Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Ali, 2020, Principles and recent advances in electronic nose for quality inspection of agricultural and food products, Trends Food Sci. Technol. Aljaafreh, 2017, Agitation and mixing processes automation using current sensing and reinforcement learning, J. Food Eng., 203, 53, 10.1016/j.jfoodeng.2017.02.001 Bechar, 2016, Agricultural robots for field operations: concepts and components, Biosyst. Eng., 149, 94, 10.1016/j.biosystemseng.2016.06.014 Baranowski, 2013, Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data, Postharvest Biol. Technol., 86, 249, 10.1016/j.postharvbio.2013.07.005 Bonah, 2020, Vis-NIR hyperspectral imaging for the classification of bacterial foodborne pathogens based on pixel-wise analysis and a novel CARS-PSO-SVM model, Infrared Phys. Technol., 10.1016/j.infrared.2020.103220 Borges, 2015, Monitoring the authenticity of organic rice via chemometric analysis of elemental data, Food Res. Int., 77, 299, 10.1016/j.foodres.2015.06.008 Boiret, 2014, Application of independent component analysis on Raman images of a pharmaceutical drug product: pure spectra determination and spatial distribution of constituents, J. Pharmaceut. Biomed. Anal., 90, 78, 10.1016/j.jpba.2013.11.025 Calvini, 2015, Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging, Chemometr. Intell. Lab. Syst., 146, 503, 10.1016/j.chemolab.2015.07.010 Cao, 2014, Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique, Pest Manag. Sci., 71, 1113, 10.1002/ps.3893 Che, 2018, Pixel based bruise region extraction of apple using Vis-NIR hyperspectral imaging, Comput. Electron. Agric., 146, 12, 10.1016/j.compag.2018.01.013 Chen, 2018, Lifelong machine learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, 12, 1, 10.2200/S00832ED1V01Y201802AIM037 Choudhary, 2008, Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples, Biosyst. Eng., 102, 115, 10.1016/j.biosystemseng.2008.09.028 Chu, 2020, Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging, Infrared Phys. Technol., 10.1016/j.infrared.2020.103242 Cho, 2017, Feature selection and parameters optimization of svm using particle swarm optimization for fault classification in power distribution systems, Comput. Intell. Neurosci., 2017, 10.1155/2017/4135465 Chuang, 2014, Integration of independent component analysis with near infrared spectroscopy for evaluation of rice freshness, J. Cereal. Sci., 60, 238, 10.1016/j.jcs.2014.03.005 Cen, 2016, Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification, Postharvest Biol. Technol., 111, 352, 10.1016/j.postharvbio.2015.09.027 Chen, 2017, A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, Catena, 151, 147, 10.1016/j.catena.2016.11.032 Cui, 2018, Plant pest detection using an artificial nose system: a review, Sensors, 18, 1, 10.3390/s18020378 Dacal-Nieto, 2011, Common scab detection on potatoes using an infrared hyperspectral imaging system, 303 Duda, 1973, vol. 3, 731 Delwiche, 2019, Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging, J. Cereal. Sci., 87, 18, 10.1016/j.jcs.2019.02.008 Dong, 2017, Discrimination of hayward kiwifruits treated with forchlorfenuron at different concentrations using hyperspectral imaging technology, Food Analytical Methods, 10, 477, 10.1007/s12161-016-0603-6 Elmasry, 2009, Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks, Postharvest Biol. Technol., 52, 1, 10.1016/j.postharvbio.2008.11.008 Erkinbaev, 2019, Single kernel wheat hardness estimation using near infrared hyperspectral imaging, Infrared Phys. Technol., 98, 250, 10.1016/j.infrared.2019.03.033 ElMasry, 2012, Meat quality evaluation by hyperspectral imaging technique, an overview, Crit. Rev. Food Sci. Nutr., 52, 689, 10.1080/10408398.2010.507908 Garbin, 2020, Dropout vs. batch normalization: an empirical study of their impact to deep learning, Multimed. Tool. Appl., 79, 12777, 10.1007/s11042-019-08453-9 Gao, 2020, Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging, Spectrochim. Acta Mol. Biomol. Spectrosc., 10.1016/j.saa.2020.118269 Gaston, 2011, Hyperspectral imaging for the detection of microbial spoilage of mushrooms Gewali, 2018 Golhani, 2018, A review of neural networks in plant disease detection using hyperspectral data, vol. 5, 354 Gómez-Sanchis, 2013, Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers, Postharvest Biol. Technol., 82, 76, 10.1016/j.postharvbio.2013.02.011 Guo, 2018, K-Nearest Neighbor combined with guided filter for hyperspectral image classification, Procedia Computer Science, 129, 159, 10.1016/j.procs.2018.03.066 Garreta, 2013 Han, 2019, Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging, Comput. Electron. Agric., 10.1016/j.compag.2019.104888 Hussain, 2019, Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: a review of recent applications, Trends Food Sci. Technol., 91, 598, 10.1016/j.tifs.2019.07.018 He, 2020, Detection of mites