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Ultra-Low-Cost Self-Referencing Multispectral Detector for Non-Destructive Measurement of Fruit Quality

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Abstract

A very low-cost sensor is developed to non-destructively test fruits by sequentially turning on light-emitting diodes at 12 different wavelengths and measuring the reflectance in the interactance mode. The detector is tested on kiwifruit to measure soluble solids content (SSC) and dry matter (DM) non-destructively, while the performance is compared with a benchtop spectrometer. For SSC and DM measurements, a total of 378 and 200 samples of kiwifruits were measured respectively. Non-parametric regression was used for the multi-spectral detector, while a partial least squares regression model was used for the benchtop spectrometer. Different regression techniques were used as they provided the best prediction for the two different measurements. For SSC measurements, coefficient of determination (R2) of 0.83, root mean square error for prediction (RMSEP) of 0.85%, bias of −0.004%, and SDR of 2.45 were observed using the multispectral detector. The corresponding values achieved with the benchtop spectrometer were 0.98, 0.37%, 0.01%, and 5.60, respectively. For dry matter measurements with 200 kiwifruits, R2, RMSEP, bias, and SDR of 0.83, 0.61%, −0.02, and 2.44 were achieved with the prototype multispectral detector compared with the values of 0.96, 0.31%, −0.11, and 4.80, respectively, achieved with the benchtop spectrometer. The efficacy of the multispectral detector was also tested by measuring 30 navel oranges, wherein an R2 and root mean square error for calibration (RMSEC) of 0.79 and 0.47% were achieved for SSC measurements. While the performance of the multispectral detection is lower than a benchtop spectrometer, its performance is still better than low-cost spectrometers and can be used to inexpensively measure the quality of fruits in a non-destructive manner.

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References

  • Abasi S, Minaei S, Jamshidi B, Fathi D (2018) Dedicated non-destructive devices for food quality measurement: a review. Trends Food Sci Technol 78:197–205

    Article  CAS  Google Scholar 

  • Ahmad MS, Siddiqui MW (2015) Factors affecting postharvest quality of fresh fruits. Postharvest quality assurance of fruits: practical approaches for developing countries. Springer International Publishing, Cham, pp 7–32

    Book  Google Scholar 

  • Arendse E, Fawole OA, Magwaza LS, Opara UL (2018) Non-destructive prediction of internal and external quality attributes of fruit with thick rind: a review. J Food Eng 217:11–23

    Article  Google Scholar 

  • Bhunase M, Patil S (1998) Near infrared spectroscopy for fruit quality analysis. Int J Eng Res Technol 10:1–15

    Google Scholar 

  • Bozanic DK, Djokovic V, Bibić N, Sreekumari Nair P, Georges MK, Radhakrishnan T (2009) Biopolymer-protected CdSe nanoparticles. Carbohydr Res 344:2383–2387

    Article  CAS  Google Scholar 

  • Büning-Pfaue H (2003) Analysis of water in food by near infrared spectroscopy. Food Chem 82(1):107–115

    Article  CAS  Google Scholar 

  • Cantin CM, Soto A, Crisosto GM, Crisosto CH (2010) Evaluation of a non-destructive dry matter sensor for kiwifruit. In: VII International Symposium on Kiwifruit, vol 913, pp 627–632

    Google Scholar 

  • Chandrasekaran I, Panigrahi SS, Ravikanth L, Singh CB (2019) Potential of near-infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: an overview. Food Anal Methods 12:2438–2458. https://doi.org/10.1007/s12161-019-01609-1

    Article  Google Scholar 

  • Chen X, Han W (2012) Spectroscopic determination of soluble solids content of ‘Qinmei’ kiwifruit using partial least squares. Afr J Biotechnol 11:2528–2536

    CAS  Google Scholar 

  • Commission for Environmental Cooperation (2017) Characterization and Management of Food Loss and Waste in North America. [PDF file]. Retrieved from http://www3.cec.org/islandora/en/item/11772-characterization-and-management-food-loss-and-waste-in-north-america-en.pdf. Accessed 29 Sept 2019

