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Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

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References

  • Acquarelli, J., T. van Laarhoven, J. Gerretzen, T. N. Tran, L. M. C. Buydens, and E. Marchiori. 2017. Convolutional neural networks for vibrational spectroscopic data analysis. Analytica Chimica Acta 954:22–31. doi: 10.1016/j.aca.2016.12.010.
  • Afsah-Hejri, L., P. Hajeb, P. Ara, and R. J. Ehsani. 2019. A comprehensive review on food applications of terahertz spectroscopy and imaging. Comprehensive Reviews in Food Science and Food Safety 18 (5):1563–621. doi: 10.1111/1541-4337.12490.
  • Ahn, D., J. Y. Choi, H. C. Kim, J. S. Cho, K. D. Moon, and T. Park. 2019. Estimating the composition of food nutrients from hyperspectral signals based on deep neural networks. Sensors 19 (7):1560. doi: 10.3390/s19071560.
  • Al-Asadi, R. A., and A. M. Mouazen. 2018. A prototype measuring system of soil bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy. Computers and Electronics in Agriculture 151:485–91. doi: 10.1016/j.compag.2018.06.045.
  • Al-Sarayreh, M., M. M. Reis, W. Q. Yan, and R. Klette. 2018a. Detection of red-meat adulteration by deep spectral-spatial features in hyperspectral images. Journal of Imaging 4 (5):63. doi: 10.3390/jimaging4050063.
  • Al-Sarayreh, M., M. M. Reis, W. Q. Yan, and R. Klette. 2018b. Deep spectral-spatial features of snapshot hyperspectral images for red-meat classification. In 2018 International Conference on Image and Vision Computing New Zealand, New York.
  • Alshejari, A., and V. S. Kodogiannis. 2017. An intelligent decision support system for the detection of meat spoilage using multispectral images. Neural Computing and Applications 28 (12):3903–20. doi: 10.1007/s00521-016-2296-6.
  • Altieri, G., F. Genovese, N. Admane, and G. C. Di Renzo. 2016. On-line measure of donkey's milk properties by near infrared spectrometry. Lwt - Food Science and Technology 69:348–57. doi: 10.1016/j.lwt.2016.01.069.
  • Amigo, J. M., H. Babamoradi, and S. Elcoroaristizabal. 2015. Hyperspectral image analysis. A tutorial. Analytica Chimica Acta 896:34–51. doi: 10.1016/j.aca.2015.09.030.
  • Amigo, J. M., I. Martí, and A. Gowen. 2013. Hyperspectral imaging and chemometrics: A perfect combination for the analysis of food structure. Composition and Quality. Data Handling in Science and Technology 28:343–70. doi: 10.1016/B978-0-444-59528-7.00009-0.
  • Bağcıoğlu, M., M. Fricker, S. Johler, and M. Ehling-Schulz. 2019. Detection and identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via machine learning based FTIR spectroscopy. Frontiers in Microbiology 10:902. doi: 10.3389/fmicb.2019.00902.
  • Bai, Y. H., Y. J. Xiong, J. C. Huang, J. Zhou, and B. H. Zhang. 2019. Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features. Postharvest Biology and Technology 156:110943. doi: 10.1016/j.postharvbio.2019.110943.
  • Baltacıoğlu, H., A. Bayındırlı, M. Severcan, and F. Severcan. 2015. Effect of thermal treatment on secondary structure and conformational change of mushroom polyphenol oxidase (PPO) as food quality related enzyme: A FTIR study. Food Chemistry 187:263–9. doi: 10.1016/j.foodchem.2015.04.097.
  • Barmpalexis, P., A. Karagianni, I. Nikolakakis, and K. Kachrimanis. 2018. Artificial neural networks (ANNs) and partial least squares (PLS) regression in the quantitative analysis of cocrystal formulations by Raman and ATR-FTIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 158:214–24. doi: 10.1016/j.jpba.2018.06.004.
  • Behkami, S., S. M. Zain, M. Gholami, and M. F. A. Khir. 2019. Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers. Food Chemistry 294:309–15. doi: 10.1016/j.foodchem.2019.05.060.
  • Cao, X. H., Y. M. Ge, R. J. Li, J. Zhao, and L. C. Jiao. 2019. Hyperspectral imagery classification with deep metric learning. Neurocomputing 356:217–27. doi: 10.1016/j.neucom.2019.05.019.
  • Carmon, N., and E. Ben-Dor. 2016. Rapid assessment of dynamic friction coefficient of Asphalt pavement using reflectance spectroscopy. IEEE Geoscience and Remote Sensing Letters 13 (5):721–4. doi: 10.1109/LGRS.2016.2539301.
  • Cevoli, C., A. Gori, M. Nocetti, L. Cuibus, M. F. Caboni, and A. Fabbri. 2013. FT-NIR and FT-MIR spectroscopy to discriminate competitors, non compliance and compliance grated Parmigiano Reggiano cheese. Food Research International 52 (1):214–20. doi: 10.1016/j.foodres.2013.03.016.
