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Perspective

Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products

by
Jiaojiao Zhou
1,
Chen Liu
2,
Yujun Zhong
3 and
Zhihui Luo
3,*
1
National R&D Center for Se-Rich Agricultural Products Processing, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
2
School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
3
Guangxi Key Lab of Agricultural Resources Chemistry and Biotechnology, College of Chemistry and Food Science, Yulin Normal University, Yulin 537000, China
*
Author to whom correspondence should be addressed.
Foods 2024, 13(24), 3992; https://doi.org/10.3390/foods13243992
Submission received: 1 November 2024 / Revised: 28 November 2024 / Accepted: 9 December 2024 / Published: 10 December 2024
(This article belongs to the Section Food Analytical Methods)
Browse Figure
Versions Notes

Abstract

Fish has become one of the most popular aquatic products for its beneficial effects. The quality of fish and fishery products may be influenced by their geographical origin, transportation, processing, and storage conditions. The availability of rapid and reliable techniques is important for nondestructive determination of their quality. Recently, near-infrared spectroscopy (NIRS) has been widely employed in the nondestructive evaluation of fish and fishery products. However, a comprehensive review on NIRS for this topic remains to be published. Based on this demand, the applications of NIRS in the nondestructive evaluation of fish and fishery products have been discussed in this review. This review firstly introduces the fundamentals of NIRS. Then the application of NIRS for the assessment of species, geographical origin, adulteration, freshness, nutrient components, and texture is summarized. In addition, the application of near-infrared hyperspectral imaging technology in fish and fishery products is also discussed. Finally, the challenges and prospects are outlined. The current review may provide a reference for research on NIRS in this field. In the future, NIRS could be used for online assessment of quality attributes in the fish industry through the development of new instruments and chemometrics.

1. Introduction

Fish and its products have become an important part of the human diet owing to their high nutrition and relatively low price. The consumers of fish and fishery products are conscious of their quality. However, fish is very perishable and susceptible, and its quality could be easily influenced by many factors, such as microbial spoilage and chemical contamination. Meanwhile, consumers are becoming more conscious of quality. Therefore, apart from a series of preservation methods such as freezing [1], cooling [2], drying [3], salting [4], smoking [5], and fermentation [6] to maintain quality, novel evaluation tools are clearly significant.
Until now, fish quality issues can be assessed by sensory evaluation, gas chromatography mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), near-infrared spectroscopy (NIRS), Raman spectroscopy, etc. Generally, sensory evaluation offers the best quality information to consumers due to its visibility [7]. But despite its general acceptance, it still has limitations in fish processing. Therefore, chemical and physical analytical methods are also used to assess the quality of fish and fishery products. Meanwhile, sensory evaluation results should be consistent with instrumental results. The physical properties of fish and fishery products include rigor mortis, color attributes, and textural features, which can be assessed through morphological analysis [8]. Chemical analytical methods are usually associated with changes in chemical composition such as protein decomposition, lipid oxidation, and microbial spoilage. Conventional methods, such as the use of a pH meter for pH determination, HPLC for K value determination, and the semimicro Kjeldahl method for total volatile basic nitrogen (TVB-N) determination, are commonly used for freshness evaluation of fish and fishery products. Meanwhile, the analytical results of these methods are generally regarded as reference values to establish corresponding prediction models.
With the development of the industry, rapid analytical methods have become an imperative demand. Near-infrared spectroscopy (NIRS) stands out due to its rapid analytical speed, equal or superior to that of reference methods, the opportunity to conduct both qualitative and quantitative tests, the low cost per test, and the ability to make non-invasive measurements. NIRS has been widely used for the assessment of species, nutrients, freshness, authenticity, and geographical origin in the fish industry [9,10,11,12]. There are several reviews on the quality evaluation of cocoa beans and cocoa bean products [13], stored grains [14], dairy production [15], oilseeds and edible oils [16], tea [17], and fish muscle [18], as well as the evaluation of the chemical, textural, and structural characteristics of meat [19]. However, there have been no reports on the application of chemometrics to NIR and HSI spectral data for evaluating the quality of fish and fishery products. Accordingly, this review summarizes the recent developments of fish and fishery products based on NIRS and HSI technique in tandem with chemometrics.

2. Fundamentals of NIRS

The NIR spectral region was discovered by William Herschel in 1800 [20]. NIR light covers an electromagnetic spectrum (approximately 780–2500 nm). NIRS is a widely used spectroscopic method, which shines NIR light on a sample and records the reflected or transmitted radiation [15]. NIRS involves a combination of overtones or basic absorption bands associated with molecular rotation and vibrational transitions [21]. Information about the characteristics of chemical bonds can be obtained via scanning the near-infrared spectrum of the sample [22]. However, there are many factors that influence NIR light absorbance. Thus, it is difficult to directly analyze the recorded NIR spectra based on wavelength and related absorbance. Meanwhile, each substance, such as grains, fruits, milk, or dairy products, has its unique NIR spectrum. Therefore, NIRS could be used for qualitative and quantitative analysis. A set of representative spectra of the test samples is required to determine its quality. In fact, NIRS is an indirect measurement, and reference measurements are essential in most cases. The commonly used method is to take both spectral information and reference measurements in a training set to establish a prediction model, which is then validated on further samples and makes predictions about unknown samples based on the validated model. A training set with both reference data and spectra can be used to establish quantitative calibration or qualitative classification models. The characteristics of samples that are similar in nature may be predicted using the trained model in a validation set. The comparison between predicted values and reference values may provide feedback on the effectiveness of the established prediction model (Figure 1). To be noted, NIR spectra contain different types of background interference. Spectral preprocessing is necessary to reduce noise, signal distorting effects, and other interfering factors. In data processing, the properties of the dataset and the research objectives will determine the form of data preprocessing and the required chemometrics. The chemometric methods employed to calibrate models include partial least squares (PLS) regression, principal components analysis (PCA), artificial neural networks (ANN), and support vector machines (SVM) [16]. Specifically, PLS is a procedure used to reduce original variables to a new variable set based on a small number of orthogonal factors.

