Combination of Spectral and Spatial Information of Hyperspectral Imaging for the Prediction of the Moisture Content and Visualizing Distribution in Daqu

Literature cited

• Tao, X.; Sun, X.; Ming, H.; Zeng, H.; Chen, C. Analysis of the Relationship between Daqu’s Physical and Chemical Parameters and Its Quality Using Rough Sets. International Conference on Advances in Energy Engineering, Beijing, China, June 19–20, 2010.
   Google Scholar
• Zhang, C. L.; Ao, Z. H.; Chui, W. Q.; Shen, C. H.; Tao, W. Y.; Zhang, S. Y. Characterization of Volatile Compounds from Daqu - A Traditional Chinese Liquor Fermentation Starter. Int. J. Food Sci. Technol. 2011, 46, 1591–1599. DOI: 10.1111/j.1365-2621.2011.02660.x.
   Web of Science ®Google Scholar
• Yan, S.; Tong, Q.; Guang, J. Yeast Dynamics and Changes in Volatile Compounds during the Fermentation of the Traditional Chinese Strong-Flavor Daqu. LWT-Food Sci. Technol. 2019, 106, 57–63. DOI: 10.1016/j.lwt.2019.02.058.
   Web of Science ®Google Scholar
• Yang, J.-G.; Dou, X.; Han, P.-J.; Bai, F.-Y.; Zhou, J.; Zhang, S.-Y.; Qin, H.; Ma, Y.-Y. Microbial Diversity in Daqu during Production of Luzhou-Flavored Liquor. J. Am. Soc. Brew. Chem. 2017, 75, 136–144. DOI: 10.1094/ASBCJ-2017-2879-01.
   Web of Science ®Google Scholar
• Wei, Y.; Wu, F.; Xu, J.; Sha, J.; Zhao, Z.; He, Y.; Li, X. Visual Detection of the Moisture Content of Tea Leaves with Hyperspectral Imaging Technology. J. Food Eng. 2019, 248, 89–96. DOI: 10.1016/j.jfoodeng.2019.01.004.
   Web of Science ®Google Scholar
• Costa, C.; Antonucci, F.; Pallottino, F.; Aguzzi, J.; Sun, D.-W.; Menesatti, P. Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision. Food Bioprocess Technol. 2011, 4, 673–692. DOI: 10.1007/s11947-011-0556-0.
   Web of Science ®Google Scholar
• Wilde, A. S.; Haughey, S. A.; Galvin-King, P.; Elliott, C. T. The Feasibility of Applying NIR and FT-IR Fingerprinting to Detect Adulteration in Black Pepper. Food Control 2019, 100, 1–7. DOI: 10.1016/j.foodcont.2018.12.039.
   Web of Science ®Google Scholar
• Zhang, Y.; Gao, J.; Cen, H.; Lu, Y.; Yu, X.; He, Y.; Pieters, J. G. Automated Spectral Feature Extraction from Hyperspectral Images to Differentiate Weedy Rice and Barnyard Grass from a Rice Crop. Comput. Electron. Agric. 2019, 159, 42–49. DOI: 10.1016/j.compag.2019.02.018.
   Web of Science ®Google Scholar
• Sun, J.; Wang, G.; Zhang, H.; Xia, L.; Zhao, W.; Guo, Y.; Sun, X. Detection of Fat Content in Peanut Kernels Based on Chemometrics and Hyperspectral Imaging Technology. Infrared Phys. Technol. 2020, 105, 103226. DOI: 10.1016/j.infrared.2020.103226.
   Web of Science ®Google Scholar
• Chen, J. Y.; Li, G. H. Prediction of Moisture Content of Wood Using Modified Random Frog and Vis-NIR Hyperspectral Imaging. Infrared Phys. Technol. 2020, 105, 103225. DOI: 10.1016/j.infrared.2020.103225.
   Web of Science ®Google Scholar
• Yuan, R.; Liu, G.; He, J.; Ma, C.; Cheng, L.; Fan, N.; Ban, J.; Li, Y.; Sun, Y. Determination of Metmyoglobin in Cooked Tan Mutton Using Vis/NIR Hyperspectral Imaging System. J. Food Sci. 2020, 85, 1403–1410. DOI: 10.1111/1750-3841.15137.
   Web of Science ®Google Scholar
• Mishra, P.; Nordon, A.; Asaari, M. S. M.; Lian, G.; Redfern, S. Fusing Spectral and Textural Information in Near-Infrared Hyperspectral Imaging to Improve Green Tea Classification Modelling. J. Food Eng. 2019, 249, 40–47. DOI: 10.1016/j.jfoodeng.2019.01.009.
   Web of Science ®Google Scholar
• Hu, X. J.; Chen, P.; Tian, J. P.; Huang, D. P.; Luo, H. B.; Huang, D. Predicting the Moisture Content of Daqu with Hyperspectral Imaging. Int. J. Food Eng. 2021, 17, 37–47. DOI: 10.1515/ijfe-2019-0243.
   Web of Science ®Google Scholar
• Onwude, D. I.; Hashim, N.; Abdan, K.; Janius, R.; Chen, G. Combination of Computer Vision and Backscattering Imaging for Predicting the Moisture Content and Colour Changes of Sweet Potato (Ipomoea batatas L.) during Drying. Comput. Electron. Agric. 2018, 150, 178–187. DOI: 10.1016/j.compag.2018.04.015.