Tyrophagus putrescentiae and Cheyletus eruditus in flour using hyperspectral imaging system coupled with chemometrics, J. Food Process. Eng., 10.1111/jfpe.13386 Ivorra, 2016, Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images, J. Food Eng., 178, 110, 10.1016/j.jfoodeng.2016.01.008 Ji, 2019, Detection of bruised potatoes using hyperspectral imaging technique based on discrete wavelet transform, Infrared Phys. Technol., 10.1016/j.infrared.2019.103054 Jia, 2020, Essential processing methods of hyperspectral images of agricultural and food products, Chemometr. Intell. Lab. Syst., 10.1016/j.chemolab.2020.103936 Jamshidi, 2003, Tools for intelligent control: Fuzzy controllers, neural networks and genetic algorithms, Phil. Trans. Math. Phys. Eng. Sci., 361, 1781, 10.1098/rsta.2003.1225 Konda Naganathan, 2015, Three dimensional chemometric analyses of hyperspectral images for beef tenderness forecasting, J. Food Eng., 169, 309, 10.1016/j.jfoodeng.2015.09.001 Khaled, 2018, Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy, Comput. Electron. Agric., 144, 297, 10.1016/j.compag.2017.11.012 Kong, 2013, Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis, Sensors, 13, 8916, 10.3390/s130708916 Liu, 2012, Detecting fertility and early embryo development of chicken eggs using near-infrared hyperspectral imaging, Food Bioprocess Technol., 6, 2503, 10.1007/s11947-012-0933-3 Liu, 2010, Categorization of pork quality using Gabor filter-based hyperspectral imaging technology, J. Food Eng., 99, 284, 10.1016/j.jfoodeng.2010.03.001 Liu, 2019, Convolutional neural network for hyperspectral data analysis and effective wavelengths selection, Anal. Chim. Acta, 1086, 46, 10.1016/j.aca.2019.08.026 Liu, 2017, Hyperspectral imaging technique for evaluating food quality and safety during various processes: a review of recent applications, Trends Food Sci. Technol., 69, 25, 10.1016/j.tifs.2017.08.013 Liu, 2018, Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects, Transactions of the ASABE, 61, 425, 10.13031/trans.12214 Lu, 2020, Evaluation and classification of five cereal fungi on culture medium using Visible/Near-Infrared (Vis/NIR) hyperspectral imaging, Infrared Phys. Technol. Luo, 2019, Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features, Sci. Rep., 9, 1, 10.1038/s41598-019-51941-z Mahesh, 2015, Hyperspectral imaging to classify and monitor quality of agricultural materials, J. Stored Prod. Res., 61, 17, 10.1016/j.jspr.2015.01.006 Mahesh, 2008, Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes, Biosyst. Eng., 101, 50, 10.1016/j.biosystemseng.2008.05.017 Minaei, 2017, VIS/NIR imaging application for honey floral origin determination, Infrared Phys. Technol., 86, 218, 10.1016/j.infrared.2017.09.001 Mishra, 2019, Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method, Sensor. Actuator. B Chem., 281, 1034, 10.1016/j.snb.2018.11.034 Nturambirwe, 2020, Machine learning applications to non-destructive defect detection in horticultural products, Biosyst. Eng., 189, 60, 10.1016/j.biosystemseng.2019.11.011 Pan, 2019, Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging, Postharvest Biol. Technol., 154, 96, 10.1016/j.postharvbio.2019.04.005 Pan, 2015, Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network, Food Chem., 192, 134, 10.1016/j.foodchem.2015.06.106 Qin, 2020, Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques, Food Contr., 10.1016/j.foodcont.2020.107234 Qin, 2013, Hyperspectral and multispectral imaging for evaluating food safety and quality: a review, J. Food Eng., 118, 157, 10.1016/j.jfoodeng.2013.04.001 Qiu, 2018, Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network, Appl. Sci., 8, 212, 10.3390/app8020212 Rady, 2017, Hyperspectral imaging for detection of codling moth infestation in GoldRush apples, Postharvest Biol. Technol., 129, 37, 10.1016/j.postharvbio.2017.03.007 Rady, 2015, Evaluation of sugar content of potatoes using hyperspectral imaging, Food Bioprocess Technol., 8, 995, 10.1007/s11947-014-1461-0 Ravikanth, 2015, Classification of contaminants from wheat using near-infrared hyperspectral imaging, Biosyst. Eng., 135, 73, 10.1016/j.biosystemseng.2015.04.007 Ren, 2020, Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality, Spectrochim. Acta Mol. Biomol. Spectrosc., 10.1016/j.saa.2020.118407 Rojas-Moraleda, 2017, A frame-based ANN for classification of hyperspectral images: assessment of mechanical damage in mushrooms, Neural Comput. Appl., 28, 969, 10.1007/s00521-016-2376-7 Ropelewska, 2018, Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis, Eur. Food Res. Technol., 244, 1453, 10.1007/s00217-018-3059-7 Rehman, 2019, Current and future applications of statistical machine learning algorithms for agricultural machine vision systems, Comput. Electron. Agric., 156, 585, 10.1016/j.compag.2018.12.006 Raschka, 2017 Sanz, 2016, Lamb muscle discrimination using hyperspectral