3,293
Views
7
CrossRef citations to date
0
Altmetric
Reviews

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

, , & ORCID Icon

References

  • Adir, O., M. Poley, G. Chen, S. Froim, N. Krinsky, J. Shklover, J. Shainsky-Roitman, T. Lammers, and A. Schroeder. 2020. Integrating artificial intelligence and nanotechnology for precision cancer medicine. Advanced Materials 32 (13):1901989. doi: 10.1002/adma.201901989.
  • Al-Sarayreh, M., M. M. Reis, W. Q. Yan, and R. Klette. 2020. Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat. Food Control. 117:107332. doi: 10.1016/j.foodcont.2020.107332.
  • Ali, M. M., N. Hashim, S. Abd Aziz, and O. Lasekan. 2020. Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends in Food Science & Technology 99:1–10.
  • An, D., L. Zhang, Z. Liu, J. Liu, and Y. Wei. 2022. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Critical Reviews in Food Science and Nutrition 61 (15):1–31. doi: 10.1080/10408398.10402022.12066062.
  • Anderssen, K. E., S. K. Stormo, T. Skara, M. H. Skjelvareid, and K. Heia. 2020. Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. LWT 133:110093. doi: 10.1016/j.lwt.2020.110093.
  • Badillo, S., B. Banfai, F. Birzele, I. I. Davydov, L. Hutchinson, T. Kam-Thong, J. Siebourg-Polster, B. Steiert, and J. D. Zhang. 2020. An introduction to machine learning. Clinical Pharmacology and Therapeutics 107 (4):871–85. doi: 10.1002/cpt.1796.
  • Bai, Z. Z., X. J. Hu, J. P. Tian, P. Chen, H. B. Luo, and D. Huang. 2020. Rapid and nondestructive detection of sorghum adulteration using optimization algorithms and hyperspectral imaging. Food Chemistry 331:127290.
  • Castro, W., J. Oblitas, M. De-la-Torre, C. Cotrina, K. Bazan, and H. Avila-George. 2019. Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces. IEEE Access. 7:27389–400. doi: 10.1109/ACCESS.2019.2898223.
  • Cheng, W. W., D.-W. Sun, and J.-H. Cheng. 2016. Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. LWT 73:13–9. doi: 10.1016/j.lwt.2016.05.031.
  • Cheng, W. W., D.-W. Sun, H. B. Pu, and Y. W. Liu. 2016. Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat. LWT - Food Science and Technology 72:322–9. doi: 10.1016/j.lwt.2016.05.003.
  • Cheng, W. W., D.-W. Sun, H. B. Pu, and Q. Y. Wei. 2017. Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods. Food Chemistry 221:1989–96. doi: 10.1016/j.foodchem.2016.11.093.
  • Cheng, W. W., D.-W. Sun, H. B. Pu, and Q. Y. Wei. 2018. Heterospectral two-dimensional correlation analysis with near-infrared hyperspectral imaging for monitoring oxidative damage of pork myofibrils during frozen storage. Food Chemistry 248:119–27. doi: 10.1016/j.foodchem.2017.12.050.
  • Cheng, J.-H., D.-W. Sun, G. Liu, and Y.-N. Chen. 2019. Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in fish fillet using physarum network and genetic algorithm (PN-GA) method. Food Chemistry 270:181–8. doi: 10.1016/j.foodchem.2018.07.013.
  • Cheplygina, V., M. de Bruijne, and J. P. W. Pluim. 2019. Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical Image Analysis 54:280–96.
  • Dai, Q., J.-H. Cheng, D.-W. Sun, Z. W. Zhu, and H. B. Pu. 2016. Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chemistry 197 (Pt A):257–65. doi: 10.1016/j.foodchem.2015.10.073.
  • Dargan, S., M. Kumar, M. R. Ayyagari, and G. Kumar. 2020. A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering 27 (4):1071–92. doi: 10.1007/s11831-019-09344-w.
  • Debska, B, and B. Guzowska-Swider. 2011. Decision trees in selection of featured determined food quality. Analytica Chimica Acta 705 (1-2):261–71.
  • Deng, X., S. Cao, and A. L. Horn. 2021. Emerging applications of machine learning in food safety. Annual Review of Food Science and Technology 12 (1):513–38.
