nondestructive detection of kiwifruit textural characteristic based on near infrared hyperspectral imaging technology

References

• Huang, Z.; Jian, L.; Zhang, J.; Gao, Y.; Hui, G. Physicochemical Properties Enhancement of Chinese Kiwi Fruit (Actinidia Chinensis Planch) via Chitosan Coating Enriched with Salicylic Acid Treatment. J. Food Meas. Charact 2017, 11(1), 184–191. DOI: 10.1007/s11694-016-9385-1.
   Web of Science ®Google Scholar
• Liu, D.; Guo, W. Identifying CPPU-Treated Kiwifruits Using Near-Infrared Hyperspectral Imaging Technology. Food Anal. Methods. 2017, 10(5), 1273–1283. DOI: 10.1007/s12161-016-0681-5.
   Web of Science ®Google Scholar
• Nødtvedt, Ø. O.; Hansen, A. L.; Bjorvatn, B.; Pallesen, S. The Effects of Kiwi Fruit Consumption in Students with Chronic Insomnia Symptoms: A Randomized Controlled Trial. Sleep Biol. Rhythms. 2017, 15(2), 159–166. DOI: 10.1007/s41105-017-0095-9.
   Web of Science ®Google Scholar
• Levine, H., and Finley, J.W. Texture. In Principles of Food Chemistry Dennis, R., Jonhn, Coupland, Mario, Ferryzzi, Richard, W. Hartel, Rubén, Morawicki, S.Suzanne, Nielsen, Juan, L. Silva; Springer International Publishing: Cham, 2018; pp 329–363.
   Google Scholar
• Liu, Y.-X.; Cao, M.-J., and Liu, G.-M. 17 - Texture Analyzers for Food Quality Evaluation. In Evaluation Technologies for Food Quality Xichang, Zhong, Xichang, Wang; Woodhead Publishing, 2019; pp 441–463.
   Google Scholar
• Paula, A. M.; Conti-Silva, A. C. Texture Profile and Correlation between Sensory and Instrumental Analyses on Extruded Snacks. J. Food Eng 2014, 121(1), 9–14. DOI: 10.1016/j.jfoodeng.2013.08.007.
   Google Scholar
• Meenu, M.; Xu, B. Application of Vibrational Spectroscopy for Classification, Authentication and Quality Analysis of Mushroom: A Concise Review. Food Chem 2019, 289, 545–557. DOI: 10.1016/j.foodchem.2019.03.091.
   PubMed Web of Science ®Google Scholar
• Vejarano, R.; Siche, R.; Tesfaye, W. Evaluation of Biological Contaminants in Foods by Hyperspectral Imaging: A Review. Int. J. Food Prop 2017, 20(sup2), 1264–1297. DOI: 10.1080/10942912.2017.1338729.
   Google Scholar
• Feng, C.-H.; Makino, Y.; Oshita, S.; García Martín, J. F. Hyperspectral Imaging and Multispectral Imaging as the Novel Techniques for Detecting Defects in Raw and Processed Meat Products: Current state-of-the-art Research Advances. Food Control. 2018, 84, 165–176. DOI: 10.1016/j.foodcont.2017.07.013.
   Web of Science ®Google Scholar
• Lan, W.; Jaillais, B.; Renard, C. M. G. C.; Leca, A.; Chen, S.; Le Bourvellec, C.; Bureau, S. A Method Using near Infrared Hyperspectral Imaging to Highlight the Internal Quality of Apple Fruit Slices. Postharvest. Biol. Technol 2021, 175, 111497. DOI: 10.1016/j.postharvbio.2021.111497.
   Web of Science ®Google Scholar
• ElMasry, M. G.; Nakauchi, S. Image Analysis Operations Applied to Hyperspectral Images for non-invasive Sensing of Food Quality – A Comprehensive Review. Biosyst. Eng 2016, 142, 53–82. DOI: 10.1016/j.biosystemseng.2015.11.009.
