https://doi.org/10.4081/jae.2022.1311
Hyperspectral imaging to measure apricot attributes during storage
Published: 28 June 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The fruit industry needs rapid and non-destructive techniques to evaluate the quality of the products in the field and during the post-harvest phase. The soluble solids content (SSC), in terms of °Brix, and the flesh firmness (FF) are typical parameters used to measure fruit quality and maturity state. Hyperspectral imaging (HSI) is a powerful technique that combines image analysis and infrared spectroscopy. This study aimed to evaluate the potential of the application of the Vis/NIR push-broom hyperspectral imaging (400 to 1000 nm) to predict the firmness and the °Brix in apricots (180 samples) during storage (11 days). Partial least squares (PLS) and artificial neural networks (ANN) were used to develop predictive models. For the PLS, R2 values (test set) up to 0.85 (RMSEP=1.64 N) and 0.72 (RMSEP=0.51 °Brix) were obtained for the FF and SSC, respectively. Concerning the ANN, the best results in the test set were achieved for the FF (R2=0.85, RMSEP=1.50 N). The study showed the potential of the HSI technique as a non-destructive tool for measuring apricot quality even along the whole supply chain.
Amoriello T., Ciccoritti R., Carbone K. 2019. Vibrational spectroscopy as a green technology for predicting nutraceutical properties and antiradical potential of early-to-late apricot genotypes. Postharv. Biol. Technol. 155:156-66.
DOI: https://doi.org/10.1016/j.postharvbio.2019.03.013
Amoriello T., Ciccoritti R., Paliotta M., Carbone K. 2018. Classification and prediction of early-to-late ripening apricot quality using spectroscopic techniques combined with chemometric tools. Sci. Hortic. 240:310-7.
DOI: https://doi.org/10.1016/j.scienta.2018.06.031
Berardinelli A., Cevoli C., Silaghi F.A., Fabbri A., Ragni L., Giunchi A. 2010. FT-NIR Spectroscopy for the Quality Characterization of Apricots (Prunus Armeniaca L.). J. Food Sci. 75:462-8.
DOI: https://doi.org/10.1111/j.1750-3841.2010.01741.x
Bureau S., Reich M., Renard C.M.G.C., Ruiz D., Audergon J.M. 2012. Rapid characterization of apricot fruit quality using near and mid-infrared spectroscopy: Study of the model robustness. Acta Hortic. 934:173-6.
DOI: https://doi.org/10.17660/ActaHortic.2012.934.20
Bureau S., Renard C.M.G.C., Fakhfackh Z., Audergon J.M. 2018. Infrared spectroscopy as a rapid tool to assess apricot fruit quality: Comparison of two strategies for a model establishment. Acta Hortic. 1214:145-9.
DOI: https://doi.org/10.17660/ActaHortic.2018.1214.24
Bureau S., Ruiz D., Reich M., Gouble B., Bertrand D., Audergon J.M., Renard C.M.G.C. 2009. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chem. 113:1323-8.
DOI: https://doi.org/10.1016/j.foodchem.2008.08.066
Buyukcan M.B., Kavdir I. 2017. Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy. J. Food Measure. Character. 11:651-9.
DOI: https://doi.org/10.1007/s11694-016-9434-9
Camps C., Christen D. 2009a. Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT - Food Sci. Technol. 42:1125-31.
DOI: https://doi.org/10.1016/j.lwt.2009.01.015
Camps C., Christen D. 2009b. On-tree follow-up of apricot fruit development using a hand-held NIR instrument. J. Food Agric. Environ. 7:394-400.
Carlini P., Massantini R., Mencarelli F. 2000. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. J. Agric. Food Chem. 48:5236-42.
DOI: https://doi.org/10.1021/jf000408f
Chandrasekaran I., Panigrahi S.S., Ravikanth L., Singh C.B. 2019. Potential of near-infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: an overview. Food Analyt. Methods. 12:2438-58.
DOI: https://doi.org/10.1007/s12161-019-01609-1
Chong I.G., Jun C.H. 2005. Performance of some variable selection methods when multicollinearity is present. Chemometr. Intell. Lab. Syst. 78:103-12.
DOI: https://doi.org/10.1016/j.chemolab.2004.12.011
Christen D., Camps C., Summermatter A., Gabioud Rebeaud S., Baumgartner D. 2012. Prediction of the pre- and postharvest apricot quality with different VIS/NIRs devices. Acta Hortic. 966:149-54.
