through the exposure of aliphatic hydrophobic groups to the solvent. Meanwhile, Herrero, Carmona, Garcia, Solas, and Careche (2005) used Raman spectroscopy to study the structural changes in hake muscle during frozen storage, and the results obtained showed that the changes in the spaces between myofibrils could be related to modifications of shear resistance. The behavior of the strong 160 cm1 band could be related to conformational transitions of muscle proteins, to changes in the structure of muscle water, and/or to alterations in protein-water interactions. The results also showed that there were intensity changes in the v(OeH) band that may be attributable to transfer of water to larger spatial domains during frozen storage. In addition, Leelapongwattana, Benjakul, Visessanguan, and Howell (2008) used FT-Raman spectroscopy to analyze natural actomyosin from haddock during refrigerated (4
C) and frozen (10
C) storage, and it was revealed that amide I and amide III bands of natural actomyosin were affected by storage temperatures. From the above studies, it is observed that Raman spectroscopy is a useful potential tool for the assessment of fish quality, especially for indicating protein structure changes.

Nuclear magnetic resonance (NMR) spectroscopy NMR spectroscopy communicates with the object by means of electromagnetic waves in the radio frequency range, and this technique has become a central analytical tool in medicine, chemistry, physics, biology and food science (Erikson, Standal, Aursand, Veliyulin, & Aursand, 2012). This technique can be extended for its uses in non-invasive quality evaluation of fish including the fat content and distribution, water content, water holding capacity, collagen content, pH, and metabolites such as lactate, glycogen and ATP. Low-field nuclear magnetic resonance (LF-NMR) operates in the frequency range of 2e25 MHz and represents a simplified and cheaper version of a traditional NMR spectrometer. Nevertheless, the instrument can provide useful information about relaxation behavior and diffusion behavior. Moreover, the instrument can also be used in connection with at-line quality control for quick analyses of fat, water and protein. Thus, among NMR technology, LF-NMR technique has been widely used for the analysis of food products. For example, Veliyulin, van der Zwaag, Burk, and Erikson (2005) employed a mobile low-field (LF-NMR) analyzer for measuring the fat content in live or slaughtered Atlantic salmon. In this study, the fat content (range 90e182 g kg1) showed significant correlation (R ¼ 0.92) with chemical extraction data obtained after slaughtering the same fish, and it was concluded that the mobile NMR spectrometer had the potential for implementation in connection with on-line quality control. LF-NMR spectroscopy was also used to analyze the frozen storage time and quality changes of hake frozen stored at 10
C for up to 6 months (S
anchez-Alonso, Martinez, S
anchezValencia, & Careche, 2012) and the results showed that water holding capacity 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. Additionally, Gudj
onsd
ottir, Arason, and Rustad (2011) performed LFNMR spectroscopy analysis to evaluate the effect of different pre-salting methods on the protein denaturation and changes in dry salted cod fillets muscle properties. Significant correlations were observed between the NMR relaxation parameters and all physicochemical quality properties measured. The study also indicated that prebrining by brine injection followed by brining, with low salt concentrations, resulted in the least protein denaturation during the dry salting and rehydration. Besides low-field nuclear magnetic resonance, other NMR spectroscopies have also been tested for their feasibility in fish quality assessment. Among them, Gribbestad, Aursand, and Martinez (2005) employed high resolution (HR-1H-NMR) spectroscopy technique for assessing and analyzing whole fish, fillets and extracts of farmed Atlantic salmon, and it was shown that this technique may have practical applications for the classification of both live specimens and fillets according to their

J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31

qualitative and quantitative content of lipids and small molecules. Bankefors et al. (2011) investigated the metabolite profiles from muscles and livers of Atlantic salmon using HR-NMR spectroscopy for aqueous extracts and magic angle spinning (MAS-NMR) spectroscopy for intact tissues, and the comparison of the results showed that most small metabolites present in the aqueous extracts were also identified in the MAS-NMR spectra of the intact tissues. Using the diffusion-edited MAS-NMR spectra, not only the total u-3 fatty acid content, but also the EPA and DHA contents, in the muscle and liver tissues could be calculated without the need for lipophilic extraction. In addition, three different methods of 23Na-NMR, LF-1H-NMR and Volhard titration methods have been conducted by Erikson, Veliyulin, Singstad, and Aursand (2004) to study the effects of salting and desalting on the waterrelated properties, salt content, and salt distribution of fresh and frozen-thawed cod fillets. Excellent correlations were obtained between quantitative salt determinations using 23 Na-NMR, LF-NMR and Volhard titration. This study illustrated that NMR spectroscopy is a useful tool for evaluating and optimizing the fish salting process, and has the potential for replacing traditional salt and water-related analytical methods. Finally, Aursand et al. (2009) applied the technique of 13C-NMR spectroscopy for the classification of Atlantic salmon according to their wild, farmed, and geographical origin and concluded that 13C-NMR spectroscopy had the potential for verification of production methods of salmon. Spectral imaging As an innovative platform technology, spectral imaging including hyperspectral imaging (HSI) and nuclear magnetic resonance imaging (NMRI) combines the techniques of spectroscopy and imaging. A typical spectrometer offers a single spectrum, I(l), while imaging provides the intensity at each pixel of the image, I(x, y). Thus, a spectral image provides a spectrum at each pixel I(x, y, l), which can be viewed as a cube of information as illustrated in Fig. 3. Therefore, this emerging technique can create a threedimensional (3D) dataset that contains many images of the same object, where each one of them is measured at a different wavelength. Owing to the abilities of space distinguishing and spectral resolution, spectral imaging can not only obtain the spatial information of the object, but also the spectral information (Sun, 2010). As a result, spectral imaging technique has been widely used in fish quality and safety evaluation and inspection mainly related to moisture determination, freshness and parasites detection. Moisture determination Moisture is the main component of agricultural and food products (Delgado & Sun, 2002; Sun 1999; Sun, & Byrne, 1998; Sun & Woods, 1993, 1994a,1994b, 1997; Wang, & Sun, 2001) and therefore moisture content is one of the key quality measures of fish. Spectral imaging can be

