Noninvasive freshness evaluation of bighead carp heads based on fluorescence spectroscopy coupled with long short-term memory network: simulation of cold chains

Abstract

To swiftly and noninvasively assess the freshness of bighead carp heads within simulated cold chain environments, an excitation-emission matrix fluorescence spectroscopy coupled with a long short-term memory network (EEM-LSTM) model was developed. Through the parallel factor algorithm based on analysis of residuals, diagnosis of core consistency, and split-half evaluation, three key fluorescent components from fish fillets were extracted, with the most significant components linked to tryptophan and NADH, both indicative of fish freshness. The EEM-LSTM model exhibited coherent trends in freshness indicators and demonstrated exceptional predictive capabilities for four freshness indicators simultaneously, achieving R2 values exceeding 0.8840 in simulated cold chain situations. Relative errors in the supermarket direct sales cold chain were less than 10%, surpassing those of the long-distance transport cold chain. Hence, the EEM-LSTM model stands validated for predicting fish freshness in simulated cold chains, holding promise for real-world aquatic product freshness forecasting within cold chain scenarios.

Keywords

Excitation-emission matrix fluorescence spectroscopy / Simulation of cold chains / Freshness prediction / Long short-term memory network / Parallel factor algorithm / Bighead carp heads

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Shi C; draft manuscript preparation: You J; conceptualization, visualization and supervision: Sun Z; methodology: Li X, Fan H; writing - revision & editing: Fan H; statistical and mathematical methods to data analysis: Ying X. All authors reviewed the results and approved the final version of the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

This study was supported by the Beijing Academy of Agriculture and Forestry Sciences Outstanding young scientist training program, the Fund of Young Beijing Scholar, Jiangsu Science and Technology Plan (Key Research and Development Plan Modern Agriculture) Project (BE2023315), and the Ministry of Finance and Ministry of Agriculture and Rural Affairs: National System of Modern Agricultural Industry Technology (CARS-45-28).

Conflict of interest

The authors declare that they have no conflict of interest.

References

[1] Wang H, Zheng Y, Shi W, Wang X. 2022. Comparison of Arrhenius model and artificial neuronal network for predicting quality changes of frozen tilapia (Oreochromis niloticus). Food Chemistry 372:131268

[2] Shi X, Zhang J, Shi C, Tan Y, Hong H, et al. 2022. Nondestructive prediction of freshness for bighead carp (Hypophthalmichthys nobilis) head by Excitation-Emission Matrix (EEM) analysis based on fish eye fluid: comparison of BPNNs and RBFNNs. Food Chemistry 382:132341

[3] Zhao H, Liu S, Tian C, Yan G, Wang D. 2018. An overview of current status of cold chain in China. International Journal of Refrigeration 88:483-95

[4] Yu J, Xiao K, Xue W, Shen Y, Tan J, et al. 2020. Excitation-emission matrix (EEM) fluorescence spectroscopy for characterization of organic matter in membrane bioreactors: principles, methods and applications. Frontiers of Environmental Science & Engineering 14:31

[5] Li L, Wang Y, Zhang W, Yu S, Wang X, et al. 2020. New advances in fluorescence excitation-emission matrix spectroscopy for the characterization of dissolved organic matter in drinking water treatment: A review. Chemical Engineering Journal 381:122676

[6] ElMasry G, Nagai H, Moria K, Nakazawa N, Tsuta M, et al. 2015. Freshness estimation of intact frozen fish using fluorescence spectroscopy and chemometrics of excitation-emission matrix. Talanta 143:145-56

[7] Rahman MM, Shibata M, ElMasry G, Nakazawa N, Nakauchi S, et al. 2019. Expeditious prediction of post-mortem changes in frozen fish meat using three-dimensional fluorescence fingerprints. Bioscience Biotechnology and Biochemistry 83:901-13

[8] Liao Q, Suzuki T, Kohno Y, Al Riza DF, Kuramoto M, et al. 2018. Potential of using uric acid fluorescence in eye fluid for freshness assessment on Red Sea bream (Pagrus major). Spectroscopy Letters 51:431-37

[9] Marini F. 2009. Artificial neural networks in foodstuff analyses: Trends and perspectives A review. Analytica Chimica Acta 635:121-31

[10] Chu Y, Tan M, Yi Z, Ding Z, Yang D, et al. 2021. Shelf-life prediction of glazed large yellow croaker (Pseudosciaena crocea) during frozen storage based on Arrhenius model and long-short-term memory neural networks model. Fishes 6(3):39

[11] Guo L, Xu C, Yu T, Wumaier T, Han X. 2024. Ultra-short-term wind power forecasting based on long short-term memory network with modified honey badger algorithm. Energy Reports 12:3548-65

[12] Ren H, Du L, Peng C, Yang J, Gao W. 2024. The composite drought index incorporated solar-induced chlorophyll fluorescence enhances the monitoring capability of short-term drought. Journal of Hydrology 637:131361

