Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Ziani, Imane; Bouakline, Hamza; El Guerraf, Abdelqader; El Bachiri, Ali; Fauconnier, Marie-Laure; Sher, Farooq

doi:10.1016/j.tifs.2024.104850

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Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Ziani, Imane; Bouakline, Hamza; El Guerraf, Abdelqader et al.

2025 • In Trends in Food Science and Technology, 156, p. 104850

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10.1016/j.tifs.2024.104850

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Keywords :

Food safety technologies; spectroscopic advancement; Mass spectrometry; sensor innovation

Abstract :

[en] Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) are widely used in food analysis but often face limitations in detecting trace contaminants at ultra-low levels or in complex matrices. This review highlights recent breakthroughs in food analysis technologies that deliver unprecedented sensitivity and accuracy for consumers' health protection. Among these advances, Wide Line Surface-Enhanced Raman scattering (WL-SERS) has delivered a tenfold increase in sensitivity, enabling the detection of contaminants like melamine in raw milk at concentrations far below conventional thresholds. Mass spectrometry imaging (MSI), particularly matrix-assisted laser desorption/ionization (MALDI-MSI), has made significant progress in spatial resolution, allowing for precise mapping of food constituents and contaminants. Additionally, two-dimensional liquid chromatography (2D-LC) and multidimensional gas chromatography have evolved rapidly, achieving detection as low as 1 ppb in complex food systems. Innovative sensor technologies, such as the Dpyt near-infrared (NIR) fluorescent probe and electrochemiluminescence (ECL) aptasensors, offer rapid and highly sensitive detection, effectively complementing traditional methods. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized food quality assessment, with models like convolutional neural networks (CNNs) reaching up to 99.85% accuracy in identifying adulterants. Despite these advancements, challenges such as high operational costs, sensor stability and AI's computational demands remain. This review highlights the integration of advanced spectroscopy, AI-driven analysis, and novel sensor technologies, outlining future strategies such as miniaturization, nanomaterial innovations, and standardized protocols. These approaches present transformative pathways for improving the precision, efficiency, and accessibility of food safety and quality management, ultimately enhancing public health protection.

Disciplines :

Food science
Chemistry

Author, co-author :

Ziani, Imane

Bouakline, Hamza

El Guerraf, Abdelqader

El Bachiri, Ali

Fauconnier, Marie-Laure ; Université de Liège - ULiège > TERRA Research Centre > Chemistry for Sustainable Food and Environmental Systems (CSFES)

Sher, Farooq

Language :

English

Title :

Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Publication date :

February 2025

Journal title :

Trends in Food Science and Technology

ISSN :

0924-2244

eISSN :

1879-3053

Publisher :

Elsevier BV

Volume :

156

Pages :

104850

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0924224424005260?httpAccept=text/xml

Available on ORBi :

