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Review

The Applications of Hyperspectral Imaging Technology for Agricultural Products Quality Analysis: A Review

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References

  • Dale, L. M.; Thewis, A.; Boudry, C.; Rotar, I.; Dardenne, P.; Baeten, V.; Pierna, J. A. F. Hyperspectral Imaging Applications in Agriculture and Agro-food Product Quality and Safety Control: A Review. Appl. Spectrosc. Rev. 2013, 48(2), 142–159. DOI: 10.1080/05704928.2012.705800.
  • Wu, D.; Sun, D.-W. Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review — Part I: Fundamentals. Innovative Food Sci. Emerg. Technol. 2013, 19, 1–14. DOI: 10.1016/j.ifset.2013.04.014.
  • Wu, D.; Sun, D.-W. Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review — Part II: Applications. Innovative Food Sci. Emerg. Technol. 2013, 19, 15–28. DOI: 10.1016/j.ifset.2013.04.016.
  • Tao, F.; Yao, H.; Hruska, Z.; Burger, L. W.; Rajasekaran, K.; Bhatnagar, D. Recent Development of Optical Methods in Rapid and Non-destructive Detection of Aflatoxin and Fungal Contamination in Agricultural Products. TrAC Trends Anal. Chem. 2018, 100, 65–81. DOI: 10.1016/j.trac.2017.12.017.
  • Hart, J.; Norris, K.; Golumbie, C. Determination of the Moisture Content of Seeds by Near-infrared Spectroscopy. Cereal Chem. 1962, 39, 94–99.
  • Ghosh, P. K.; Jayas, D. S. Use of Spectroscopic Data for Automation in Food Processing Industry. Sens. Instrum. Food Qual. Saf. 2009, 3(1), 3–11. DOI: 10.1007/s11694-008-9068-7.
  • Osborne B G . Practical NIR spectroscopy with applications in food and beverage analysis. Practical Nir Spectroscopy with Applications in Food & Beverage Analysis, 1993,45(12), 227.
  • Liu, D.; Zeng, X. A.; Sun, D. W. Nir Spectroscopy and Imaging Techniques for Evaluation of Fish Quality—a Review. Appl. Spectrosc. Rev. 2013, 48(8), 609–628. DOI: 10.1080/05704928.2013.775579.
  • Lenhardt, L.; Bro, R.; Zekovic, I.; Dramicanin, T.; Dramicanin, M. D. Fluorescence Spectroscopy Coupled with Parafac and Pls Da for Characterization and Classification of Honey. Food Chem. 2015, 175, 284–291. DOI: 10.1016/j.foodchem.2014.11.162.
  • He, H.-J.; Sun, D.-W. Microbial Evaluation of Raw and Processed Food Products by Visible/ Infrared,Raman and Fluorescence Spectroscopy. Trends Food Sci. Technol. 2015, 46(2), 199–210. DOI: 10.1016/j.tifs.2015.10.004.
  • Lunadei, L.; Ruiz-Garcia, L.; Bodria, L.; Guidetti, R. Image-based Screening for the Identification of Bright Greenish Yellow Fluorescence on Pistachio Nuts and Cashews. Food Bioprocess. Technol. 2013, 6(5), 1261–1268. DOI: 10.1007/s11947-012-0815-8.
  • Jia, B.; Wang, W.; Ni, X.; Lawrence, K. C.; Zhuang, H.; Yoon, S.-C.; Gao, Z. Essential Processing Methods of Hyperspectral Images of Agricultural and Food Products. Chemom. Intell. Lab. Syst. 2020, 198, 103936. DOI: 10.1016/j.chemolab.2020.103936.
  • Goetz, A. F. H.; Vane, G.; Solomon, J. E.; Rock, B. N. Imaging Spectroscopy for Earth Remote Sensing. Science. 1985, 228, 1147–1153. DOI: 10.1126/science.228.4704.1147.