25

Fig. 3. Schematic illustration of hyperspectral imaging cube (Zhang, Liu, et al., 2012; Zhang, Ying, et al., 2012).

used to predict this component. In a study conducted by Wold et al. (2006), NIR multispectral imaging was developed for on-line determination of moisture in 70 dried salted coalfish. The PLSR prediction models established had good correlation value of R2 ¼ 0.92 with RMSECV of 1.07%. Also Wold and co-workers (ElMasry & Wold, 2008) used NIR interactance spectral imaging technique with a spectral region of 760e1040 nm to determine water content distribution in the fillets of six fish species in real time, and the result proved that this technique was suitable for high-speed assessment of quality parameters of water content distribution in fish fillets. Freshness Compared to conventional RGB imaging and NIR spectroscopy, hyperspectral imaging (HSI) provides images in a three-dimensional form called “hypercube”, which can be analyzed to ascertain minor and/or subtle physical and chemical characteristics of a sample as well as their spatial distributions (Zhang, Liu, et al., 2012; Zhang, Ying, et al., 2012). Recently application of HSI technique has been extended to the area of fish quality evaluation and some attempts have been made for using it to predict and evaluate fish quality and safety. A number of studies have proved HSI technique to be a valuable tool for assessing fish quality as discussed below. Color is a principal attribute of fish freshness quality related to sensory evaluation and application of NIR-HSI technique for measurement of color distribution in salmon fillet was carried out using successive projection algorithm by Wu, Sun, and He (2012), who demonstrated that NIRHIS was a potential technique to quantitatively measure color distribution of salmon fillet in a rapid and non-invasive way and could be used as a reliable and rapid alternative to traditional colorimeter for measuring color of salmon fillet. Very recently, Zhu, Zhang, He, Liu, and Sun (2012) applied VIS/NIR-HIS technique combined with LS-SVM to differentiate between fresh and frozen-thawed fish fillets. The

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J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31

results indicated that VIS/NIR-HSI had the potential to be used as an online technique for rapid and non-destructive differentiation of fresh from fresh and frozen-thawed fish. Another application of HSI involved in classification of fresh Atlantic salmon fillets stored under different packaging atmospheres (air, 60% CO2/40% N2 and 90% vacuum) using PCA and PLSR methods, achieving the correct classification rate of >88% for fillets, which was largely dependent on spectral characteristics at the wavelengths of 606 nm and 636 nm (Sone, Olsen, Dahl, et al., 2011; Sone, Olsen, Sivertsen, et al., 2011). Parasites detection Parasitic nematodes can be present in fillets of fish species, which is a serious quality and safety concern for the fishing industry. Therefore in order to fulfill market requirements and avoid complaints, the fish industry must be able to provide parasite-free products. However, there are no effective methods available to detect parasites except by manual vision inspection on candling tables, leading to inaccurate, time-consuming and laborious detection. To overcome this difficulty, spectral imaging in the VIS/NIR region was used for automatic detection of parasites in cod fillets. Results indicated by Wold, Westad, and Heia (2001) showed that the spectral characteristics of nematodes differed from those of fish flesh, thus allowing one to obtain fairly good classifications. Similar studies on automatic nematode detection in cod fillets were also conducted by Sivertsen, Heia, et al. (2011), Sivertsen, Kimiya, et al. (2011) and Sivertsen, Heia, Hindberg, and Godtliebsen (2012), who also illustrated that HSI technique could be effective for industrial on-line detection of parasites in fish and fish fillets. Besides hyperspectral imaging, nuclear magnetic resonance imaging (NMRI) is also popularly considered as another one of the best-known non-invasive, nondestructive internal spectral imaging technique particularly with respect to the 3-D mapping of NMR parameters in medical whole body scanner. NMRI can therefore be used for studying the chemical and physical properties and anatomical structure of fish. For example, NMRI was performed at room temperature to study belly bursting in the frozen-thawed herring for approximately 50 h. The results showed that the stomachs were filled with prey and that they were very resistant to degradation. The ventral muscle together with the upper part of the intestine seemed to be the most sensible structures where the autolysis initiated and extended to the rest of the abdominal cavity (Veliyulin, Felberg, Digre, & Martinez, 2007). Another relevant study was reported by Veliyulin and Aursand (2007), who used 1H and 23Na-NMRI for investigation of Atlantic salmon and cod fillet pieces salted in different brine concentrations. Their results indicated that observed changes in proton and sodium NMR relaxation times with the salt content reflected complex counteraction of several factors related to the muscle structural changes associated

