Approaching Authenticity Issues in Fish and Seafood Products by Qualitative Spectroscopy and Chemometrics

Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs

BACKGROUND

Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace.

RESULTS

This paper proposes a rapid detection method for PST in mussels using near-infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950-1700 nm for PST-contaminated and non-contaminated mussel samples were used to build the detection model. Near-Bayesian support vector machines (NBSVM) with unequal misclassification costs (u-NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non-contaminated mussels was far less than that of PST-contaminated mussels in practice. The u-NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u-NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G-mean and accuracy reached 0.9898 and 0.9944, respectively.

CONCLUSION

Compared with the traditional support vector machines (SVMs) and the NBSVM, the u-NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u-NBSVM model can be used for rapid and non-destructive PST detection in mussels. © 2023 Society of Chemical Industry.

Cuttlefish Species Authentication: Advancing Label Control through Near-Infrared Spectroscopy as Rapid, Eco-Friendly, and Robust Approach

Accurate species identification, especially in the fishery sector, is critical for ensuring food safety, consumer protection and to prevent economic losses. In this study, a total of 93 individual frozen-thawed cuttlefish samples from four different species (S. officinalis, S. bertheloti, S. aculeata, and Sepiella inermis) were collected from two wholesale fish plants in Chioggia, Italy. Species identification was carried out by inspection through morphological features using dichotomic keys and then through near-infrared spectroscopy (NIRS) measurements. The NIRS data were collected using a handled-portable spectrophotometer, and the spectral range scanned was from 900-1680 nm. The collected spectra were processed using principal component analysis for unsupervised analysis and a support vector machine for supervised analysis to evaluate the species identification capability. The results showed that NIRS classification had a high overall accuracy of 93% in identifying the cuttlefish species. This finding highlights the robustness and effectiveness of spectral analysis as a tool for species identification, even in complex spatial contexts. The findings emphasize the potential of NIRS as a valuable tool in the field of fishery product authentication, offering a rapid and eco-friendly approach to species identification in the post-processing stages.

What about the Arsenic? Health Risk Assessment in Canned Tuna Commercialized in Northern Spain

The incorrect labeling, as well as the bioaccumulation of heavy metals in seafood, represent a recurring problem worldwide, not only for natural resources but also for the consumers’ health. Heavy metals can be accumulated through the food chain and transferred to the final human consumer. Despite its toxicology, arsenic does not have a concentration limit on food, unlike other heavy metals like cadmium, mercury, and lead. Tuna species, with a worldwide distribution and high per capita consumption, represent a well-known toxicological issue caused by heavy metals. In this context, 80 samples of canned tuna were analyzed to check if the information contained in the label was correct and complete. Genetic identification was made by sequencing a fragment of 16S rDNA from 80 samples. For the heavy metal quantification, only those samples with the complete FAO fishing area information on the label were analyzed. Only 29 out of 80 samples presented enough information on the labels for the analysis. Some of the canned tuna commercialized in Spanish markets surpassed the safety standard levels established by the Joint FAO/WHO Expert Committee on Food Activities (JECFA) under the consumption rates of 300 g and 482 g per week. However, the carcinogenic risk (CRlim) for arsenic in all cans and all scenarios was higher than the safety levels.

Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review

Food adulteration refers to the alteration of food quality that takes place deliberately. It includes the addition of ingredients to modify different properties of food products for economic advantage. Color, appearance, taste, weight, volume, and shelf life are such food properties. Substitution of food or its nutritional content is also accomplished to spark the apparent quality. Substitution with species, protein content, fat content, or plant ingredients are major forms of food substitution. Origin misrepresentation of food is often practiced to increase the market demand of food. Organic and synthetic compounds are added to ensure a rapid effect on the human body. Adulterated food products are responsible for mild to severe health impacts as well as financial damage. Diarrhea, nausea, allergic reaction, diabetes, cardiovascular disease, etc., are frequently observed illnesses upon consumption of adulterated food. Some adulterants have shown carcinogenic, clastogenic, and genotoxic properties. This review article discusses different forms of food adulteration. The health impacts also have been documented in brief.

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Fish is an essential source of many nutrients necessary for human health. However, the deliberate mislabeling of similar fish fillet types is common in markets to make use of the relatively high price difference. This is a type of explicit food adulteration. In the present work, spectrochemical analysis and chemometric methods are adopted to disclose this type of fish species cheating. Laser-induced breakdown spectroscopy (LIBS) was utilized to differentiate between the fillets of the low-priced tilapia and the expensive Nile perch. Furthermore, the acquired spectroscopic data were analyzed statistically using principal component analysis (PCA) and artificial neural network (ANN) showing good discrimination in the PCA score plot and a 99% classification accuracy rate of the implemented ANN model. The recorded spectra of the two fish indicated that tilapia has a higher fat content than Nile perch, as evidenced by higher CN and C2 bands and an atomic line at 247.8 nm in its spectrum. The obtained results demonstrated the potential of using LIBS as a simple, fast, and cost-effective analytical technique, combined with statistical analysis for the decisive discrimination between fish fillet species.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Harnessing the Full Power of Chemometric-Based Analysis of Total Reflection X-ray Fluorescence Spectral Data to Boost the Identification of Seafood Provenance and Fishing Areas