  • Ariana, D. P.; Lu, R. Quality Evaluation of Pickling Cucumbers Using Hyperspectral Reflectance and Transmittance Imaging—part Ii. Performance of a Prototype. Sens. Instrum. Food Qual. Saf. 2008, 2(3), 152–160. DOI: 10.1007/s11694-008-9058-9.
  • Ariana, D. P.; Lu, R. Quality Evaluation of Pickling Cucumbers Using Hyperspectral Reflectance and Transmittance Imaging: Part I. Development of a Prototype. Sens. Instrum. Food Qual. Saf. 2008, 2(3), 144–151. DOI: 10.1007/s11694-008-9057-x.
  • 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.
  • El, M. G.; Sun, D. W. Principles of Hyperspectral Imaging Technology, San Diego: Academic Press, 2010; pp 3–43.
  • Burger, J.; Geladi, P. Hyperspectral NIR Imaging for Calibration and Prediction: A Comparison between Image and Spectrometer Data for Studying Organic and Biological Samples. Analyst. 2006, 131, 1152–1160. DOI: 10.1039/b605386f.
  • Wang, S.;. Infrared Spectroscopy for Food Quality Analysis and Control. Trends Food Sci. Technol. 2010, 21(1), 52. DOI: 10.1016/j.tifs.2009.08.004.
  • Sun, J.; Shi, X.; Zhang, H.; Xia, L.; Guo, Y.; Sun, X. Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics. J. Food Process Eng, 2019, 42(7), 13263. DOI: 10.1111/jfpe.13263.
  • Chen, Y. N.; Sun, D. W.; Cheng, J. H.; Gao, W. H. Recent Advances for Rapid Identification of Chemical Information of Muscle Foods by Hyperspectral Imaging Analysis. Food Eng. Rev. 2016, 8(3), 336–350. DOI: 10.1007/s12393-016-9139-1.
  • Feng, C. H.; Makino, Y.; Oshita, S.; Martín, J. F. G. 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.
  • Skvaril, J.; Kyprianidis, K. G.; Dahlquist, E. Applications of near Infrared Spectroscopy (Nirs) in Biomass Energy Conversion Processes: A Review. Appl. Spectrosc. Rev. 2017, 52(8), 675–728. DOI: 10.1080/05704928.2017.1289471.
  • Nicola, B. M.; Defraeye, T.; De Ketelaere, B.; Herremans, E.; Hertog, M. L. A. T. M.; Saeys, W.; Torricelli, A.; Vandendriessche, T.; Verboven, P. Nondestructive Measurement of Fruit and Vegetable Quality. Ann. Rev. Food Sci. Technol. 2014, 5(1), 285–312. DOI: 10.1146/annurev-food-030713-092410.
  • Pu, Y. Y.; Feng, Y. Z.; Sun, D. W. Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Compr. Rev. Food Sci. Food Saf. 2015, 14(2), 176–188. DOI: 10.1111/1541-4337.12123.
  • Elmasry, G.; Barbin, D. F.; Sun, D. W.; Allen, P. Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview. Crit. Rev. Food Sci. Nutr. 2012, 52(8), 689–711. DOI: 10.1080/10408398.2010.507908.
  • Caporaso, N.; Whitworth, M. B.; Fisk, I. D. Near-infrared Spectroscopy and Hyperspectral Imaging for Non-destructive Quality Assessment of Cereal Grains. Appl. Spectrosc. Rev. 2018, 53(8), 667-687. DOI: 10.1080/05704928.2018.1425214.
  • Landgrebe, D.;. Hyperspectral Image Data Analysis. IEEE Signal Process. Mag. 2002, 19(1), 17–28. DOI: 10.1109/79.974718.
  • Raychaudhuri, B.;. Imaging Spectroscopy: Origin and Future Trends. Appl. Spectrosc. Rev. 2016, 51(1), 23–25. DOI: 10.1080/05704928.2015.1087405.
  • Huang, H.; Liu, L.; Ngadi, M. O. Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety. Sensors. 2014, 14(4), 7248–7276. DOI: 10.3390/s140407248.