with muscle swelling, shrinking and denaturation. Most recently, in order to gain quantitative analysis of subcutaneous fat in fish, NMRI technique was applied by Collewet et al. (2012) and the results were compared with vision measurements. Relatively high correlations between both techniques were obtained (R2 ¼ 0.77 and 0.87 for the ventral and dorsal part), confirming the potentiality of NMRI for fat measurement in fish particularly for a large number of samples. The above-mentioned studies show that spectral imaging techniques such as HSI and NMRI can be implemented as an alternative to conventional destructive and time-consuming analytical methods. Moreover, combining with appropriate chemometric methods, spectral imaging techniques have found their way for potential applications in evaluation and monitoring of fish quality and safety online or off-line in a rapid and nondestructive manner. Advantages and limitations Spectroscopic techniques including VIS spectroscopy, NIR spectroscopy, MIR spectroscopy, Raman spectroscopy, NMR spectroscopy and spectral imaging have been successfully applied for fish quality evaluation and inspection as illustrated in Table 1. Compared with the traditional chemical and instrumental methods, the spectroscopic techniques have been proved to be non-destructive, non-contact, objective and cost-effective, which can be used as routine procedures implemented in the fish industry for automated sorting and grading of fish and fish products and other services. This is mostly likely due to the fact that these innovative techniques will provide on-line detection, save a lot of time for workers and produce higher economic and social benefits, and further avoid producing environmental pollution and offer guaranteed quality products for human consumption and international trades. In particular, these techniques requires minimal or no sample preparation. However, there are also some limitations about the applications of these spectroscopic techniques. Concerning the NIR spectroscopy, for quantitative analyses, NIR spectroscopy is not independent of the disadvantages arising from the reference method applied for calibration, which requires a set of samples at least 20e50 with known analyte concentrations. Thus to a great extent, the success of NIR depends on the reliability of the reference method employed and the sample presentation. It is also difficult to interpret the complex spectra obtained from the spectrometer, although a lot of time has been contributed to understanding of the models through multivariate analysis or chemometrics (Cozzolino, 2012). With respect to Raman spectroscopy, some restrictions such as inherently weaker effect of Raman scattering, stronger interfering of biological fluorescence, higher instrumental costs and some heat generated by the laser may affect the measurement effectiveness (Afseth et al., 2005). The limitation of NMR spectroscopy using for fish quality evaluation is expensive and it

J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31

27

Table 1. Applications of spectroscopic techniques for fish quality and safety evaluation and inspection. Fish species

Technique

Detection of

Method

R2

RMSE/RMSECV

Reference

Atlantic salmon Herring, sardine

NIR NIR NIR

PCA, PLS PLS PLS SW-MLR

0.95 0.95 0.80 0.95

0.12 14.03 3.52 0.8%

Tito et al. (2012) Masoum et al. (2012)

Herring

Total aerobic counts Crude protein Moisture content Fat content

Atlantic cod Cod fillets European sea bass

VIS/NIR NIR NIR

Residual blood Freshness Water content

PCA, PLSR PLSR PLSR

0.83 0.90 0.90

e 3.4 0.67%

Live abalone Shucked abalone Freeze-dried abalone Cured Atlantic salmon

NIR

Glycogen concentrations

PLSR

SW-NIR

Salt content

ANN PLSR ANN PLSR PCA, PLS BPNN PLSR BPNN PLSR PLSR

0.97 0.86 0.90 0.70 0.73 0.78 0.80 0.97 0.82 0.78 0.95 0.94 0.83 0.82 0.97 0.95 0.87 0.99e0.97 0.99 1.00 0.97 0.94 0.91

1.71 3.46 3.07 1.43% 1.37% 2.08% 2.04% 0.38 0.55% 0.63% 2.44% 2.65 0.32% 0.25% 0.33 15.5 2.5 0.2e0.8 e e e 2.73% 2.99%

Water content Rainbow trout Cold-smoked salmon

SW-NIR SW-NIR

Microbial loads Salt content Water content

Pacific king Chum salmon Atlantic salmon

SW-NIR

Salt content

Raman

Salmon Hake Atlantic salmon

FT-Raman Raman 13 C-NMR

Carotenoids content Fat content Iodine value Protein structure Wild or farmed

Halibut Atlantic halibut

VIS/NIR-HSI Spectral imaging

Fresh or frozen-thawed Moisture content Fat content

PLSR PLSR PLSR PLSR BNN SVM LS-SVM PLSR

Vogt, Gormley, Downey, and Somers (2002) Olsen et al. (2008) Bøknæs et al. (2002) Majolini, Trocino, Xiccato, and Santulli (2010) Fluckiger et al. (2011)

Huang et al. (2003)

Lin et al. (2006) Huang et al. (2006)

Lin et al. (2003) Wold, Marquardt, Dable, Robb, and Hatlen (2004) Afseth, Wold, and Segtnan (2006) Herrero et al. (2004) Aursand et al. (2009) Zhu et al. (2012) ElMasry and Wold (2008)

RMSE: Root means square error; RMSECV: Root means square error of crossing validation; BPNN: Back propagation neural networks; LS-SVM: Least-squares support vector machine; BPP: Probabilistic neural networks.