  • Che, W. K., L. J. Sun, Q. Zhang, D. Zhang, D. D. Ye, W. Y. Tan, L. K. Wang, and C. J. Dai. 2017. Application of visible/near-infrared spectroscopy in the prediction of azodicarbonamide in wheat flour. Journal of Food Science 82 (10):2516–25. doi: 10.1111/1750-3841.13859.
  • Chen, L. P., Z. J. Li, F. Q. H. Yu, X. Zhang, Y. Xue, and C. H. Xue. 2019. Hyperspectral imaging and chemometrics for nondestructive quantification of total volatile basic nitrogen in pacific oysters (Crassostrea gigas). Food Analytical Methods 12 (3):799–810. doi: 10.1007/s12161-018-1400-1.
  • Chen, L. Z., J. H. Wang, Z. H. Ye, J. Zhao, X. F. Xue, Y. Vander Heyden, and Q. Sun. 2012. Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chemistry 135 (2):338–42. doi: 10.1016/j.foodchem.2012.02.156.
  • Chen, X. Y., Q. Q. Chai, N. Lin, X. H. Li, and W. Wang. 2019. 1D convolutional neural network for the discrimination of aristolochic acids and their analogues based on near-infrared spectroscopy. Analytical Methods 11 (40):5118–25. doi: 10.1039/C9AY01531K.
  • Chen, Y. Y., and Z. B. Wang. 2018. Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks. Chemometrics and Intelligent Laboratory Systems 181:1–10. doi: 10.1016/j.chemolab.2018.08.001.
  • Chen, Y. Y., and Z. B. Wang. 2019. End-to-end quantitative analysis modeling of near-infrared spectroscopy based on convolutional neural network. Journal of Chemometrics 33 (5):e3122. doi: 10.1002/cem.3122.
  • Cheng, J. H., and D. W. Sun. 2015. Recent applications of spectroscopic and hyperspectral imaging techniques with chemometric analysis for rapid inspection of microbial spoilage in muscle foods. Comprehensive Reviews in Food Science and Food Safety 14 (4):478–90. doi: 10.1111/1541-4337.12141.
  • Conceicao, D. G., B. R. F. Goncalves, F. F. da Hora, A. S. Faleiro, L. S. Santos, and S. P. B. Ferrao. 2018. Use of FTIR-ATR spectroscopy combined with multivariate analysis as a screening tool to identify adulterants in raw milk. Journal of the Brazilian Chemical Society 30 (4):780–5. doi: 10.21577/0103-5053.20180208.
  • Coronel-Reyes, J., I. Ramirez-Morales, E. Fernandez-Blanco, D. Rivero, and A. Pazos. 2018. Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques. Computers and Electronics in Agriculture 145:1–10. doi: 10.1016/j.compag.2017.12.030.
  • Cubero-Leon, E., R. Penalver, and A. Maquet. 2014. Review on metabolomics for food authentication. Food Research International 60:95–107. doi: 10.1016/j.foodres.2013.11.041.
  • Cui, C. H., and T. Fearn. 2018. Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration. Chemometrics and Intelligent Laboratory Systems 182:9–20. doi: 10.1016/j.chemolab.2018.07.008.
  • da Costa, A. Z., H. E. H. Figueroa, and J. A. Fracarolli. 2020. Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering 190:131–44. doi: 10.1016/j.biosystemseng.2019.12.003.
  • Ding, X., Y. Guo, Y. Ni, and S. Kokot. 2016. A novel NIR spectroscopic method for rapid analyses of lycopene, total acid, sugar, phenols and antioxidant activity in dehydrated tomato samples. Vibrational Spectroscopy 82:1–9. doi: 10.1016/j.vibspec.2015.10.004.
  • Dixit, Y., M. P. Casado-Gavalda, R. Cama-Moncunill, X. Cama-Moncunill, M. Markiewicz-Keszycka, P. J. Cullen, and C. Sullivan. 2017. Developments and challenges in online NIR spectroscopy for meat processing. Comprehensive Reviews in Food Science and Food Safety 16 (6):1172–87. doi: 10.1111/1541-4337.12295.
  • Duan, C., C. G. Chen, M. N. Khan, Y. Z. Liu, R. Zhang, H. Lin, and L. M. Cao. 2014. Non-destructive determination of the total bacteria in by portable near infrared spectrometer. Food Control 42:18–22. doi: 10.1016/j.foodcont.2014.01.023.
  • Efenberger-Szmechtyk, M., A. Nowak, and D. Kregiel. 2018. Implementation of chemometrics in quality evaluation of food and beverages. Critical Reviews in Food Science and Nutrition 58 (10):1747–66. doi: 10.1080/10408398.2016.1276883.
  • Feng, L., S. S. Zhu, L. Zhou, Y. Y. Zhao, Y. D. Bao, C. Zhang, and Y. He. 2019. Detection of subtle bruises on Winter Jujube using hyperspectral imaging with pixel-wise deep learning method. IEEE Access 7:64494–505. doi: 10.1109/ACCESS.2019.2917267.