3. Applications in Fish and Fish Products Quality and Safety Analysis

3.1. Qualitative Analysis

Fish authentication is a major concern to stakeholders. The substitution of high-priced fish species with cheap substitutes is an economic deception, and could also affect consumer health [23]. Traditional methods are ineffective at identifying adulterated fish and fish products due to such products’ similar physical and chemical attributes. Therefore, it is proposed to employ NIRS for the identification of adulteration and authentication of fish and fish products. Their qualitative analysis mainly focuses on two aspects: variety and geographical origin. Table 1 presents applications of NIRS in identifying adulteration and authenticating fish and fish products.
NIRS has been successfully used in distinguishing fresh and frozen/thawed fillets [24,25,26,27], organic and conventionally farmed European sea bass [28], wild and farmed European sea bass [29], raw and cooked freeze-dried rainbow trout [30], and fillets and patties of Atlantic cod from those of haddock [31]. The classification accuracies range from 65% to 100%. Different species of fish fillets with the same geographical origin could also be identified by vis-NIR combined with principal component analysis (PCA). Qin et al. used vis-NIR coupled with 24 machine-learning classifiers to distinguish fish fillets of six species, including red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia [23]. The overall classification accuracy for species differentiation is 98.5%. However, it is difficult to differentiate adulterated fish or fish products from those of cheaper fish or fish products, or to detect the mislabeling of fish fillets.
In addition, the geographical origin of fish and fishery products will also influence their quality. NIRS was utilized to discriminate fish meal produced in China and Peru with good accuracy and precision [32], which provided a precise tool to identify commercial fish meal products. The discrimination accuracy for the validation set increased to 94.87% after the selection of characteristic wavelength variables by competitive adaptive reweighted sampling (CARS). The principle of the CARS algorithm is that through adaptive reweighted sampling (ARS), a PLS model is established based on the new subset using the wavenumbers with a greater absolute weight of the regression coefficients. After multiple calculations, the wavenumbers in the subset with the smallest root mean square error (RMSECV) were selected [33]. Therefore, the CARS algorithm could be used to extract the optimal variables. Meanwhile, NIR reflectance spectroscopy was employed to authenticate European sea bass from different fishing areas [34]. Briefly, orthogonal partial least square discriminant analysis (OPLS-DA) was used to develop classifiers to discriminate fish samples. As a result, the samples were predicted with good accuracy. Soft independent modelling of class analogy (SIMCA) includes a first step of independent PCA applied to spectral variables of the calibration sets for the two considered classes [35]. Then, for each class, the number of principal components with a minimum threshold of 90% explained variance is chosen and the standard deviation of the residuals is calculated. Liu et al. investigated the combination of NIRS and SIMCA to assess the origin traceability of tilapia fillets and reported good precision [36]. Similarly, NIR reflectance spectroscopy coupled with PLS was used to discriminate salted ripened anchovies from different fishing areas [9]. The discrimination models exhibited good prediction precision. From the above applications, we can conclude that NIRS can effectively discriminate fish and fish products from different geographical origins.
Table 1. Summary of application of NIRS in identifying adulteration of fish and fish products.
Table 1. Summary of application of NIRS in identifying adulteration of fish and fish products.
SamplesApplicationsSpectral Range (nm)ModelsAccuraciesReferences
West African GoatfishClassification300~1100PLS100%[24]
Fresh and frozen/thawed fishClassification1100~2500PLS80~91%[25]
Fresh and frozen/thawed tuna filletsClassification350~2500PLS>82%[26]
European sea
bass		Classification1100~2500 PCA + SIMCA90%[28]
European seabassClassification1100~2500PLS [29]
Atlantic codClassification950~1650LDA + SIMCA100%[31]
Seven freshwater fish speciesClassification1000~1799PCA + LDA100%[37]
Fish mealClassification1000–2500PLS-DA + CARS94.87%[32]
European sea bassGeographical origins1100–2500PCA + OPLS-DA85–100%[34]
Tilapia filletsGeographical origins1000–2500SIMCA75%[36]
Salted
ripened anchovies		Geographical origins1000–2400OPLS-DA99%[9]