imaging: comparison of various machine learning algorithms, J. Food Eng., 174, 92, 10.1016/j.jfoodeng.2015.11.024 Swamynathan, 2017 Sutton, 1998, vol. 135 Shao, 2018, Identification of adulterated cooked millet flour with Hyperspectral Imaging Analysis, IFAC-PapersOnLine, 51, 96, 10.1016/j.ifacol.2018.08.068 Sharma, 2019, Application of computer vision technique for physical quality monitoring of turmeric slices during direct solar drying, Journal of Food Measurement and Characterization, 13, 545, 10.1007/s11694-018-9968-0 Shuaibu, 2018, Unsupervised hyperspectral band selection for apple Marssonina blotch detection, Comput. Electron. Agric., 148, 45, 10.1016/j.compag.2017.09.038 Siedliska, 2014, Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data, Comput. Electron. Agric., 106, 66, 10.1016/j.compag.2014.05.012 Siedliska, 2017, Detection of pits in fresh and frozen cherries using a hyperspectral system in transmittance mode, J. Food Eng., 215, 61, 10.1016/j.jfoodeng.2017.07.028 Siedliska, 2018, Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging, Postharvest Biol. Technol., 139, 115, 10.1016/j.postharvbio.2018.01.018 Singh, 2007, Fungal detection in wheat using near-infrared hyperspectral imaging, Transactions of the ASABE, 50, 2171, 10.13031/2013.24077 Siripatrawan, 2011, Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging, Talanta, 85, 276, 10.1016/j.talanta.2011.03.061 Sone, 2012, Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging, J. Food Eng., 109, 482, 10.1016/j.jfoodeng.2011.11.001 Sun, 2016, Classification of black beans using visible and near infrared hyperspectral imaging, Int. J. Food Prop., 19, 1687, 10.1080/10942912.2015.1055760 Tharwat, 2018, Independent component analysis: an introduction, Applied Computing and Informatics Tan, 2018, Study on bruising degree classification of apples using hyperspectral imaging and GS-SVM, Optik, 154, 581, 10.1016/j.ijleo.2017.10.090 Velásquez, 2017, An application based on the decision tree to classify the marbling of beef by hyperspectral imaging, Meat Sci., 133, 43, 10.1016/j.meatsci.2017.06.002 Vu, 2016, November). Spatial and spectral features utilization on a Hyperspectral imaging system for rice seed varietal purity inspection, 169 Wakholi, 2018, Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics, Sensor. Actuator. B, 255, 498, 10.1016/j.snb.2017.08.036 Wang, 2018, Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data, Sensors, 18, 1126, 10.3390/s18041126 Washburn, 2017, Non-invasive assessment of packaged cod freeze-thaw history by hyperspectral imaging, J. Food Eng., 205, 64, 10.1016/j.jfoodeng.2017.02.025 Weng, 2020, Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion, Spectrochim. Acta Mol. Biomol. Spectrosc. Xia, 2019, Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis, Infrared Phys. Technol., 10.1016/j.infrared.2019.103077 Xin, 2019, Research on moldy tea feature classification based on WKNN algorithm and NIR hyperspectral imaging, Spectrochim. Acta Mol. Biomol. Spectrosc., 206, 378, 10.1016/j.saa.2018.07.049 Xu, 2016, Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm, Int. J. Refrig., 74, 151, 10.1016/j.ijrefrig.2016.10.014 Yasin, 2014, Optimal least squares support vector machines parameter selection in predicting the output of distributed generation, 152 Yu, 2018, Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging, Postharvest Biol. Technol., 141, 39, 10.1016/j.postharvbio.2018.02.013 Yu, 2019, A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei), Biosyst. Eng., 178, 244, 10.1016/j.biosystemseng.2018.11.018 Zhan-qi, 2018, Identification of different concentrations pesticide residues of dimethoate on spinach leaves by hyperspectral image technology, IFAC-PapersOnLine, 51, 758, 10.1016/j.ifacol.2018.08.104 Zhang, 2019, Identification of wheat grain in different states based on hyperspectral imaging technology, Spectrosc. Lett., 52, 356, 10.1080/00387010.2019.1639762 Zhao, 2020, Shape induced reflectance correction for non-destructive determination and visualization of soluble solids content in winter jujubes using hyperspectral imaging in two different spectral ranges, Postharvest Biol. Technol., 10.1016/j.postharvbio.2019.111080 Zhongzhi, 2020, Aflatoxin contaminated degree detection by hyperspectral data using band index, Food Chem. Toxicol., 10.1016/j.fct.2020.111159 Zhu, 2015, Visible near-infrared (VNIR) reflectance hyperspectral imagery for identifying aflatoxin-contaminated corn kernels, 1 Zhu, 2017, Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers, Sci. Rep., 7, 1 Zhang, 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 Chem. Zhang, 2020, Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging, Biosyst. Eng., 10.1016/j.biosystemseng.2020.01.018 Zhou, 2019, Application of deep learning in food: a review, Compr. Rev. Food Sci. Food Saf., 18, 1793, 10.1111/1541-4337.12492