  • Fernandes, A. M., C. Franco, A. Mendes-Ferreira, A. Mendes-Faia, P. L. da Costa, and P. Melo-Pinto. 2015. Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks. Computers and Electronics in Agriculture 115:88–96. doi: 10.1016/j.compag.2015.05.013.
  • Fukuhara, M., K. Fujiwara, Y. Maruyama, and H. Itoh. 2019. Feature visualization of Raman spectrum analysis with deep convolutional neural network. Analytica Chimica Acta 1087:11–9. doi: 10.1016/j.aca.2019.08.064.
  • Funes, E., Y. Allouche, G. Beltran, M. P. Aguliera, and A. Jimenez. 2018. Predictive ANN models for the optimization of extra virgin olive oil clarification by means of vertical centrifugation. Journal of Food Process Engineering 41 (1):e12593. doi: 10.1111/jfpe.12593.
  • Gomes, V., A. Fernandes, P. Martins-Lopes, L. Pereira, A. M. Faia, and P. Melo-Pinto. 2017. Characterization of neural network generalization in the determination of pH and anthocyanin content of wine grape in new vintages and varieties. Food Chemistry 218:40–6. doi: 10.1016/j.foodchem.2016.09.024.
  • Gomes, V. M., A. M. Fernandes, A. Faia, and P. Melo-Pinto. 2017. Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging. Computers and Electronics in Agriculture 140:244–54. doi: 10.1016/j.compag.2017.06.009.
  • Gomez-Caravaca, A. M., R. M. Maggio, and L. Cerretani. 2016. Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review. Analytica Chimica Acta 913:1–21. doi: 10.1016/j.aca.2016.01.025.
  • Gonzalez-Fernandez, I., M. A. Iglesias-Otero, M. Esteki, O. A. Moldes, J. C. Mejuto, and J. Simal-Gandara. 2019. A critical review on the use of artificial neural networks in olive oil production, characterization and authentication. Critical Reviews in Food Science and Nutrition 59 (12):1913–26. doi: 10.1080/10408398.2018.1433628.
  • Grunert, T., R. Stephan, M. Ehling-Schulz, and S. Johler. 2016. Fourier Transform Infrared Spectroscopy enables rapid differentiation of fresh and frozen/thawed chicken. Food Control 60:361–4. doi: 10.1016/j.foodcont.2015.08.016.
  • Guan, B. B., J. W. Zhao, H. J. Jin, and H. Lin. 2014. The qualitative and quantitative analysis of aromatic vinegar produced during different seasons by near infrared spectroscopy. Analytical Methods 6 (24):9634–42. doi: 10.1039/C4AY02165G.
  • Hameed, S., L. J. Xie, and Y. B. Ying. 2018. Conventional and emerging detection techniques for pathogenic bacteria in food science: A review. Trends in Food Science & Technology 81:61–73. doi: 10.1016/j.tifs.2018.05.020.
  • Han, Z. Z., and J. Y. Gao. 2019. Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging. Computers and Electronics in Agriculture 164:104888. doi: 10.1016/j.compag.2019.104888.
  • Harrington, P. D. 2018. Feature expansion by a continuous restricted Boltzmann machine for near-infrared spectrometric calibration. Analytica Chimica Acta 1010:20–8. doi: 10.1016/j.aca.2018.01.026.
  • Hong, S. J., S. J. Rho, A. Y. Lee, H. Park, J. Cui, J. Park, S. J. Hong, Y. R. Kim, and G. Kim. 2017. Rancidity estimation of perilla seed oil by using near-infrared spectroscopy and multivariate analysis techniques. Journal of Spectroscopy 2017:1–10. 10. doi: 10.1155/2017/1082612.
  • Huang, F. R., Y. P. Li, J. Wu, J. Dong, and Y. Wang. 2016. Identification of repeatedly frozen meat based on near-infrared spectroscopy combined with self-organizing competitive neural networks. International Journal of Food Properties 19 (5):1007–15. doi: 10.1080/10942912.2014.968789.
  • Huang, L., S. Y. Guo, Y. Wang, S. Wang, Q. B. Chu, L. Li, and T. Bai. 2019. Attention based residual network for medicinal fungi near infrared spectroscopy analysis. Mathematical Biosciences and Engineering 16 (4):3003–17. doi: 10.3934/mbe.2019149.
  • Huang, L., J. W. Zhao, Q. S. Chen, and Y. H. Zhang. 2014. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chemistry 145:228–36. doi: 10.1016/j.foodchem.2013.06.073.
  • Huang, X., H. Xu, L. Wu, H. Dai, L. Yao, and F. Han. 2016. A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Analytical Methods 8 (14):2929–35. doi: 10.1039/C5AY03005F.