  • Du, D. D., J. Wang, B. Wang, L. Y. Zhu, and X. Z. Hong. 2019. Ripeness prediction of postharvest kiwifruit using a MOS e-nose combined with chemometrics. Sensors 19 (2):419. doi: 10.3390/s19020419.
  • Ezugwu, A. E., A. M. Ikotun, O. O. Oyelade, L. Abualigah, J. O. Agushaka, C. I. Eke, and A. A. Akinyelu. 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence 110:104743. doi: 10.1016/j.engappai.2022.104743.
  • Fan, S. X., J. B. Li, Y. H. Zhang, X. Tian, Q. Y. Wang, X. He, C. Zhang, and W. Q. Huang. 2020. On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering 286:110102. doi: 10.1016/j.jfoodeng.2020.110102.
  • Gu, S., J. Wang, and Y. Wang. 2019. Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose. Food Chemistry 292:325–35.
  • Hamamoto, R., K. Suvarna, M. Yamada, K. Kobayashi, N. Shinkai, M. Miyake, M. Takahashi, S. Jinnai, R. Shimoyama, A. Sakai, et al. 2020. Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers 12 (12):3532. doi: 10.3390/cancers12123532.
  • Han, Y. F., Z. J. Liu, K. Khoshelham, and S. H. Bai. 2021. Quality estimation of nuts using deep learning classification of hyperspectral imagery. Computers and Electronics in Agriculture 180:105868. doi: 10.1016/j.compag.2020.105868.
  • Han, Z. Z, and J. Y. Gao. 2019. Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging. Computers and Electronics in Agriculture 164:104888. doi: 10.1016/j.compag.2019.104888.
  • Hong, X., J. Wang, and G. Qi. 2015a. Comparison of semi-supervised and supervised approaches for classification of e-nose datasets: Case studies of tomato juices. Chemometrics and Intelligent Laboratory Systems 146:457–63. doi: 10.1016/j.chemolab.2015.07.001.
  • Hong, X. Z., J. Wang, and G. D. Qi. 2014. Comparison of spectral clustering, K-clustering and hierarchical clustering on e-nose datasets: Application to the recognition of material freshness, adulteration levels and pretreatment approaches for tomato juices. Chemometrics and Intelligent Laboratory Systems 133:17–24. doi: 10.1016/j.chemolab.2014.01.017.
  • Hong, X. Z., J. Wang, and G. D. Qi. 2015b. E-nose combined with chemometrics to trace tomato-juice quality. Journal of Food Engineering 149:38–43. doi: 10.1016/j.jfoodeng.2014.10.003.
  • Huang, Y. R., J. Wang, N. Li, J. Yang, and Z. H. Ren. 2021. Predicting soluble solids content in "Fuji" apples of different ripening stages based on multiple information fusion. Pattern Recognition Letters 151:76–84. doi: 10.1016/j.patrec.2021.08.003.
  • Hussain, N., D.-W. Sun, and H. Pu. 2019. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science & Technology 91:598–608. doi: 10.1016/j.tifs.2019.07.018.
  • Ji, Y., L. Sun, Y. Li, and D. Ye. 2019. Detection of bruised potatoes using hyperspectral imaging technique based on discrete wavelet transform. Infrared Physics & Technology 103:103054. doi: 10.1016/j.infrared.2019.103054.
  • Jiang, Y. P., S. F. Chen, B. Bian, Y. H. Li, Y. Sun, and X. C. Wang. 2021. Discrimination of tomato maturity using hyperspectral imaging combined with graph-based semi-supervised method considering class probability information. Food Analytical Methods 14 (5):968–83. doi: 10.1007/s12161-020-01955-5.
  • Kalinichenko, A, and L. Arseniyeva. 2020. Electronic nose combined with chemometric approaches to assess authenticity and adulteration of sausages by soy protein. Sensors and Actuators B: Chemical 303:127250. doi: 10.1016/j.snb.2019.127250.
  • Kamath, C. N., S. S. Bukhari, and A. Dengel. 2018. Comparative study between traditional machine learning and deep learning approaches for text classification. Proceedings of the ACM Symposium on Document Engineering 2018, pp. 14. Halifax, Canada: Association for Computing Machinery.
  • Kamruzzaman, M., Y. Makino, and S. Oshita. 2016. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of Food Engineering 170:8–15. doi: 10.1016/j.jfoodeng.2015.08.023.