   Web of Science ®Google Scholar
• Weng, S.; Yu, S.; Dong, R.; Pan, F.; Liang, D. Nondestructive Detection of Storage Time of Strawberries Using visible/near-infrared Hyperspectral Imaging. Int. J. Food Prop 2020, 23(1), 269–281. DOI: 10.1080/10942912.2020.1716793.
   Web of Science ®Google Scholar
• Li, Y.; Ma, B.; Li, C.; Yu, G. Accurate Prediction of Soluble Solid Content in Dried Hami Jujube Using SWIR Hyperspectral Imaging with Comparative Analysis of Models. Comput. Electron. Agric 2022, 193, 106655. DOI: 10.1016/j.compag.2021.106655.
   Web of Science ®Google Scholar
• Phuangsombut, K.; Ma, T.; Inagaki, T.; Tsuchikawa, S.; Terdwongworakul, A. Near-infrared Hyperspectral Imaging for Classification of Mung Bean Seeds. Int. J. Food Prop 2018, 21(1), 799–807. DOI: 10.1080/10942912.2018.1476378.
   Web of Science ®Google Scholar
• Steinbrener, J.; Posch, K.; Leitner, R. Hyperspectral Fruit and Vegetable Classification Using Convolutional Neural Networks. Comput. Electron. Agric 2019, 162, 364–372. DOI: 10.1016/j.compag.2019.04.019.
   Web of Science ®Google Scholar
• Li, J.; Zhang, Y.; Liu, M.; Chen, J.; Xue, L. Rapid Detection and Visualization of Mechanical Bruises on “Nanfeng” Mandarin Using the Hyperspectral Imaging Combined with ICA_LSQ Method. Food Anal. Methods. 2019, 12(9), 2025–2034. Available. DOI: 10.1007/s12161-019-01546-z.
   Web of Science ®Google Scholar
• Fazari, A.; Pellicer-Valero, O. J.; Gómez-Sanchıs, J.; Bernardi, B.; Cubero, S.; Benalia, S.; Zimbalatti, G.; Blasco, J. Application of Deep Convolutional Neural Networks for the Detection of Anthracnose in Olives Using VIS/NIR Hyperspectral Images. Comput. Electron. Agric 2021, 187, 106252. DOI: 10.1016/j.compag.2021.106252.
   Web of Science ®Google Scholar
• Garhwal, A. S.; Pullanagari, R. R.; Li, M.; Reis, M. M.; Archer, R. Hyperspectral Imaging for Identification of Zebra Chip Disease in Potatoes. Biosyst. Eng 2020, 197, 306–317. DOI: 10.1016/j.biosystemseng.2020.07.005.
   Web of Science ®Google Scholar
• Pieczywek, P. M.; Cybulska, J.; Szymańska-Chargot, M.; Siedliska, A.; Zdunek, A.; Nosalewicz, A.; Baranowski, P.; Kurenda, A. Early Detection of Fungal Infection of Stored Apple Fruit with Optical Sensors – Comparison of Biospeckle, Hyperspectral Imaging and Chlorophyll Fluorescence. Food Control. 2018, 85, 327–338. DOI: 10.1016/j.foodcont.2017.10.013.
   Web of Science ®Google Scholar
• Siedliska, A.; Baranowski, P.; Zubik, M.; Mazurek, W.; Sosnowska, B. Detection of Fungal Infections in Strawberry Fruit by VNIR/SWIR Hyperspectral Imaging. Postharvest. Biol. Technol 2018, 139, 115–126. Available DOI: 10.1016/j.postharvbio.2018.01.018.
   Web of Science ®Google Scholar
• Yang, D.; Jiang, J.; Jie, Y.; Li, Q.; Shi, T. Detection of the Moldy Status of the Stored Maize Kernels Using Hyperspectral Imaging and Deep Learning Algorithms. Int. J. Food Prop 2022, 25(1), 170–186. DOI: 10.1080/10942912.2022.2027963.