is generally not easy to achieve on-line detection of fish quality changes. Regarding the spectral imaging technique, a spectral image contains significantly more information and data than a single color image and therefore it needs time and skills to acquire the desired information from the spectral images. Difficulties also exist in eliminating data redundancy, accelerating detection speed and selecting optimized wavelengths for multispectral imaging systems. Conclusions Spectroscopic techniques have been dramatically developed in the past decade, and these non-destructive and noninvasive spectroscopic techniques have been widely and extensively applied for the analysis of fish characteristics and components which could reflect fish quality and safety. In this review, several spectroscopic techniques including VIS spectroscopy, NIR spectroscopy, MIR spectroscopy, Raman spectroscopy, NMR spectroscopy and spectral imaging are described for their great potentials in fish quality and safety evaluation. VIS spectroscopy correlates well with fish sensory evaluation and is suitable for measuring the freshness quality of fish and fish fillets related to color,

texture, and storage period. NIR spectroscopy as another versatile technique may quickly provide extensive information on fish quality and safety evaluation and inspection, and it has already been successfully used to predict microbial contamination and spoilage and chemical compositions, in terms of fat, protein, moisture, salt content and distribution. The technique can also be used to discriminate fresh fish from frozen-thawed fish with appropriate chemometric methods. Due to the complexity and rapid changes of fish quality after slaughter, NIR spectroscopy is becoming increasingly recognized as a complementary method for providing quantitative information on fish quality. MIR spectroscopy as a powerful tool has been successfully used to evaluate fish freshness and analyze chemical composition related to fish protein structures and salt content. Raman spectroscopy is valuable for the assessment of fish quality in terms of monitoring the variations of protein structures and the degree of lipid oxidation. NMR spectroscopy has also been considered as an effective technique to evaluate fish quality mainly concentrating on fat content, chemical oxidation and metabolite measurement. Finally as an emerging technique, spectral imaging such as HSI and

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NMRI, which integrates advantages from both spectroscopy and computer imaging, has great potential for uses in fish quality classification, fish freshness discrimination and parasite detection. In general, applications of nondestructive spectroscopic techniques show great promise in rapid online detection of fish quality and safety, leading to the enhancement of consumer confidence and acceptability, and to supplementing and replacing of industrial traditional methods. Acknowledgments The authors are grateful to the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun)”. Special thanks to Dr. Qijun Wang and Zhong Han from South China University of Technology for their kind suggestions. References Abdel-Nour, N., Ngadi, M., Prasher, S., & Karimi, Y. (2011). Prediction of egg freshness and albumen quality using visible/near infrared spectroscopy. Food and Bioprocess Technology, 4(5), 731e736. Addis, M. F., Pisanu, S., Preziosa, E., Bernardini, G., Pagnozzi, D., Roggio, T., et al. (2012). 2D-DIGE/MS to investigate the impact of slaughtering techniques on postmortem integrity of fish filet proteins. Journal of Proteomics, 75(12), 3654e3664. Afseth, N., Segtnan, V., Marquardt, B., & Wold, J. (2005). Raman and near-infrared spectroscopy for quantification of fat composition in a complex food model system. Applied Spectroscopy, 59(11), 1324e1332. Afseth, N. K., Wold, J. P., & Segtnan, V. H. (2006). The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon. Analytica Chimica Acta, 572(1), 85e92. Alexandrakis, D., Downey, G., & Scannell, A. G. (2012). Rapid nondestructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier transform mid-infrared spectroscopy and multivariate statistics. Food and Bioprocess Technology, 5(1), 338e347. Antonucci, F., Pallottino, F., Paglia, G., Palma, A., D’Aquino, S., & Menesatti, P. (2011). Non-destructive estimation of mandarin maturity status through portable VIS-NIR spectrophotometer. Food and Bioprocess Technology, 4(5), 809e813. Aursand, M., Standal, I. B., Pra€el, A., McEvoy, L., Irvine, J., & Axelson, D. E. (2009). 13C NMR pattern recognition techniques for the classification of Atlantic salmon (Salmo salar L.) according to their wild, farmed, and geographical origin. Journal of Agricultural and Food Chemistry, 57(9), 3444e3451. Badii, F., & Howell, N. K. (2002). Effect of antioxidants, citrate, and cryoprotectants on protein denaturation and texture of frozen cod (Gadus morhua). Journal of Agricultural and Food Chemistry, 50(7), 2053e2061. Bankefors, J., Kaszowska, M., Schlechtriem, C., Pickova, J., Br€ ann€ as, E., Edebo, L., et al. (2011). A comparison of the metabolic profile on intact tissue and extracts of muscle and liver of juvenile Atlantic salmon (Salmo salar L.) e Application to a short feeding study. Food Chemistry, 129(4), 1397e1405. van den Berg, F., Lyndgaard, C. B., Sørensen, K. M., & Engelsen, S. B. (2012). Process analytical technology in the food industry. Trends in Food Science & Technology, 31(1), 27e35. Bøknæs, N., Jensen, K. N., Andersen, C. M., & Martens, H. (2002). Freshness assessment of thawed and chilled cod fillets packed in