Provenance and traceability are crucial aspects of seafood safety, supporting managers and regulators, and allowing consumers to have clear information about the origin of the seafood products they consume. In the present study, we developed an innovative spectral approach based on total reflection X-ray fluorescence (TXRF) spectroscopy to identify the provenance of seafood and present a case study for five economically relevant marine species harvested in different areas of the Atlantic Portuguese coast: three bony fish-Merluccius merluccius, Scomber colias, and Sparus aurata; one elasmobranch-Raja clavata; one cephalopod-Octopus vulgaris. Applying a first-order Savitzky-Golay transformation to the TXRF spectra reduced the potential matrix physical effects on the light scattering of the X-ray beam while maintaining the spectral differences inherent to the chemical composition of the samples. Furthermore, a variable importance in projection partial least-squares discriminant analysis (VIP-PLS-DA), with k - 1 components (where k is the number of geographical origins of each seafood species), produced robust high-quality models of classification of samples according to their geographical origin, with several clusters well-evidenced in the dispersion plots of all species. Four of the five species displayed models with an overall classification above 80.0%, whereas the lowest classification accuracy for S. aurata was 74.2%. Notably, about 10% of the spectral features that significantly contribute to class differentiation are shared among all species. The results obtained suggest that TXRF spectra can be used for traceability purposes in seafood species (from bony and cartilaginous fishes to cephalopods) and that the presented chemometric approach has an added value for coupling with classic TXRF spectral peak deconvolution and elemental quantification, allowing characterization of the geographical origin of samples, providing a highly accurate and informative dataset in terms of food safety.

Myoglobin from Atlantic and Tinker mackerels: Purification, characterization and its possible use as a molecular marker

Here, we report the characterization (purification, autoxidation rate, pseudoperoxidase activity) and amino acid sequence determination of S. scombrus (Atlantic mackerel) and S. colias (Tinker mackerel) mioglobins (Mbs), considering the increasing consumption of fresh and canned mackerel meat and Mb implication in meat storage (e.g.: browning and lipid oxidation). We found that Atlantic mackerel Mb has major autoxidation rate (0.204 ± 0.013 h^-1) compared to Tinker mackerel Mb (0.140 ± 0.009 h^-1), while the pseudoperoxidase activity is major for Tinker mackerel (K_m: 87.71 ± 7.19 μM; k_cat: 0.32 s^-1) Mb with respect to Atlantic mackerel (K_m: 96.08 ± 6.91 μM; k_cat: 0.50 s^-1). These functional differences are confirmed by primary structure determination, in which six amino acid substitutions are found, with the first N-terminal amino acid residue acetylated. Furthermore, we predicted by AphaFold 3D model both fish Mbs and used them to investigate the possible structural differences. In addition, phylogenetic analysis using Mb sequences from Scombridae family confirms that Atlantic and Tinker mackerels are two distinct species. Finally, an analytic qualitative RP-HPLC method to distinguish S. scombrus and S. colias specimens was developed considering the different retention times of the two mackerel apoMbs.

Rapid Identification between Two Fish Species Using UV-Vis Spectroscopy for Substitution Detection

Fish species substitution and fraud has become a worldwide economic issue in the seafood industry. In this study, an ultraviolet-visible (UV-Vis) spectroscopy-based method was developed for the identification of fish samples. Sixty fish samples from twelve commonly consumed fish species in China were analyzed as models to testify the protocol. The obtained results show that UV-Vis spectroscopy combined with chemometric analysis, such as principal component analysis (PCA), can accurately distinguish two fish species by boiling fish tissue sample in trifluoroacetic acid (TFA) solution for 2 min and analyzing the resultant samples using a UV-Vis spectrometer. The developed strategy was successfully applied to the classification and identification of fish samples on the market. It is a promising strategy that can be applied to the classification and authenticity testing of closely related fish species in order to detect and recognize fish substitution.

Fast and Green Method to Control Frauds of Geographical Origin in Traded Cuttlefish Using a Portable Infrared Reflective Instrument

An appropriate seafood origin identification is essential for labelling regulation but also economic and ecological issues. Near infrared (NIRS) reflectance spectroscopy was employed to assess the origins of cuttlefish caught from five fishing FAO areas (Adriatic Sea, northeastern and eastern central Atlantic Oceans, and eastern Indian and western central Pacific Oceans). A total of 727 cuttlefishes of the family Sepiidae (Sepia officinalis and Sepiella inermis) were collected with a portable spectrophotometer (902–1680 nm) in a wholesale fish plant. NIR spectra were treated with standard normal variate, detrending, smoothing, and second derivative before performing chemometric approaches. The random forest feature selection procedure was executed to select the most significative wavelengths. The geographical origin classification models were constructed on the most informative bands, applying support vector machine (SVM) and K nearest neighbors algorithms (KNN). The SVM showed the best performance of geographical classification through the hold-out validation according to the overall accuracy (0.92), balanced accuracy (from 0.83 to 1.00), sensitivity (from 0.67 to 1.00), and specificity (from 0.88 to 1.00). Thus, being one of the first studies on cuttlefish traceability using NIRS, the results suggest that this represents a rapid, green, and non-destructive method to support on-site, practical inspection to authenticate geographical origin and to contrast fraudulent activities of cuttlefish mislabeled as local.