  • Dong, X.; Jakobi, M.; Wang, S.; Khler, M. H.; Zhang, X.; Koch, A. W. A Review of Hyperspectral Imaging for Nanoscale Materials Research. Appl. Spectrosc. Rev. 2018, 6, 1–21.
  • Elmasry, G.; Kamruzzaman, M.; Sun, D. W.; Allen, P. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-food Products: A Review. Crit. Rev. Food Sci. Nutr. 2012, 52(11), 999–1023. DOI: 10.1080/10408398.2010.543495.
  • Wang, H.; Peng, J.; Xie, C.; Bao, Y.; He, Y. Fruit Quality Evaluation Using Spectroscopy Technology: A Review. Sensors. 2015, 15(5), 11889–11927. DOI: 10.3390/s150511889.
  • Liu, J. Y.; Zeng, L. H.; Ren, Z. H. Recent Application of Spectroscopy for the Detection of Microalgae Life Information: A Review. Appl. Spectrosc. Rev. 55(1), 26-59. DOI: 10.1080/05704928.2018.1509345.
  • Kucha, C. T.; Liu, L.; Ngadi, M. O. Non-destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review. Sensors. 2018, 18(2), 377–400.
  • Wu, D.; Nie, P.; He, Y.; Bao, Y. Determination of Calcium Content in Powdered Milk Using near and Mid-infrared Spectroscopy with Variable Selection and Chemometrics. Food Bioprocess. Technol. 2012, 5(4), 1402–1410. DOI: 10.1007/s11947-010-0492-4.
  • Wu, D.; Chen, X.; Zhu, X.; Guan, X.; Wu, G. Uninformative Variable Elimination for Improvement of Successive Projections Algorithm on Spectral Multivariable Selection with Different Calibration Algorithms for the Rapid and Non-destructive Determination of Protein Content in Dried Laver. Anal. Methods. 2011, 3(8), 1790–1796. DOI: 10.1039/c1ay05075c.
  • Xiaobo, Z.; Jiewen, Z.; Povey, M. J. W.; Holmes, M.; Hanpin, M. Variables Selection Methods in Near-infrared Spectroscopy. Anal. Chim. Acta. 2010, 667(1–2), 14–32. DOI: 10.1016/j.aca.2010.03.048.
  • Fan, S.; Zhang, B.; Li, J.; Liu, C.; Huang, W.; Tian, X. Prediction of Soluble Solids Content of Apple Using the Combination of Spectra and Textural Features of Hyperspectral Reflectance Imaging Data. Postharvest Biol. Technol. 2016, 121, 51–61. DOI: 10.1016/j.postharvbio.2016.07.007.
  • Zhang, D.; Xu, Y.; Huang, W.; Tian, X.; Xia, Y.; Xu, L.; Fan, S. Nondestructive Measurement of Soluble Solids Content in Apple Using near Infrared Hyperspectral Imaging Coupled with Wavelength Selection Algorithm. Infrared Phys. Technol. 2019, 98, 297–304. DOI: 10.1016/j.infrared.2019.03.026.
  • Guo, W.; Zhao, F.; Dong, J. Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-infrared Hyperspectral Imaging. Food Anal. Methods. 2016, 9(1), 38–47. DOI: 10.1007/s12161-015-0165-z.
  • Dong, J.; Guo, W. Nondestructive Determination of Apple Internal Qualities Using Near-infrared Hyperspectral Reflectance Imaging. Food Anal. Methods. 2015, 8(10), 2635–2646. DOI: 10.1007/s12161-015-0169-8.
  • Zhang, D.; Xu, L.; Liang, D.; Xu, C.; Jin, X.; Weng, S. Fast Prediction of Sugar Content in Dangshan Pear (Pyrus Spp.) Using Hyperspectral Imagery Data. Food Anal. Methods. 2018, 11(8), 2336–2345. DOI: 10.1007/s12161-018-1212-3.
  • Rui, L.; Longsheng, F. Nondestructive Measurement of Firmness and Sugar Content of Blueberries Based on Hyperspectral Imaging. Trans. Chin. Soc. Agric. Eng. 2017, 33, 362–366.