[13] Matenda RT, Rip D, Marais J, Williams PJ. 2024. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy 315:124261

[14] Huang W, Zhao J, Wang X. 2024. Model-driven multimodal LSTMCNN for unbiased structural forecasting of European Union allowances open-high-low-close price. Energy Economics 132:107459

[15] Gao K, Huang Z, Lyu C, Liu C. 2024. Multi-scale prediction of remaining useful life of lithium-ion batteries based on variational mode decomposition and integrated machine learning. Journal of Energy Storage 99:113372

[16] Zhang Y, Chu G, Shen D. 2021. The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters 38:101484

[17] Boutheina B, Leila K, Besbes N, Messina CM, Santulli A, et al. 2023. Evaluation of the qualitative properties and consumer perception of marinated sardine Sardina pilchardus: The effect of fucoxanthin addition. International Journal of Gastronomy and Food Science 31:100611

[18] Koel M. 2024. Developments in analytical chemistry initiated from green chemistry. Sustainable Chemistry for the Environment 5:100078

[19] Takefuji Y. 2023. An updated tutorial on reproducible PyPI applications for advancing chemometrics and boosting learner motivation. Chemometrics and Intelligent Laboratory Systems 241:104941

[20] Smilde AK, Doornbos DA. 1992. Simple validatory tools for judging the predictive performance of parafac and three-way PLS. Journal of Chemometrics 6:11-28

[21] Câmara ABF, da Silva WJO, Neves ACdO, Moura HOMA, de Lima KMG, de Carvalho LS. 2024. Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination. Talanta 266:125126

[22] Mangalgiri K, Cheng Z, Cervantes S, Spencer S, Liu H. 2021. UVbased advanced oxidation of dissolved organic matter in reverse osmosis concentrate from a potable water reuse facility: a parallelfactor (PARAFAC) analysis approach. Water Research 204:117585

[23] Yu Y, Si X, Hu C, Zhang J. 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation 31:1235-70

[24] Smagulova K, James AP. 2019. A survey on LSTM memristive neural network architectures and applications. European Physical Journal-Special Topics 228:2313-24

[25] Tran TTK, Bateni SM, Ki SJ, Vosoughifar H. 2021. A review of neural networks for air temperature forecasting. Water 13:1294

[26] Bekhit AEDA, Holman BWB, Giteru SG, Hopkins DL. 2021. Total volatile basic nitrogen (TVB-N) and its role in meat spoilage: A review. Trends in Food Science & Technology 109:280-302

[27] Lorentzen G, Ageeva TN, Heide M, Esaiassen M. 2020. Temperature fluctuations in processing and distribution: Effect on the shelf life of fresh cod fillets (Gadus morhua L.). Food Control 112:107102

[28] Gayer AV, Yakimov BP, Sluchanko NN, Shirshin EA. 2023. Multifarious analytical capabilities of the UV/Vis protein fluorescence in blood plasma. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 286:122028

[29] Wang X, Xie J, Qian Y. 2020. A non-invasive method for quantitative monitoring of quality changes and water migration in bigeye tuna (Thunnus obesus) during simulated cold chain logistics using low-field nuclear magnetic resonance coupled with PCA. Food Science and Technology International 26:475-84

[30] Wu Y, Deng J, Xu F, Li X, Kong L, et al. 2023. The mechanism of Leuconostoc mesenteroides subsp. IMAU:80679 in improving meat color: Myoglobin oxidation inhibition and myoglobin derivatives formation based on multi enzyme-like activities. Food Chemistry 428:136751

[31] Karoui R, Thomas E, Dufour E. 2006. Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Research International 39:349-55

[32] Wu B, Dahlberg K, Gao X, Smith J, Bailin J. 2018. A rapid method based on fluorescence spectroscopy for meat spoilage detection. International Journal of High Speed Electronics and Systems 27:1840025

[33] Sun F, Zong W, Liu R, Chai J, Liu Y. 2010. Micro-environmental influences on the fluorescence of tryptophan. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 76:142-45

[34] Zhang YY, Li XS, Ren KD, Peng J, Luo XJ. 2023. Restoration of metal homeostasis: a potential strategy against neurodegenerative diseases. Ageing Research Reviews 87:101931

[35] Hassoun A, Karoui R. 2015. Front-face fluorescence spectroscopy coupled with chemometric tools for monitoring fish freshness stored under different refrigerated conditions. Food Control 54:240-49

[36] Shibu A, Jones S, Tolley PL, Diaz D, Kwiatkowski CO, et al. 2023. Correlating structure and photophysical properties in thiazolo[5, 4-d]thiazole crystal derivatives for use in solid-state photonic and fluorescence-based optical devices. Materials Advances 4:6321-32

[37] Chicco D, Warrens MJ, Jurman G. 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science 7:e623