since 02 January 2025

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Bibliography

Abid, H.M.R., Khan, N., Hussain, A., Anis, Z.B., Nadeem, M., Khalid, N., Quantitative and qualitative approach for accessing and predicting food safety using various web-based tools. Food Control, 110471, 2024.
Adunphatcharaphon, S., Elliott, C.T., Sooksimuang, T., Charlermroj, R., Petchkongkaew, A., Karoonuthaisiri, N., The evolution of multiplex detection of mycotoxins using immunoassay platform technologies. Journal of Hazardous Materials, 432, 2022, 128706.
Ahuja, V., Singh, A., Paul, D., Dasgupta, D., Urajová, P., Ghosh, S., Singh, R., Sahoo, G., Ewe, D., Saurav, K., Recent advances in the detection of food toxins using mass spectrometry. Chemical Research in Toxicology 36:12 (2023), 1834–1863.
Akinsemolu, A.A., Onyeaka, H.N., Microorganisms associated with food spoilage and foodborne diseases. Food safety and quality in the global south, 2024, Springer, 489–531.
Al-Habsi, N., Al-Julandani, R., Al-Hadhrami, A., Al-Ruqaishi, H., Al-Sabahi, J., Al-Attabi, Z., Rahman, M.S., Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation. Journal of Food Science and Technology, 2024, 1–11.
Ali, M.M., Hashim, N., Abd Aziz, S., Lasekan, O., Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends in Food Science & Technology 99 (2020), 1–10.
An, J.-M., Hur, S.H., Kim, H., Lee, J.H., Kim, Y.-K., Sim, K.S., Lee, S.-E., Kim, H.J., Determination of the geographical origin of chicken (breast and drumstick) using ICP-OES and ICP-MS: Chemometric analysis. Food Chemistry, 437, 2024, 137836.
Ateia, M., Wei, H., Andreescu, S., Sensors for emerging water contaminants: Overcoming roadblocks to innovation. Environmental Science & Technology 58:6 (2024), 2636–2651.
Ayres, L., Benavidez, T., Varillas, A., Linton, J., Whitehead, D.C., Garcia, C.D., Predicting antioxidant synergism via artificial intelligence and benchtop data. Journal of Agricultural and Food Chemistry 71:42 (2023), 15644–15655.
Ayres, L.B., Gomez, F.J.V., Linton, J.R., Silva, M.F., Garcia, C.D., Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Analytica Chimica Acta, 1161, 2021, 338403.
Barthwal, R., Kathuria, D., Joshi, S., Kaler, R.S.S., Singh, N., New trends in the development and application of artificial intelligence in food processing. Innovative Food Science & Emerging Technologies, 2024, 103600.
Bayen, S., Elliott, C., Arlorio, M., Ballin, N.Z., Birse, N., Brockmeyer, J., Chahal, S., Corradini, M.G., Hanner, R., Hann, S., Towards a harmonized approach for food authenticity marker validation and accreditation. Trends in Food Science & Technology, 149, 2024, 104550.
Beecher, G.R., Evolution of food composition knowledge in the United States from its beginning. Journal of Food Composition and Analysis, 126, 2024, 105802.
Belianinov, A., Ievlev, A.V., Lorenz, M., Borodinov, N., Doughty, B., Kalinin, S.V., Fernández, F.M., Ovchinnikova, O.S., Correlated materials characterization via multimodal chemical and functional imaging. ACS Nano 12:12 (2018), 11798–11818.
Bento-Silva, A., Duarte, N., Santos, M., Costa, C.P., Vaz Patto, M.C., Rocha, S.M., Bronze, M.R., Comprehensive two-dimensional gas chromatography as a powerful strategy for the exploration of broas volatile composition. Molecules, 27(9), 2022, 10.3390/molecules27092728.
Bouguettaya, A., Zarzour, H., Kechida, A., Taberkit, A.M., Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing & Applications 34:12 (2022), 9511–9536.
Cacciola, F., Rigano, F., Dugo, P., Mondello, L., Comprehensive two-dimensional liquid chromatography as a powerful tool for the analysis of food and food products. TrAC, Trends in Analytical Chemistry, 127, 2020, 115894.
Cai, R., Zhang, Z., Chen, H., Tian, Y., Zhou, N., A versatile signal-on electrochemical biosensor for Staphylococcus aureus based on triple-helix molecular switch. Sensors and Actuators B: Chemical, 326, 2021, 128842.
Caratti, A., Squara, S., Bicchi, C., Liberto, E., Vincenti, M., Reichenbach, S.E., Tao, Q., Geschwender, D., Alladio, E., Cordero, C., Boosting comprehensive two-dimensional chromatography with artificial intelligence: Application to food-omics. TrAC, Trends in Analytical Chemistry, 2024, 117669.
Caratti, A., Squara, S., Bicchi, C., Tao, Q., Geschwender, D., Reichenbach, S.E., Ferrero, F., Borreani, G., Cordero, C., Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome. Journal of Chromatography A, 1699, 2023, 464010.
Chaker, J., Gilles, E., Léger, T., Jégou, B., David, A., From metabolomics to HRMS-based exposomics: Adapting peak picking and developing scoring for MS1 suspect screening. Analytical Chemistry 93:3 (2020), 1792–1800.
Charlebois, S., Juhasz, M., Music, J., Vézeau, J., A review of Canadian and international food safety systems: Issues and recommendations for the future. Comprehensive Reviews in Food Science and Food Safety 20:5 (2021), 5043–5066.
Chen, Y., Bao, J., Pan, X., Chen, Q., Yan, J., Yang, G., Khan, B., Zhang, K., Han, X., A near-infrared fluorescent probe with large Stokes shift for sensitive detection of hydrogen sulfide in environmental water, food spoilage, and biological systems. The Journal of Physical Chemistry B 128 (2024), 5846–5854 https://pubs.acs.org/doi/10.1021/acs.jpcb.4c02258?ref=pdf.
Chen, C., Li, S.-L., Xu, Y.-Y., Liu, J., Graham, D.W., Zhu, Y.-G., Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis. Environment International, 187, 2024, 108680.
Chen, X., Xu, J., Zhang, L., Bi, N., Gou, J., Li, Y., Zhao, T., Jia, L., A sensitive fluorometric-colorimetric dual-mode intelligent sensing platform for the detection of formaldehyde. Food Chemistry, 439, 2024, 138095.
Chen, R., Zhan, C., Huang, C., He, Q., Bao, J., Zhang, X., Pi, Z., Chen, Y., Tyramine signal amplification on polystyrene microspheres for highly sensitive quantification of Aflatoxin B1 in peanut samples. Sensors and Actuators B: Chemical, 378, 2023, 133120.