3.2. Quantitative Quality Analysis of Fish and Fishery Products

Recently, NIRS coupled with chemometrics has been successfully applied to the evaluation of chemical properties, such as nutritional composition and freshness index. An overview of quantitative quality analysis of fish and fishery products using NIRS is listed in Table 2.
Free fatty acids and moisture in fish oils were determined with partial least squares regression (PLSR) analysis by means of NIRS [38]. Likewise, the fatty acids and lipid classes in salmon oil were studied by NIRS combined with PLSR. The results indicated RMSEP values between 0.29 and 1.78% and residual predictive deviation (RPD) values in the range of 1.27 and 6.44 [39]. NIRS was also successfully used to monitor sodium chloride during the cod fish desalting process [40]. These studies clearly demonstrated that NIRS can be successfully used for inspection of the chemical composition of fish and fishery products.
Freshness is an important quality parameter of fishery products. The combination of spectral and freshness features has been reported in applications related to the grading of fishery products and differentiation between fresh and frozen/thawed fillets [41]. Trimethylamine (TMA) is another quality indicator for assessing fish freshness [42]. A genetic algorithm PLS (GA-PLS) model was established using FT-NIR for quantitative prediction of TMA in fish [43]. Several different PLSR models were compared using FT-NIR for quantification of TMA. Among the established calibration models, the GA-PLS achieved the best results with Rp2 of 0.977 and RMSEP of 5.10. As a kind of nitrogen-containing compound, TVB-N is also an indicator of fish freshness. TVB-N value has been determined by NIRS with a back-propagation artificial neural network (BP-ANN) as well as a computer visualization of Parabramis pekinensis samples purchased from a local market. Acceptable prediction results were obtained with a discrimination rate of 93.33% in the prediction set [44]. Microbial spoilage is another factor that shortens the shelf life for fishery products. NIRS has been applied to estimate the microbial spoilage on Atlantic salmon [45]. A PLSR calibration model achieved good prediction results with RCV2 and RMSECV of 0.95 and 0.12, respectively. Different from the usually exploited PLS, excellent results were obtained using a combination of GA and BP-ANN for the evaluation of total bacteria in flounder fillet [46], and the Rp2 and RMSEP were calculated as 0.985 and 0.095, respectively. Histamine levels in frozen/thawed tuna were also measured by NIRS with PLSR as well as machine learning. Good predictive performance was obtained with Rp2 of 0.74 and RCV2 of 0.888 [47]. Similarly, Pauline et al. used an optimized wavelet neural network (WNN) model to predict the histamine levels in mackerel with acceptable results [48]. The K value is also frequently used for freshness evaluation and represents adenosine triphosphate (ATP) decomposition [49]. Ding et al. found that NIRS together with SVM model outperformed PLS-related models in predicting the K value in silver chub, with Rp2 of 0.9374 and RMSEP of 0.036525 [50]. In another study, modeling of the K value in silver carp pieces using NIRS was also satisfactory [51]. The performance of the established PLS model showed better K value prediction ability compared with other chemometric algorithms, with Rp2 of 0.9786 and RMSEP of 3.98. Apart from individual indicators, simultaneous prediction multiple freshness indicators have also been reported based on NIRS and PLSR calibration analysis [52,53]. These results indicate that NIRS combined with chemometrics has great capability for freshness assessment.
NIRS with chemometric analysis has also been developed to determine the textural properties of fish fillets. For instance, Zhou et al. revealed the feasibility of NIRS based on PLSR models to predict the textural properties of silver carp flesh [54]. Based on full spectral analysis, the established PLSR models displayed less successful performance. To reduce redundancy, key wavelengths were chosen, and RP2 values between 0.83 and 0.95 with the corresponding RMSEP values ranging from 0.08 and 2.63 were obtained. The statistical results indicated that NIRS could assess the textural properties of fish.
Overall, these results indicate that NIRS combined with chemometrics for the prediction of the physicochemical properties and quality of fish and fishery products is feasible.
Table 2. Overview of application of NIRS for quantitative quality analysis of fish and fishery products.
Table 2. Overview of application of NIRS for quantitative quality analysis of fish and fishery products.
SamplesParametersSpectral Range (nm)ModelPerformance Ref.
Silver carptrimethylamine1000~2500PLSR2P: 0.977, RMSEP: 5.10[43]
Parabramis pekinensisTVB-N1000~2500ANNAccuracy: 0.9333[44]
Atlantic salmonmicrobial numbers800~2500PLSR2P: 0.95, RMSEP: 0.12[45]
Flounder filletstotal bacteria600~1100ANNR2P: 0.966, RMSEP: 0.083[46]
Tuna fishhistamine400~2500PLSR2P: 0.74, R2cv: 0.88[47]
Mackerelhistamine400~2498WNNR2P: 0.79, RMSEP: 70[48]
Silver chubK value1000~2500SVMR2P: 0.9374, RMSEP: 0.036525[50]
Silver carpK value1000~2500PLSR2P: 0.9786, RMSEP: 3.98[51]
Catfish filletsTVB-N, thiobarbituric acid (TBA), drip loss, L*, and whiteness1000~2500PLSRAccuracy: 0.86–0.90,
residual predictive deviation (RPD): 2.66–3.19		[52]
Bighead carppH, TVB-N, TBA, and K value1000~1799PLSRR2P: 0.807–0.954, RMSEP: 0.081–6.509[53]
Silver carp fleshtextural properties1000~1799PLSRR2P: 0.83–0.95, RMSEP: 0.08–2.63[54]