  • Fan, S.; Huang, W.; Guo, Z.; Zhang, B.; Zhao, C. Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. Food Anal. Methods. 2015, 8(8), 1936–1946. DOI: 10.1007/s12161-014-0079-1.
  • Pu, H.; Liu, D.; Wang, L.; Sun, D.-W. Soluble Solids Content and Ph Prediction and Maturity Discrimination of Lychee Fruits Using Visible and near Infrared Hyperspectral Imaging. Food Anal. Methods. 2016, 9(1), 235–244. DOI: 10.1007/s12161-015-0186-7.
  • Xu, D.; Wang, H.; Ji, H.; Zhang, X.; Cerbu, C.; Hu, E.; Dong, F. Quantitative Evaluation of Impact Damage to Apple by Hyperspectral Imaging and Mechanical Parameters. Food Anal. Methods. 2018, 12(2), 371–380. DOI: 10.1007/s12161-018-1369-9.
  • Zhang, M.; Li, G. Visual Detection of Apple Bruises Using Adaboost Algorithm and Hyperspectral Imaging. Int. J. Food Prop. 2018, 21(1), 1598–1607. DOI: 10.1080/10942912.2018.1503299.
  • Munera, S.; José Blasco, J. M.; Amigo, S. C.; Talens, P.; Aleixos, N. Use of Hyperspectral Transmittance Imaging to Evaluate the Internal Quality of Nectarines. Biosyst. Eng. 2019, 182, 54–64. DOI: 10.1016/j.biosystemseng.2019.04.001.
  • Fan, S.; Li, C.; Huang, W.; Chen, L. Detection of Blueberry Internal Bruising over Time Using Nir Hyperspectral Reflectance Imaging with Optimum Wavelengths. Postharvest. Biol. Technol. 2017, 134, 55–66. DOI: 10.1016/j.postharvbio.2017.08.012.
  • Sun, Y.; Xiao, H.; Tu, S.; Sun, K.; Pan, L.; Tu, K. Detecting Decayed Peach Using a Rotating Hyperspectral Imaging Testbed. LWT. 2018, 87, 326–332. DOI: 10.1016/j.lwt.2017.08.086.
  • Pan, L.; Zhang, Q.; Zhang, W.; Sun, Y.; Hu, P.; Tu, K. Detection of Cold Injury in Peaches by Hyperspectral Reflectance Imaging and Artificial Neural Network. Food Chem. 2016, 192, 134–141. DOI: 10.1016/j.foodchem.2015.06.106.
  • Sun, Y.; Gu, X.; Sun, K.; Hu, H.; Xu, M.; Wang, Z.; Tu, K.; Pan, L. Hyperspectral Reflectance Imaging Combined with Chemometrics and Successive Projections Algorithm for Chilling Injury Classification in Peaches. LWT. 2017, 75, 557–564. DOI: 10.1016/j.lwt.2016.10.006.
  • Li, J.; Huang, W.; Tian, X.; Wang, C.; Fan, S.; Zhao, C. Fast Detection and Visualization of Early Decay in Citrus Using Vis-nir Hyperspectral Imaging. Comput. Electron. Agric. 2016, 127, 582–592. DOI: 10.1016/j.compag.2016.07.016.
  • Ye, D.; Sun, L.; Tan, W.; Che, W.; Yang, M. Detecting and Classifying Minor Bruised Potato Based on Hyperspectral Imaging. Chemom. Intell. Lab. Syst. 2018, 177, 129–139. DOI: 10.1016/j.chemolab.2018.04.002.
  • Ji, Y.; Sun, L.; Li, Y.; Li, J.; Liu, S.; Xie, X.; Xu, Y. Non-destructive Classification of Defective Potatoes Based on Hyperspectral Imaging and Support Vector Machine. Infrared Phys. Technol. 2019, 99, 71–79. DOI: 10.1016/j.infrared.2019.04.007.