  • Huang, Y., S. Meng, P. Zhao, and C. Li. 2019. Wood quality of Chinese zither panel based on convolutional neural network and near-infrared spectroscopy. Applied Optics 58 (18):5122–7. doi: 10.1364/ao.58.005122.
  • Hussain, N., D. W. Sun, and H. B. Pu. 2019. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science & Technology 91:598–608. doi: 10.1016/j.tifs.2019.07.018.
  • Jiang, H., and J. G. Lu. 2018. Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy 196:131–40. doi: 10.1016/j.saa.2018.02.017.
  • Jiang, X. H., H. R. Xue, L. N. Zhang, X. J. Gao, G. D. Wu, and J. Bai. 2018. Nondestructive detection of chilled mutton freshness based on multi-label information fusion and adaptive BP neural network. Computers and Electronics in Agriculture 155:371–7. doi: 10.1016/j.compag.2018.10.019.
  • Jin, X., L. Jie, S. Wang, H. J. Qi, and S. W. Li. 2018. Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensing 10 (3)20.395. doi: 10.3390/rs10030:.
  • Kavdir, I., M. B. Buyukcan, and F. Kurtulmus. 2018. Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers. Journal of Food Measurement and Characterization 12 (4):2493–502. doi: 10.1007/s11694-018-9866-5.
  • Khulal, U., J. W. Zhao, W. W. Hu, and Q. S. Chen. 2016. Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chemistry 197:1191–9. doi: 10.1016/j.foodchem.2015.11.084.
  • Kiani, S., S. M. van Ruth, L. W. D. van Raamsdonk, and S. Minaei. 2019. Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study. Lwt 104:61–9. doi: 10.1016/j.lwt.2019.01.045.
  • Kodogiannis, V. S., and A. Alshejari. 2014. An adaptive neuro-fuzzy identification model for the detection of meat spoilage. Applied Soft Computing 23:483–97. doi: 10.1016/j.asoc.2014.06.009.
  • Kodogiannis, V. S., E. Kontogianni, and J. N. Lygouras. 2014. Neural network based identification of meat spoilage using Fourier-transform infrared spectra. Journal of Food Engineering 142:118–31. doi: 10.1016/j.jfoodeng.2014.06.018.
  • Le, B. T., D. Xiao, Y. Mao, and D. He. 2018. Coal analysis based on visible-infrared spectroscopy and a deep neural network. Infrared Physics & Technology 93:34–40. doi: 10.1016/j.infrared.2018.07.013.
  • Li, W. W., M. Lin, Y. M. Huang, H. J. Liu, and X. Q. Zhou. 2017. Near infrared spectroscopy detection of the content of wheat based on improved deep belief network. In 2nd Annual International Conference on Information System and Artificial Intelligence, Bristol.
  • Liu, C. H., W. Liu, J. B. Yang, Y. Chen, and L. Zheng. 2017. Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics. Journal of the Science of Food and Agriculture 97 (7):2094–9. doi: 10.1002/jsfa.8014.
  • Liu, D., D. W. Sun, and X. A. Zeng. 2014. Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food and Bioprocess Technology 7 (2):307–23. doi: 10.1007/s11947-013-1193-6.
  • Liu, L. F., M. Ji, and M. Buchroithner. 2018. Transfer learning for soil spectroscopy based on convolutional neural networks and its application in soil clay content mapping using hyperspectral imagery. Sensors 18 (9):3169. doi: 10.3390/s18093169.
  • Liu, Y., L. J. Sun, Z. Y. Ran, X. Y. Pan, S. Zhou, and S. C. Liu. 2019. Prediction of talc content in wheat flour based on a near-infrared spectroscopy technique. Journal of Food Protection 82 (10):1655–62. doi: 10.4315/0362-028x.Jfp-18-582.
  • Liu, Y. S., S. B. Zhou, W. Han, W. X. Liu, Z. F. Qiu, and C. Li. 2019. Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Analytica Chimica Acta 1086:46–54. doi: 10.1016/j.aca.2019.08.026.
  • Liu, Z., Y. He, H. Cen, and R. Lu. 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 (2):425–36. doi: 10.13031/trans.12214.
  • Luna, A. S., A. P. da Silva, E. A. Alves, R. B. Rocha, I. C. A. Lima, and J. S. de Gois. 2017. Evaluation of chemometric methodologies for the classification of Coffea canephora cultivars via FT-NIR spectroscopy and direct sample analysis. Analytical Methods 9 (29):4255–60. doi: 10.1039/C7AY01167A.
  • Maione, C., and R. M. Barbosa. 2019. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Critical Reviews in Food Science and Nutrition 59 (12):1868–79. doi: 10.1080/10408398.2018.1431763.
  • Mao, X. D., L. J. Sun, G. Y. Hui, and L. L. Xu. 2014. Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network. Journal of Food and Drug Analysis 22 (2):230–5. doi: 10.1016/j.jfda.2014.01.023.
  • Mariani, N. C. T., R. C. da Costa, K. M. G. de Lima, V. Nardini, L. C. Cunha, and G. H. D. Teixeira. 2014. Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics. Food Chemistry 159:458–62. doi: 10.1016/j.foodchem.2014.03.066.