DOI: https://doi.org/10.17660/ActaHortic.2012.966.23
Ciacciulli A., Bassi D., Castellari L., Foschi S. 2018. Fruit ripening evolution in diverse commercial apricots by conventional and non-destructive methods: Preliminary results. Acta Hortic. 1214:165-70.
DOI: https://doi.org/10.17660/ActaHortic.2018.1214.27
De Oliveira G.A., Bureau S., Renard C.M.G.C., Pereira-Netto A.B., De Castilhos F. 2014. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 143:223-30.
DOI: https://doi.org/10.1016/j.foodchem.2013.07.122
ElMasry G., Wang N., ElSayed A., Ngadi M. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J. Food Engine. 81:98-107.
DOI: https://doi.org/10.1016/j.jfoodeng.2006.10.016
Guo W., Li W., Yang B., Zhu Z.Z., Liu D., Zhu X. 2019. A novel noninvasive and cost-effective handheld detector on soluble solids content of fruits. J. Food Engine. 257:1-9.
DOI: https://doi.org/10.1016/j.jfoodeng.2019.03.022
Helin R., Indahl U.G., Tomic O., Liland K.H. 2021. On the possible benefits of deep learning for spectral preprocessing. J. Chemometr. 36:e3374.
DOI: https://doi.org/10.1002/cem.3374
Manley M., Joubert E., Myburgh L., Kidd M. 2007. Prediction of soluble solids content and post-storage internal quality of Bulida apricots using near infrared spectroscopy. J. Near Infrared Spectros. 15:179-88.
DOI: https://doi.org/10.1255/jnirs.725
McGlone V.A., Kawano S. 1998. Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharv. Biol. Technol. 13:131-41.
DOI: https://doi.org/10.1016/S0925-5214(98)00007-6
Munera S., Besada C., Blasco J., Cubero S., Salvador A., Talens P., Aleixos N. 2017. Astringency assessment of persimmon by hyperspectral imaging. Postharv. Biol. Technol. 125:35-41.
DOI: https://doi.org/10.1016/j.postharvbio.2016.11.006
Nicolaï B.M., Beullens K., Bobelyn E., Peirs A., Saeys W., Theron K.I., Lammertyn J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharv. Biol. Technol. 46:99-118.
DOI: https://doi.org/10.1016/j.postharvbio.2007.06.024
Pu H., Liu D., Wang L., Sun D.W. 2016. Soluble solids content and ph prediction and maturity discrimination of Lychee fruits using visible and near infrared hyperspectral imaging. Food Analyt. Methods. 9:235-44.
DOI: https://doi.org/10.1007/s12161-015-0186-7
Ruiz D., Reich M., Bureau S., Renard C.M.G.C., Audergon J.M. 2008. Application of reflectance colorimeter measurements and infrared spectroscopy methods to rapid and nondestructive evaluation of carotenoids content in apricot (Prunus armeniaca L.). J. Agric. Food Chem. 56:4916-922.
DOI: https://doi.org/10.1021/jf7036032
Uwadaira Y., Sekiyama Y., Ikehata A. 2017. An examination of the principle of non-destructive flesh firmness measurement of peach fruit by using VIS-NIR spectroscopy. Heliyon. 4:e00531.
DOI: https://doi.org/10.1016/j.heliyon.2018.e00531
Wendel A., Underwood J., Walsh K. 2018. Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform. Comput. Electron. Agric. 155:298-313.
DOI: https://doi.org/10.1016/j.compag.2018.10.021
Witherspoon J.M., Jackson J.F. 1995. Analysis of fresh and dried apricot. In: Linskens H.F., Jackson J.F. (Eds.), Fruit analysis. Modern methods of plant analysis, Vol. 18. Springer-Verlag, Berlin-Heidelberg, Germany, pp. 111-131.
DOI: https://doi.org/10.1007/978-3-642-79660-9_7
Xue J, Zhang S, Zhang J, 2015. Ripeness classification of Shajin apricot using hyperspectral imaging technique. Nongye Gongcheng Xuebao/Trans. Chinese Soc. Agric. Engine. 31:300-7.
How to Cite
Benelli, A. (2022) “Hyperspectral imaging to measure apricot attributes during storage”, Journal of Agricultural Engineering, 53(2). doi: 10.4081/jae.2022.1311.
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