Cheng, W., Tang, X., Zhang, Y., Wu, D., Yang, W., Applications of metal-organic framework (MOF)-based sensors for food safety: Enhancing mechanisms and recent advances. Trends in Food Science & Technology 112 (2021), 268–282.
Chhetri, K.B., Applications of artificial intelligence and machine learning in food quality control and safety assessment. Food Engineering Reviews 16:1 (2024), 1–21.
Chorti, P., Kazi, A.P., Haque, A.-M.J., Wiederoder, M., Christodouleas, D.C., Flow-through electrochemical immunoassay for targeted bacteria detection. Sensors and Actuators B: Chemical, 351, 2022, 130965.
Chotwanvirat, P., Prachansuwan, A., Sridonpai, P., Kriengsinyos, W., Automated artificial intelligence–based Thai food dietary assessment system: Development and validation. Current Developments in Nutrition, 8(5), 2024, 102154.
Chu, F., Zhao, G., Wei, W., Shuaibu, N.S., Feng, H., Pan, Y., Wang, X., Wide-energy programmable microwave plasma-ionization for high-coverage mass spectrometry analysis. Nature Communications, 15(1), 2024, 6075.
Cui, F., Yue, Y., Zhang, Y., Zhang, Z., Zhou, H.S., Advancing biosensors with machine learning. ACS Sensors 5:11 (2020), 3346–3364.
da Silva, M.K.L., Vanzela, H.C., Defavari, L.M., Cesarino, I., Determination of carbamate pesticide in food using a biosensor based on reduced graphene oxide and acetylcholinesterase enzyme. Sensors and Actuators B: Chemical 277 (2018), 555–561.
De Vijlder, T., Valkenborg, D., Lemière, F., Romijn, E.P., Laukens, K., Cuyckens, F., A tutorial in small molecule identification via electrospray ionization‐mass spectrometry: The practical art of structural elucidation. Mass Spectrometry Reviews 37:5 (2018), 607–629.
De Vos, J., Broeckhoven, K., Eeltink, S., Advances in ultrahigh-pressure liquid chromatography technology and system design. Analytical Chemistry 88:1 (2016), 262–278.
Deng, X., Cao, S., Horn, A.L., Emerging applications of machine learning in food safety. Annual Review of Food Science and Technology 12:1 (2021), 513–538.
Deng, Z., Wang, T., Zheng, Y., Zhang, W., Yun, Y.-H., Deep learning in food authenticity: Recent advances and future trends. Trends in Food Science & Technology, 2024, 104344.
Desmet, G., de Beeck, J.O., Van Raemdonck, G., Van Mol, K., Claerebout, B., Van Landuyt, N., Jacobs, P., Separation efficiency kinetics of capillary flow micro-pillar array columns for liquid chromatography. Journal of Chromatography A, 1626, 2020, 461279.
Dirks, M., Poole, D., Automatic neural network hyperparameter optimization for extrapolation: Lessons learned from visible and near-infrared spectroscopy of mango fruit. Chemometrics and Intelligent Laboratory Systems, 231, 2022, 104685.
Du, L.-J., Chu, C., Warner, E., Wang, Q.-Y., Hu, Y.-H., Chai, K.-J., Cao, J., Peng, L.-Q., Chen, Y.-B., Yang, J., Zhang, Q.-D., Rapid microwave-assisted dispersive micro-solid phase extraction of mycotoxins in food using zirconia nanoparticles. Journal of Chromatography A 1561 (2018), 1–12.
Dusza, H.M., Katrukha, E.A., Nijmeijer, S.M., Akhmanova, A., Vethaak, A.D., Walker, D.I., Legler, J., Uptake, transport, and toxicity of pristine and weathered micro-and nanoplastics in human placenta cells. Environmental Health Perspectives, 130(9), 2022, 97006.
Eş, I., Khaneghah, A.M., Advancing food quality assurance: Integrating microneedle technology with advanced analytical methods. Nano Today, 54, 2024, 102115.
El-Moghazy, A.Y., Wisuthiphaet, N., Yang, X., Sun, G., Nitin, N., Electrochemical biosensor based on genetically engineered bacteriophage T7 for rapid detection of Escherichia coli on fresh produce. Food Control, 135, 2022, 108811.
Fakayode, S.O., Baker, G.A., Bwambok, D.K., Bhawawet, N., Elzey, B., Siraj, N., Macchi, S., Pollard, D.A., Perez, R.L., Duncan, A.V., Molecular (Raman, NIR, and FTIR) spectroscopy and multivariate analysis in consumable products analysis1. Applied Spectroscopy Reviews 55:8 (2020), 647–723.
Fakayode, S.O., Lisse, C., Medawala, W., Brady, P.N., Bwambok, D.K., Anum, D., Alonge, T., Taylor, M.E., Baker, G.A., Mehari, T.F., Fluorescent chemical sensors: Applications in analytical, environmental, forensic, pharmaceutical, biological, and biomedical sample measurement, and clinical diagnosis. Applied Spectroscopy Reviews 59:1 (2024), 1–89.
Fan, C., Yang, J., Ni, W., Wu, J., Liu, X., Li, Z., Zhang, Y., Quan, W., Zeng, M., Hu, N., Real-time and wireless transmission of a nitrogen-doped Ti3C2Tx wearable gas sensor for efficient detection of food spoilage and ammonia leakage. ACS Sensors 9 (2024), 4870–4878.
Fan, Y., Zhang, L., Zheng, C., Zu, Y., Wang, X., Zhu, J., Real-time and accurate meal detection for meal-assisting robots. Journal of Food Engineering, 371, 2024, 111996.
Faraji Rad, Z., Microneedle technologies for food and crop health: Recent advances and future perspectives. Advanced Engineering Materials, 25(4), 2023, 2201194.
Farka, Z., Jurik, T., Kovar, D., Trnkova, L., Skládal, P., Nanoparticle-based immunochemical biosensors and assays: Recent advances and challenges. Chemical Reviews 117:15 (2017), 9973–10042.
Feng, Y., Soni, A., Brightwell, G., Reis, M.M., Wang, Z., Wang, J., Wu, Q., Ding, Y., The potential new microbial hazard monitoring tool in food safety: Integration of metabolomics and artificial intelligence. Trends in Food Science & Technology, 2024, 104555.
Ferrão, L.F.V., Dhakal, R., Dias, R., Tieman, D., Whitaker, V., Gore, M.A., Messina, C., Resende Jr, M.F.R., Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics. Current Opinion in Biotechnology, 83, 2023, 102968.
Ferrero, F., Prencipe, S., Spadaro, D., Gullino, M.L., Cavallarin, L., Piano, S., Tabacco, E., Borreani, G., Increase in aflatoxins due to Aspergillus section Flavi multiplication during the aerobic deterioration of corn silage treated with different bacteria inocula. Journal of Dairy Science 102:2 (2019), 1176–1193.
