Applications

Seafood Analysis

The US Seafood Industry brought in upwards of $3 billion in revenue in 2019 and showed annual growth over 3% from 2014 to 2019.

Introduction

The global seafood industry has expanded due to the advancements in high-seas fishing vessels, improvements in food processing and storage, and the establishment of fishing industries in developing countries. The consequences of this expansion include an expanded market and variety of species available to the consumer as well as greater demand by the consumer. This greater demand has brought on expansion of illegal practices and fraud. Stringent laws and regulations exist for seafood products but in practice these are difficult to enforce. The average consumer does not have the ability to visually determine if the seafood they are buying is authentic. Further, processing of seafood products often requires the removal of significant morphological characteristics, making it difficult for even experts to make a visual determination of authenticity. Wet chemistry methods do exist for determining the chemical and physical properties of seafood but they are expensive and time-consuming to implement. Spoilage and determination of optimal shelf life for both quality and safety are also of paramount importance in the seafood industry, especially considering the time it can take from catching the product to selling it on the shelf. Deterioration of fish muscle due to degradation of lipids is one parameter that needs to be monitored for quality assurance. One potential analytical tool to replace wet chemistry to detect seafood fraud and adulteration as well as measure quality is NIR spectroscopy.

Analytes

  • Species Authenticity and Adulteration
  • Fat, Moisture, and Fatty Acids for Classifying Wild vs. Fresh Species
  • Frozen-Thawed Cycle Determination and Classification
  • Fishmeal Classification
  • Spoilage & Microbial Numbers for Shelf Life Determination
  • Total Lipids (TL) & Phospholipids (PL)
  • Polyunsaturated Fatty Acids (PUFA) & Monounsaturated Fatty Acids (MUFA)
  • Free Fatty Acids (FFA)
  • Thiobarbituric Acid Reactive Substances (TBARS)
  • Fluorescent Interaction Compounds (OFR)

Summary of Published Papers, Articles, and Reference Materials

Studies have been conducted using NIR spectroscopy to identify fraud and measure quality in seafood. The studies show promise as a potential tool but more work is necessary to fully validate NIR spectroscopy for these purposes. Natural products always present challenges in building calibrations and this is especially true for seafood. Crabmeat adulteration usually consists of mixing a higher quality species with a cheaper product and this is detectable by NIR. One form of adulteration in fish is a farmed species presented as a wild species and this has been studied for sea bass. Results showed a marked difference in fat, fatty acids, and water content between the wild and farmed species and the spectra could differentiate between the two groups. Another issue for seafood wholesalers and consumers is frozen-thawed cycles. Fish that have been frozen and thawed repeatedly during the shipping process exhibit deterioration in nutritional value, appearance, texture, flavor, and other functional properties. Wet chemistry analysis proved the difference in composition between tilapia fillets that were once thawed and thawed multiple times and processed NIR spectra showed enough differences to classify the two groups with reasonable accuracy. The feasibility of classifying fishmeal from different species, which is a crucial by-product produced after filleting fish for human consumption, has also been determined using NIR spectroscopy. Spoilage from bacteria is a big issue with seafood and there is potential for using NIR to determine changes in the flesh of fish that are related to the presence of bacteria. This can then be used to determine shelf life. Two specific quality control parameters in fish are the oxidation and hydrolytic degradation of lipids in fish muscle. One study measured various fatty acid parameters in two different species of frozen fish as well as classifying fish based on low, medium, and high concentrations of TBARS and OFR. Results show potential to use NIR spectroscopy to inspect lipid characteristics and the quality of frozen fish. While there are inherent challenges to using NIR spectroscopy as an analytical tool in the seafood industry, current studies show promise with the goal of replacing wet chemistry methods for such analysis.

Scientific References and Statistics

Detection and Quantification of Species Authenticity and Adulteration in Crabmeat Using Visible and Near-Infrared Spectroscopy – Gayo, Hale – Journal of Agricultural and Food Chemistry, 2007, 55, 585-592

Two different species (Atlantic Blue and Blue Swimmer) of crabmeat were used in the study. Atlantic Blue was the authentic species and Blue Swimmer was the adulterant due to its year-round availability, reduced cost, and import status. One hundred ten samples of both the two pure species and mixtures ranging from 10% to 90% Blue Swimmer were homogenized using a blender. Ten absorbance spectra of each set were collected from 400 nm to 2498 nm at 2 nm intervals with an average of thirty-two scans per spectrum. Various pretreatment methods were performed on the collected data.