modified atmosphere using near-infrared spectroscopy. LWT e Food Science and Technology, 35(7), 628e634. Boubellouta, T., & Dufour,
E. (2012). Cheese-matrix characteristics during heating and cheese melting temperature prediction by synchronous fluorescence and mid-infrared spectroscopies. Food and Bioprocess Technology, 5(1), 273e284. Carbonaro, M., & Nucara, A. (2010). Secondary structure of food proteins by Fourier transform spectroscopy in the mid-infrared region. Amino Acids, 38(3), 679e690. Carrera, M., Ca~ nas, B., & Gallardo, J. M. (2012). Proteomics for the assessment of quality and safety of fishery products. Food Research International, in press. http://dx.doi.org/10.1016/j/foodres.2012.10. 027. Carton, I., Bocker, U., Ofstad, R., Sørheim, O., & Kohler, A. (2009). Monitoring secondary structural changes in salted and smoked salmon muscle myofiber proteins by FT-IR microspectroscopy. Journal of Agricultural and Food Chemistry, 57(9), 3563e3570. Celedon, A., & Aguilera, J. (2002). Applications of microprobe Raman spectroscopy in food science. Food Science and Technology International, 8(2), 101e108. Collewet, G., Bugeon, J., Idier, J., Quellec, S., Quittet, B., Cambert, M., et al. (2012). Rapid quantification of muscle fat content and subcutaneous adipose tissue in fish using MRI. Food Chemistry, 138(2e3), 2008e2015. Costa, C., D’Andrea, S., Russo, R., Antonucci, F., Pallottino, F., & Menesatti, P. (2011). Application of non-invasive techniques to differentiate sea bass (Dicentrarchus labrax, L. 1758) quality cultured under different conditions. Aquaculture International, 19(4), 765e778. Cozzolino, D. (2012). Recent trends on the use of infrared spectroscopy to trace and authenticate natural and agricultural food products. Applied Spectroscopy Reviews, 47(7), 518e530. Cozzolino, D., & Murray, I. (2012). A review on the application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood. Applied Spectroscopy Reviews, 47(3), 207e218. Delgado, A. E., & Sun, D.-W. (2002). Desorption isotherms for cooked and cured beef and pork. Journal of food engineering, 51(2), 163e170. ElMasry, G., & Wold, J. P. (2008). High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. Journal of Agricultural and Food Chemistry, 56(17), 7672e7677. ElMasry, G., Sun, D.-W., & Allen, P. (2011). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624e2633. ElMasry, G., Sun, D.-W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127e140. Erikson, U., Standal, I. B., Aursand, I. G., Veliyulin, E., & Aursand, M. (2012). Use of NMR in fish processing optimization: a review of recent progress. Magnetic Resonance in Chemistry, 50(7), 471e480. Erikson, U., Veliyulin, E., Singstad, T., & 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 1 H NMR, and physicochemical analytical methods. Journal of Food Science, 69(3), FEP107eFEP114. Fluckiger, M., Brown, M. R., Ward, L. R., & Moltschaniwskyj, N. A. (2011). Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). Food Chemistry, 126(4), 1817e1820. Folkestad, A., Wold, J. P., Rørvik, K. A., Tschudi, J., Haugholt, K. H., Kolstad, K., et al. (2008). Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquaculture, 280(1), 129e135.

J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31 Forn
e, I., Abi
an, J., & Cerda, J. (2010). Fish proteome analysis: model organisms and non-sequenced species. Proteomics, 10(4), 858e872. Galvis-S
anchez, A. C., T
oth, I. V., Portela, A., Delgadillo, I., & Rangel, A. O. S. S. (2011). Monitoring sodium chloride during cod fish desalting process by flow injection spectrometry and infrared spectroscopy. Food Control, 22(2), 277e282. Gribbestad, I. S., Aursand, M., & Martinez, I. (2005). High-resolution 1H magnetic resonance spectroscopy of whole fish, fillets and extracts of farmed Atlantic salmon (Salmo salar) for quality assessment and compositional analyses. Aquaculture, 250(1), 445e457. Gudj
onsd
ottir, M., Arason, S., & Rustad, T. (2011). The effects of presalting methods on water distribution and protein denaturation of dry salted and rehydrated cod e a low-field NMR study. Journal of Food Engineering, 104(1), 23e29. G€ unaydın, B., S¸ir, N., Kavlak, S., G€ uner, A., & Mutlu, M. (2010). A new approach for the electrochemical detection of phenolic compounds. Part I: modification of graphite surface by plasma polymerization technique and characterization by Raman Spectroscopy. Food and Bioprocess Technology, 3(3), 473e479. Heia, K., Esaiassen, M., Nilsen, H., Sigernes, F., Luten, J., Oehlenschl€ ager, J., et al. (2003). Visible spectroscopy e Evaluation of storage time of ice stored cod and frozen stored hakeIn Quality of fish from catch to consumer: Labelling, monitoring and traceability, . Herrero, A. M. (2008). Raman spectroscopy for monitoring protein structure in muscle food systems. Critical Reviews in Food Science and Nutrition, 48(6), 512e523. 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), 2147e2153. Herrero, A., Carmona, P., Garcia, M., Solas, M., & Careche, M. (2005). Ultrastructural changes and structure and mobility of myowater in frozen-stored hake (Merluccius merluccius L.) muscle: relationship with functionality and texture. Journal of Agricultural and Food Chemistry, 53(7), 2558e2566. Huang, Y., Cavinato, A., Mayes, D., Bledsoe, G., & Rasco, B. (2006). Nondestructive prediction of moisture and sodium chloride in cold smoked Atlantic salmon (Salmo salar). Journal of Food Science, 67(7), 2543e2547. Huang, Y., Cavinato, A., Mayes, D., Kangas, L., Bledsoe, G., & Rasco, B. (2003). Nondestructive determination of moisture and sodium chloride in cured Atlantic salmon (Salmo salar) (Teijin) using short-wavelength Near-infrared spectroscopy (SW-NIR). Journal of Food Science, 68(2), 482e486. Iglesias, J., Medina, I., Bianchi, F., Careri, M., Mangia, A., & Musci, M. (2009). Study of the volatile compounds useful for the characterisation of fresh and frozen-thawed cultured gilthead sea bream fish by solid-phase microextraction gas chromatographymass spectrometry. Food Chemistry, 115(4), 1473e1478. Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104(3), 332e340. Karoui, R., Lefur, B., Grondin, C., Thomas, E., Demeulemester, C., Baerdemaeker, J. D., et al. (2007). Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness. International Journal of Food Science & Technology, 42(1), 57e64. Khodabux, K., L’Omelette, M. S. S., Jhaumeer-Laulloo, S., Ramasami, P., & Rondeau, P. (2007). Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chemistry, 102(3), 669e675. Ko, W. C., Cheng, J. Y., Chen, P. Y., & Hsieh, C. W. (2012). Optimized Extraction method of acetic acid in vinegar and its effect on SNIFNMR analysis to control the authenticity of vinegar. Food and Bioprocess Technology, 6(8), 2202e2206.