Gallo, M., Ferrara, L., Calogero, A., Montesano, D., Naviglio, D., Relationships between food and diseases: What to know to ensure food safety. Food Research International, 137, 2020, 109414.
Gamela, R.R., Costa, V.C., Sperança, M.A., Pereira-Filho, E.R., Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples. Food Research International, 132, 2020, 109037.
Gangwar, R., Ray, D., Rao, K.T., Khatun, S., Subrahmanyam, C., Rengan, A.K., Vanjari, S.R.K., Plasma functionalized carbon interfaces for biosensor application: Toward the real-time detection of Escherichia coli O157: H7. ACS Omega 7:24 (2022), 21025–21034.
Gao, C., Yu, X.-Y., Xiong, S.-Q., Liu, J.-H., Huang, X.-J., Electrochemical detection of arsenic (III) completely free from noble metal: Fe3O4 microspheres-room temperature ionic liquid composite showing better performance than gold. Analytical Chemistry 85:5 (2013), 2673–2680.
Garcia-Vozmediano, A., Maurella, C., Ceballos, L.A., Crescio, E., Meo, R., Martelli, W., Pitti, M., Lombardi, D., Meloni, D., Pasqualini, C., Ru, G., Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy. Veterinary Research, 55(1), 2024, 72.
Ge, F., Chen, G., Qian, M., Xu, C., Liu, J., Cao, J., Li, X., Hu, D., Xu, Y., Xin, Y., Artificial intelligence aided lipase production and engineering for enzymatic performance improvement. Journal of Agricultural and Food Chemistry 71:41 (2023), 14911–14930.
Goto-Inoue, N., Sato, T., Morisasa, M., Igarashi, Y., Mori, T., Characterization of metabolite compositions in wild and farmed red sea bream (Pagrus major) using mass spectrometry imaging. Journal of Agricultural and Food Chemistry 67:25 (2019), 7197–7203.
Guo, Y., Chen, K., Bao, Q., Sun, X., An intelligent and portable fiber optic real-time fluorescence detection system for pathogenic microorganisms detection. Sensors and Actuators B: Chemical, 423, 2025, 136733, 10.1016/j.snb.2024.136733.
Guo, S., Popp, J., Bocklitz, T., Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling. Nature Protocols 16:12 (2021), 5426–5459.
Guo, M., Wang, K., Lin, H., Wang, L., Cao, L., Sui, J., Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Comprehensive Reviews in Food Science and Food Safety, 23(1), 2024, e13301.
Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z., Ji, S., Cai, J., Xu, C., Chen, X., Portable food‐freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Advanced Materials, 32(45), 2020, 2004805.
Guo, Z., Wu, X., Jayan, H., Yin, L., Xue, S., El-Seedi, H.R., Zou, X., Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables. Food Chemistry, 137469, 2023.
Handford, C.E., Campbell, K., Elliott, C.T., Impacts of milk fraud on food safety and nutrition with special emphasis on developing countries. Comprehensive Reviews in Food Science and Food Safety 15:1 (2016), 130–142.
Hansen, J., Kunert, C., Münstermann, H., Raezke, K.-P., Seifert, S., Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning. Scientific Reports, 14(1), 2024, 16594.
Hassoun, A., Bekhit, A.E.-D., Jambrak, A.R., Regenstein, J.M., Chemat, F., Morton, J.D., Gudjónsdóttir, M., Carpena, M., Prieto, M.A., Varela, P., The fourth industrial revolution in the food industry—part II: Emerging food trends. Critical Reviews in Food Science and Nutrition 64:2 (2024), 407–437.
He, Y., Wang, S., Wen, J., Feng, N., Ma, R., Zhang, H., Chen, G., Chu, X., Chen, Y., Redesigned guide DNA enhanced Clostridium butyricum argonaute activity for amplification-free and multiplexed detection of pathogens. Nano Letters 24:31 (2024), 9750–9759.
Hernandez-Jaimes, M.L., Martinez-Cruz, A., Ramírez-Gutiérrez, K.A., Feregrino-Uribe, C., Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and cloud-fog-edge architectures. Internet of Things, 2023, 100887.
Huang, J., Zhu, Y., Li, Y., Lam, E.Y., Snapshot polarization-sensitive holography for detecting microplastics in turbid water. ACS Photonics 10:12 (2023), 4483–4493.
Huertas-Pérez, J.F., Baslé, Q., Dubois, M., Theurillat, X., Multi-residue pesticides determination in complex food matrices by gas chromatography tandem mass spectrometry. Food Chemistry, 436, 2024, 137687.
Ilchenko, O., Slipets, R., Rindzevicius, T., Durucan, O., Morelli, L., Schmidt, M.S., Wu, K., Boisen, A., Wide line surface‐enhanced Raman scattering mapping. Advanced Materials Technologies, 5(6), 2020, 1900999.
Izadi, Z., Sheikh-Zeinoddin, M., Ensafi, A.A., Soleimanian-Zad, S., Fabrication of an electrochemical DNA-based biosensor for Bacillus cereus detection in milk and infant formula. Biosensors and Bioelectronics 80 (2016), 582–589.
Izquierdo, M., Lastra-Mejías, M., González-Flores, E., Cancilla, J.C., Pérez, M., Torrecilla, J.S., Convolutional decoding of thermographic images to locate and quantify honey adulterations. Talanta, 209, 2020, 120500.
Jahn, I.J., Žukovskaja, O., Zheng, X.-S., Weber, K., Bocklitz, T.W., Cialla-May, D., Popp, J., Surface-enhanced Raman spectroscopy and microfluidic platforms: Challenges, solutions and potential applications. Analyst 142:7 (2017), 1022–1047.
Ji, X., Deng, T., Xiao, Y., Jin, C., Lyu, W., Wang, W., Tang, B., Wu, Z., Yang, H., Evaluation of Alternaria toxins in fruits, vegetables and their derivatives marketed in China using a QuEChERS method coupled with ultra-high performance liquid chromatography-tandem mass spectrometry: Analytical methods and occurrence. Food Control, 147, 2023, 109563.
Jia, H., Lan, G., Li, X., Chen, L., Feng, L., Mao, X., Rapid and simultaneous detection of cadmium and mercury in foods based on solid sampling integrated electrothermal vaporization technique. Food Chemistry, 2024, 140087.
Jiang, L., Hassan, M.M., Ali, S., Li, H., Sheng, R., Chen, Q., Evolving trends in SERS-based techniques for food quality and safety: A review. Trends in Food Science & Technology 112 (2021), 225–240.