  • Yu, P.; Huang, M.; Zhang, M.; Yang, B. Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying. Appl. Sci. 2019, 9(2), 331. DOI: 10.3390/app9020331.
  • Rady, A.; Guyer, D.; Lu, R. Evaluation of Sugar Content of Potatoes Using Hyperspectral Imaging. Food Bioprocess. Technol. 2015, 8(5), 995–1010. DOI: 10.1007/s11947-014-1461-0.
  • Rahman, A.; Kandpal, L. M.; Lohumi, S.; Kim, M. S.; Lee, H.; Mo, C.; Cho, B.-K. Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging. Appl. Sci. 2017, 7(1), 109. DOI: 10.3390/app7010109.
  • Zhao, Y.; Yu, K.; Feng, C.; Cen, H.; He, Y. Early Detection of Aphid (Myzus Persicae) Infestation on Chinese Cabbage by Hyperspectral Imaging and Feature Extraction. Trans. ASABE. 2017, 60(4), 1045–1051. DOI: 10.13031/trans.11886.
  • Zhao, M.; Esquerre, C.; Downey, G.; O’Donnell, C. P. Process Analytical Technologies for Fat and Moisture Determination in Ground Beef - a Comparison of Guided Microwave Spectroscopy and near Infrared Hyperspectral Imaging. Food Control. 2016, 73, 1082–1094. DOI: 10.1016/j.foodcont.2016.10.023.
  • Xiong, Z.; Sun, D. W.; Xie, A.; Pu, H.; Han, Z.; Luo, M. Quantitative Determination of Total Pigments in Red Meats Using Hyperspectral Imaging and Multivariate Analysis. Food Chem. 2015, 178, 339–345. DOI: 10.1016/j.foodchem.2015.01.071.
  • Cheng, W.; Sun, D. W.; Cheng, J. H. Pork Biogenic Amine Index (Bai) Determination Based on Chemometric Analysis of Hyperspectral Imaging Data. LWT - Food Sci. Technol. 2016, 73, 13–19. DOI: 10.1016/j.lwt.2016.05.031.
  • Huang, H.; Liu, L.; Ngadi, M. O. Assessment of Intramuscular Fat Content of Pork Using Nir Hyperspectral Images of Rib End. J. Food Eng. 2017, 193, 29–41. DOI: 10.1016/j.jfoodeng.2016.07.005.
  • Liu, L.; Ngadi, M. O. Predicting Intramuscular Fat Content of Pork Using Hyperspectral Imaging. J. Food Eng. 2014, 134, 16–23. DOI: 10.1016/j.jfoodeng.2014.02.007.
  • Ma, J.; Pu, H.; Sun, D. W. Predicting Intramuscular Fat Content Variations in Boiled Pork Muscles by Hyperspectral Imaging Using a Novel Spectral Pre-processing Technique. LWT. 2018, 94, 119–128. DOI: 10.1016/j.lwt.2018.04.030.
  • Ma, J.; Sun, D. W.; Pu, H. Model Improvement for Predicting Moisture Content (Mc) in Pork Longissimus Dorsi Muscles under Diverse Processing Conditions by Hyperspectral Imaging. J. Food Eng. 2017, 196, 65–72. DOI: 10.1016/j.jfoodeng.2016.10.016.
  • Yang, D.; He, D.; Lu, A.; Ren, D.; Wang, J. Combination of Spectral and Textural Information of Hyperspectral Imaging for the Prediction of the Moisture Content and Storage Time of Cooked Beef. Infrared Phys. Technol. 2017, 83, 206–216. DOI: 10.1016/j.infrared.2017.05.005.
  • Liu, Y.; Sun, D. W.; Cheng, J. H.; Han, Z. Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef during Microwave Heating Process. Food Anal. Methods. 2018, 11, 2472–2484. DOI: 10.1007/s12161-018-1234-x.