29

Leelapongwattana, K., Benjakul, S., Visessanguan, W., & Howell, N. K. (2008). Raman spectroscopic analysis and rheological measurements on natural actomyosin from haddock (Melanogrammus aeglefinus) during refrigerated (4
C) and frozen (10
C) storage in the presence of trimethylamine-N-oxide demethylase from kidney of lizardfish (Saurida tumbil ). Food Chemistry, 106(3), 1253e1263. Lin, M., Cavinato, A. G., Huang, Y., & Rasco, B. A. (2003). Predicting sodium chloride content in commercial king (Oncorhynchus tshawytscha) and chum (O. keta) hot smoked salmon fillet portions by short-wavelength near-infrared (SW-NIR) spectroscopy. Food Research International, 36(8), 761e766. Lin, M., Mousavi, M., Al-Holy, M., Cavinato, A. G., & Rasco, B. A. (2006). Rapid near infrared spectroscopic method for the detection of spoilage in rainbow trout (Oncorhynchus mykiss) fillet. Journal of Food Science, 71(1), S18eS23. Liu, R., Zhao, S. M., Xie, B. J., & Xiong, S. B. (2011). Contribution of protein conformation and intermolecular bonds to fish and pork gelation properties. Food Hydrocolloids, 25(5), 898e906. Liu, B., Zhou, P., Liu, X., Sun, X., Li, H., & Lin, M. (2013). Detection of pesticides in fruits by surface-enhanced raman spectroscopy coupled with gold nanostructures. Food and Bioprocess Technology, 6(3), 710e718. Lu, X., Al-Qadiri, H. M., Lin, M., & Rasco, B. A. (2011). Application of mid-infrared and Raman spectroscopy to the study of bacteria. Food and Bioprocess Technology, 4(6), 919e935. Lu, X., Liu, Q., Wu, D., Al-Qadiri, H. M., Al-Alami, N. I., Kang, D. H., et al. (2011). Using of infrared spectroscopy to study the survival and injury of Escherichia coli O157: H7, Campylobacter jejuni and Pseudomonas aeruginosa under cold stress in low nutrient media. Food Microbiology, 28(3), 537e546. Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J., Saeys, W., & Nicola€ı, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruitea review. Food and Bioprocess Technology, 5(2), 425e444. Majolini, D., Trocino, A., Xiccato, G., & Santulli, A. (2010). Near infrared reflectance spectroscopy (NIRS) characterization of European sea bass (Dicentrarchus labrax) from different rearing systems. Italian Journal of Animal Science, 8(2s), 860e862. Marquardt, B. J., & Wold, J. P. (2004). Raman analysis of fish: a potential method for rapid quality screening. LWT e Food Science and Technology, 37(1), 1e8. Masoum, S., Alishahi, A. R., Farahmand, H., Shekarchi, M., & Prieto, N. (2012). Determination of protein and moisture in fishmeal by near-infrared reflectance spectroscopy and multivariate regression based on partial least squares. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 31(3), 51e59. Mendes, R., Cardoso, C., & Pestana, C. (2009). Measurement of malondialdehyde in fish: a comparison study between HPLC methods and the traditional spectrophotometric test. Food Chemistry, 112(4), 1038e1045. Nielsen, D., Hyldig, G., Nielsen, J., & Nielsen, H. H. (2005). Lipid content in herring (Clupea harengus L.) e influence of biological factors and comparison of different methods of analyses: solvent extraction, Fatmeter, NIR and NMR. LWT e Food Science and Technology, 38(5), 537e548. Nilsen, H., & Esaiassen, M. (2005). Predicting sensory score of cod (Gadus morhua) from visible spectroscopy. LWT e Food Science and Technology, 38(1), 95e99. Nilsen, H., Esaiassen, M., Heia, K., & Sigernes, F. (2002). Visible/nearinfrared spectroscopy: a new tool for the evaluation of fish freshness? Journal of Food Science, 67(5), 1821e1826. Olsen, S. H., Sørensen, N. K., Larsen, R., Elvevoll, E. O., & Nilsen, H. (2008). Impact of pre-slaughter stress on residual blood in fillet portions of farmed Atlantic cod (Gadus morhua) e measured