Junior, G.J.S., Selva, J.S.G., Sukeri, A., Gonçalves, J.M., Regiart, M., Bertotti, M., Fabrication of dendritic nanoporous gold via a two-step amperometric approach: Application for electrochemical detection of methyl parathion in river water samples. Talanta, 226, 2021, 122130.
Kamilaris, A., Fonts, A., Prenafeta-Boldύ, F.X., The rise of blockchain technology in agriculture and food supply chains. Trends in Food Science & Technology 91 (2019), 640–652.
Kanu, A.B., Recent developments in sample preparation techniques combined with high-performance liquid chromatography: A critical review. Journal of Chromatography A, 1654, 2021, 462444.
Kim, S.H., Kim, J., Lee, Y.J., Lee, T.G., Yoon, S., Sample preparation of corn seed tissue to prevent analyte relocations for mass spectrometry imaging. Journal of the American Society for Mass Spectrometry 28:8 (2017), 1729–1732.
Kleboth, J.A., Kosorus, H., Rechberger, T., Luning, P.A., Using data mining as a tool for anomaly detection in food safety audit data. Food Control, 138, 2022, 109004.
Kokesch-Himmelreich, J., Wittek, O., Race, A.M., Rakete, S., Schlicht, C., Busch, U., Römpp, A., MALDI mass spectrometry imaging: From constituents in fresh food to ingredients, contaminants and additives in processed food. Food Chemistry, 385, 2022, 132529.
Kou, X., Shi, P., Gao, C., Ma, P., Xing, H., Ke, Q., Zhang, D., Data-driven elucidation of flavor chemistry. Journal of Agricultural and Food Chemistry 71:18 (2023), 6789–6802.
Kumar, S., Verma, N., Singh, A.K., Development of cadmium specific recombinant biosensor and its application in milk samples. Sensors and Actuators B: Chemical 240 (2017), 248–254.
Li, C., Chu, S., Tan, S., Yin, X., Jiang, Y., Dai, X., Gong, X., Fang, X., Tian, D., Towards higher sensitivity of mass spectrometry: A perspective from the mass analyzers. Frontiers in Chemistry, 9, 2021, 813359.
Li, Y., Dvořák, M., Nesterenko, P.N., Stanley, R., Nuchtavorn, N., Krčmová, L.K., Aufartová, J., Macka, M., Miniaturised medium pressure capillary liquid chromatography system with flexible open platform design using off-the-shelf microfluidic components. Analytica Chimica Acta 896 (2015), 166–176.
Li, Y., Gao, X., Fang, Y., Cui, B., Shen, Y., Nanomaterials-driven innovative electrochemiluminescence aptasensors in reporting food pollutants. Coordination Chemistry Reviews, 485, 2023, 215136.
Li, Q., Gong, S., Zhang, H., Huang, F., Zhang, L., Li, S., Tailored necklace-like Ag@ZIF-8 core/shell heterostructure nanowires for high-performance plasmonic SERS detection. Chemical Engineering Journal 371 (2019), 26–33.
Li, Y.-Y., Li, H.-D., Fang, W.-K., Liu, D., Liu, M.-H., Zheng, M.-Q., Zhang, L.-L., Yu, H., Tang, H.-W., Amplification of the fluorescence signal with clustered regularly interspaced short palindromic repeats-Cas12a based on Au nanoparticle-DNAzyme probe and on-site detection of Pb2+ via the photonic crystal chip. ACS Sensors 7:5 (2022), 1572–1580.
Li, M., Lin, H., Paidi, S.K., Mesyngier, N., Preheim, S., Barman, I., A fluorescence and surface-enhanced Raman spectroscopic dual-modal aptasensor for sensitive detection of cyanotoxins. ACS Sensors 5:5 (2020), 1419–1426.
Li, Y., Liu, H., Huang, H., Deng, J., Fang, L., Luo, J., Zhang, S., Huang, J., Liang, W., Zheng, J., A sensitive electrochemical strategy via multiple amplification reactions for the detection of E. coli O157: H7. Biosensors and Bioelectronics, 147, 2020, 111752.
Li, T., Lu, C., Huang, J., Chen, Y., Zhang, J., Wei, Y., Wang, Y., Ning, J., Qualitative and quantitative analysis of the pile fermentation degree of Pu-erh tea. LWT, 173, 2023, 114327.
Li, J., Wang, X., Zhao, G., Chen, C., Chai, Z., Alsaedi, A., Hayat, T., Wang, X., Metal–organic framework-based materials: Superior adsorbents for the capture of toxic and radioactive metal ions. Chemical Society Reviews 47:7 (2018), 2322–2356.
Li, H., Xu, H., Li, Y., Li, X., Application of artificial intelligence (AI)-enhanced biochemical sensing in molecular diagnosis and imaging analysis: Advancing and challenges. TrAC, Trends in Analytical Chemistry, 2024, 117700.
Li, C.-Y., Zhang, J.-T., Chatbots or me? Consumers' switching between human agents and conversational agents. Journal of Retailing and Consumer Services, 72, 2023, 103264.
Li, C., Zhu, L., Yang, W., He, X., Zhao, S., Zhang, X., Tang, W., Wang, J., Yue, T., Li, Z., Amino-functionalized Al–MOF for fluorescent detection of tetracyclines in milk. Journal of Agricultural and Food Chemistry 67:4 (2019), 1277–1283.
Liu, C., Ahmad, N., Jiang, M., Arshad, M.Z., Steering the path to safer food: The role of transformational leadership in food services to combat against foodborne illness. Journal of Retailing and Consumer Services, 81, 2024, 103958.
Liu, C., Cao, G.-H., Qu, Y.-Y., Cheng, Y.-M., An improved PSO algorithm for time-optimal trajectory planning of Delta robot in intelligent packaging. The International Journal of Advanced Manufacturing Technology 107:3 (2020), 1091–1099.
Liu, C., Chu, Z., Weng, S., Zhu, G., Han, K., Zhang, Z., Huang, L., Zhu, Z., Zheng, S., Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. Food Chemistry, 385, 2022, 132651.
Liu, J., Wu, D., Wu, Y., Shi, Y., Liu, W., Sun, Z., Li, G., Recent advances in optical sensors and probes for the detection of freshness in food samples: A comprehensive review (2020–2023). TrAC, Trends in Analytical Chemistry, 2024, 117793.
Liu, L., Yan, Y., Wang, J., Wu, W., Xu, L., Generation of mt: Egfp transgenic zebrafish biosensor for the detection of aquatic zinc and cadmium. Environmental Toxicology and Chemistry 35:8 (2016), 2066–2073.
Lu, Y., Zhong, J., Yao, G., Huang, Q., A label-free SERS approach to quantitative and selective detection of mercury (II) based on DNA aptamer-modified SiO2@ Au core/shell nanoparticles. Sensors and Actuators B: Chemical 258 (2018), 365–372.
