Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features

Barbin, 2013, Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging, Innov. Food Sci. Emerg. Technol., 17, 180, 10.1016/j.ifset.2012.11.001 Bengio, 2007, Greedy layer-wise training of deep networks, 153 Bobelyn, 2010, Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance, Postharvest Biol. Technol., 55, 133, 10.1016/j.postharvbio.2009.09.006 Bochereau, 1992, A method for prediction by combining data analysis and neural networks: application to prediction of apple quality using near infra-red spectra, J. Agric. Eng. Res., 51, 207, 10.1016/0021-8634(92)80038-T Cheng, 2015, Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis, LWT-Food Sci. Technol., 62, 1060, 10.1016/j.lwt.2015.01.021 Chen, 2014, Deep learning-based classification of hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 7, 2094, 10.1109/JSTARS.2014.2329330 ElMasry, 2007, Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry, J. Food Eng., 81, 98, 10.1016/j.jfoodeng.2006.10.016 Fan, 2016, Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple, Biosyst. Eng., 143, 9, 10.1016/j.biosystemseng.2015.12.012 Fan, 2016, Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data, Postharvest Biol. Technol., 121, 51, 10.1016/j.postharvbio.2016.07.007 Fan, 2019, Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method, Postharvest Biol. Technol., 151, 79, 10.1016/j.postharvbio.2019.02.001 Fu, 2016, Food safety evaluation based on near infrared spectroscopy and imaging: a review, Crit. Rev. Food Sci. Nutr., 56, 1913, 10.1080/10408398.2013.807418 Fukushima, 1980, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol. Cybern., 36, 193, 10.1007/BF00344251 Guthrie, 2006, NIR model development and robustness in prediction of melon fruit total soluble solids, Aust. J. Agric. Res., 57, 411, 10.1071/AR05123 Hinton, 2012, A practical guide to training restricted Boltzmann machines, 599 Hu, 2015, Estimating blueberry mechanical properties based on random frog selected hyperspectral data, Postharvest Biol. Technol., 106, 1, 10.1016/j.postharvbio.2015.03.014 Jiang, 2019, Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues, Artif. Intell. Agric., 1, 1 Kim, 2009, Rapid discrimination of commercial strawberry cultivars using Fourier transform infrared spectroscopy data combined by multivariate analysis, Plant Biotechnol. Rep., 3, 87, 10.1007/s11816-008-0078-z Le Roux, 2010, Deep belief networks are compact universal approximators, Neural Comput., 22, 2192, 10.1162/neco.2010.08-09-1081 LeCun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541, 10.1162/neco.1989.1.4.541 Leiva-Valenzuela, 2013, Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging, J. Food Eng., 115, 91, 10.1016/j.jfoodeng.2012.10.001 Li, 2014, Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of ‘Ya’ pear, Food Anal. Methods, 7, 1891, 10.1007/s12161-014-9832-8 Li, 2012, Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification, Anal. Chim. Acta, 740, 20, 10.1016/j.aca.2012.06.031 Li, 2013, A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy, J. Food Eng., 116, 324, 10.1016/j.jfoodeng.2012.11.007 Li, 2018, Determination of soluble solid content in multi-origin ‘Fuji’apples by using FT-NIR spectroscopy and an origin discriminant strategy, Comput. Electron. Agric., 155, 23, 10.1016/j.compag.2018.10.003 Li, 2019, Factors affecting human hand grasp type in tomato fruit-picking: a statistical investigation for ergonomic development of harvesting robot, Comput. Electron. Agric., 157, 90, 10.1016/j.compag.2018.12.047 Luo, 2018, Robustness improvement of NIR-based determination of soluble solids in apple fruit by local calibration, Postharvest Biol. Technol., 139, 82, 10.1016/j.postharvbio.2018.01.019 Magwaza, 2012, NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review, Food Bioproc. Technol., 5, 425, 10.1007/s11947-011-0697-1 Mehmood, 2012, A review of variable selection methods in partial least squares regression, Chemom. Intell. Lab. Syst., 118, 62, 10.1016/j.chemolab.2012.07.010 Nicolai, 2007, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review, Postharvest Biol. Technol., 46, 99, 10.1016/j.postharvbio.2007.06.024 Paz, 2009, Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy, Comput. Electron. Agric., 69, 24, 10.1016/j.compag.2009.06.008 Peirs, 2003, Effect of biological variability on the robustness of NIR models for soluble solids content of apples, Postharvest Biol. Technol., 28, 269, 10.1016/S0925-5214(02)00196-5 Sánchez, 2012, Non-destructive characterization and quality control of intact strawberries based on NIR spectral data, J. Food Eng., 110, 102, 10.1016/j.jfoodeng.2011.12.003 Sutskever, 2008, Deep, narrow sigmoid belief networks are universal approximators, Neural Comput., 20, 2629, 10.1162/neco.2008.12-07-661 Tian, 2018, Non-destructive prediction of soluble solids content of pear based on fruit surface feature classification and multivariate regression analysis, Infrared Phys. Technol., 92, 336, 10.1016/j.infrared.2018.06.019 Travers, 2014, Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy, LWT-Food Sci. Technol., 59, 1107, 10.1016/j.lwt.2014.04.048 Wang, 2016, Hyperspectral image classification based on PCA network, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1 Workman, 1996, Interpretive spectroscopy for near infrared, Appl. Spectrosc. Rev., 31, 251, 10.1080/05704929608000571 Yu, 2014, Shedding new light on lipid functions with CARS and SRS microscopy, Biochimica et Biophysica Acta (BBA)-Mol. Cell Biol. Lipids, 1841, 1120, 10.1016/j.bbalip.2014.02.003 Yun, 2013, An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration, Spectrochim. Acta A. Mol. Biomol. Spectrosc., 111, 31, 10.1016/j.saa.2013.03.083 Zhang, 2017, Influence of physical and biological variability and solution methods in fruit and vegetable quality non-destructive inspection by using imaging and near-infrared spectroscopy techniques: a review, Crit. Rev. Food Sci. Nutr. Zhang, 2018, Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: a technical review, Trends Food Sci. Technol., 10.1016/j.tifs.2018.09.018