  • Karakaya, D., O. Ulucan, and M. Turkan. 2020. Electronic nose and its applications: A survey. International Journal of Automation and Computing 17 (2):179–209. doi: 10.1007/s11633-019-1212-9.
  • Khan, S., M. Sajjad, T. Hussain, A. Ullah, and A. S. Imran. 2021. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. IEEE Access. 9:10657–73. doi: 10.1109/ACCESS.2020.3048172.
  • Khojastehnazhand, M, and H. Ramezani. 2020. Machine vision system for classification of bulk raisins using texture features. Journal of Food Engineering 271:109864. doi: 10.1016/j.jfoodeng.2019.109864.
  • Kim, S., M. H. Lee, T. Wiwasuku, A. S. Day, S. Youngme, D. S. Hwang, and J. Y. Yoon. 2021. Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification. Biosensors & Bioelectronics 188:113335.
  • Klerkx, L., E. Jakku, and P. Labarthe. 2019. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences 90-91 (1):1–16. doi: 10.1016/j.njas.2019.100315.
  • Lashgari, M., A. Imanmehr, and H. Tavakoli. 2020. Fusion of acoustic sensing and deep learning techniques for apple mealiness detection. Journal of Food Science and Technology 57 (6):2233–40. doi: 10.1007/s13197-020-04259-y.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi: 10.1038/nature14539.
  • Leggieri, M. C., M. Mazzoni, S. Fodil, M. Moschini, T. Bertuzzi, A. Prandini, and P. Battilani. 2021. An electronic nose supported by an artificial neural network for the rapid detection of aflatoxin B-1 and fumonisins in maize. Food Control. 123:107722. doi: 10.1016/j.foodcont.2020.107722.
  • Lei, T., X.-H. Lin, and D.-W. Sun. 2019. Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA-LDA models built by hyperspectral data. Journal of Food Measurement and Characterization 13 (4):3119–29. doi: 10.1007/s11694-019-00234-0.
  • Li, L., S. Xie, J. Ning, Q. Chen, and Z. Zhang. 2019. Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. Journal of the Science of Food and Agriculture 99 (4):1787–94.
  • Li, L. Q., Y. J. Wang, S. S. Jin, M. H. Li, Q. S. Chen, J. M. Ning, and Z. Z. Zhang. 2021. Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy 246:118991.
  • Lin, X. H, and D.-W. Sun. 2022. Development of a general model for monitoring moisture distribution of four vegetables undergoing microwave-vacuum drying by hyperspectral imaging. Drying Technology 40 (7):1478–92. doi: 10.1080/07373937.2021.1950171.
  • Lin, X. H., J.-L. Xu, and D.-W. Sun. 2019. Investigation of moisture content uniformity of microwave-vacuum dried mushroom (Agaricus bisporus) by NIR hyperspectral imaging. LWT 109:108–17. doi: 10.1016/j.lwt.2019.03.034.
  • Lin, X. H., J.-L. Xu, and D.-W. Sun. 2020. Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique. Food Chemistry 332:127407. doi: 10.1016/j.foodchem.2020.127407.
  • Lin, X. H., J.-L. Xu, and D.-W. Sun. 2021. Comparison of moisture uniformity between microwave-vacuum and hot-air dried ginger slices using hyperspectral information combined with semivariogram. Drying Technology 39 (8):1044–58. doi: 10.1080/07373937.2020.1741006.
  • Liu, Y. W., H. B. Pu, and D.-W. Sun. 2017. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends in Food Science & Technology 69:25–35. doi: 10.1016/j.tifs.2017.08.013.
  • Liu, W., Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi. 2017. A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. doi: 10.1016/j.neucom.2016.12.038.
  • Liu, Y. W., D.-W. Sun, J.-H. Cheng, and Z. Han. 2018. Hyperspectral imaging sensing of changes in moisture content and color of beef during microwave heating process. Food Analytical Methods 11(9):2472–84. doi: 10.1007/s12161-018-1234-x.
  • Liu, Y., H. B. Pu, and D. W. Sun. 2021. Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science & Technology 113:193–204. doi: 10.1016/j.tifs.2021.04.042.
  • Ma, J, and D.-W. Sun. 2020. Prediction of monounsaturated and polyunsaturated fatty acids of various processed pork meats using improved hyperspectral imaging technique. Food Chemistry 321:126695. doi: 10.1016/j.foodchem.2020.126695.