30

J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31

chemically and by visible and near-infrared spectroscopy. Aquaculture, 284(1), 90e97. Ottavian, M., Facco, P., Fasolato, L., Novelli, E., Mirisola, M., Perini, M., et al. (2012). Use of Near-Infrared Spectroscopy for fast fraud detection in seafood: application to the authentication of wild European sea bass (Dicentrarchus labrax). Journal of Agricultural and Food Chemistry, 60(2), 639e648. Pink, J., Naczk, M., & Pink, D. (1998). Evaluation of the quality of frozen minced red hake: use of Fourier transform infrared spectroscopy. Journal of Agricultural and Food Chemistry, 46(9), 3667e3672. Poji
c, M. M., & Mastilovi
c, J. S. (2013). Near infrared spectroscopyeadvanced analytical tool in wheat breeding, trade, and processing. Food and Bioprocess Technology, 6(2), 330e352. Pons-Sanchez-Cascado, S., Vidal-Carou, M., Nunes, M., & VecianaNogues, M. (2006). Sensory analysis to assess the freshness of Mediterranean anchovies (Engraulis encrasicholus) stored in ice. Food Control, 17(7), 564e569. Rodriguez-Casado, A., Carmona, P., Moreno, P., S
anchezGonz
alez, I., Macagnano, A., Natale, C. D., et al. (2007). Structural changes in sardine (Sardina pilchardus) muscle during iced storage: investigation by DRIFT spectroscopy. Food Chemistry, 103(3), 1024e1030.
Eim, V. S., Simal, S., Femenia, A., & Rossell
Rodr
ıguez, O., o, C. (2013). Validation of a difussion model using moisture profiles measured by means of TD-NMR in apples (Malus domestica). Food and Bioprocess Technology, 6(2), 542e552. S
anchez-Alonso, I., Carmona, P., & Careche, M. (2011). Vibrational spectroscopic analysis of hake (Merluccius merluccius L.) lipids during frozen storage. Food Chemistry, 132(1), 160e167. S
anchez-Alonso, I., Martinez, I., S
anchez-Valencia, J., & Careche, M. (2012). Estimation of freezing storage time and quality changes in hake (Merluccius merluccius, L.) by low field NMR. Food Chemistry, 135(3), 1626e1634. Santagapita, P., Laghi, L., Panarese, V., Tylewicz, U., Rocculi, P., & Dalla Rosa, M. (2012). Modification of transverse NMR relaxation times and water diffusion coefficients of kiwifruit pericarp tissue subjected to osmotic dehydration. Food and Bioprocess Technology, 6(6), 1434e1443. Scotter, C. N. G. (1997). Non-destructive spectroscopic techniques for the measurement of food quality. Trends in Food Science & Technology, 8(9), 285e292. Shao, X., & Li, Y. (2011). Application of low-field NMR to analyze water characteristics and predict unfrozen water in blanched sweet corn. Food and Bioprocess Technology, 6(6), 1593e1599. Shao, J. H., Zou, Y. F., Xu, X. L., Wu, J. Q., & Zhou, G. H. (2011). Evaluation of structural changes in raw and heated meat batters prepared with different lipids using Raman spectroscopy. Food Research International, 44(9), 2955e2961. Sivam, A. S., Waterhouse, G. I., Zujovic, Z. D., Perera, C. O., & SunWaterhouse, D. (2013). Structure and dynamics of wheat starch in breads fortified with polyphenols and pectin: an ESEM and solidstate CP/MAS 13C NMR spectroscopic study. Food and Bioprocess Technology, 6(1), 110e123. Sivertsen, A. H., Heia, K., Hindberg, K., & Godtliebsen, F. (2012). Automatic nematode detection in cod fillets (Gadus morhua L.) by hyperspectral imaging. Journal of Food Engineering, 111(4), 675e681. Sivertsen, A. H., Heia, K., Stormo, S. K., Elvevoll, E., & Nilsen, H. (2011). Automatic nematode detection in cod fillets (Gadus morhua) by transillumination hyperspectral imaging. Journal of Food Science, 76(1), S77eS83. 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), 317e323. Sone, I., Olsen, R. L., Dahl, R., & Heia, K. (2011). Visible/nearinfrared spectroscopy detects autolytic changes during storage of

