[1] KING T, COLE M, FARBER J M, et al. Food safety for food security: Relationship between global megatrends and developments in food safety[J]. Trends in Food Science & Technology, 2017, 68:160-175.
[2] KUMAR P, MAHATO D K, KAMLE M, et al. Aflatoxins: A global concern for food safety, human health and their management[J]. Frontiers in Microbiology, 2017, 7:2170.
[3] 缪伊雯, 白菲, 童华荣. 茶叶中霉菌毒素危害与质量控制研究进展[J]. 食品科学, 2023, 44(17):352-362.
MIAO Y W, BAI F, TONG H R. Research progress on the hazards and control of mycotoxins in tea[J]. Food Science, 2023, 44(17):352-362.
[4] XU H W, WANG L Z, SUN J D, et al. Microbial detoxification of mycotoxins in food and feed[J]. Critical Reviews in Food Science and Nutrition, 2022, 62(18):4951-4969.
[5] KOŚCIELECKA K, KUĆ A, KUBIK-MACHURA D, et al. Endocrine effect of some mycotoxins on humans: A clinical review of the ways to mitigate the action of mycotoxins[J]. Toxins, 2023, 15(9):515.
[6] ZAIN M E. Impact of mycotoxins on humans and animals[J]. Journal of Saudi Chemical Society, 2011, 15(2):129-144.
[7] PAVLENKO R, BERZINA Z, REINHOLDS I, et al. An occurrence study of mycotoxins in plant-based beverages using liquid chromatography-mass spectrometry[J]. Toxins, 2024, 16(1):53.
[8] TSAGKARIS A S, PRUSOVA N, DZUMAN Z, et al. Regulated and non-regulated mycotoxin detection in cereal matrices using an ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) method[J]. Toxins, 2021, 13(11):783.
[9] LIANG Y F, ZHOU X W, WANG F, et al. Development of a monoclonal antibody-based ELISA for the detection of Alternaria mycotoxin tenuazonic acid in food samples[J]. Food Analytical Methods, 2020, 13(8):1594-1602.
[10] MAGNUS I, VIRTE M, THIENPONT H, et al. Combining optical spectroscopy and machine learning to improve food classification[J]. Food Control, 2021, 130:108342.
[11] ELLIS D I, BROADHURST D, CLARKE S J, et al. Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning[J]. The Analyst, 2005, 130(12):1648-1654.
[12] MENICHETTI G, RAVANDI B, MOZAFFARIAN D, et al. Machine learning prediction of the degree of food processing[J]. Nature Communications, 2023, 14(1):2312.
[13] CHHETRI K B. Applications of artificial intelligence and machine learning in food quality control and safety assessment[J]. Food Engineering Reviews, 2024, 16(1):1-21.
[14] RASHIDI H H, TRAN N, ALBAHRA S, et al. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML[J]. International Journal of Laboratory Hematology, 2021, 43(Suppl 1):15-22.
[15] ALSUBARI S N. Data analytics for the identification of fake reviews using supervised learning[J]. Computers, Materials & Continua, 2022, 70(2):3189-3204.
[16] SEBAG M. A tour of machine learning: An AI perspective[J]. AI Communications, 2014, 27(1):11-23.
[17] INGLIS A, PARNELL A C, SUBRAMANI N, et al. Machine learning applied to the detection of mycotoxin in food: A systematic review[J]. Toxins, 2024, 16(6):268.
[18] HENDRICKX K, PERINI L, VAN DER PLAS D, et al. Machine learning with a reject option: A survey[J]. Machine Learning, 2024, 113(5):3073-3110.
[19] GOODMAN K E, LESSLER J, COSGROVE S E, et al. A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-lactamase-producing organism[J]. Clinical Infectious Diseases, 2016, 63(7):896-903.
[20] ASHINO K, SUGANO K, AMAGASA T, et al. Predicting the decision making chemicals used for bacterial growth[J]. Scientific Reports, 2019, 9(1):7251.
[21] SUN L, LIANG K B, SONG Y X, et al. An improved CNN-based apple appearance quality classification method with small samples[J]. IEEE Access, 2021, 9:68054-68065.