   Web of Science ®Google Scholar
• Romano, G.; Argyropoulos, D.; Nagle, M.; Khan, M. T.; Müller, J. Combination of Digital Images and Laser Light to Predict Moisture Content and Color of Bell Pepper Simultaneously during Drying. J. Food Eng. 2012, 109, 438–448. DOI: 10.1016/j.jfoodeng.2011.10.037.
   Web of Science ®Google Scholar
• Xiong, Z.; Sun, D.-W.; Pu, H.; Zhu, Z.; Luo, M. Combination of Spectra and Texture Data of Hyperspectral Imaging for Differentiating between Free-Range and Broiler Chicken Meats. LWT-Food Sci. Technol. 2015, 60, 649–655. DOI: 10.1016/j.lwt.2014.10.021.
   Web of Science ®Google Scholar
• Cheng, J.-H.; Sun, D.-W. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. Food Bioprocess Technol. 2015, 8, 951–959. DOI: 10.1007/s11947-014-1457-9.
   Web of Science ®Google Scholar
• Bilgin, S.; Tanrikulu, H. O. The Changes in Chemical Composition of Holothuria tubulosa (Gmelin, 1788) with Ambient-Drying and Oven-Drying Methods. Food Sci. Nutr. 2018, 6, 1456–2461. DOI: 10.1002/fsn3.703.
   PubMed Web of Science ®Google Scholar
• Sinija, V. R.; Mishra, H. N. FTNIR Spectroscopic Method for Determination of Moisture Content in Green Tea Granules. Food Bioprocess Technol. 2011, 4, 136–141. DOI: 10.1007/s11947-008-0149-8.
   Web of Science ®Google Scholar
• Pan, L.; Sun, Y.; Xiao, H.; Gu, X.; Hu, P.; Wei, Y.; Tu, K. Hyperspectral Imaging with Different Illumination Patterns for the Hollowness Classification of White Radish. Postharvest. Biol. Technol. 2017, 126, 40–49. DOI: 10.1016/j.postharvbio.2016.12.006.
   Web of Science ®Google Scholar
• Zhao, W.; Li, S.; Li, A.; Zhang, B.; Li, Y. Hyperspectral Images Classification with Convolutional Neural Network and Textural Feature Using Limited Training Samples. Remote Sens. Lett. 2019, 10, 449–458. DOI: 10.1080/2150704X.2019.1569274.
   Web of Science ®Google Scholar
• Li, Y. T.; Sun, J.; Wu, X. H.; Lu, B.; Wu, M. M.; Dai, C. X. Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms. J. Food Sci. 2019, 84, 2234–4221. DOI: 10.1111/1750-3841.14706.
   Web of Science ®Google Scholar
• Chen, L.; Li, Z.; Yu, F.; Zhang, X.; Xue, Y.; Xue, C. Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas). Food Anal. Methods 2019, 12, 799–810. DOI: 10.1007/s12161-018-1400-1.
   Web of Science ®Google Scholar
• Fan, Y. M.; Ma, S. C.; Wu, T. T. Individual Wheat Kernels Vigor Assessment Based on NIR Spectroscopy Coupled with Machine Learning Methodologies. Infrared Phys. Technol. 2020, 105, 103213. DOI: 10.1016/j.infrared.2020.103213.
   Web of Science ®Google Scholar
• Fu, X.; Ying, Y.; Xu, H.; Yu, H. Support Vector Machines and near Infrared Spectroscopy for Quantification of Vitamin C Content in Kiwifruit. In 2008 ASABE Annual International Meeting Presentation, Providence, Rhode Island; 2008; Vol. 19.
   Google Scholar
• Li, H. Z.; Guo, S.; Zhao, H. R.; Su, C. B.; Wang, B. Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm. Energies 2012, 5, 4430–4445. DOI: 10.3390/en5114430.
   Web of Science ®Google Scholar
• Yu, X.; Wang, J.; Wen, S.; Yang, J.; Zhang, F. A Deep Learning Based Feature Extraction Method on Hyperspectral Images for Nondestructive Prediction of TVB-N Content in Pacific White Shrimp (Litopenaeus vannamei). Biosys. Eng. 2019, 178, 244–255. DOI: 10.1016/j.biosystemseng.2018.11.018.
   Web of Science ®Google Scholar
• Chen, J.; Gu, S.; Shen, M.; Tang, Y.; Matsushita, B. Estimating Aboveground Biomass of Grassland Having a High Canopy Cover: An Exploratory Analysis of in Situ Hyperspectral Data. Int. J. Remote Sens. 2009, 30, 6497–6517. DOI: 10.1080/01431160902882496.
   Web of Science ®Google Scholar
• Wang, Y. Z.; Guo, W. C.; Zhu, X. H.; Liu, Q. Effect of Homogenisation on Detection of Milk Protein Content Based on NIR Diffuse Reflectance Spectroscopy. Int. J. Food Sci. Technol. 2019, 54, 387–395. DOI: 10.1111/ijfs.13948.
   Web of Science ®Google Scholar