  • Martelo-Vidal, M. J., and M. Vazquez. 2015. Application of artificial neural networks coupled to UV-VIS-NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures. Cyta - Journal of Food 13 (1):32–9. doi: 10.1080/19476337.2014.908955.
  • Meenu, M., Q. X. Cai, and B. J. Xu. 2019. A critical review on analytical techniques to detect adulteration of extra virgin olive oil. Trends in Food Science & Technology 91:391–408. doi: 10.1016/j.tifs.2019.07.045.
  • Minaei, S., S. Shafiee, G. Polder, N. Moghadam-Charkari, S. van Ruth, M. Barzegar, J. Zahiri, M. Alewijn, and P. M. Kus. 2017. VIS/NIR imaging application for honey floral origin determination. Infrared Physics & Technology 86:218–25. doi: 10.1016/j.infrared.2017.09.001.
  • Mohammadi-Moghaddam, T., S. M. A. Razavi, M. Taghizadeh, B. Pradhan, A. Sazgarnia, and A. Shaker-Ardekani. 2018. Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels. Journal of Food Measurement and Characterization 12 (3):1493–502. doi: 10.1007/s11694-018-9764-x.
  • Moomkesh, S., S. A. Mireei, M. Sadeghi, and M. Nazeri. 2017. Early detection of freezing damage in sweet lemons using Vis/SWNIR spectroscopy. Biosystems Engineering 164:157–70. doi: 10.1016/j.biosystemseng.2017.10.009.
  • Murru, C., C. Chimeno-Trinchet, M. E. Diaz-Garcia, R. Badia-Laino, and A. Fernandez-Gonzalez. 2019. Artificial neural network and attenuated total reflectance-fourier transform infrared spectroscopy to identify the chemical variables related to ripeness and variety classification of grapes for protected. Designation of origin wine production. Computers and Electronics in Agriculture 164:104922. doi: 10.1016/j.compag.2019.104922.
  • Nagasubramanian, K., S. Jones, A. K. Singh, S. Sarkar, A. Singh, and B. Ganapathysubramanian. 2019. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15 (1):10. doi: 10.1186/s13007-019-0479-8.
  • Neto, H. A., W. L. F. Tavares, D. Ribeiro, R. C. O. Alves, L. M. Fonseca, and S. V. A. Campos. 2019. On the utilization of deep and ensemble learning to detect milk adulteration. BioData Mining 12:13. doi: 10.1186/s13040-019-0200-5.
  • Ng, W., B. Minasny, M. Montazerolghaem, J. Padarian, R. Ferguson, S. Bailey, and A. B. McBratney. 2019. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma 352:251–67. doi: 10.1016/j.geoderma.2019.06.016.
  • Ni, C., D. Y. Wang, and Y. Tao. 2019. Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy 209:32–9. doi: 10.1016/j.saa.2018.10.028.
  • Nie, P. C., J. N. Zhang, X. P. Feng, C. L. Yu, and Y. He. 2019. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sensors and Actuators B-Chemical 296 (12):126630. doi: 10.1016/j.snb.2019.126630.
  • Nie, P. C., Z. Y. Xia, D. W. Sun, and Y. He. 2013. Application of visible and near infrared spectroscopy for rapid analysis of chrysin and galangin in Chinese propolis. Sensors 13 (8):10539–49. doi: 10.3390/s130810539.
  • Oliveira, M. M., J. P. Cruz-Tirado, and D. F. Barbin. 2019. Nontargeted analytical methods as a powerful tool for the authentication of spices and herbs: A review. Comprehensive Reviews in Food Science and Food Safety 18 (3):670–89. doi: 10.1111/1541-4337.12436.
  • Ozbalci, B., I. H. Boyaci, A. Topcu, C. Kadilar, and U. Tamer. 2013. Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks. Food Chemistry 136 (3-4):1444–52. doi: 10.1016/j.foodchem.2012.09.064.
  • Pallone, J. A. L., E. T. D. Carames, and P. D. Alamar. 2018. Green analytical chemistry applied in food analysis: Alternative techniques. Current Opinion in Food Science 22:115–21. doi: 10.1016/j.cofs.2018.01.009.
  • Pan, W. X., J. W. Zhao, Q. S. Chen, and D. L. Zhang. 2015. Simultaneous and rapid measurement of main compositions in black tea infusion using a developed spectroscopy system combined with multivariate calibration. Food Analytical Methods 8 (3):749–57. doi: 10.1007/s12161-014-9954-z.
  • Pu, H. B., D. W. Sun, J. Ma, and J. H. Cheng. 2015. Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Science 99:81–8. doi: 10.1016/j.meatsci.2014.09.001.
  • Qiu, Z. J., J. Chen, Y. Y. Zhao, S. S. Zhu, Y. He, and C. Zhang. 2018. Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Applied Sciences 8 (2):212. doi: 10.3390/app8020212.