[22] 章海亮, 周孝文, 刘雪梅, 等. 基于卷积神经网络和高光谱成像技术的多宝鱼新鲜度鉴别[J]. 光谱学与光谱分析, 2024, 44(2):367-371.
ZHANG H L, ZHOU X W, LIU X M, et al. Freshness identification of turbot based on convolutional neural network and hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2024, 44(2):367-371.
[23] 蔡健荣, 黄楚钧, 马立鑫, 等. 一维卷积神经网络的手持式可见/近红外柑橘可溶性固形物含量无损检测系统[J]. 光谱学与光谱分析, 2023, 43(9):2792-2798.
CAI J R, HUANG C J, MA L X, et al. Hand-held visible/near infrared nondestructive detection system for soluble solid content in mandarin by 1D-CNN model[J]. Spectroscopy and Spectral Analysis, 2023, 43(9):2792-2798.
[24] 刘静, 杜广全, 管骁. 基于近红外光谱的果蔬脆片品质评价方法研究[J]. 分析科学学报, 2017, 33(1):71-75.
LIU J, DU G Q, GUAN X. Study on quality evaluation of fruit and vegetable chips based on near infrared spectroscopy[J]. Journal of Analytical Science, 2017, 33(1):71-75.
[25] MANNARO K, BAIRE M, FANTI A, et al. A robust SVM color-based food segmentation algorithm for the production process of a traditional carasau bread[J]. IEEE Access, 2022, 10:15359-15377.
[26] ZOUNEMAT-KERMANI M, BATELAAN O, FADAEE M, et al. Ensemble machine learning paradigms in hydrology: A review[J]. Journal of Hydrology, 2021, 598:126266.
[27] 郭灿, 岳晓凤, 白艺珍, 等. 花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究[J]. 中国农业科学, 2022, 55(17):3426-3449.
GUO C, YUE X F, BAI Y Z, et al. Research on the application of a balanced sampling-random forest early warning model for aflatoxin risk in peanut[J]. Scientia Agricultura Sinica, 2022, 55(17):3426-3449.
[28] WU H, PRASAD S. Semi-supervised deep learning using pseudo labels for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2018, 27(3):1259-1270.
[29] VAN ENGELEN J E, HOOS H H. A survey on semi-supervised learning[J]. Machine Learning, 2020, 109(2):373-440.
[30] MA J N, GUAN Y, XING F G, et al. Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array coupling with machine-learning prediction models[J]. Journal of Hazardous Materials, 2023, 449:131030.
[31] WANG X X, LIU C, VAN DER FELS-KLERX H J. Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning[J]. Food Research International, 2022, 159:111588.
[32] CASTANO-DUQUE L, VAUGHAN M, LINDSAY J, et al. Gradient boosting and Bayesian network machine learning models predict aflatoxin and fumonisin contamination of maize in Illinois-First USA case study[J]. Frontiers in Microbiology, 2022, 13:1039947.
[33] WU Q S, XU L J, ZOU Z Y, et al. Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models[J]. Frontiers in Plant Science, 2022, 13:1047479.
[34] CHENG X Y, LI R X, XIE P D, et al. Predictive modeling of patulin accumulation in apple lesions infected by Penicillium expansum using machine learning[J]. Postharvest Biology and Technology, 2024, 217:113115.
[35] MATEO F, GADEA R, MATEO R, et al. Neural network models for prediction of trichothecene content in wheat[J]. World Mycotoxin Journal, 2008, 1(3):349-356.
[36] MATEO F, GADEA R, MATEO E M, et al. Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum[J]. Food Control, 2011, 22(1):88-95.
[37] CAMARDO LEGGIERI M, MAZZONI M, FODIL S, et al. An electronic nose supported by an artificial neural network for the rapid detection of aflatoxin B1 and fumonisins in maize[J]. Food Control, 2021, 123:107722.
[38] LEGGIERI M C, MAZZONI M, BATTILANI P. Machine learning for predicting mycotoxin occurrence in maize[J]. Frontiers in Microbiology, 2021, 12:661132.
[39] HAN Z Z, GAO J Y. Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2019, 164:104888.