4. Near-Infrared Hyperspectral Imaging Technology in Fish and Fishery Products

NIR hyperspectral imaging (NIR-HSI) technology is a spatially resolved spectral technology that utilizes the advantages of spectroscopy and imaging technology [55]. As an alternative to traditional methods, it has been widely used in food quality and safety analysis [56]. A summary of its applications in fish and fishery products is presented in Table 3.
Many studies have reported the application of NIR-HSI for the identification of fraud and mislabeling in fish fillets [23], for the classification of fresh and frozen/thawed fish fillets [57,58,59], for the automated sorting of Atlantic cod roe, milt, and liver [60], for the automatic differentiation of organic and conventional salmon fillets [61], and for the investigation of mass changes of vacuum freeze-dried grass carp fillets [62]. These successful applications have proved that using NIR-HSI to qualitatively predict the quality attributes in fish industry is feasible.
NIR-HSI techniques have been widely used to predict the chemical properties of fish and fishery products, such as moisture and lipid content, total volatile basic nitrogen, K index value, and microbial spoilage. For example, He et al. used NIR-HSI to predict the moisture distribution, drip loss, and pH value of salmon fillets [63,64]. Moreover, it has been also used for the determination of fish caloric density. Xu et al. used this technique for accurate determination of the gross energy density values of salmon fillets [65]. Good results were obtained, with R2P of 0.908 and RMSEP of 6.871%. Subsequently, Qu et al. proved the feasibility of NIR-HSI to determine moisture contents in grass carp slices [66]. In another work, Anderssen et al. used this technique to predict liquid loss in frozen and thawed cod and further proved its feasibility [67]. Zhang et al. measured fat and moisture content in salmon fillets with an SVM model based on characteristic wavelengths [68]. Similarly, Aurora Lintvedt et al. measured the fatty acids in salmon fillets using NIR-HSI, with R2 ranging from 0.831 to 0.92 [69]. Thus, chemometrics applied to NIR-HSI can be used for the prediction of chemical information. In terms of freshness measurement of fish, NIR-HSI has been successfully applied to establish a prediction model of the total volatile basic nitrogen (TVB-N) content in fish fillets [70,71], the shelf life of expired vacuum-packed smoked salmon [72,73], the determination of K value [74], and the evaluation of microbial spoilage [75,76,77,78].
NIR-HSI techniques are also available for evaluating the physical attributes of fish [79]. Images were taken with a least square support vector machine (LS-SVM) to assess the tenderness of raw farmed salmon fillets [80]. After wavelength selection by a successful projections algorithm (SPA), an R2P value of 0.905 and RMSEP of 1.089 were obtained for the optimized SPA-LS-SVM prediction model. Cheng revealed that LS-SVM has the ability to evaluate the firmness of grass carp fillets [81]. After selecting the important wavelengths by GA, the best performance showed an R2P of 0.941 and the RMSEP was 1.229. Therefore, NIR-HSI combined with GA-LS-SVM could detect the firmness of grass carp fillets. Color is another physical parameter in the quality evaluation of fillet. Color distribution measurements using NIR-HSI are also available for salmon fillets based on multiple linear regression (MLR) models [82]. Similarly, Wang et al. have successfully applied NIR-HSI to monitor the color changes of large yellow croaker fillets during low-temperature storage using a PLSR model [83]. These results indicate that NIR-HSI can be used as a non-invasive and rapid alternative to traditional colorimeters in color measurements of fish fillets.
Table 3. A summary of application of NIR-HSI technology in fish and fishery products.
Table 3. A summary of application of NIR-HSI technology in fish and fishery products.
SamplesParametersSpectral Range (nm)ModelPerformanceRef.
Fish filletsinspect substitution and mislabeling 400~1000 and 900~1700SVM;
SVM		Accuracy: 95.0% and 95.5%[23]
Halibut filletsfresh and frozen/thawed samples380~1030SVMAccuracy: 97.22%[57]
Grass carp filletsfresh and frozen/thawed samples400~1000SVMAccuracy: 94.29%[58]
Crucian carpfresh and froze/thawed samples400~1000PLSAccuracy: over 92.5%[59]
Atlantic codroe, milt, and liver400~1000Spectral Angle Mapper (SAM)Sensitivity: 96%; specificity: 98%[60]
Salmon filletsorganic and conventional salmon fillets400~1000SVMAccuracy: 98.2%[61]
Dried grass carp filletsdehydrating and rehydrating mass changes400~1000PLSRR2P: 0.8278, RMSEP: 9.79%[62]
Salmon filletsmoisture400~1000;
900~1700;
400~1700		PLSRR2P: 0.893, RMSEP: 1.513%;
R2P: 0.902, RMSEP: 1.450%;
R2P: 0.849, RMSEP: 1.800%;		[63]
Salmon filletsdrip loss and pH distribution400~1700PLSRR2P: 0.834, RMSEP: 0.067 (drip loss);
R2P: 0.877, RMSEP: 0.046 (pH)		[64]
Salmon filletsgross energy density900~1700PLSRR2P: 0.908, RMSEP: 6.871%[65]
Grass carp slicesmoisture 400~1000PLSRR2P: 0.9021, RMSEP: 0.0561 [66]
Frozen and thawed codliquid loss430~1000PLSR2P: 0.88, RMSEP: 0.0062[67]
Salmon filletsfat and moisture900~1700LS-SVMR2P: 0.9685, RMSEP: 1.1750 (fat);
R2P: 0.9688, RMSEP: 0.8021 (moisture)		[68]
Salmon filletsfatty acids930~2500PLSRR2P: 0.9, RMSEP: 0.86[69]
Tilapia filletTVB-N900~1700PLSRR2P: 0.8524, RMSEP: 2.4487[70]
Rainbow trout filletsTVB-N430~1010Linear Deep Neural Network
(LDNN)		R2P: 0.853, RMSEP: 3.159[71]
Vacuum-packed smoked salmonshelf life400~1000PLSR2P: 0.97, RMSEP: 0.08[72]
Vacuum-packed chilled smoked salmonshelf life400~1000support vector
machine (SVM)		R2P: 0.89, RMSECV: 7.7[73]
Grass carp and silver carp filletsK value400~1000SVMR2P: 0.936, RMSEP: 5.21%[74]
Salmon fleshtotal viable counts400~1700PLSRR2P: 0.985, residual predictive deviation (RPD): 5.127[75]
Salmon fleshlactic acid bacteria 900~1700SVMR2P: 0.925, RMSEP: 0.531[76]
Salmon fleshtotal counts of Enterobacteriaceae and Pseudomonas spp. (EPC)900~1700PLSR2P: 0.964, RMSEP: 0.429[77]
Grass carp filletstotal viable counts400~1000PLSRR2P: 0.90, RMSEP: 0.57[78]
Salmon filletstenderness400~1720SVMR2P: 0.905, RMSEP: 1.089[80]
Grass carp filletsfirmness400~1000SVMR2P: 0.941, RMSEP: 1.229[81]
Salmon filletscolor964~1631MLR R2P: 0.876, 0.744, and 0.803 for L*, a*, and b*, respectively.[82]
Large yellow croaker filletscolor400~1000PLSRR2P: 0.908, 0.915, and 0.977 for L*, a*, and b*, respectively.[83]

5. Conclusions and Future Trends

In summary, NIRS combined with a chemometric algorithm has been proved to be an effective tool for nondestructive and rapid quality analysis in the fishery industry. The application of NIRS to fish and fishery products will greatly benefit the quality and safety of the industry. However, there are still some limitations affecting the reliability and accuracy of prediction models based on NIRS. Firstly, prediction models based on NIRS require abundant sample analysis as reference results. Moreover, spectral information is complicated and largely relies on analytical methods. For instance, improper wavelength selection may leave out useful information or retain useless information in the spectra. More attention should be paid to developing more advanced algorithms. Second, sample preparation methods will influence the performance of prediction models, especially in terms of the reference value determination of samples. Third, veterinary drug residues and heavy metal ions are also hazards in the fishery industry, and innovative methods should be established to deal with these contaminants in the future. Finally, online measurement in the food industry is another direction to be explored in the future.

Funding

This study was supported by Guangxi Key Lab of Agricultural Resources Chemistry and Biotechnology (Se-2024KF03).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

All of authors declare that they have no conflicts of interest.