Adulteration Content R2= 0.988

The one hundred ten samples were split evenly into a calibration and validation set. Classification analysis was performed and the data plot showed a clear pattern as the amount of adulteration increased in the samples. Quantitative analysis using a calibration model for measuring the percent adulterant was able to accurately predict the test set within 6% error. The results prove the ability to use NIR spectra to determine adulteration in crabmeat.
https://pubs.acs.org/doi/abs/10.1021/jf061801%2B

Use of Near-Infrared Spectroscopy for Fast Fraud Detection in Seafood: Application to the Authentication of Wild European Sea Bass – Ottavian, Facco, Fasolato, et al., Journal of Agricultural and Food Chemistry, 2012, 60, 639-648

Farmed and wild sea bass were collected from different distribution centers and different cities. A total of thirty-eight calibration samples with a determined (proven to be 100% accurate) attribution of production method and sixty-six validation samples with declared (not proven) methods of production (thirty-two declared wild and thirty-four declared farmed). Thirty-five chemical properties and morphometric traits were measured for each sample. NIR absorbance spectra were collected in reflectance mode from 1100 nm to 2500 nm. Thirty-two scans were collected per spectrum and averaged.

Fat R2= 0.98
Moisture R2 =0.99
δ13C R2 =0.67

Three different chemometrics techniques were used to process the spectral data. Two used pure discrimination analysis and the third using a regression model based on the fat, moisture, and δ13C and subsequent discrimination analysis. All three methods showed comparable results to the chemical analysis techniques currently used to discriminate between wild and farmed sea bass. The validity of the analysis was confirmed by the statistics for the chemometric analysis, which showed that the most predictive spectral regions for classification were related to wavelength absorbing areas for fat, fatty acids, and water content.
https://pubs.acs.org/doi/abs/10.1021/jf203385e

Detection of Frozen-Thawed Cycles for Frozen Tilapia (Oreochromis) Fillets Using Near-Infrared Spectroscopy – Wang, Chen, Tian, Liu, Journal of Aquatic Food Product Technology, 2018, Vol. 27, No. 5, 609-618

Sixty fillets from thirty tilapia fish were frozen at -18oC for twelve hours and thawed at 40oC for another twelve hours. This process is known as a frozen-thawed cycle. Fillets were then divided into six equal groups and each group went through the cycle again from two to seven times. Tests were done for thawing loss, cooking loss, moisture, total volatile basic nitrogen (TVB-N), and texture analysis. NIR reflectance spectra were collected from 10000 cm-1 to 4000 cm-1 with 4 cm-1 resolution and thirty-two scans per spectrum. Three spectra were collected for each sample and averaged into one spectrum. This process was done for both the dorsal and belly portion of each fillet. Every sample was scanned in this way for both the frozen and thawed state for all frozen-thawed cycles from one to seven.

As expected, the reference testing showed moisture loss, protein degradation, and destruction of texture after the samples went through repeated frozen-thawed cycles. Visual examination of the spectral data of the reflectance spectra shows a clear distinction between the once frozen-thawed samples and those that went through the cycle multiple times. Various pretreatments were applied to the data and four separate qualitative models using Mahalanobis distance discrimination analysis for Thawed Dorsal (TD), Thawed Belly (TB), Frozen Dorsal (FD), and Frozen Belly (FB) were created. Validation set spectra for the FD samples showed the best results with the ability to detect repeated frozen-thawed cycle samples with an accuracy of 93.33%, proving the feasibility of using NIR spectra to classify single and multiple frozen-thawed cycle samples of tilapia fillets.
https://www.tandfonline.com/doi/abs/10.1080/10498850.2018.1461156?journalCode=wafp20

Usefulness of Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics To Discriminate Fishmeal Batches Made with Different Fish Species – Cozzolino, Chree, Scaife, Murray, Journal of Agricultural and Food Chemistry, 2000, 53, 4459-4463

Sixty fishmeal samples were collected from four different species – mackerel, herring, salmon, and blue whiting. Reference method tests for moisture and total volatile nitrogen (TVN) were performed. All samples were scanned in reflectance from 1100 nm to 2500 nm at 2 nm intervals and the spectra were processed into absorbance. Thirty-two scans were collected for each data point and averaged into one spectrum. Samples were kept stationary during scanned and were not rotated.