  • Reinholds, I., V. Bartkevics, I. C. J. Silvis, S. M. van Ruth, and S. Esslinger. 2015. Analytical techniques combined with chemometrics for authentication and determination of contaminants in condiments: A review. Journal of Food Composition and Analysis 44:56–72. doi: 10.1016/j.jfca.2015.05.004.
  • Rodriguez, S. D., M. P. Lopez-Fernandez, S. Maldonado, and M. P. Buera. 2019. Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy. Journal of Food Science and Technology 56 (10):4457–64. doi: 10.1007/s13197-019-03948-7.
  • Ropodi, A. I., E. Z. Panagou, and G. J. E. Nychas. 2016. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends in Food Science & Technology 50:11–25. doi: 10.1016/j.tifs.2016.01.011.
  • Sanz, J. A., A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Goncalves, D. Paternain, A. Jurio, and P. Melo-Pinto. 2016. Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms. Journal of Food Engineering 174:92–100. doi: 10.1016/j.jfoodeng.2015.11.024.
  • Sexton, J., Y. Everingham, D. Donald, S. Staunton, and R. White. 2018. A comparison of non-linear regression methods for improved on-line near infrared spectroscopic analysis of a sugarcane quality measure. Journal of near Infrared Spectroscopy 26 (5):297–310. doi: 10.1177/0967033518802448.
  • Shi, C., J. P. Qian, W. Y. Zhu, H. Liu, S. Han, and X. T. Yang. 2019. Nondestructive determination of freshness indicators for tilapia fillets stored at various temperatures by hyperspectral imaging coupled with RBF neural networks. Food Chemistry 275:497–503. doi: 10.1016/j.foodchem.2018.09.092.
  • Siedliska, A., P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska. 2018. Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biology and Technology 139:115–26. doi: 10.1016/j.postharvbio.2018.01.018.
  • Su, W. H., S. Bakalis, and D. W. Sun. 2020. Chemometric determination of time series moisture in both potato and sweet potato tubers during hot air and microwave drying using near/mid-infrared (NIR/MIR) hyperspectral techniques. Drying Technology 38 (5-6):806–23. doi: 10.1080/07373937.2019.1593192.
  • Sun, Y., K. L. Wei, Q. Liu, L. Q. Pan, and K. Tu. 2018. Classification and discrimination of different fungal diseases of three infection levels on peaches using hyperspectral reflectance imaging analysis. Sensors 18 (4):1295. doi: 10.3390/s1804.1295.
  • Sun, Y., X. Z. Gu, K. Sun, H. J. Hu, M. Xu, Z. J. Wang, K. Tu, and L. Q. Pan. 2017. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. Lwt 75:557–64. doi: 10.1016/j.lwt.2016.10.006.
  • Tamouridou, A. A., X. E. Pantazi, T. Alexandridis, A. Lagopodi, G. Kontouris, and D. Moshou. 2018. Spectral identification of disease in weeds using multilayer perceptron with automatic relevance determination. Sensors 18 (9):2770. doi: 10.3390/s18092770.
  • Tao, F., H. Yao, Z. Hruska, L. W. Burger, K. Rajasekaran, and D. Bhatnagar. 2018. Recent development of optical methods in rapid and non-destructive detection of aflatoxin and fungal contamination in agricultural products. Trac Trends in Analytical Chemistry 100:65–81. doi: 10.1016/j.trac.2017.12.017.
  • Tao, F. F., Y. K. Peng, C. L. Gomes, K. L. Chao, and J. W. Qin. 2015. A comparative study for improving prediction of total viable count in beef based on hyperspectral scattering characteristics. Journal of Food Engineering 162:38–47. doi: 10.1016/j.jfoodeng.2015.04.008.
  • Teixeira, A. M., and C. Sousa. 2019. A review on the application of vibrational spectroscopy to the chemistry of nuts. Food Chemistry 277:713–24. doi: 10.1016/j.foodchem.2018.11.030.
  • Teye, E., X. Y. Huang, L. K. Sam-Amoah, J. Takrama, D. Boison, F. Botchway, and F. Kumi. 2015. Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis. Food Chemistry 176:403–10. doi: 10.1016/j.foodchem.2014.12.042.
  • Tian, H., Zhang, L. N. M., Li, Y., Wang, D. G., Sheng, J., Liu, C. M., Wang. 2019. WSPXY combined with BP-ANN method for hemoglobin determination based on near-infrared spectroscopy. Infrared Physics & Technology 102:103003. doi: 10.1016/j.infrared.2019.103003.
  • Valand, R., S. Tanna, G. Lawson, and L. Bengtstrom. 2020. A review of Fourier Transform Infrared (FTIR) spectroscopy used in food adulteration and authenticity investigations. Food Additives & Contaminants. Part A, Chemistry, Analysis, Control, Exposure & Risk Assessment 37 (1):19–38. doi: 10.1080/19440049.2019.1675909.