References

  1. Wei, P.; Zhu, K.; Cao, J.; Lin, X.; Shen, X.; Duan, Z.; Li, C. Relationship between Micromolecules and Quality Changes of Tilapia Fillets After Partial Freezing Treatment with Polyphenols. J. Agric. Food Chem. 2021, 69, 8213–8226. [Google Scholar] [CrossRef] [PubMed]
  2. Zang, J.; Xu, Y.; Xia, W.; Jiang, Q. The impact of desmin on texture and water-holding capacity of ice-stored grass carp (Ctenopharyngodon idella) fillet. Int. J. Food Sci. Technol. 2016, 52, 464–471. [Google Scholar] [CrossRef]
  3. Sinthupachee, A.; Matan, N.; Matan, N. Development of smoke flavour-antimicrobial packaging from coconut fibre using Litsea cubeba essential oil and wood smoke for dried fish preservation and reduction of PAH. Food Control 2023, 148, 109629. [Google Scholar] [CrossRef]
  4. Yan, D.; Xu, W.; Yu, Q.; You, J.; Gao, R.; Bao, Y. Pre-rigor salting improves gel strength and water-holding of surimi gel made from snakehead fish (Channa argus): The role of protein oxidation. Food Chem. 2024, 450, 139269. [Google Scholar] [CrossRef]
  5. Nie, Y.; Chen, L.; Zi, Y.; Chen, J.; Xu, J.; Shi, W.; Wang, X.; Zhong, J. Development of tilapia muscle-based Shanghai smoked fish and the effect of salt amounts in the preparation process. LWT 2023, 186, 115240. [Google Scholar] [CrossRef]
  6. Chen, J.-N.; Zhang, Y.-Y.; Huang, X.; Wang, H.-P.; Dong, X.; Zhu, B.; Qin, L. Analysis of Lipid Molecule Profiling and Conversion Pathway in Mandarin Fish (Siniperca chuatsi) during Fermentation via Untargeted Lipidomics. J. Agric. Food Chem. 2023, 71, 8673–8684. [Google Scholar] [CrossRef]
  7. López-García, M.M.; Ramil-Novo, L.A.; Vázquez-Odériz, M.L.; Romero-Rodríguez, M.Á. A QIM-Based Evaluation of Sensory Quality of Frozen–Thawed Roundnose Grenadier (Coryphaenoides rupestris) and Its Applications. ACS Food Sci. Technol. 2021, 1, 392–398. [Google Scholar] [CrossRef]
  8. Arvanitoyannis, I.S.; Kotsanopoulos, K.V. Smoking of Fish and Seafood: History, Methods and Effects on Physical, Nutritional and Microbiological Properties. Food Bioprocess Technol. 2011, 5, 831–853. [Google Scholar] [CrossRef]
  9. Varrà, M.O.; Ghidini, S.; Ianieri, A.; Zanardi, E. Near infrared spectral fingerprinting: A tool against origin-related fraud in the sector of processed anchovies. Food Control 2020, 123, 107778. [Google Scholar] [CrossRef]
  10. Wu, L.; Pu, H.; Sun, D.-W. Novel techniques for evaluating freshness quality attributes of fish: A review of recent developments. Trends Food Sci. Technol. 2019, 83, 259–273. [Google Scholar] [CrossRef]
  11. Hassoun, A.; Sahar, A.; Lakhal, L.; Aït-Kaddour, A. Fluorescence spectroscopy as a rapid and non-destructive method for monitoring quality and authenticity of fish and meat products: Impact of different preservation conditions. LWT 2019, 103, 279–292. [Google Scholar] [CrossRef]
  12. Chapman, J.; Elbourne, A.; Truong, V.K.; Newman, L.; Gangadoo, S.; Rajapaksha Pathirannahalage, P.; Cheeseman, S.; Cozzolino, D. Sensomics—From conventional to functional NIR spectroscopy—Shining light over the aroma and taste of foods. Trends Food Sci. Technol. 2019, 91, 274–281. [Google Scholar] [CrossRef]
  13. Teye, E.; Anyidoho, E.; Agbemafle, R.; Sam-Amoah, L.K.; Elliott, C. Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A review. Infrared Phys. Technol. 2020, 104, 103127. [Google Scholar] [CrossRef]
  14. Johnson, J.B. An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains. J. Stored Prod. Res. 2020, 86, 101558. [Google Scholar] [CrossRef]
  15. Yakubu, H.G.; Kovacs, Z.; Toth, T.; Bazar, G. The recent advances of near-infrared spectroscopy in dairy production—A review. Crit. Rev. Food Sci. Nutr. 2020, 62, 810–831. [Google Scholar] [CrossRef]
  16. Li, X.; Zhang, L.; Zhang, Y.; Wang, D.; Wang, X.; Yu, L.; Zhang, W.; Li, P. Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils. Trends Food Sci. Technol. 2020, 101, 172–181. [Google Scholar] [CrossRef]
  17. Lin, X.; Sun, D.-W. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci. Technol. 2020, 104, 163–176. [Google Scholar] [CrossRef]
  18. Cheng, J.-H.; Sun, D.-W. Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. Food Eng. Rev. 2016, 9, 36–49. [Google Scholar] [CrossRef]
  19. Reis, M.M.; Van Beers, R.; Al-Sarayreh, M.; Shorten, P.; Yan, W.Q.; Saeys, W.; Klette, R.; Craigie, C. Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat. Meat Sci. 2018, 144, 100–109. [Google Scholar] [CrossRef]
  20. Jerome Workman, J. A Brief review of near infrared in Petroleum product analysis. J. Near Infrared Spectrosc. 1996, 4, 69–74. [Google Scholar] [CrossRef]
  21. Louw, E.D.; Theron, K.I. Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biol. Technol. 2010, 58, 176–184. [Google Scholar] [CrossRef]
  22. Bogomolov, A.; Melenteva, A.; Dahm, D.J. Fat Globule Size Effect on Visible and Shortwave near Infrared Spectra of Milk. J. Near Infrared Spectrosc. 2013, 21, 435–440. [Google Scholar] [CrossRef]