   Web of Science ®Google Scholar
• Li, X.; Wei, Y.; Jie, X.; Feng, X.; Wu, F.; Zhou, R.; Jin, J.; Xu, K.; Yu, X.; Yong, H. SSC and pH for Sweet Assessment and Maturity Classification of Harvested Cherry Fruit Based on NIR Hyperspectral Imaging Technology. Postharvest. Biol. Technol 2018, 143, 112–118. Available DOI: 10.1016/j.postharvbio.2018.05.003.
   Web of Science ®Google Scholar
• Zhu, N.; Lin, M.; Nie, Y.; Wu, D.; Chen, K. Study on the Quantitative Measurement of Firmness Distribution Maps at the Pixel Level inside Peach Pulp. Comput. Electron. Agric 2016, 130, 48–56. DOI: 10.1016/j.compag.2016.09.018.
   Web of Science ®Google Scholar
• Hu, M.-H.; Dong, Q.-L.; Liu, B.-L.; Opara, U. L. Prediction of Mechanical Properties of Blueberry Using Hyperspectral Interactance Imaging. Postharvest. Biol. Technol 2016, 115, 122–131. DOI: 10.1016/j.postharvbio.2015.11.021.
   Web of Science ®Google Scholar
• Sun, M.; Zhang, D.; Liu, L.; Wang, Z. How to Predict the Sugariness and Hardness of Melons: A near-infrared Hyperspectral Imaging Method. Food Chem 2017, 218, 413–421. DOI: 10.1016/j.foodchem.2016.09.023.
   PubMed Web of Science ®Google Scholar
• Zhang, H.; Gu, B.; Mu, J.; Ruan, P.; Li, D. Wheat Hardness Prediction Research Based on NIR Hyperspectral Analysis Combined with Ant Colony Optimization Algorithm. Procedia Engineering. 2017, 174, 648–656. DOI: 10.1016/j.proeng.2017.01.202.
   Google Scholar
• Pu, H.; Sun, D.-W.; Ma, J.; Cheng, J.-H. Classification of Fresh and frozen-thawed Pork Muscles Using Visible and near Infrared Hyperspectral Imaging and Textural Analysis. Meat Sci 2015, 99, 81–88. DOI: 10.1016/j.meatsci.2014.09.001.
   PubMed Web of Science ®Google Scholar
• Cheng, J.-H.; Qu, J.-H.; Sun, D.-W.; Zeng, X.-A. Visible/near-infrared Hyperspectral Imaging Prediction of Textural Firmness of Grass Carp (Ctenopharyngodon Idella) as Affected by Frozen Storage. Food Res. Int 2014, 56, 190–198. DOI: 10.1016/j.foodres.2013.12.009.
   Web of Science ®Google Scholar
• Wu, D.; Sun, D.-W.; He, Y. Novel non-invasive Distribution Measurement of Texture Profile Analysis (TPA) in Salmon Fillet by Using Visible and near Infrared Hyperspectral Imaging. Food Chem 2014, 145, 417–426. DOI: 10.1016/j.foodchem.2013.08.063.
   PubMed Web of Science ®Google Scholar
• Shiu, J. W.; Slaughter, D. C.; Boyden, L. E.; Barrett, D. M. Effect of the shear-to-compressive Force Ratio in Puncture Tests Quantifying Watermelon Mechanical Properties. J. Food Eng 2015, 150, 125–131. DOI: 10.1016/j.jfoodeng.2014.11.007.
   Web of Science ®Google Scholar
• Zhang, W.; Cui, D.; Ying, Y. Nondestructive Measurement of Pear Texture by Acoustic Vibration Method. Postharvest. Biol. Technol 2014, 96, 99–105. DOI: 10.1016/j.postharvbio.2014.05.006.
   Web of Science ®Google Scholar
• Zhang, L.; Li, G.; Sun, M.; Li, H.; Wang, Z.; Li, Y.; Lin, L. Kennard-Stone Combined with Least Square Support Vector Machine Method for Noncontact Discriminating Human Blood Species. Infrared. Phys. Technol. 2017, 86, 116–119. DOI: 10.1016/j.infrared.2017.08.020.