  • Datt B (1999) Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. Int J Remote Sens 20(14):2741–2759

    Article  Google Scholar 

  • Dos Santos CAT, Lopo M, Páscoa RN, Lopes JA (2013) A review on the applications of portable near-infrared spectrometers in the agro-food industry. Appl Spectrosc 67(11):1215–1233

    Article  CAS  Google Scholar 

  • Doymaz I (2009) Mathematical modelling of thin-layer drying of kiwifruit slices. J Food Process Preserv 33:145–160

    Article  Google Scholar 

  • Dufour É (2009) Principles of infrared spectroscopy. In: Sun DW (ed) Infrared spectroscopy for food quality analysis and control. Academic Press, San Diego, pp 1–27

    Google Scholar 

  • Feng J (2003) Segregation of ‘Hayward’ kiwifruit for storage potential: a thesis presented in partial fulfilment of the requirements for the Degree of Doctor of Philosophy. Massey University, Palmerston North, New Zealand.

  • Foster K (2018) Spoiled, rotten, and left behind. In: How to feed the world (pp. 132–147). Island Press, Washington, DC

  • González-Caballero V, Sánchez MT, López MI, Pérez-Marín D (2010) First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery. J Food Eng 101(2):158–165

    Article  CAS  Google Scholar 

  • Hazelton ML (2015) International encyclopedia of the social and behavioral sciences (2nd edition), 867-877.

  • Ignat T, Lurie S, Nyasordzi J, Ostrovsky V, Egozi H, Hoffman A, Friedman H, Weksler A, Schmilovitch Z (2014) Forecast of apple internal quality indices at harvest and during storage by vis–NIR spectroscopy. Food Bioprocess Technol 7:2951–2961

    Article  Google Scholar 

  • Jha SN (2010) Near infrared spectroscopy. In: Jha SN (ed) Nondestructive evaluation of food quality: theory and practice. Springer-Verlag, Berlin, pp 141–212

    Chapter  Google Scholar 

  • Junfang X, Xiaoyu L, Peiwu L, Wei W, Xiaoxia D (2007) Approach to nondestructive measurement of Vitamin C content of orange with near-infrared spectroscopy treated by wavelet transform. Trans Chin Soc Agric Eng 2007(6)

  • Kaipia R, Dukovska-Popovska I, Loikkanen L (2013) Creating sustainable fresh food supply chains through waste reduction. Int J Phys Distrib Logist Manag 43(3):262–276

    Article  Google Scholar 

  • Kaur H, Kunnemeyer R, McGlone A (2017) Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment. J Near Infrared Spect 25(4):267–277

    Article  CAS  Google Scholar 

  • Kawano S (1994) Present condition of nondestructive quality evaluation of fruits and vegetables in Japan. Jpn Agric Res Q 28:212–216

    Google Scholar 

  • Kemps B, Leon L, Best S, De Baerdemaeker J, De Ketelaere B (2010) Assessment of the quality parameters in grapes using VIS/NIR spectroscopy. Biosyst Eng 105(4):507–513

    Article  Google Scholar 

  • Knight A, & Davis C (2010) What a waste! Surplus fresh foods research project. Food Climate Research Network

  • Lee JS, Kim SC, Seong KC, Kim CH, Um YC, Lee SK (2012) Quality prediction of kiwifruit based on near infrared spectroscopy. Korean J Hortic Sci 30:709–717

    CAS  Google Scholar 

  • Li M, Pullanagari RR, Pranamornkith T, Yule IJ, East AR (2017) Quantitative prediction of post storage ‘Hayward’ kiwifruit attributes using at harvest vis–NIR spectroscopy. J Food Eng 202:46–55