  23. Qin, J.; Vasefi, F.; Hellberg, R.S.; Akhbardeh, A.; Isaacs, R.B.; Yilmaz, A.G.; Hwang, C.; Baek, I.; Schmidt, W.F.; Kim, M.S. Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques. Food Control 2020, 114, 107234. [Google Scholar] [CrossRef]
  24. Ottavian, M.; Fasolato, L.; Serva, L.; Facco, P.; Barolo, M. Data Fusion for Food Authentication: Fresh/Frozen–Thawed Discrimination in West African Goatfish (Pseudupeneus prayensis) Fillets. Food Bioprocess Technol. 2013, 7, 1025–1036. [Google Scholar] [CrossRef]
  25. Ottavian, M.; Fasolato, L.; Facco, P.; Barolo, M. Foodstuff authentication from spectral data: Toward a species-independent discrimination between fresh and frozen–thawed fish samples. J. Food Eng. 2013, 119, 765–775. [Google Scholar] [CrossRef]
  26. Reis, M.M.; Martínez, E.; Saitua, E.; Rodríguez, R.; Pérez, I.; Olabarrieta, I. Non-invasive differentiation between fresh and frozen/thawed tuna fillets using near infrared spectroscopy (Vis-NIRS). LWT 2017, 78, 129–137. [Google Scholar] [CrossRef]
  27. Alamprese, C.; Casiraghi, E. Application of FT-NIR and FT-IR spectroscopy to fish fillet authentication. LWT Food Sci. Technol. 2015, 63, 720–725. [Google Scholar] [CrossRef]
  28. Trocino, A.; Xiccato, G.; Majolini, D.; Tazzoli, M.; Bertotto, D.; Pascoli, F.; Palazzi, R. Assessing the quality of organic and conventionally-farmed European sea bass (Dicentrarchus labrax). Food Chem. 2012, 131, 427–433. [Google Scholar] [CrossRef]
  29. Ottavian, M.; Facco, P.; Fasolato, L.; Novelli, E.; Mirisola, M.; Perini, M.; Barolo, M. Use of near-infrared spectroscopy for fast fraud detection in seafood: Application to the authentication of wild European sea bass (Dicentrarchus labrax). J. Agric. Food Chem. 2012, 60, 639–648. [Google Scholar] [CrossRef]
  30. Dalle Zotte, A.; Ottavian, M.; Concollato, A.; Serva, L.; Martelli, R.; Parisi, G. Authentication of raw and cooked freeze-dried rainbow trout (Oncorhynchus mykiss) by means of near infrared spectroscopy and data fusion. Food Res. Int. 2014, 60, 180–188. [Google Scholar] [CrossRef]
  31. Grassi, S.; Casiraghi, E.; Alamprese, C. Handheld NIR device: A non-targeted approach to assess authenticity of fish fillets and patties. Food Chem. 2018, 243, 382–388. [Google Scholar] [CrossRef] [PubMed]
  32. Shen, G.; Han, L.; Fan, X.; Liu, X.; Cao, Y.; Yang, Z. Classification of fish meal produced in China and Peru by online near infrared spectroscopy with characteristic wavelength variables. J. Near Infrared Spectrosc. 2017, 25, 63–71. [Google Scholar] [CrossRef]
  33. Lu, Z.; Yu, H.; Yin, Y.; Yuan, Y.; Liang, H.; Li, F.; Li, Z. Determination of the Acid and Peroxide Values of Vegetable Oils by Raman Spectroscopy with Competitive Adaptive Reweighted Sampling (CARS) and Back Propagation Neural Network (BPNN). Anal. Lett. 2023, 57, 2289–2306. [Google Scholar] [CrossRef]
  34. Ghidini, S.; Varra, M.O.; Dall’Asta, C.; Badiani, A.; Ianieri, A.; Zanardi, E. Rapid authentication of European sea bass (Dicentrarchus labrax L.) according to production method, farming system, and geographical origin by near infrared spectroscopy coupled with chemometrics. Food Chem. 2019, 280, 321–327. [Google Scholar] [CrossRef]
  35. Wold, S.; SjÖStrÖM, M. SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy. In Chemometrics: Theory and Application; American Chemical Society: Washington, DC, USA, 1977; pp. 243–282. [Google Scholar] [CrossRef]
  36. Liu, Y.; Ma, D.-h.; Wang, X.-c.; Liu, L.-p.; Fan, Y.-x.; Cao, J.-x. Prediction of chemical composition and geographical origin traceability of Chinese export tilapia fillets products by near infrared reflectance spectroscopy. LWT Food Sci. Technol. 2015, 60, 1214–1218. [Google Scholar] [CrossRef]
  37. Lv, H.; Xu, W.; You, J.; Xiong, S. Classification of freshwater fish species by linear discriminant analysis based on near infrared reflectance spectroscopy. J. Near Infrared Spectrosc. 2017, 25, 54–62. [Google Scholar] [CrossRef]
  38. Cozzolino, D.; Murray, I.; Chree, A.; Scaife, J.R. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. LWT Food Sci. Technol. 2005, 38, 821–828. [Google Scholar] [CrossRef]
  39. Cascant, M.M.; Breil, C.; Fabiano-Tixier, A.S.; Chemat, F.; Garrigues, S.; de la Guardia, M. Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy. Food Chem. 2018, 239, 865–871. [Google Scholar] [CrossRef]
  40. Galvis-Sánchez, A.C.; Tóth, I.V.; Portela, A.; Delgadillo, I.; Rangel, A.O.S.S. Monitoring sodium chloride during cod fish desalting process by flow injection spectrometry and infrared spectroscopy. Food Control 2011, 22, 277–282. [Google Scholar] [CrossRef]
  41. Taheri-Garavand, A.; Fatahi, S.; Banan, A.; Makino, Y. Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches. Comput. Electron. Agric. 2019, 159, 16–27. [Google Scholar] [CrossRef]
  42. Xiao-wei, H.; Zhi-hua, L.; Xiao-bo, Z.; Ji-yong, S.; Han-ping, M.; Jie-wen, Z.; Li-min, H.; Holmes, M. Detection of meat-borne trimethylamine based on nanoporous colorimetric sensor arrays. Food Chem. 2016, 197, 930–936. [Google Scholar] [CrossRef] [PubMed]