[40] KIM Y, KANG S, AJANI O S, et al. Predicting early mycotoxin contamination in stored wheat using machine learning[J]. Journal of Stored Products Research, 2024, 106:102294.
[41] LIU W, DENG H Y, SHI Y L, et al. Application of multispectral imaging combined with machine learning methods for rapid and non-destructive detection of zearalenone (ZEN) in maize[J]. Measurement, 2022, 203:111944.
[42] DENG J H, NI L H, BAI X, et al. Simultaneous analysis of mildew degree and aflatoxin B1 of wheat by a multi-task deep learning strategy based on microwave detection technology[J]. LWT, 2023, 184:115047.
[43] SUN B Y, WU H Y, FANG T R, et al. Dual-mode colorimetric/SERS lateral flow immunoassay with machine learning-driven optimization for ultrasensitive mycotoxin detection[J]. Analytical Chemistry, 2025, 97(9):4824-4831.
[44] MA P H, ZHANG Z K, JIA X X, et al. Neural network in food analytics[J]. Critical Reviews in Food Science and Nutrition, 2024, 64(13):4059-4077.
[45] Rezaee Z, Mohtasebi S S, Firouz S M. Monitoring pistachio health using data fusion of machine vision and electronic nose (E-nose)[J]. Journal of Food Measurement and Characterization, 2024, 19(3): 1-8.
[46] ZHENG S Y, WEI Z S, LI S, et al. Near-infrared reflectance spectroscopy-based fast versicolorin A detection in maize for early aflatoxin warning and safety sorting[J]. Food Chemistry, 2020, 332:127419.
[47] BERTANI F R, BUSINARO L, GAMBACORTA L, et al. Optical detection of aflatoxins B in grained almonds using fluorescence spectroscopy and machine learning algorithms[J]. Food Control, 2020, 112:107073.
[48] KIM Y K, BAEK I, LEE K M, et al. Rapid detection of single- and co-contaminant aflatoxins and fumonisins in ground maize using hyperspectral imaging techniques[J]. Toxins, 2023, 15(7):472.
[49] ZHAO Y Q, ZHU C Y, JIANG H. Quantitative detection of Zearalenone in wheat using intervals selection coupled to near-infrared spectroscopy[J]. Infrared Physics & Technology, 2024, 136:105004.
[50] CEBRIÁN E, NÚÑEZ F, RODRÍGUEZ M, et al. Potential of near infrared spectroscopy as a rapid method to discriminate OTA and non-OTA-producing mould species in a dry-cured ham model system[J]. Toxins, 2021, 13(9):620.
[51] DENG J H, JIANG H, CHEN Q S. Characteristic wavelengths optimization improved the predictive performance of near-infrared spectroscopy models for determination of aflatoxin B1 in maize[J]. Journal of Cereal Science, 2022, 105:103474.
[52] 杨承霖, 刘嘉祺, 郭芸成, 等. 结合太赫兹光谱与机器学习的小麦霉变程度判别[J]. 食品科学, 2023, 44(12):343-350.
YANG C L, LIU J Q, GUO Y C, et al. Detection of mildew degree of wheat using terahertz spectroscopy and machine learning[J]. Food Science, 2023, 44(12):343-350.
[53] DIB A A, ASSAF J C, DEBS E, et al. A comparative review on methods of detection and quantification of mycotoxins in solid food and feed: a focus on cereals and nuts[J]. Mycotoxin Research, 39: 319-345.
[54] KIM D Y, GETACHEW F, TILLMAN B L, et al. Developing statistical models of aflatoxin risk in peanuts using historical weather data[J]. Agronomy Journal, 2024, 116(5):2346-2361.
[55] KIM Y K, QIN J W, BAEK I, et al. Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy system with machine learning[J]. Current Research in Food Science, 2023, 7:100647.
[56] ZHANG S, LI Z X, AN J, et al. Identification of aflatoxin B1 in peanut using near-infrared spectroscopy combined with naive Bayes classifier[J]. Spectroscopy Letters, 2021, 54(5):340-351.
[57] 赵雪, 靳欣迪, 刘斌, 等. 辣椒粉中黄曲霉菌生长及其产毒规律的预测模型构建[J]. 食品科学, 2021, 42(14):62-69.