  • Ma, J., H. B. Pu, and D.-W. Sun. 2018. Predicting intramuscular fat content variations in boiled pork muscles by hyperspectral imaging using a novel spectral pre-processing technique. LWT 94:119–28. doi: 10.1016/j.lwt.2018.04.030.
  • Ma, J., D.-W. Sun, B. Nicolai, H. B. Pu, P. Verboven, Q. Y. Wei, and Z. P. Liu. 2019. Comparison of spectral properties of three hyperspectral imaging (HSI) sensors in evaluating main chemical compositions of cured pork. Journal of Food Engineering 261:100–8. doi: 10.1016/j.jfoodeng.2019.05.024.
  • Ma, J., D.-W. Sun, and H. B. Pu. 2017. Model improvement for predicting moisture content (MC) in pork longissimus dorsi muscles under diverse processing conditions by hyperspectral imaging. Journal of Food Engineering 196:65–72. doi: 10.1016/j.jfoodeng.2016.10.016.
  • Ma, J., D.-W. Sun, H. B. Pu, Q. Y. Wei, and X. M. Wang. 2019. Protein content evaluation of processed pork meats based on a novel single shot (snapshot) hyperspectral imaging sensor. Journal of Food Engineering 240:207–13. doi: 10.1016/j.jfoodeng.2018.07.032.
  • Ma, J., J.-H. Cheng, D.-W. Sun, and D. Liu. 2019. Mapping changes in sarcoplasmatic and myofibrillar proteins in boiled pork using hyperspectral imaging with spectral processing methods. LWT 110:338–45. doi: 10.1016/j.lwt.2019.04.095.
  • Ma, J., D.-W. Sun, H. Pu, J.-H. Cheng, and Q. Wei. 2019. Advanced techniques for hyperspectral imaging in the food industry: Principles and recent applications. Annual Review of Food Science and Technology 10 (1):197–220.
  • Ma, T., S. Tsuchikawa, and T. Inagaki. 2020. Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach. Computers and Electronics in Agriculture 177:105683. doi: 10.1016/j.compag.2020.105683.
  • Mao, J., Y. Yu, X. Rao, and J. Wang. 2016. Firmness prediction and modeling by optimizing acoustic device for watermelons. Journal of Food Engineering 168:1–6. doi: 10.1016/j.jfoodeng.2015.07.009.
  • Marcelo, M. C. A., F. L. F. Soares, J. A. Ardila, J. C. Dias, R. Pedo, S. Kaiser, O. F. S. Pontes, C. E. Pulcinelli, and G. P. Sabin. 2019. Fast inline tobacco classification by near-infrared hyperspectral imaging and support vector machine-discriminant analysis. Analytical Methods 11 (14):1966–75. doi: 10.1039/C9AY00413K.
  • Merghadi, A., A. P. Yunus, J. Dou, J. Whiteley, B. ThaiPham, D. T. Bui, R. Avtar, and B. Abderrahmane. 2020. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews 207:103225. doi: 10.1016/j.earscirev.2020.103225.
  • Mirzaee-Ghaleh, E., A. Taheri-Garavand, F. Ayari, and J. Lozano. 2020. Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an e-nose machine coupled fuzzy KNN. Food Analytical Methods 13 (3):678–89. doi: 10.1007/s12161-019-01682-6.
  • Mountrakis, G., J. Im, and C. Ogole. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66 (3):247–59. doi: 10.1016/j.isprsjprs.2010.11.001.
  • Munera, S., J. Gomez-Sanchis, N. Aleixos, J. Vila-Frances, G. Colelli, S. Cubero, E. Soler, and J. Blasco. 2021. Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology 171:111356. doi: 10.1016/j.postharvbio.2020.111356.
  • Nagasubramanian, K., S. Jones, A. K. Singh, S. Sarkar, A. Singh, and B. Ganapathysubramanian. 2019. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods. 15 (1):1–10. doi: 10.1186/s13007-019-0479-8.
  • Nasiri, A., M. Omid, and A. Taheri-Garavand. 2020. An automatic sorting system for unwashed eggs using deep learning. Journal of Food Engineering 283:110036. doi: 10.1016/j.jfoodeng.2020.110036.
  • Ni, C., Z. Li, X. Zhang, X. Sun, Y. Huang, L. Zhao, T. Zhu, and D. Wang. 2020. Online sorting of the film on cotton based on deep learning and hyperspectral imaging. IEEE Access. 8:93028–38. doi: 10.1109/ACCESS.2020.2994913.