Applications of Non-invasive and Novel Methods of Low-Field Nuclear Magnetic Resonance and Magnetic Resonance Imaging in Aquatic Products

Aquatic products, such as fish, are popular throughout the world due to their satisfying flavor characteristics as well as rich animal nutrition, and they provide high-value food therapy, but they are easily oxidized and spoiled. It is necessary to detect aquatic products through rapid and accurate technology. Low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) have been widely used in the aquatic product industry due to their sensitivity, fast analysis, non-destructive nature and low cost. The applications of LF-NMR in the measurement of aquatic product quality and nutrients (water, fat, and protein) are summarized in this paper. Applications in aquatic products have been shown to depend on deep processing, storage and authentication. This review discusses the application of MRI technology in the quality control of aquatic products. Therefore, this review will guide the application of the aquatic products industry and aims to supply the reader with both the theory of the method and practical applications of the method for use as a rapid and non-destructive technology in scientific research and the industrial industry.

Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics

Objectives

Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models.

Materials and Methods

Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures.

Results

PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS).

Conclusions

NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.

Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis

Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R²p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R²_p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.

The use of the so-called 'superchilling' technique for the transport of fresh fishery products

Superchilling entails lowering the fish temperature to between the initial freezing point of the fish and about 1-2°C lower. The temperature of superchilled fresh fishery products (SFFP) in boxes without ice was compared to that of products subject to the currently authorised practice in boxes with ice (CFFP) under the same conditions of on-land storage and/or transport. A heat transfer model was developed and made available as a tool to identify under which initial configurations of SFFP the fish temperature, at any time of storage/transport, is lower or equal to CFFP. A minimum degree of superchilling, corresponding to an ice fraction in the fish matrix of SFFP equal or higher than the proportion of ice added per mass of fish in CFFP, will ensure with 99-100% certainty (almost certain) that the fish temperature of SFFP and the consequent increase of relevant hazards will be lower or equal to that of CFFP. In practice, the degree of superchilling can be estimated using the fish temperature after superchilling and its initial freezing point, which are subject to uncertainties. The tool can be used as part of 'safety-by-design' approach, with the reliability of its outcome being dependent on the accuracy of the input data. An evaluation of methods capable of detecting whether a previously frozen fish is commercially presented as 'superchilled' was carried out based on, amongst others, their applicability for different fish species, ability to differentiate fresh fish from fish frozen at different temperatures, use as a stand-alone method, ease of use and classification performance. The methods that were considered 'fit for purpose' are Hydroxyacyl-coenzyme A dehydrogenase (HADH) test, α-glucosidase test, histology, ultraviolet-visible-near-infrared (UV-VIS/NIR) spectroscopy and hyperspectral imaging. These methods would benefit from standardisation, including the establishment of threshold values or classification algorithms to provide a practical routine test.

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

BACKGROUND

Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS-DA), first with regard to whether the milk was heat-treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk.

RESULTS

First, discrimination of milk samples as raw or pasteurized was achieved using PLS-DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples.

CONCLUSION

The results suggest that Raman spectroscopy coupled with PLS-DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry.

Emerging Techniques for Differentiation of Fresh and Frozen-Thawed Seafoods: Highlighting the Potential of Spectroscopic Techniques

Fish and other seafood products have a limited shelf life due to favorable conditions for microbial growth and enzymatic alterations. Various preservation and/or processing methods have been developed for shelf-life extension and for maintaining the quality of such highly perishable products. Freezing and frozen storage are among the most commonly applied techniques for this purpose. However, frozen–thawed fish or meat are less preferred by consumers; thus, labeling thawed products as fresh is considered a fraudulent practice. To detect this kind of fraud, several techniques and approaches (e.g., enzymatic, histological) have been commonly employed. While these methods have proven successful, they are not without limitations. In recent years, different emerging methods have been investigated to be used in place of other traditional detection methods of thawed products. In this context, spectroscopic techniques have received considerable attention due to their potential as being rapid and non-destructive analytical tools. This review paper aims to summarize studies that investigated the potential of emerging techniques, particularly those based on spectroscopy in combination with chemometric tools, to detect frozen–thawed muscle foods.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Prediction of various freshness indicators in fish fillets by one multispectral imaging system

In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R²_P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R²_p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R²_p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R²_p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R²_P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.