Dummy Classification Number R2= 0.94

Visual examination of the spectral data and classification analysis showed marked differences in water, oil, and unsaturated compound absorbing areas of the NIR spectral ranges, especially between salmon and the other samples. Similar results were obtained for two different methods – DPLS (Dummy Partial Least Squares) where an arbitrary value (1 for blue herring, 2 for mackerel & herring, and 3 for salmon) was assigned to each group for classification purposes and predicted from the chemometric model and LDA (Linear Discrimination Analysis), a standard classification algorithm that uses the spectra for the classification. Both methods showed prediction results between 80% to 90% accuracy. The study shows the feasibility of classifying fishmeal using NIR spectroscopy but more detailed work is needed to improve results and extend the discrimination analysis to other species.
https://pubs.acs.org/doi/pdf/10.1021/jf050303i

Use of Near Infrared Spectroscopy to Predict Microbial Numbers on Atlantic Salmon – Tito, Rodemann, Powell – Food Microbiology 32 (2012) 431-436

Salmon fillets were purchased at the market on day of delivery and cut in half with a sterile scalpel. Each fillet was cut in half and one half was immediately scanned. The other half was stored in a sterile Petri dish for nine days at 4oC and then scanned. Microbiological analysis was conducted to determine plate counts. Seventy-two total data points were collected. Spectral data parameters were from 12500 cm-1 to 4000 cm-1 scanning sixteen times for each measurement at 4 cm-1 resolution. Spectra were collected at eight random spots on each fillet and these spectra were averaged into one data point.

Total Aerobic Plate Counts R2= 0.95

Classification analysis between NIR spectra of Day 0 and Day 9 samples showed a clear grouping between the two sets of samples. Reference analysis of the samples showed a range from 4.7-7.4 (log cfu/g) for Day 0 samples and 7-10.7 (log cfu/g) for Day 9 samples. Good correlation is shown from the calibration models but there are many factors that must be considered when interpreting the results. Predictions on the validation test set showed an error around 0.3 (log cfu/g). Considering the small sample size, error in the reference test method (0.2 log cfu/g), and the distribution of reference values (only a few samples were at the high and low end of the values), the results are promising. While more detailed calibration work is necessary, the results do indicate that changes in NIR spectra do occur from an increase in bacteria on salmon fillets and there is potential for using NIR spectroscopy as a tool for predicting microbial number and determining shelf life. https://www.sciencedirect.com/science/article/pii/S0740002012001554

The Application of Near infrared Spectroscopy to Study Lipid Characteristics and Deterioration of Frozen Lean Fish Muscles – Karlsdottir, Ararson, Kirstinsson, Sveinsdottir, Food Chemistry 159 (2014) 420-427

Lipid deterioration occurs from oxidation and hydrolytic reactions during storage of fish and is a major factor in muscle quality loss. Two lean fish species (Hoki & Saithe) were scanned using NIR spectroscopy and reference analysis was done for total lipids (TL) content and composition, free fatty acids (FFA), thiobarbituric acid reactive substances (TBARS), and fluorescent interaction compounds (OFR). NIR spectra were collected on thawed and minced homogenized samples from 800 nm to 2500 nm. Sixteen scans were collected per spectrum. Five spectra were collected per sample and averaged to make one data point.

Hoki:
Total Lipids (TL) R2=0.98
Phospolipids (PL) R2= 0.98
Polyunsaturated Fatty Acids (PUFA) R2= 0.97
Monounsaturated Fatty Acids (MUFA) R2= 0.99
Free Fatty Acids (FFA) R2= 0.86
Fluorescent Interaction Compounds (OFR) R2= 0.86
Thiobarbituric Acid Reactive Substances (TBARS) R2= 0.82
Saithe:
Total Lipids (TL) R2= 0.96
Phospolipids (PL) R2= 0.99
Polyunsaturated Fatty Acids (PUFA) R2= 0.89
Monounsaturated Fatty Acids (MUFA) R2= 0.83
Free Fatty Acids (FFA) R2= 0.93
Fluorescent Interaction Compounds (OFR) R2= 0.64
Thiobarbituric Acid Reactive Substances (TBARS) R2= 0.93

Quantitative calibration models for lipid and fatty acid measurements for both species showed good results and indicate that NIR spectroscopy can estimate these parameters with reasonable accuracy. OFR and TBARS are an indication of lipid damage and while the calibration models were not as good as the lipids and fatty acids measurements, they still showed decent results. In the case of OFR for Saithe, the sample range was small and this surely contributed to the poor correlation coefficient. Classification analysis does indicate that samples can be accurately classified into high, medium, and low OFR and TBARS content. More samples and extending the range of reference values should improve the results but this study proves the feasibility of using NIR spectroscopy to measure muscle quality deterioration in frozen fish.
https://www.sciencedirect.com/science/article/pii/S0308814614004415

Commerical References

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