  • Ma, J.; Sun, D.-W.; Pu, H.; Wei, Q.; Wang, X. Protein Content Evaluation of Processed Pork Meats Based on a Novel Single Shot (Snapshot) Hyperspectral Imaging Sensor. J. Food Eng. 2019, 240, 207–213. DOI: 10.1016/j.jfoodeng.2018.07.032.
  • Jia, B.; Yoon, S. C.; Zhuang, H.; Wang, W.; Li, C. Prediction of Ph of Fresh Chicken Breast Fillets by Vnir Hyperspectral Imaging. J. Food Eng. 2017, 208, 57–65. DOI: 10.1016/j.jfoodeng.2017.03.023.
  • Khulal, U.; Zhao, J.; Hu, W.; Chen, Q. Nondestructive Quantifying Total Volatile Basic Nitrogen (TVB-N) Content in Chicken Using Hyperspectral Imaging (HSI) Technique Combined with Different Data Dimension Reduction Algorithms. Food Chem. 2016, 197, 1191–1199. DOI: 10.1016/j.foodchem.2015.11.084.
  • Qiu, Y.; Zhu, R.; Fan, Z.; Yao, X.; Lewis, E. Comparison of Models and Visualization of Total Volatile Basic Nitrogen Content in Mutton Using Hyperspectral Imaging and Variable Selection Methods. Spectrosc. Lett. 2018, 51(5), 226–235. DOI: 10.1080/00387010.2018.1452268.
  • Zhu, R.-G.; Yao, X.-D.; Duan, H.-W.; Ma, B.-X.; Tang, T. Study on the Rapid Evaluation of Total Volatile Basic Nitrogen(tvb-n) of Mutton by Hyperspectral Imaging Technique. Spectrosc. Spectral Anal. 2016, 36(3), 806–810.
  • Qiao, L.; Tang, X.; Dong, J. A Feasibility Quantification Study of Total Volatile Basic Nitrogen (Tvb-n) Content in Duck Meat for Freshness Evaluation. Food Chem. 2017, 237, 1179–1185. DOI: 10.1016/j.foodchem.2017.06.031.
  • Li, H.; Chen, Q.; Zhao, J.; Wu, M. Nondestructive Detection of Total Volatile Basic Nitrogen (Tvb-n) Content in Pork Meat by Integrating Hyperspectral Imaging and Colorimetric Sensor Combined with a Nonlinear Data Fusion. LWT Food Sci. Technol. 2015, 63(1), 268–274. DOI: 10.1016/j.lwt.2015.03.052.
  • Zheng, X.; Peng, Y.; Wang, W. A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique. Appl. Sci. 2017, 7(3), 213. DOI: 10.3390/app7030213.
  • Tao, F.; Peng, Y.; Gomes, C. L.; Chao, K.; Qin, J. A Comparative Study for Improving Prediction of Total Viable Count in Beef Based on Hyperspectral Scattering Characteristics. J. Food Eng. 2015, 162, 38–47. DOI: 10.1016/j.jfoodeng.2015.04.008.
  • Ye, X.; Iino, K.; Zhang, S. Monitoring of Bacterial Contamination on Chicken Meat Surface Using a Novel Narrowband Spectral Index Derived from Hyperspectral Imagery Data. Meat Sci. 2016, 122, 25–31. DOI: 10.1016/j.meatsci.2016.07.015.
  • Kamruzzaman, M.; Makino, Y.; Oshita, S. Hyperspectral Imaging in Tandem with Multivariate Analysis and Image Processing for Non-invasive Detection and Visualization of Pork Adulteration in Minced Beef. Anal. Methods. 2015, 7(18), 7496–7502. DOI: 10.1039/C5AY01617G.
  • Zhao, H.-T.; Feng, Y.-Z.; Chen, W.; Jia, G.-F. Application of Invasive Weed Optimization and Least Square Support Vector Machine for Prediction of Beef Adulteration with Spoiled Beef Based on Visible Near-infrared (Vis-nir) Hyperspectral Imaging. Meat Sci. 2019, 151, 75–81. DOI: 10.1016/j.meatsci.2019.01.010.