Luo, Y., Abidian, M.R., Ahn, J.-H., Akinwande, D., Andrews, A.M., Antonietti, M., Bao, Z., Berggren, M., Berkey, C.A., Bettinger, C.J., Technology roadmap for flexible sensors. ACS Nano 17:6 (2023), 5211–5295.
Luo, X., Han, Y., Chen, X., Tang, W., Yue, T., Li, Z., Carbon dots derived fluorescent nanosensors as versatile tools for food quality and safety assessment: A review. Trends in Food Science & Technology 95 (2020), 149–161.
Ma, H., Sun, J., Zhang, Y., Bian, C., Xia, S., Zhen, T., Label-free immunosensor based on one-step electrodeposition of chitosan-gold nanoparticles biocompatible film on Au microelectrode for determination of aflatoxin B1 in maize. Biosensors and Bioelectronics 80 (2016), 222–229.
Mahmoud, M.A.A., Zhang, Y., Enhancing odor analysis with gas chromatography–olfactometry (GC-O): Recent breakthroughs and challenges. Journal of Agricultural and Food Chemistry 72:17 (2024), 9523–9554.
Maslov, D.R., Svirkova, A., Allmaier, G., Marchetti-Deschamann, M., Pavelić, S.K., Optimization of MALDI-TOF mass spectrometry imaging for the visualization and comparison of peptide distributions in dry-cured ham muscle fibers. Food Chemistry 283 (2019), 275–286.
Mat Yeh, R.M., Taha, B.A., Bachok, N.N., Sapiee, N.M., Othman, A.R., Abd Karim, N.H., Arsad, N., Advancements in detecting porcine-derived proteins and DNA for enhancing food integrity: Taxonomy, challenges, and future directions. Food Control, 161, 2024, 110399.
Mathelié-Guinlet, M., Cohen-Bouhacina, T., Gammoudi, I., Martin, A., Beven, L., Delville, M.-H., Grauby-Heywang, C., Silica nanoparticles-assisted electrochemical biosensor for the rapid, sensitive and specific detection of Escherichia coli. Sensors and Actuators B: Chemical 292 (2019), 314–320.
Mayer, M., Baeumner, A.J., A megatrend challenging analytical chemistry: Biosensor and chemosensor concepts ready for the internet of things. Chemical Reviews 119:13 (2019), 7996–8027.
Medina, D.A.V., Borsatto, J.V.B., Maciel, E.V.S., Lancas, F.M., Current role of modern chromatography and mass spectrometry in the analysis of mycotoxins in food. TrAC, Trends in Analytical Chemistry, 135, 2021, 116156.
Meira, D.I., Barbosa, A.I., Borges, J., Reis, R.L., Correlo, V.M., Vaz, F., Recent advances in nanomaterial-based optical biosensors for food safety applications: Ochratoxin-A detection, as case study. Critical Reviews in Food Science and Nutrition 64:18 (2024), 6318–6360.
Meliana, C., Liu, J., Show, P.L., Low, S.S., Biosensor in smart food traceability system for food safety and security. Bioengineered, 15(1), 2024, 2310908.
Menon, S., Jain, K., Blockchain technology for transparency in agri-food supply chain: Use cases, limitations, and future directions. IEEE Transactions on Engineering Management 71 (2021), 106–120.
Mphaga, K.V., Moyo, D., Rathebe, P.C., Unlocking food safety: A comprehensive review of South Africa's food control and safety landscape from an environmental health perspective. BMC Public Health, 24(1), 2024, 2040.
Nath, P.C., Mishra, A.K., Sharma, R., Bhunia, B., Mishra, B., Tiwari, A., Nayak, P.K., Sharma, M., Bhuyan, T., Kaushal, S., Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chemistry, 2024, 138945.
Ncube, N., Thatyana, M., Tancu, Y., Mketo, N., Quantitative analysis and health risk assessment of selected heavy metals in pet food samples using ultrasound assisted hydrogen peroxide extraction followed by ICP-OES analysis. Food and Chemical Toxicology, 2024, 114915.
Ngure, F.M., Makule, E., Mgongo, W., Phillips, E., Kassim, N., Stoltzfus, R., Nelson, R., Processing complementary foods to reduce mycotoxins in a medium scale Tanzanian mill: A hazard analysis critical control point (HACCP) approach. Food Control, 162, 2024, 110463.
Niu, H., Ye, T., Yao, L., Lin, Y., Chen, K., Zeng, Y., Li, L., Guo, L., Wang, J., A novel red-to-near-infrared AIE fluorescent probe for detection of Hg2+ with large Stokes shift in plant and living cells. Journal of Hazardous Materials, 475, 2024, 134914, 10.1016/j.jhazmat.2024.134914.
Nolvachai, Y., Amaral, M.S.S., Marriott, P.J., Foods and contaminants analysis using multidimensional gas chromatography: An update of recent studies, technology, and applications. Analytical Chemistry 95:1 (2023), 238–263.
Nolvachai, Y., McGregor, L., Spadafora, N.D., Bukowski, N.P., Marriott, P.J., Comprehensive two-dimensional gas chromatography with mass spectrometry: Toward a super-resolved separation technique. Analytical Chemistry 92:18 (2020), 12572–12578, 10.1021/acs.analchem.0c02522.
Nontipichet, N., Khumngern, S., Choosang, J., Thavarungkul, P., Kanatharana, P., Numnuam, A., An enzymatic histamine biosensor based on a screen-printed carbon electrode modified with a chitosan–gold nanoparticles composite cryogel on Prussian blue-coated multi-walled carbon nanotubes. Food Chemistry, 364, 2021, 130396.
Nowak, P.M., Wietecha-Posłuszny, R., Pawliszyn, J., White analytical chemistry: An approach to reconcile the principles of green analytical chemistry and functionality. TrAC, Trends in Analytical Chemistry, 138, 2021, 116223.
Ouyang, Q., Zhang, M., Wang, B., Ahmad, W., Chen, Q., Development of a novel upconversion fluorescence nanosensor based on metalloporphyrin element for sensitive detection of N-nitrosodimethylamine. Sensors and Actuators B: Chemical, 393, 2023, 134260.
Ozturk, O., Golparvar, A., Acar, G., Guler, S., Yapici, M.K., Single-arm diagnostic electrocardiography with printed graphene on wearable textiles. Sensors and Actuators A: Physical, 349, 2023, 114058.
Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M.D.M., Blanco Díaz, G.D., Cima Cabal, M.D., Moreno Rojas, J.M., López Sánchez, J.I., Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(5), 2021, e1516.