  43. Agyekum, A.A.; Kutsanedzie, F.Y.H.; Mintah, B.K.; Annavaram, V.; Zareef, M.; Hassan, M.M.; Arslan, M.; Chen, Q. Rapid and Nondestructive Quantification of Trimethylamine by FT-NIR Coupled with Chemometric Techniques. Food Anal. Methods 2019, 12, 2035–2044. [Google Scholar] [CrossRef]
  44. Huang, X.; Xu, H.; Wu, L.; Dai, H.; Yao, L.; Han, F. A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Anal. Methods 2016, 8, 2929–2935. [Google Scholar] [CrossRef]
  45. Tito, N.B.; Rodemann, T.; Powell, S.M. Use of near infrared spectroscopy to predict microbial numbers on Atlantic salmon. Food Microbiol. 2012, 32, 431–436. [Google Scholar] [CrossRef] [PubMed]
  46. Duan, C.; Chen, C.; Khan, M.N.; Liu, Y.; Zhang, R.; Lin, H.; Cao, L. Non-destructive determination of the total bacteria in flounder fillet by portable near infrared spectrometer. Food Control 2014, 42, 18–22. [Google Scholar] [CrossRef]
  47. Currò, S.; Savini, F.; Fasolato, L.; Indio, V.; Tomasello, F.; Rampazzo, G.; Zironi, E.; Pagliuca, G.; Gazzotti, T.; Prandini, L.; et al. Application of near-infrared spectroscopy as at line method for the evaluation of histamine in tuna fish (Thunnus albacares). Food Control 2025, 167, 110778. [Google Scholar] [CrossRef]
  48. Pauline, O.; Chang, H.-T.; Tsai, I.L.; Lin, C.-H.; Chen, S.; Chuang, Y.-K. Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy. LWT 2021, 145, 111524. [Google Scholar] [CrossRef]
  49. Prabhakar, P.K.; Vatsa, S.; Srivastav, P.P.; Pathak, S.S. A comprehensive review on freshness of fish and assessment: Analytical methods and recent innovations. Food Res. Int. 2020, 133, 109157. [Google Scholar] [CrossRef]
  50. Ding, R.; Huang, X.; Han, F.; Dai, H.; Teye, E.; Xu, F. Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis. Anal. Methods 2014, 6, 9675–9683. [Google Scholar] [CrossRef]
  51. Agyekum, A.A.; Kutsanedzie, F.Y.H.; Annavaram, V.; Mintah, B.K.; Asare, E.K.; Wang, B. FT-NIR coupled chemometric methods rapid prediction of K-value in fish. Vib. Spectrosc. 2020, 108, 103044. [Google Scholar] [CrossRef]
  52. Cao, L.; Huang, Z.; Wu, D.; Ruan, R.; Liu, Y. Rapid and nondestructive determination of qualities in vacuum-packaged catfish (Clarias leather) fillets during slurry ice storage. J. Food Process. Preserv. 2021, 45, e15262. [Google Scholar] [CrossRef]
  53. Zhou, J.; Wu, X.; Chen, Z.; You, J.; Xiong, S. Evaluation of freshness in freshwater fish based on near infrared reflectance spectroscopy and chemometrics. LWT 2019, 106, 145–150. [Google Scholar] [CrossRef]
  54. Zhou, J.; Wu, X.; You, J.; Xiong, S. Rapid determination of the textural properties of silver carp (Hypophthalmichthys molitrix) using near-infrared reflectance spectroscopy and chemometrics. LWT 2020, 129, 109545. [Google Scholar] [CrossRef]
  55. Washburn, K.E.; Stormo, S.K.; Skjelvareid, M.H.; Heia, K. Non-invasive assessment of packaged cod freeze-thaw history by hyperspectral imaging. J. Food Eng. 2017, 205, 64–73. [Google Scholar] [CrossRef]
  56. Pu, H.; Lin, L.; Sun, D.W. Principles of Hyperspectral Microscope Imaging Techniques and Their Applications in Food Quality and Safety Detection: A Review. Compr. Rev. Food Sci. Food Saf. 2019, 18, 853–866. [Google Scholar] [CrossRef]
  57. Zhu, F.; Zhang, D.; He, Y.; Liu, F.; Sun, D.-W. Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets. Food Bioprocess Technol. 2012, 6, 2931–2937. [Google Scholar] [CrossRef]
  58. Cheng, J.-H.; Sun, D.-W.; Pu, H.-B.; Chen, X.; Liu, Y.; Zhang, H.; Li, J.-L. Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. J. Food Eng. 2015, 161, 33–39. [Google Scholar] [CrossRef]
  59. Shan, J.; Wang, X.; Russel, M.; Zhao, J.; Zhang, Y. Comparisons of Fish Morphology for Fresh and Frozen-Thawed Crucian Carp Quality Assessment by Hyperspectral Imaging Technology. Food Anal. Methods 2018, 11, 1701–1710. [Google Scholar] [CrossRef]
  60. Paluchowski, L.A.; Misimi, E.; Grimsmo, L.; Randeberg, L.L. Towards automated sorting of Atlantic cod (Gadus morhua) roe, milt, and liver—Spectral characterization and classification using visible and near-infrared hyperspectral imaging. Food Control 2016, 62, 337–345. [Google Scholar] [CrossRef]
  61. Xu, J.-L.; Riccioli, C.; Sun, D.-W. Comparison of hyperspectral imaging and computer vision for automatic differentiation of organically and conventionally farmed salmon. J. Food Eng. 2017, 196, 170–182. [Google Scholar] [CrossRef]
  62. Ma, J.; Qu, J.-H.; Sun, D.-W. Developing hyperspectral prediction model for investigating dehydrating and rehydrating mass changes of vacuum freeze dried grass carp fillets. Food Bioprod. Process. 2017, 104, 66–76. [Google Scholar] [CrossRef]
  63. He, H.-J.; Wu, D.; Sun, D.-W. Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innov. Food Sci. Emerg. Technol. 2013, 18, 237–245. [Google Scholar] [CrossRef]