  • Caporaso, N.; Whitworth, M. B.; Fisk, I. D. Protein Content Prediction in Single Wheat Kernels Using Hyperspectral Imaging. Food Chem. 2018, 240, 32–42. DOI: 10.1016/j.foodchem.2017.07.048.
  • Mahesh, S.; Jayas, D. S.; Paliwal, J.; White, N. D. G. Comparison of Partial Least Squares Regression (Plsr) and Principal Components Regression (Pcr) Methods for Protein and Hardness Predictions Using the Near-infrared (Nir) Hyperspectral Images of Bulk Samples of Canadian Wheat. Food Bioprocess. Technol. 2015, 8(1), 31–40. DOI: 10.1007/s11947-014-1381-z.
  • Sun, J.; Lu, X.; Mao, H.; Wu, X.; Gao, H. Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and Bcc‐ls‐svr Algorithm. J. Food Process Eng. 2017, 40(3), e12446. DOI: 10.1111/jfpe.12446.
  • Lu, X.; Sun, J.; Mao, H.; Wu, X.; Gao, H. Quantitative Determination of Rice Starch Based on Hyperspectral Imaging Technology. Int. J. Food Prop. 2017, 20(1), 1037–1044. DOI: 10.1080/10942912.2017.1326058.
  • Kandpal, L. M.; Sangdae Lee, M. S.; Kim, M. S.; Bae, H.; Cho, B.-K. Short Wave Infrared (SWIR) Hyperspectral Imaging Technique for Examination of Aflatoxin B1 (AFB1) on Corn Kernels. Food Control. 2015, 51, 171–176. DOI: 10.1016/j.foodcont.2014.11.020.
  • Kimuli, D.; Wang, W.; Lawrence, K. C.; Yoon, S. C.; Ni, X.; Heitschmidt, G. W. Utilisation of Visible/near-infrared Hyperspectral Images to Classify Aflatoxin B 1, Contaminated Maize Kernels. Biosyst. Eng. 2018, 166, 150–160.
  • Wang, W.; Lawrence, K. C.; Ni, X.; Yoon, S. C.; Heitschmidt, G. W.; Feldner, P. Near-infrared Hyperspectral Imaging for Detecting Aflatoxin B1 of Maize Kernels. Food Control. 2015, 51, 347–355. DOI: 10.1016/j.foodcont.2014.11.047.
  • Kimuli, D.; Wang, W.; Wang, W.; Jiang, H.; Zhao, X.; Chu, X. Application of SWIR Hyperspectral Imaging and Chemometrics for Identification of Aflatoxin B1 Contaminated Maize Kernels. Infrared Phys. Technol. 2018, 89, 351–362. DOI: 10.1016/j.infrared.2018.01.026.
  • Chu, X.; Wang, W.; Yoon, S. C.; Ni, X.; Heitschmidt, G. W. Detection of Aflatoxin B1 (AFB1) in Individual Maize Kernels Using Short Wave Infrared (SWIR) Hyperspectral Imaging. Biosyst. Eng. 2017, 157, 13–23. DOI: 10.1016/j.biosystemseng.2017.02.005.
  • Xuan, X.; Wei, W.; Zhang, L.-D.; Guo, L.-H.; Feldner, P.; Heitschmidt, G. Hyperspectral Optimum Wavelengths and Fisher Discrimination Analysis to Distinguish Different Concentrations of Aflatoxin on Corn Kernel Surface. Spectrosc. Spectral Anal. 2014, 34(7), 1811–5.
  • Huang, M.; Zhu, X.; Zhu, Q.; Feng, Z. Hyperspectral Image Classification of Maize Seeds Based on Active Contour Model. J. Data Acquisition Process 2013, 28(3), 289–293.
  • Jun, S.; Xinzi, L.; Hanping, M. Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and Bcc-ls-svr Algorithm. J. Food Process Eng. 2016, 40(3), 12446. DOI: 10.1111/jfpe.12446.