Pandey, A.K., Samota, M.K., Sanches Silva, A., Mycotoxins along the tea supply chain: A dark side of an ancient and high valued aromatic beverage. Critical Reviews in Food Science and Nutrition 63:27 (2023), 8672–8697.
Peveler, W.J., Food for thought: Optical sensor arrays and machine learning for the food and beverage industry. ACS Sensors 9:4 (2024), 1656–1665.
Pirok, B.W.J., Stoll, D.R., Schoenmakers, P.J., Recent developments in two-dimensional liquid chromatography: Fundamental improvements for practical applications. Analytical Chemistry 91:1 (2018), 240–263.
Popoola, O., Finny, A., Dong, I., Andreescu, S., Smart and sustainable 3D-printed nanocellulose-based sensors for food freshness monitoring. ACS Applied Materials & Interfaces 16 (2024), 60920–60932.
Prabhu, G.R.D., Urban, P.L., Elevating chemistry research with a modern electronics toolkit. Chemical Reviews 120:17 (2020), 9482–9553.
Pua, A., Huang, Y., Vivian Goh, R.M., Ee, K.-H., Li, L., Cornuz, M., Lassabliere, B., Jublot, L., Liu, S.Q., Yu, B., Multidimensional gas chromatography of organosulfur compounds in coffee and structure–odor analysis of 2-Methyltetrahydrothiophen-3-one. Journal of Agricultural and Food Chemistry 71:10 (2023), 4337–4345.
Qiao, L., Lang, W., Sun, C., Huang, Y., Wu, P., Cai, C., Xing, B., Near infrared-II photothermal and colorimetric synergistic sensing for intelligent onsite dietary myrosinase profiling. Analytical Chemistry 95:7 (2023), 3856–3863.
Quinn, T.P., Jacobs, S., Senadeera, M., Le, V., Coghlan, S., The three ghosts of medical AI: Can the black-box present deliver?. Artificial Intelligence in Medicine, 124, 2022, 102158.
Raju, C.M., Elpa, D.P., Urban, P.L., Automation and computerization of (bio) sensing systems. ACS Sensors 9:3 (2024), 1033–1048.
Rodriguez, R.S., O'Keefe, T.L., Froehlich, C., Lewis, R.E., Sheldon, T.R., Haynes, C.L., Sensing food contaminants: Advances in analytical methods and techniques. Analytical Chemistry 93:1 (2020), 23–40.
Romero-Sánchez, I., Ramírez-García, L., Gracia-Lor, E., Madrid-Albarrán, Y., Simultaneous determination of aflatoxins B1, B2, G1 and G2 in commercial rices using immunoaffinity column clean-up and HPLC-MS/MS. Food Chemistry, 395, 2022, 133611.
Saha, D., Manickavasagan, A., Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science 4 (2021), 28–44.
Samad, S., Ahmed, F., Naher, S., Kabir, M.A., Das, A., Amin, S., Islam, S.M.S., Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps. Intelligent Systems with Applications, 15, 2022, 200103.
Shaikh, T.A., Rasool, T., Lone, F.R., Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 2022, 107119.
Shang, Z., Meng, Q., Tian, D., Wang, Y., Zhang, Z., Zhang, Z., Zhang, R., Red-emitting fluorescent probe for hydrogen sulfide detection and its applications in food freshness determination and in vivo bioimaging. Food Chemistry, 427, 2023, 136701.
Sharma, R., Nath, P.C., Lodh, B.K., Mukherjee, J., Mahata, N., Gopikrishna, K., Tiwari, O.N., Bhunia, B., Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges. Food Chemistry, 139817, 2024.
Shen, Q., Wang, S., Wang, H., Liang, J., Zhao, Q., Cheng, K., Imran, M., Xue, J., Mao, Z., Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges. Comprehensive Reviews in Food Science and Food Safety, 23(4), 2024, e13398.
Silva, N.F.D., Magalhães, J.M.C.S., Freire, C., Delerue-Matos, C., Electrochemical biosensors for Salmonella: State of the art and challenges in food safety assessment. Biosensors and Bioelectronics 99 (2018), 667–682.
Sindhu, S., Manickavasagan, A., Nondestructive testing methods for pesticide residue in food commodities: A review. Comprehensive Reviews in Food Science and Food Safety 22:2 (2023), 1226–1256.
Singh, K., Blümich, B., NMR spectroscopy with compact instruments. TrAC, Trends in Analytical Chemistry 83 (2016), 12–26.
Sonwani, E., Bansal, U., Alroobaea, R., Baqasah, A.M., Hedabou, M., An artificial intelligence approach toward food spoilage detection and analysis. Frontiers in Public Health, 9, 2022, 816226.
Souza, V.M.A., dos Reis, D.M., Maletzke, A.G., Batista, G.E., Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery 34:6 (2020), 1805–1858.
Spengler, B., Mass spectrometry imaging of biomolecular information. Analytical Chemistry 87:1 (2015), 64–82.
Squara, S., Caratti, A., Fina, A., Liberto, E., Spigolon, N., Genova, G., Castello, G., Cincera, I., Bicchi, C., Cordero, C., Artificial intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: The challenge of quantitative volatilomics in food quality assessment. Journal of Chromatography A, 1700, 2023, 464041.
Taiwo, O.R., Onyeaka, H., Oladipo, E.K., Oloke, J.K., Chukwugozie, D.C., Advancements in predictive microbiology: Integrating new technologies for efficient food safety models. International Journal of Microbiology, 2024(1), 2024, 6612162.
Tan, J.-X., Lv, H., Wang, F., Dao, F.-Y., Chen, W., Ding, H., A survey for predicting enzyme family classes using machine learning methods. Current Drug Targets 20:5 (2019), 540–550.
Tang, Y., Jones, E., Minasny, B., Evaluating low-cost portable near infrared sensors for rapid analysis of soils from South Eastern Australia. Geoderma Regional, 20, 2020, e00240.
Tao, D., Zhang, D., Hu, R., Rundensteiner, E., Feng, H., Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media. Scientific Reports, 11(1), 2021, 21678.
Tian, Y., Du, L., Zhu, P., Chen, Y., Chen, W., Wu, C., Wang, P., Recent progress in micro/nano biosensors for shellfish toxin detection. Biosensors and Bioelectronics, 176, 2021, 112899.
Trinklein, T.J., Cain, C.N., Ochoa, G.S., Schöneich, S., Mikaliunaite, L., Synovec, R.E., Recent advances in GC× GC and chemometrics to address emerging challenges in nontargeted analysis. Analytical Chemistry 95:1 (2023), 264–286.