ZHAO X, JIN X D, LIU B, et al. Prediction model construction for Aspergillus flavus growth and toxin accumulation in chili powder[J]. Food Science, 2021, 42(14):62-69.
[58] RANBIR, SINGH G, KAUR N, et al. Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of mycotoxins in corn sample[J]. Food Chemistry, 2025, 464(Pt 3):141869.
[59] WANG Q A, CHEN J, NI Y Q, et al. Application of Bayesian networks in reliability assessment: A systematic literature review[J]. Structures, 2025, 71:108098.
[60] RAHMAT F, ZULKAFLI Z, ISHAK A J, et al. Supervised feature selection using principal component analysis[J]. Knowledge and Information Systems, 2024, 66(3):1955-1995.
[61] ATAŞ M, YARDIMCI Y, TEMIZEL A. A new approach to aflatoxin detection in chili pepper by machine vision[J]. Computers and Electronics in Agriculture, 2012, 87:129-141.
[62] LI S L, SHAO X J, GUO Z, et al. Novel detection method for Aspergillus flavus contamination in maize kernels based on spatial-spectral features using short-wave infrared hyperspectral imaging[J]. Journal of Food Composition and Analysis, 2025, 140:107219.
[63] 王蓓, 沈飞, 何学明, 等. 电子鼻同步检测花生霉菌及霉菌毒素[J]. 食品科学, 2022, 43(12): 310-316.
WANG B, SHEN F, HE X M, et al.Simultaneous Detection of Harmful Fungi and Mycotoxin Contamination in Peanuts by Electronic Nose[J]. Food Science, 2022, 43(12): 310-316.
[64] CHEN M, HE X M, PANG Y Y, et al. Laser induced fluorescence spectroscopy for detection of Aflatoxin B1 contamination in peanut oil[J]. Journal of Food Measurement and Characterization, 2021, 15(3):2231-2239.
[65] WANG B, SHEN F, HE X M, et al. Simultaneous detection of Aspergillus moulds and aflatoxin B1 contamination in rice by laser induced fluorescence spectroscopy[J]. Food Control, 2023, 145:109485.
[66] SALEHI A, KHEDMATI M. Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification[J]. Scientific Reports, 2025, 15(1):3460.
[67] RABBANI Y, BEHJATI S, LAMBERT B, et al. Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning[J]. ECS Meeting Abstracts, 2023, MA2023-01(10):1223.
[68] PURCHASE J, DONATO R, SACCO C, et al. The association of food ingredients in breakfast cereal products and fumonisins production: Risks identification and predictions[J]. Mycotoxin Research, 2023, 39(3):165-175.
[69] ROCCHETTI G, GHILARDELLI F, MASOERO F, et al. Screening of regulated and emerging mycotoxins in bulk milk samples by high-resolution mass spectrometry[J]. Foods, 2021, 10(9):2025.
[70] 陈靓, 阳佳红, 田星. 机器学习在食品风味领域的研究进展与未来趋势[J]. 食品科学, 2024, 45(10): 28-37.
CHEN J, YANG J H, TIAN X. Research Progress and Future Trends of Machine Learning in the Field of Food Flavor[J]. Food Science, 2024, 45(10): 28-37.
[71] 郭香兰, 王立, 金学波, 等. 机器学习-基于GAN和DF结合的粮食加工过程污染物小样本数据扩充及预测[J]. 食品科学, 2024, 45(12): 22-30.
GUO X L, YU L, JIN X B, et al. Machine Learning-Small Sample Data Expansion and Prediction of Pollutants in the Grain Processing Process Based on the Combination of GAN and DF[J]. Food Science, 2024, 45(12): 22-30.
[72] KOYAMA H. Machine learning application in otology[J]. Auris, Nasus, Larynx, 2024, 51(4):666-673.
[73] BANG J, YANG B. Application of machine learning to predict the engineering characteristics of construction material[J]. Multiscale Science and Engineering, 2023, 5(1):1-9.
[74] LOU R R, LV Z H, DANG S P, et al. Application of machine learning in ocean data[J]. Multimedia Systems, 2023, 29(3):1815-1824.