  • Valdes, A., A. Beltran, C. Mellinas, A. Jimenez, and M. C. Garrigos. 2018. Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content. Trends in Food Science & Technology 77:120–30. doi: 10.1016/j.tifs.2018.05.014.
  • Valinger, D., M. Kusen, A. J. Tusek, M. Panic, T. Jurina, M. Benkovic, I. R. Redovnikovic, and J. G. Kljusuric. 2019. Development of near infrared spectroscopy models for quantitative prediction of the content of bioactive compounds in olive leaves. Chemical and Biochemical Engineering Quarterly 32 (4):535–43. doi: 10.15255/CABEQ.2018.1396.
  • Versari, A., V. F. Laurie, A. Ricci, L. Laghi, and G. P. Parpinello. 2014. Progress in authentication, typification and traceability of grapes and wines by chemometric approaches. Food Research International 60:2–18. doi: 10.1016/j.foodres.2014.02.007.
  • Viejo, C. G., S. Fuentes, D. Torrico, K. Howell, and F. R. Dunshea. 2018. Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms. Journal of the Science of Food and Agriculture 98 (2):618–27. doi: 10.1002/jsfa.8506.
  • Wang, K. Q., H. B. Pu, and D. W. Sun. 2018. Emerging spectroscopic and spectral imaging techniques for the rapid detection of microorganisms: An overview. Comprehensive Reviews in Food Science and Food Safety 17 (2):256–73. doi: 10.1111/1541-4337.12323.
  • Wang, Y. W., W. Ding, L. P. Kou, L. Li, C. Wang, and W. M. Jurick. 2015. A non-destructive method to assess freshness of raw bovine milk using FT-NIR spectroscopy. Journal of Food Science and Technology 52 (8):5305–10. doi: 10.1007/s13197-014-1574-5.
  • Wang, Z. D., M. H. Hu, and G. T. Zhai. 2018. Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors 18 (4):14. doi: 10.3390/s18041126.
  • Wu, M. M., J. Sun, B. Lu, X. Ge, X. Zhou, and M. L. Zou. 2019. Application of deep brief network in transmission spectroscopy detection of pesticide residues in lettuce leaves. Journal of Food Process Engineering 42 (3):e13005. doi: 10.1111/jfpe.13005.
  • Wu, N., C. Zhang, X. L. Bai, X. Y. Du, and Y. He. 2018. Discrimination of Chrysanthemum varieties using hyperspectral imaging combined with a deep convolutional neural network. Molecules 23 (11):2831. doi: 10.3390/molecules23112831.
  • Wu, N., Y. Zhang, R. S. Na, C. X. Mi, S. S. Zhu, Y. He, and C. Zhang. 2019. Variety identification of oat seeds using hyperspectral imaging: Investigating the representation ability of deep convolutional neural network. RSC Advances 9 (22):12635–44. doi: 10.1039/C8RA10335F.
  • Wu, Y., L. L. Li, L. Liu, and Y. Liu. 2019. Nondestructive measurement of internal quality attributes of apple fruit by using NIR spectroscopy. Multimedia Tools and Applications 78 (4):4179–95. doi: 10.1007/s11042-017-5388-0.
  • Xia, Z. Y., Y. M. Sun, C. Y. Cai, Y. He, and P. C. Nie. 2019. Rapid determination of chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum using near-infrared spectroscopy. Sensors 19 (9):1981. doi: 10.3390/s19091981.
  • Xu, S., H. Z. Lu, C. Ference, and Q. Q. Zhang. 2019. Visible/near infrared reflection spectrometer and electronic nose data fusion as an accuracy improvement method for portable total soluble solid content detection of orange. Applied Sciences 9 (18):3761. doi: 10.3390/app9183761.
  • Xu, Y., F. Y. H. Kutsanedzie, H. Sun, M. X. Wang, Q. S. Chen, Z. M. Guo, and J. Z. Wu. 2018. Rapid pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy. Food Analytical Methods 11 (4):1199–208. doi: 10.1007/s12161-017-1095-8.
  • Xue, J. T., Q. W. Yang, C. Y. Li, Y. Jing, S. X. Wang, M. X. Zhang, and P. Li. 2019. Rapid and simultaneous determination of three active components in raw and processed root samples of scutellaria baicalensis by near-infrared spectroscopy. Planta Medica 85 (1):72–80. doi: 10.1055/a-0655-2211.
  • Xue, Z. H., Y. X. Zhang, W. C. Yu, J. C. Zhang, J. Y. Wang, F. Wan, Y. Kim, Y. D. Liu, and X. H. Kou. 2019. Recent advances in aflatoxin B1 detection based on nanotechnology and nanomaterials—A review. Analytica Chimica Acta 1069:1–27. doi: 10.1016/j.aca.2019.04.032.
  • Yang, D., D. D. He, A. X. Lu, D. Ren, and J. H. Wang. 2017. Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef. Infrared Physics & Technology 83:206–16. doi: 10.1016/j.infrared.2017.05.005.