Tun, W.S.T., Talodthaisong, C., Daduang, S., Daduang, J., Rongchai, K., Patramanon, R., Kulchat, S., A machine learning colorimetric biosensor based on acetylcholinesterase and silver nanoparticles for the detection of dichlorvos pesticides. Materials Chemistry Frontiers 6:11 (2022), 1487–1498.
Valdés, A., Alvarez-Rivera, G., Socas-Rodríguez, B., Herrero, M., Ibanez, E., Cifuentes, A., Foodomics: Analytical opportunities and challenges. Analytical Chemistry 94:1 (2021), 366–381.
Wang, J., Feng, J., Lian, Y., Sun, X., Wang, M., Sun, M., Advances of the functionalized covalent organic frameworks for sample preparation in food field. Food Chemistry, 405, 2023, 134818.
Wang, N., Zang, Z.-H., Sun, B.-B., Li, B., Tian, J.-L., Recent advances in computational prediction of molecular properties in food chemistry. Food Research International, 114776, 2024.
Wen, J., Han, M., Feng, N., Chen, G., Jiang, F., Lin, J., Chen, Y., A digital platform for One-Pot signal enhanced foodborne pathogen detection based on mesophilic argonaute-driven polydisperse microdroplet reactors and machine learning. Chemical Engineering Journal, 482, 2024, 148845.
Wu, Z., Pu, H., Sun, D.-W., Fingerprinting and tagging detection of mycotoxins in agri-food products by surface-enhanced Raman spectroscopy: Principles and recent applications. Trends in Food Science & Technology 110 (2021), 393–404.
Wu, S., Xia, J., Li, R., Cao, H., Ye, D., Perspectives for the role of single-atom nanozymes in assisting food safety inspection and food nutrition evaluation. Analytical Chemistry 96:5 (2024), 1813–1824.
Xu, Y., Zhong, P., Jiang, A., Shen, X., Li, X., Xu, Z., Shen, Y., Sun, Y., Lei, H., Raman spectroscopy coupled with chemometrics for food authentication: A review. TrAC, Trends in Analytical Chemistry, 131, 2020, 116017.
Xuesong, H., Pu, C., Jingyan, L., Yupeng, X., Dan, L., Xiaoli, C., Commentary on the review articles of spectroscopy technology combined with chemometrics in the last three years. Applied Spectroscopy Reviews 59:4 (2024), 423–482.
Yan, M., Li, H., Li, M., Cao, X., She, Y., Chen, Z., Advances in surface-enhanced Raman scattering-based aptasensors for food safety detection. Journal of Agricultural and Food Chemistry 69:47 (2021), 14049–14064.
Yang, T., Luo, Z., Bewal, T., Li, L., Xu, Y., Jafari, S.M., Lin, X., When smartphone enters food safety: A review in on-site analysis for foodborne pathogens using smartphone-assisted biosensors. Food Chemistry, 394, 2022, 133534.
Ye, Y., Guo, H., Sun, X., Recent progress on cell-based biosensors for analysis of food safety and quality control. Biosensors and Bioelectronics 126 (2019), 389–404.
Yi, L., Wang, W., Diao, Y., Yi, S., Shang, Y., Ren, D., Ge, K., Gu, Y., Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review. TrAC, Trends in Analytical Chemistry, 2024, 117944.
Yousefi, H., Su, H.-M., Imani, S.M., Alkhaldi, K., Filipe, C.D.M., Didar, T.F., Intelligent food packaging: A review of smart sensing technologies for monitoring food quality. ACS Sensors 4:4 (2019), 808–821.
Yu, P., Low, M.Y., Zhou, W., Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends in Food Science & Technology 71 (2018), 202–215.
Yu, H., Tai, Q., Yang, C., Gao, M., Zhang, X., Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research. Journal of Chromatography A, 1700, 2023, 464048.
Zalnezhad, A., Rahman, A., Nasiri, N., Vafakhah, M., Samali, B., Ahamed, F., Comparing performance of ANN and SVM methods for regional flood frequency analysis in South-East Australia. Water, 14(20), 2022, 3323.
Zar, A., Zar, L., Mohsen, S., Magdi, Y., Zughaier, S.M., A comprehensive review of algorithms developed for rapid pathogen detection and surveillance. Surveillance, prevention, and control of infectious diseases: An AI perspective, 2024, Springer, 23–49.
Zeng, X., Cao, R., Xi, Y., Li, X., Yu, M., Zhao, J., Cheng, J., Li, J., Food flavor analysis 4.0: A cross-domain application of machine learning. Trends in Food Science & Technology 138 (2023), 116–125.
Zhan, L., Huang, X., Xue, J., Liu, H., Xiong, C., Wang, J., Nie, Z., MALDI-TOF/TOF tandem mass spectrometry imaging reveals non-uniform distribution of disaccharide isomers in plant tissues. Food Chemistry, 338, 2021, 127984.
Zhang, Y., Zheng, M., Zhu, R., Ma, R., Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network. Meat Science, 192, 2022, 108900.
Zhao, X., Chen, L., Wongmaneepratip, W., He, Y., Zhao, L., Yang, H., Effect of vacuum impregnated fish gelatin and grape seed extract on moisture state, microbiota composition, and quality of chilled seabass fillets. Food Chemistry, 354, 2021, 129581.
Zhao, Z., Lu, M., Wang, N., Li, Y., Zhao, L., Zhang, Q., Man, S., Ye, S., Ma, L., Nanomaterials-assisted CRISPR/Cas detection for food safety: Advances, challenges and future prospects. TrAC, Trends in Analytical Chemistry, 2023, 117269.
Zhong, K., Li, Y., Hu, X., Li, Y., Tang, L., Sun, X., Li, X., Zhang, J., Meng, Y., Ma, R., A colorimetric and NIR fluorescent probe for ultrafast detecting bisulfite and organic amines and its applications in food, imaging, and monitoring fish freshness. Food Chemistry, 438, 2024, 137987.
Zhou, J., Brereton, P., Campbell, K., Progress towards achieving intelligent food assurance systems. Food Control, 110548, 2024.
Zhou, J., Guo, W., Hu, Z., Jin, L., Hu, S., Evaluation of an internal standard-free laser ablation-ICP-OES method for elemental analysis in solid food samples. Journal of Food Composition and Analysis, 126, 2024, 105910.
Zhou, Z., Tian, D., Yang, Y., Cui, H., Li, Y., Ren, S., Han, T., Gao, Z., Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection. Current Research in Food Science, 2024, 100679.
Zou, L., Wu, C., Wang, Q., Zhou, J., Su, K., Li, H., Hu, N., Wang, P., An improved sensitive assay for the detection of PSP toxins with neuroblastoma cell-based impedance biosensor. Biosensors and Bioelectronics 67 (2015), 458–464.