    Article  CAS  Google Scholar 

  • Li M, Qian Z, Shi B, Medlicott J, East A (2018) Evaluating the performance of a consumer scale SCiOTM molecular sensor to predict quality of horticulture products. Postharvest Biol Technol 145:183–192

    Article  Google Scholar 

  • Li Y, Ahluwalia KS, & Saini SS (2020) Reinforcement learning integrated with supervised learning for training of near infrared spectrum data for non-destructive testing of fruits. SPIE Sensing for Agriculture and Food Quality and Safety XII, April, 2020, Anaheim, CA

  • Liu YD; Ying YB; Chen ZM; Fu XP (2004) Appli3 peaches. In: Monitoring food safety, agriculture, and plant health. Bennedsen, B.S., Chen, Y.R., Meyer, G.E., Senecal, A.G., Tu, S.I., (Eds.); SPIE: Providence, USA, 1. Volume 5271, pp. 347–355

  • Liu YD, Ouyang AG, Luo J, Chen XM (2007) Near infrared diffuse reflectance spectroscopy for rapid analysis of soluble solids content in navel orange. Spectrosc Spectr Anal 27:2190–2192

    CAS  Google Scholar 

  • Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solids content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT Food Sci Technol 43(4):602–607

    Article  CAS  Google Scholar 

  • Mari M, Bautista-Baños S, Sivakumar D (2016) Decay control in the postharvest system: role of microbial and plant volatile organic compounds. Postharvest Biol Technol 122:70–81

    Article  CAS  Google Scholar 

  • McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13:131–141

    Article  Google Scholar 

  • McGlone VA, Clark CJ, Jordan RB (2007) Comparing density and VNIR methods for predicting quality parameters of yellow-fleshed kiwifruit (Actinidia chinensis). Postharvest Biol Technol 46:1–9

    Article  CAS  Google Scholar 

  • Mitchell BG, Kiefer DA (1988) Chlorophyll α specific absorption and fluorescence excitation spectra for light-limited phytoplankton. Deep Sea Res A Oceanogr Res Papers 35(5):639–663

    Article  CAS  Google Scholar 

  • Moghimi A, Aghkhani MH, Sazgarnia A, Sarmad M (2010) Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosyst Eng 106:295–302

    Article  Google Scholar 

  • Park B, Abbott JA, Lee KJ, Choi CH, Choi KH (2003) Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of Delicious and Gala apples. Trans ASAE 46(6):1721–1731

    Article  Google Scholar 

  • Qiang L, Mingjie T, Jianrong C, Huazhu L, Chaitep S (2010) Selection of efficient wavelengths in NIR spectrum for determination of dry matter in kiwi fruit. Maejo Int J Sci Tech 4(01):113–124

  • Rateni G, Dario P, Cavallo F (2017) Smartphone-based food diagnostic technologies: a review. Sensors 17(6):1453

    Article  Google Scholar 

  • Saeys W, Mouazen AM, Ramon H (2005) Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst Eng 91(4):393–402

    Article  Google Scholar 

  • Schaare PN, Fraser DG (2000) Comparison of reflectance, interactance and transmission modes of visible near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biol Technol 20:175–184

    Article  Google Scholar 

  • Slaughter DC, & Crisosto CH (1998) Nondestructive internal quality assessment of kiwifruit using near-infrared spectroscopy. In: Seminars in food analysis (Vol. 3, pp. 131-140). CHAPMAN & HALL

  • Srivastava S, Sadistap S (2018) Non-destructive sensing methods for quality assessment of on-tree fruits: a review. J Food Meas Charact 12(1):497–526

    Article  Google Scholar 

  • Van Beers R, Aernouts B, Watté R, Schenk A, Nicolaï BM, Saeys W (2015) Evolution of vis/NIR bulk optical properties of apple skin and flesh during fruit maturation. In: Proceedings of the International Conference of Near Infrared Spectroscopy, Foz do Iguassu. Brazil. pp. 74–78

  • Walsh K, Golic M, