Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

Open access

Abstract

The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained via the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries.

Allen K.M., Vandyke L.H., and Brunsbach R.L., 1966. Apparatus for detecting seeds in fruit. U.S. Patent No. 3, 275-136.

Baiano A., Terracone C., Peri G., and Romaniello R., 2012. Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Computers Electronics Agric., 87, 142-151.

Baranowski P., Jedryczka M., Mazurek W., Babula-Skowronska D., Siedliska A., and Kaczmarek J., 2015. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PloS one, 10(3), e0122913.

Baranowski P., Mazurek W., and Pastuszka-Woźniak J., 2013. Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data. Postharvest Biol. Technol., 86, 249-258.

Becker B.L., Lusch D.P., and Qi J., 2005. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis. Remote Sensing Environ., 97, 238-248.

Carlini P., Massantini R., and Mencarelli F., 2000. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. J. Agric. Food Chemistry, 48, 5236-5242.

Donis-González I.R., Guyer D.E., Kavdir I., Shahriari D., and Pease A., 2015. Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging. J. Food Measurement Characterization, 9(3), 290-298.

Fan G., Zha J., Du R., and Gao L., 2009. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. J. Food Eng., 93 (4), 416-420.

Fan S., Huang W., Guo Z., Zhang B., and Zhao C., 2005. Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging. Food Anal. Method, 8, 1936-1946.

FAOSTAT, 2016. http://wwandw.fao.org/faostat/en/?#data/QC Accessed 5.01.2017.

Golic M. and Walsh K.B., 2006. Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stone fruit for total soluble solids content. Analytica Chimica Acta, 555(2), 286-291.

Haff R.P., Jackson E.S., and Pearson T.C., 2005. Non-destructive detection of pits in dried plums. Small, 175, 6-6.

Haff R., Pearson T., and Jackson E., 2013. One dimensional linescan x-ray detection of pits in fresh cherries. American J. Agric. Sci. Technol., 1, 18-26.

Haff R.P. and Toyofuku N., 2008. X-ray detection of defects and contaminants in the food industry. Sensing Instrumentation for Food Quality and Safety, 2 (4), 262-273.

Herrera J., Guesalaga A., and Agosin E., 2003. Shortwave-near infrared spectroscopy for non-destructive determination of maturity of wine grapes. Measurement Sci. Technol., 14(5), 689.

Kawano S., 2016. Past, present and future near infrared spectroscopy applications for fruit and vegetables. NIR news, 27(1), 7-9.

Leiva-Valenzuela G.A, Lu R., and Aguilera J.M., 2014. Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Innovative Food Sci. Emerg. Technol., 24, 2-13.

Liaghat S., Ehsani R., Manso S., Shafr H.Z., Meon S., Sankaran S., and Azam S.H., 2014. Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. Int. J. Remote Sensing, 35(10), 3427-3439.

Liu Y., Sun X., and Aiguo O., 2010. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci. Technol., 44 (4), 602-607.

Lu R., 2007. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety, 1(1), 19.

Lu Y., Huang Y., and Lu R., 2017. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A Review. Applied Sci., 7(2), 189.

Munera S., Besada C., Aleixos N., Talens P., Salvador A., Sun D.W., Cubero S., and Blasco J., 2017. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT-Food Sci. Technol., 77, 241-248.

Nicolaï B.M., Defraeye T., De Ketelaere B., Herremans E., Hertog M.L., Saeys W., Toricelli A., Vandendriessche T. and Verboven P., 2014. Nondestructive measurement of fruit and vegetable quality. Annual Review Food Sci. Technol., 5, 285-312.

Pan L., Sun Y., Xiao H., Gu X., Hu P., Wei Y., and Tu K., 2017. Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish. Postharvest Biology Technol., 126, 40-49.

Pu Y.Y., Feng Y.Z., and Sun D.W., 2015. Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: a review. Comprehensive Reviews in Food Sci. Food Safety, 14(2), 176-188.

Qin J. and Lu R., 2005. Detection of pits in tart cherries by hyperspectral transmission imaging. Trans. ASAE, 48 (5), 1700-1963.

Schaare P.N. and Fraser D.G., 2000. Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidiachinensis). Postharvest Biol. Technol., 20(2), 175-184.

Siedliska A., Baranowski P., and Mazurek W., 2014. Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data. Computers Electronics Agric., 106, 66-74.

Song D., Song L., Sun Y., Hu P., Tu K., Pan L., Yang H., and Huang M., 2016. Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm. Applied Sci., 6(9), 249.

Sun J., Ma B., Dong J., Zhu R., Zhang R., and Jiang W., 2016. Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms. J. Food Process Eng., 40, doi: 10.1111/jfpe.12496

Sun T., Lin H., Xu H., and Ying Y., 2009. Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceaepyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol. Technol., 51(1), 86-90.

Sun Y., Gu X., Sun K., Hu H., Xu M., Wang Z., Kang T., and Pan L., 2017. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. LWT-Food Sci.Technol., 75, 557-564.

Szuvandzsiev P., Helyes L., Lugasi A., Szántó C., Baranowski P., and Pék Z., 2014. Estimation of antioxidant components of tomato using VIS-NIR reflectance data by handheld portable spectrometer. Int. Agrophys., 28(4), 521-527.

Timm E.J., Gilliland P.V., Brown G.K., and Affeldt H.A., 1991. Potential methods for detecting pits in tart cherries. Applied Eng. Agric., 7(1), 103-109.

USDA foreign agricultural service - EU-28, 2016. Stone Fruit Annual, https://gain.fas.usda.gov/Recent%20GAIN%20Publications/Stone%20Fruit%20Annual_Madrid_EU-28_8-19-2016.pdf

Wang N.N., Sun D.W., Yang Y.C., Pu H., and Zhu Z., 2016. Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality. Food Analytical Methods, 9(1), 178-191.

Williams P. and Norris K. (Eds), 2001. Near-Infrared Techno-logy in the Agricultural and Food Industries. American Association of Cereal Chemists, St. Paul, MIN, USA.

Witten I.H. and Frank E., 2005. Data mining. Practical machine learning tools and techniques. Morgan Kaufmann.

Wojdyło A., Nowicka P., Laskowski P., and Oszmianìski J., 2014. Evaluation of sour cherry (Prunuscerasus L.) fruits fortheir polyphenol content, antioxidant properties, and nutritional components. J. Agric. Food Chemistry, 62(51), 12332-12345.

Xing J., Guyer D., Ariana D., and Lu R., 2008. Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry. Sensing and Instrumentation for Food Quality and Safety, 2(3), 161-167.

Zion B., Kim S.M., McCarthy M.J., and Chen P., 1997. Detection of pits in olives under motion by nuclear magnetic resonance. J. Sci. Food Agric., 75(4), 496-502.

Zion B., McCarthy M.J., and Chen P., 1994. Real-time detection of pits in processed cherries by magnetic resonance projections. LWT-Food Sci. Technol., 27(5), 457-462.

International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2017: 1.242
5-year IMPACT FACTOR: 1.267

CiteScore 2018: 1.44

SCImago Journal Rank (SJR) 2018: 0.399
Source Normalized Impact per Paper (SNIP) 2018: 0.891

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 242 168 7
PDF Downloads 134 116 6