  • Ni, C., D. Wang, R. Vinson, M. Holmes, and Y. Tao. 2019. Automatic inspection machine for maize kernels based on deep convolutional neural networks. Biosystems Engineering 178:131–44. doi: 10.1016/j.biosystemseng.2018.11.010.
  • Nturambirwe, J. F. I, and U. L. Opara. 2020. Machine learning applications to non-destructive defect detection in horticultural products. Biosystems Engineering 189:60–83. doi: 10.1016/j.biosystemseng.2019.11.011.
  • Oyelade, J., I. Isewon, O. Oladipupo, O. Emebo, Z. Omogbadegun, O. Aromolaran, E. Uwoghiren, D. Olaniyan, and O. Olawole. 2019. Data clustering: Algorithms and its applications. 19th International Conference on Computational Science and Its Applications (ICCSA), pp. 71–81. Saint Petersburg, Russia: Saint Petersburg State University.
  • Pan, Y. Y., D.-W. Sun, J.-H. Cheng, and Z. Han. 2018. Non-destructive detection and screening of non-uniformity in microwave sterilization using hyperspectral imaging analysis. Food Analytical Methods 11 (6):1568–80. doi: 10.1007/s12161-017-1134-5.
  • Patricio, D. I, and R. Rieder. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture 153:69–81. doi: 10.1016/j.compag.2018.08.001.
  • Pu, H., D.-W. Sun, J. Ma, and J.-H. Cheng. 2015. Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Science 99:81–8.
  • Pu, H. B., L. Lin, and D.-W. Sun. 2019. Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection: A review. Comprehensive Reviews in Food Science and Food Safety 18 (4):853–66. doi: 10.1111/1541-4337.12432.
  • Qi, X., J. Jiang, X. Cui, and D. Yuan. 2020. Moldy peanut kernel identification using wavelet spectral features extracted from hyperspectral images. Food Analytical Methods 13 (2):445–56. doi: 10.1007/s12161-019-01670-w.
  • Raczkowska, M. K., P. Koziol, S. Urbaniak-Wasik, C. Paluszkiewicz, W. M. Kwiatek, and T. P. Wrobel. 2019. Influence of denoising on classification results in the context of hyperspectral data: High definition FT-IR imaging. Analytica Chimica Acta 1085:39–47.
  • Ravikanth, L., C. B. Singh, D. S. Jayas, and N. D. G. White. 2015. Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering 135:73–86. doi: 10.1016/j.biosystemseng.2015.04.007.
  • Rehman, T. U., M. S. Mahmud, Y. K. Chang, J. Jin, and J. Shin. 2019. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture 156:585–605. doi: 10.1016/j.compag.2018.12.006.
  • Ren, Z.-q., Z.-h. Rao, and H.-y. Ji. 2018. Identification of different concentrations pesticide residues of dimethoate on spinach leaves by hyperspectral image technology. IFAC Papersonline 51 (17):758–63.
  • Ren, Y. Q, and D.-W. Sun. 2022. Monitoring of moisture contents and rehydration rates of microwave vacuum and hot air dehydrated beef slices and splits using hyperspectral imaging. Food Chemistry 382:132346. doi: 10.1016/j.foodchem.2022.132346.
  • Saha, D, and A. Manickavasagan. 2021. Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science 4:28–44.
  • Sajedi, H., F. Mohammadipanah, and A. Pashaei. 2020. Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models. Multimedia Tools and Applications 79 (43-44):32711–30. doi: 10.1007/s11042-020-09284-9.
  • Sarno, R., K. Triyana, S. I. Sabilla, D. R. Wijaya, D. Sunaryono, and C. Fatichah. 2020. Detecting pork adulteration in beef for halal authentication using an optimized electronic nose system. IEEE Access. 8:221700–11. doi: 10.1109/ACCESS.2020.3043394.
  • Shao, Y. N., Y. Li, L. J. Jiang, J. Pan, Y. He, and X. M. Dou. 2016. Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using visible/near infrared hyperspectral imaging and Raman microspectroscopy technology. Water Research 104:432–40. doi: 10.1016/j.watres.2016.08.042.
  • Sheikhpour, R., M. A. Sarram, S. Gharaghani, and M. A. Z. Chahooki. 2017. A survey on semi-supervised feature selection methods. Pattern Recognition 64:141–58. doi: 10.1016/j.patcog.2016.11.003.