  • Yang, D. Z., H. Li, C. C. Cao, F. D. Chen, Y. B. Zhou, and Z. L. Xiu. 2014. Analysis of the oil content of rapeseed using artificial neural networks based on near infrared spectral data. Journal of Spectroscopy 2014:1–5. doi: 10.1155/2014/901310.
  • Yang, J., J. F. Xu, X. L. Zhang, C. Y. Wu, T. Lin, and Y. B. Ying. 2019. Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica Chimica Acta 1081:6–17. doi: 10.1016/j.aca.2019.06.012.
  • Yang, W., C. Yang, Z. Y. Hao, C. Q. Xie, and M. Z. Li. 2019. Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network. IEEE Access. 7:118239–48. doi: 10.1109/ACCESS.2019.2936892.
  • Yin, W. X., C. Zhang, H. Y. Zhu, Y. R. Zhao, and Y. He. 2017. Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries. PLoS One 12 (7):e0180534. doi: 10.1371/journal.pone.0180534.
  • Yu, J., J. C. Zhan, and W. D. Huang. 2017. Identification of wine according to grape variety using near-infrared spectroscopy based on radial basis function neural networks and least-squares support vector machines. Food Analytical Methods 10 (10):3306–11. doi: 10.1007/s12161-017-0887-1.
  • Yu, X. J., H. D. Lu, and Q. Y. Liu. 2018. Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf. Chemometrics and Intelligent Laboratory Systems 172:188–93. doi: 10.1016/j.chemolab.2017.12.010.
  • Yu, X. J., H. D. Lu, and D. Wu. 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 Biology and Technology 141:39–49. doi: 10.1016/j.postharvbio.2018.02.013.
  • Yu, X. J., L. Tang, X. F. Wu, and H. D. Lu. 2018. Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm. Food Analytical Methods 11 (3):768–80. doi: 10.1007/s12161-017-1050-8.
  • Yu, X. J., J. P. Wang, S. T. Wen, J. Q. Yang, and F. F. Zhang. 2019. A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei). Biosystems Engineering 178:244–55. doi: 10.1016/j.biosystemseng.2018.11.018.
  • Yu, X. J., X. Yu, S. T. Wen, J. Q. Yang, and J. P. Wang. 2019. Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp. Journal of Food Measurement and Characterization 13 (3):2082–94. doi: 10.1007/s11694-019-00129-0.
  • Yuan, Q. Q., Q. Zhang, J. Li, H. F. Shen, and L. P. Zhang. 2019. Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing 57 (2):1205–18. doi: 10.1109/TGRS.2018.2865197.
  • Zhang, X., L. X. Han, Y. Y. Dong, Y. Shi, W. J. Huang, L. H. Han, P. Gonzalez-Moreno, H. Q. Ma, H. C. Ye, and T. Sobeih. 2019. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing 11 (13):1554. doi: 10.3390/rs11131554.
  • Zhang, X. L., T. Lin, J. F. Xu, X. Luo, and Y. B. Ying. 2019. DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis. Analytica Chimica Acta 1058:48–57. doi: 10.1016/j.aca.2019.01.002.
  • Zhang, Y. Y., Y. L. Li, S. Y. Li, H. Zhang, and H. L. Ma. 2018. In situ monitoring of the effect of ultrasound on the sulfhydryl groups and disulfide bonds of wheat gluten. Molecules 23 (6):1376. doi: 10.3390/molecules23061376.
  • Zhao, X. H., M. Li, and Z. B. Xu. 2018. Detection of foodborne pathogens by surface enhanced Raman spectroscopy. Frontiers in Microbiology 9:1236. doi: 10.3389/fmicb.2018.01236.
  • Zheng, W. B., X. P. Fu, and Y. B. Ying. 2014. Spectroscopy-based food classification with extreme learning machine. Chemometrics and Intelligent Laboratory Systems 139:42–7. doi: 10.1016/j.chemolab.2014.09.015.
  • Zhou, L., C. Zhang, F. Liu, Z. J. Qiu, and Y. He. 2019. Application of deep learning in food: A review. Comprehensive Reviews in Food Science and Food Safety 18 (6):1793–811. doi: 10.1111/1541-4337.12492.
  • Zhou, X., J. Sun, Y. Tian, B. Lu, Y. Y. Hang, and Q. S. Chen. 2020. Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images. International Journal of Remote Sensing 41 (6):2263–76. doi: 10.1080/01431161.2019.1685721.
  • Zhu, S. S., L. Zhou, P. Gao, Y. D. Bao, Y. He, and L. Feng. 2019. Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties. Molecules 24 (18):17. doi: 10.3390/molecules24183268.
  • Zhu, S. S., L. Zhou, C. Zhang, Y. D. Bao, B. H. Wu, H. J. Chu, Y. Yu, Y. He, and L. Feng. 2019. Identification of soybean varieties using hyperspectral imaging coupled with convolutional neural network. Sensors 19 (19):15. doi: 10.3390/s19194065.

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