  • Wakholi, C.; Kandpal, L. M.; Lee, H.; Bae, H.; Eunsoo Park, M. S.; Kim, C. M.; Lee, W.-H.; Cho, B.-K. Rapid Assessment of Corn Seed Viability Using Short Wave Infrared Line-scan Hyperspectral Imaging and Chemometrics. Sens. Actuators B Chem. 2018, 255(Part 1), 498–507. DOI: 10.1016/j.snb.2017.08.036.
  • Jin, H.; Ma, Y.; Li, L.; Cheng, J. H. Rapid and Non-destructive Determination of Oil Content of Peanut (Arachis Hypogaea L.) Using Hyperspectral Imaging Analysis. Food Anal. Methods. 2016, 9(7), 2060–2067. DOI: 10.1007/s12161-015-0384-3.
  • Cheng, J. H.; Jin, H.; Liu, Z. Developing a Nir Multispectral Imaging for Prediction and Visualization of Peanut Protein Content Using Variable Selection Algorithms. Infrared Phys. Technol. 2017, 88, 92–96. DOI: 10.1016/j.infrared.2017.11.018.
  • Jin, H.; Li, L.; Cheng, J. Rapid and Non-destructive Determination of Moisture Content of Peanut Kernels Using Hyperspectral Imaging Technique. Food Anal. Methods. 2015, 8(10), 2524–2532. DOI: 10.1007/s12161-015-0147-1.
  • Huang, H.; Shen, Y.; Guo, Y.; Yang, P.; Wang, H.; Zhan, S.; Liu, H.; Song, H.; He, Y. Characterization of Moisture Content in Dehydrated Scallops Using Spectral Images. J. Food Eng. 2017, 205, 47–55. DOI: 10.1016/j.jfoodeng.2017.02.018.
  • Dai, Q.; Cheng, J. H.; Sun, D. W.; Zhu, Z.; Pu, H. Prediction of Total Volatile Basic Nitrogen Contents Using Wavelet Features from Visible/near-infrared Hyperspectral Images of Prawn (Metapenaeus Ensis). Food Chem. 2016, 197, 257–265. DOI: 10.1016/j.foodchem.2015.10.073.
  • 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). Biosyst. Eng. 2019, 178, 244–255. DOI: 10.1016/j.biosystemseng.2018.11.018.
  • Cheng, J.-H.; Sun, D.-W.; Pu, H.-B.; Wang, Q.-J.; Chen, Y.-N. Suitability of Hyperspectral Imaging for Rapid Evaluation of Thiobarbituric Acid (TBA) Value in Grass Carp (Ctenopharyngodon Idella) Fillet. Food Chem. 2015, 171, 258–265. DOI: 10.1016/j.foodchem.2014.08.124.
  • Yu, X.; Yu, X.; Wen, S.; Yang, J.; Wang, J. Using Deep Learning and Hyperspectral Imaging to Predict Total Viable Count (Tvc) in Peeled Pacific White Shrimp. J. Food Meas. Charact. 2019, 13(2), 2082–2094. DOI: 10.1007/s11694-019-00129-0.
  • Jiang, J.; Qiao, X.; He, R. Use of Near-infrared Hyperspectral Images to Identify Moldy Peanuts. J. Food Eng. 2016, 169, 284–290. DOI: 10.1016/j.jfoodeng.2015.09.013.
  • Qiao, X.; Jiang, J.; Qi, X.; Guo, H.; Yuan, D. Utilization of Spectral-spatial Characteristics in Shortwave Infrared Hyperspectral Images to Classify and Identify Fungi-contaminated Peanuts. Food Chem. 2017, 220, 393–399. DOI: 10.1016/j.foodchem.2016.09.119.
  • Kheiralipour, K.; Ahmadi, H.; Rajabipour, A.; Rafiee, S.; Javan-Nikkhah, M.; Jayas, D. S.; Siliveru, K. Detection of Fungal Infection in Pistachio Kernel by Long-wave Near-infrared Hyperspectral Imaging Technique. Qual. Assur. Saf. Crops Food. 2016, 8(1), 129–135.

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