Atlantic salmon (Salmo salar L.). Journal of Food Science, 76(3), S203eS209. Sone, I., Olsen, R. L., Sivertsen, A. H., Eilertsen, G., & Heia, K. (2011). Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. Journal of Food Engineering, 109(3), 482e489. Song, Y., Luo, Y., You, J., Shen, H., & Hu, S. (2011). Biochemical, sensory and microbiological attributes of bream (Megalobrama amblycephala) during partial freezing and chilled storage. Journal of the Science of Food and Agriculture, 92(1), 197e202. Sowoidnich, K., Schmidt, H., Maiwald, M., Sumpf, B., & Kronfeldt, H. D. (2010). Application of diode-laser Raman spectroscopy for in situ investigation of meat spoilage. Food and Bioprocess Technology, 3(6), 878e882. Sun, D.-W. (1999). Comparison and selection of EMC/ERH isotherm equations for rice. Journal of Stored Products Research, 35(3), 249e264. Sun, D.-W. (Ed.), (2010). Hyperspectral imaging for food quality analysis and control (pp. 496). San Diego, California, USA: Academic Press/Elsevier, ISBN 978-0-12-374753-2. Sun, D.-W., & Byrne, C. (1998). Selection of EMC/ERH isotherm equations for rapeseed. Journal of Agricultural Engineering Research, 69(4), 307e315. Sun, D.-W., & Woods, J. L. (1993). The moisture content/relative humidity equilibrium relationship of wheatea review. Drying Technology, 11(7), 1523e1551. Sun, D.-W., & Woods, J. L. (1994a). Low temperature moisture transfer characteristics of wheat in thin layers. Transactions of the ASAE, 37. Sun, D.-W., & Woods, J. L. (1994b). Low temperature moisture transfer characteristics of barley: thin-layer models and equilibrium isotherms. Journal of Agricultural Engineering Research, 59(4), 273e283. Sun, D.-W., & Woods, J. L. (1997). Simulation of the heat and moisture transfer process during drying in deep grain beds. Drying Technology, 15(10), 2479e2492. Tan, C., Chen, H., Xu, Z., Wu, T., Wang, L., & Zhu, W. (2012). Improvement of spectral calibration for food analysis through multi-model fusion. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 96(10), 526e531. Tito, N., Rodemann, T., & Powell, S. (2012). Use of near-infra red spectroscopy to predict microbial numbers on Atlantic salmon. Food Microbiology, 32(2), 431e436. Trocino, A., Xiccato, G., Majolini, D., Tazzoli, M., Bertotto, D., Pascoli, F., et al. (2012). Assessing the quality of organic and conventionally-farmed European sea bass (Dicentrarchus labrax). Food Chemistry, 131(2), 427e433. Uddin, M., Turza, S., & Okazaki, E. (2007). Rapid determination of intact sardine fat by NIRS using surface interactance fibre probe. International Journal of Food Engineering, 3(6), 1556e3758. Uddin, M., & Okazaki, E. (2010). Applications of Vibrational Spectroscopy to the Analysis of Fish and Other Aquatic Food Products. Handbook of Vibrational Spectroscopy. Veliyulin, E., & Aursand, I. G. (2007). 1H and 23Na MRI studies of Atlantic salmon (Salmo salar) and Atlantic cod (Gadus morhua) fillet pieces salted in different brine concentrations. Journal of the Science of Food and Agriculture, 87(14), 2676e2683. Veliyulin, E., Felberg, H. S., Digre, H., & Martinez, I. (2007). Nondestructive nuclear magnetic resonance image study of belly bursting in herring (Clupea harengus). Food Chemistry, 101(4), 1545e1551. Veliyulin, E., van der Zwaag, C., Burk, W., & Erikson, U. (2005). In vivo determination of fat content in Atlantic salmon (Salmo salar) with a mobile NMR spectrometer. Journal of the Science of Food and Agriculture, 85(8), 1299e1304.

J.-H. Cheng et al. / Trends in Food Science & Technology 34 (2013) 18e31 Vogt, A., Gormley, T. R., Downey, G., & Somers, J. (2002). A comparison of selected rapid methods for fat measurement in fresh herring (Clupea harengus). Journal of Food Composition and Analysis, 15(2), 205e215. Wang, L., & Sun, D.-W. (2001). Rapid cooling of porous and moisture foods by using vacuum cooling technology. Trends in food science & technology, 12(5), 174e184. Wold, J. P., Johansen, I. R., Haugholt, K. H., Tschudi, J., Thielemann, J., Segtnan, V. H., et al. (2006). Non-contact transflectance near infrared imaging for representative on-line sampling of dried salted coalfish (bacalao). Journal of Near Infrared Spectroscopy, 14(1), 59e66. Wold, J. P., Marquardt, B. J., Dable, B. K., Robb, D., & Hatlen, B. (2004). Rapid quantification of carotenoids and fat in Atlantic salmon (Salmo salar L.) by Raman spectroscopy and chemometrics. Applied Spectroscopy, 58(4), 395e403. Wold, J. P., Westad, F., & Heia, K. (2001). Detection of parasites in cod fillets by using SIMCA classification in multispectral images in the visible and NIR region. Applied Spectroscopy, 55(8), 1025e1034. Woodcock, T., Fagan, C. C., O’Donnell, C. P., & Downey, G. (2008). Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food and Bioprocess Technology, 1(2), 117e129.

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Wu, D., Nie, P., He, Y., & Bao, Y. (2012). Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics. Food and Bioprocess Technology, 5(4), 1402e1410. Wu, D., Sun, D.-W., & He, Y. (2012). Application of long-wave near infrared hyperspectral imaging for measurement of colour distribution in salmon fillet. Innovative Food Science & Emerging Technologies, 16(10), 361e372. Zhang, X., Liu, F., He, Y., & Li, X. (2012). Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors, 12(12), 17234e17246. Zhang, R., Ying, Y., Rao, X., & Li, J. (2012). Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture, 92(12), 2397e2408. Zhu, F., Cheng, S., Wu, D., & He, Y. (2011). Rapid discrimination of fish feeds brands based on visible and short-wave near-infrared spectroscopy. Food and Bioprocess Technology, 4(4), 597e602. Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D.-W. (2012). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen-thawed fish fillets. Food and Bioprocess Technology, http://dx.doi.org/10.1007/s11947012-0825-6.