  • Steinbrener, J., K. Posch, and R. Leitner. 2019. Hyperspectral fruit and vegetable classification using convolutional neural networks. Computers and Electronics in Agriculture 162:364–72. doi: 10.1016/j.compag.2019.04.019.
  • Sun, D.-W. 2016. Computer vision technology for food quality evaluation. San Diego, CA: Academic Press/Elsevier.
  • Sun, Q., M. Zhang, and A. S. Mujumdar. 2019. Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition 59 (14):2258–75.
  • Taheri-Garavand, A., S. Fatahi, A. Banan, and Y. Makino. 2019. Real-time nondestructive monitoring of common carp fish freshness using robust vision-based intelligent modeling approaches. Computers and Electronics in Agriculture 159:16–27. doi: 10.1016/j.compag.2019.02.023.
  • Tan Nhat, P., T. Ly Van, and D. Son Vu Truong. 2020. Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access. 8:189960–73.
  • Tang, Y., K. Xu, B. Zhao, M. Zhang, C. Gong, H. Wan, Y. Wang, and Z. Yang. 2021. A novel electronic nose for the detection and classification of pesticide residue on apples. RSC Advances 11 (34):20874–83.
  • Tian, Y. T., J. Yan, Y. Y. Zhang, T. H. Yu, P. Y. Wang, D. B. Shi, and S. K. Duan. 2020. A drift-compensating novel deep belief classification network to improve gas recognition of electronic noses. IEEE Access. 8:121385–97. doi: 10.1109/ACCESS.2020.3006729.
  • Tokunaga, K., C. Saeki, S. Taniguchi, S. Nakano, H. Ohta, and M. Nakamura. 2020. Nondestructive evaluation of fish meat using ultrasound signals and machine learning methods. Aquacultural Engineering 89:102052. doi: 10.1016/j.aquaeng.2020.102052.
  • Voss, H. G. J., S. L. Stevan, and R. A. Ayub. 2019. Peach growth cycle monitoring using an electronic nose. Computers and Electronics in Agriculture 163:104858. doi: 10.1016/j.compag.2019.104858.
  • Wang, D. Y., M. Zhang, A. S. Mujumdar, and D. X. Yu. 2022. Advanced detection techniques using artificial intelligence in processing of berries. Food Engineering Reviews 14 (1):176–99. doi: 10.1007/s12393-021-09298-5.
  • Wang, H.-P., P. Chen, J.-W. Dai, D. Liu, J.-Y. Li, Y.-P. Xu, and X.-L. Chu. 2022. Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues. TrAC Trends in Analytical Chemistry 153:116648. doi: 10.1016/j.trac.2022.116648.
  • Wang, L., Q. Hu, F. Pei, M. A. Mugambi, and W. Yang. 2020. Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors. Journal of the Science of Food and Agriculture 100 (7):3136–46.
  • Wang, X., M. Russel, Y. Zhang, J. Zhao, Y. Zhang, and J. Shan. 2019. A clustering-based partial least squares method for improving the freshness prediction model of crucian carps fillets by hyperspectral image technology. Food Analytical Methods 12 (9):1988–97. doi: 10.1007/s12161-019-01541-4.
  • Wang, Z., M. Hu, and G. Zhai. 2018. Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors 18 (4):1126. doi: 10.3390/s18041126.
  • Wei, X., J. C. He, S. H. Zheng, and D. P. Ye. 2020. Modeling for SSC and firmness detection of persimmon based on NIR hyperspectral imaging by sample partitioning and variables selection. Infrared Physics & Technology 105:103099. doi: 10.1016/j.infrared.2019.103099.
  • Wu, D., L. W. Meng, L. Yang, J. Y. Wang, X. P. Fu, X. Q. Du, S. J. Li, Y. He, and L. X. Huang. 2019. Feasibility of laser-induced breakdown spectroscopy and hyperspectral imaging for rapid detection of thiophanate-methyl residue on mulberry fruit. International Journal of Molecular Sciences 20 (8):2017. doi: 10.3390/ijms20082017.
  • Wu, X. H., J. Zhu, B. Wu, C. Zhao, J. Sun, and C. X. Dai. 2019. Discrimination of Chinese liquors based on electronic nose and fuzzy discriminant principal component analysis. Foods 8 (1):38. doi: 10.3390/foods8010038.