   Web of Science ®Google Scholar
• Zhao, N.; Wu, Z.; Cheng, Y.; Shi, X.; Qiao, Y. MDL and RMSEP Assessment of Spectral Pretreatments by Adding Different Noises in calibration/validation Datasets. Spectrochim. Acta, Part A: Mol. Biomol. Spectrosc 2016, 163, 20–27. DOI: 10.1016/j.saa.2016.03.017.
   PubMed Web of Science ®Google Scholar
• Du, C.; Dai, S.; Qiao, Y.; Wu, Z. Error Propagation of Partial Least Squares for Parameters Optimization in NIR Modeling. Spectrochim. Acta, Part A: Mol. Biomol. Spectrosc 2018, 192, 244–250. DOI: 10.1016/j.saa.2017.10.069.
   PubMed Web of Science ®Google Scholar
• Meacham-Hensold, K.; Montes, C. M.; Wu, J.; Guan, K.; Fu, P.; Ainsworth, E. A.; Pederson, T.; Moore, C. E.; Brown, K. L.; Raines, C.; et al. High-throughput Field Phenotyping Using Hyperspectral Reflectance and Partial Least Squares Regression (PLSR) Reveals Genetic Modifications to Photosynthetic Capacity. Remote Sens. Environ. 2019, 231, 111176. DOI: 10.1016/j.rse.2019.04.029.
   PubMed Web of Science ®Google Scholar
• Zhang, G.; Peng, S.; Cao, S.; Zhao, J.; Xie, Q.; Han, Q.; Wu, Y.; Huang, Q. A Fast Progressive Spectrum Denoising Combined with Partial Least Squares Algorithm and Its Application in Online Fourier Transform Infrared Quantitative Analysis. Anal. Chim. Acta. 2019, 1074, 62–68. DOI: 10.1016/j.aca.2019.04.055.
   PubMed Web of Science ®Google Scholar
• Zhao, Z.; Yan, W.; Yin, H.; Batool, A.; Wang, H.; Yu, H. Quantitative Detection of Turbid Media Components Using Textural Features Extracted from Hyperspectral Images. Microchem. J 2019, 149, 104009. DOI: 10.1016/j.microc.2019.104009.
   Web of Science ®Google Scholar
• Zou, A., and Qu, Y. Identification of Singular Samples in near Infrared Spectrum Correction Set by Using Monte Carlo Cross Validation. In. 2016 4th International Conference on Machinery, Materials and Information Technology Applications Xi'an, China; 2016.
   Google Scholar
• Shimomura, K.; Horie, H.; Sugiyama, M.; Kawazu, Y.; Yoshioka, Y. Quantitative Evaluation of Cucumber Fruit Texture and Shape Traits Reveals Extensive Diversity and Differentiation. Sci. Hortic 2016, 199, 133–141. DOI: 10.1016/j.scienta.2015.12.033.
   Web of Science ®Google Scholar
• Dahdouh, L.; Delalonde, M.; Ricci, J.; Ruiz, E.; Wisnewski, C. Influence of High Shear Rate on Particles Size, Rheological Behavior and Fouling Propensity of Fruit Juices during Crossflow Microfiltration: Case of Orange Juice. Innovative Food Sci. Emerging Technol 2018, 48, 304–312. DOI: 10.1016/j.ifset.2018.07.006.
   Web of Science ®Google Scholar
• Burdon, J.; Punter, M.; Billing, D.; Pidakala, P.; Kerr, K. Shrivel Development in Kiwifruit. Postharvest. Biol. Technol 2014, 87, 1–5. DOI: 10.1016/j.postharvbio.2013.07.031.
   Web of Science ®Google Scholar
• Valerga, L.; Darré, M.; Zaro, M. J.; Vicente, A. R.; Lemoine, M. L.; Concellón, A. The Plant Age Influences Eggplant Fruit Growth, Metabolic Activity, Texture and shelf-life. Sci. Hortic 2020, 272, 109590. Available. DOI: 10.1016/j.scienta.2020.109590.
   Web of Science ®Google Scholar