  64. He, H.-J.; Wu, D.; Sun, D.-W. Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis–NIR) hyperspectral imaging. Food Chem. 2014, 156, 394–401. [Google Scholar] [CrossRef] [PubMed]
  65. Xu, J.-L.; Riccioli, C.; Sun, D.-W. Development of an alternative technique for rapid and accurate determination of fish caloric density based on hyperspectral imaging. J. Food Eng. 2016, 190, 185–194. [Google Scholar] [CrossRef]
  66. Qu, J.-H.; Sun, D.-W.; Cheng, J.-H.; Pu, H. Mapping moisture contents in grass carp (Ctenopharyngodon idella) slices under different freeze drying periods by Vis-NIR hyperspectral imaging. LWT 2017, 75, 529–536. [Google Scholar] [CrossRef]
  67. Anderssen, K.E.; Stormo, S.K.; Skåra, T.; Skjelvareid, M.H.; Heia, K. Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. LWT 2020, 133, 110093. [Google Scholar] [CrossRef]
  68. Zhang, H.; Zhang, S.; Chen, Y.; Luo, W.; Huang, Y.; Tao, D.; Zhan, B.; Liu, X. Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features. J. Food Compos. Anal. 2020, 92, 103567. [Google Scholar] [CrossRef]
  69. Lintvedt, T.A.; Andersen, P.V.; Afseth, N.K.; Heia, K.; Lindberg, S.-K.; Wold, J.P. Raman spectroscopy and NIR hyperspectral imaging for in-line estimation of fatty acid features in salmon fillets. Talanta 2023, 254, 124113. [Google Scholar] [CrossRef]
  70. Yu, H.-D.; Qing, L.-W.; Yan, D.-T.; Xia, G.; Zhang, C.; Yun, Y.-H.; Zhang, W. Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness. Food Chem. 2021, 348, 129129. [Google Scholar] [CrossRef]
  71. Moosavi-Nasab, M.; Khoshnoudi-Nia, S.; Azimifar, Z.; Kamyab, S. 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. Sci. Rep. 2021, 11, 5094. [Google Scholar] [CrossRef]
  72. Ivorra, E.; Girón, J.; Sánchez, A.J.; Verdú, S.; Barat, J.M.; Grau, R. Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images. J. Food Eng. 2013, 117, 342–349. [Google Scholar] [CrossRef]
  73. Ivorra, E.; Sánchez, A.J.; Verdú, S.; Barat, J.M.; Grau, R. Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images. J. Food Eng. 2016, 178, 110–116. [Google Scholar] [CrossRef]
  74. Cheng, J.-H.; Sun, D.-W.; Pu, H.; Zhu, Z. Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chem. 2015, 185, 245–253. [Google Scholar] [CrossRef] [PubMed]
  75. Wu, D.; Sun, D.-W. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta 2013, 111, 39–46. [Google Scholar] [CrossRef] [PubMed]
  76. He, H.-J.; Sun, D.-W.; Wu, D. Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Res. Int. 2014, 62, 476–483. [Google Scholar] [CrossRef]
  77. He, H.-J.; Sun, D.-W. Inspection of harmful microbial contamination occurred in edible salmon flesh using imaging technology. J. Food Eng. 2015, 150, 82–89. [Google Scholar] [CrossRef]
  78. Cheng, J.-H.; Sun, D.-W. Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT Food Sci. Technol. 2015, 62, 1060–1068. [Google Scholar] [CrossRef]
  79. Wu, D.; Sun, D.W.; He, Y. Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. Food Chem. 2014, 145, 417–426. [Google Scholar] [CrossRef]
  80. He, H.-J.; Wu, D.; Sun, D.-W. Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. J. Food Eng. 2014, 126, 156–164. [Google Scholar] [CrossRef]
  81. Cheng, J.-H.; Qu, J.-H.; Sun, D.-W.; Zeng, X.-A. Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage. Food Res. Int. 2014, 56, 190–198. [Google Scholar] [CrossRef]
  82. Wu, D.; Sun, D.-W.; He, Y. Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innov. Food Sci. Emerg. Technol. 2012, 16, 361–372. [Google Scholar] [CrossRef]
  83. Wang, S.; Das, A.K.; Pang, J.; Liang, P. Real-time monitoring the color changes of large yellow croaker (Larimichthys crocea) fillets based on hyperspectral imaging empowered with artificial intelligence. Food Chem. 2022, 382, 132343. [Google Scholar] [CrossRef]
Foods 13 03992 g001
Figure 1. Generalized workflow of near-infrared spectroscopic modeling procedures. Continuous arrows: training process of multivariate calibration; dashed arrows: testing of the trained models with validation.
Figure 1. Generalized workflow of near-infrared spectroscopic modeling procedures. Continuous arrows: training process of multivariate calibration; dashed arrows: testing of the trained models with validation.
Foods 13 03992 g001
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Zhou, J.; Liu, C.; Zhong, Y.; Luo, Z. Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products. Foods 2024, 13, 3992. https://doi.org/10.3390/foods13243992

AMA Style

Zhou J, Liu C, Zhong Y, Luo Z. Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products. Foods. 2024; 13(24):3992. https://doi.org/10.3390/foods13243992

Chicago/Turabian Style

Zhou, Jiaojiao, Chen Liu, Yujun Zhong, and Zhihui Luo. 2024. "Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products" Foods 13, no. 24: 3992. https://doi.org/10.3390/foods13243992

APA Style

Zhou, J., Liu, C., Zhong, Y., & Luo, Z. (2024). Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products. Foods, 13(24), 3992. https://doi.org/10.3390/foods13243992

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