  • Xie, T. H., X. X. Li, X. S. Zhang, J. Y. Hu, and Y. Fang. 2021. Detection of Atlantic salmon bone residues using machine vision technology. Food Control. 123:107787. doi: 10.1016/j.foodcont.2020.107787.
  • Xu, M., J. Wang, and S. Gu. 2019. Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. Journal of Food Engineering 241:10–7. doi: 10.1016/j.jfoodeng.2018.07.020.
  • Xu, M., J. Wang, and L. Zhu. 2019. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chemistry 289:482–9. doi: 10.1016/j.foodchem.2019.03.080.
  • Yang, X., H. Li, Y. Wang, X. Liang, C. Chen, X. Zhou, F. Zeng, J. Fang, A. Frangi, Z. Chen, et al. 2021. Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound. Medical Image Analysis 73:102134. doi: 10.1016/j.media.2021.102134.
  • Yang, X. Z., J. H. Chen, L. W. Jia, W. Q. Yu, D. Wang, W. W. Wei, S. J. Li, S. Y. Tian, and D. Wu. 2020. Rapid and non-destructive detection of compression damage of yellow peach using an electronic nose and chemometrics. Sensors 20 (7):1866. doi: 10.3390/s20071866.
  • Yu, K.-H., A. L. Beam, and I. S. Kohane. 2018. Artificial intelligence in healthcare. Nature Biomedical Engineering 2 (10):719–31.
  • Zarezadeh, M. R., M. Aboonajmi, and M. G. Varnamkhasti. 2021. Fraud detection and quality assessment of olive oil using ultrasound. Food Science & Nutrition 9 (1):180–9.
  • Zeng, N. Y., Z. D. Wang, H. Zhang, W. B. Liu, and F. E. Alsaadi. 2016. Deep belief networks for quantitative analysis of a gold immunochromatographic strip. Cognitive Computation 8 (4):684–92. doi: 10.1007/s12559-016-9404-x.
  • Zhang, B., W. Huang, J. Li, C. Zhao, S. Fan, J. Wu, and C. Liu. 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International 62:326–43. doi: 10.1016/j.foodres.2014.03.012.
  • Zhang, C., W. Y. Wu, L. Zhou, H. Cheng, X. Q. Ye, and Y. He. 2020. Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging. Food Chemistry 319:126536. doi: 10.1016/j.foodchem.2020.126536.
  • Zhang, D. Y., G. Chen, X. Yin, R. J. Hu, C. Y. Gu, Z. G. Pan, X. G. Zhou, and Y. Chen. 2020. Integrating spectral and image data to detect Fusarium head blight of wheat. Computers and Electronics in Agriculture 175:105588. doi: 10.1016/j.compag.2020.105588.
  • Zhang, J. N., Y. Yang, X. P. Feng, H. X. Xu, J. P. Chen, and Y. He. 2020. Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network. Frontiers in Plant Science 11:821. doi: 10.3389/fpls.2020.00821.
  • Zhang, W., Z. Lv, and S. Xiong. 2018. Nondestructive quality evaluation of agro-products using acoustic vibration methods-A review. Critical Reviews in Food Science and Nutrition 58 (14):2386–97.
  • Zhang, X., J. Yang, T. Lin, and Y. Ying. 2021. Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends in Food Science & Technology 112:431–41. doi: 10.1016/j.tifs.2021.04.008.
  • Zhao, K., Z. Zha, H. Li, and J. Wu. 2021. Early detection of moldy apple core based on time-frequency images of vibro-acoustic signals. Postharvest Biology and Technology 179:111589. doi: 10.1016/j.postharvbio.2021.111589.
  • Zhou, L., C. Zhang, F. Liu, Z. Qiu, and Y. He. 2019. Application of deep learning in food: A review. Comprehensive Reviews in Food Science and Food Safety 18 (6):1793–811. doi: 10.1111/1541-4337.12492.
  • Zhou, X., S. Jun, T. Yan, L. Bing, Y. Y. Hang, and C. Quansheng. 2020. Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce. Food Chemistry 321:126503.
  • Zhou, X., J. Sun, X. H. Wu, B. Lu, N. Yang, and C. X. Dai. 2019. Research on moldy tea feature classification based on WKNN algorithm and NIR hyperspectral imaging. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy 206:378–83.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.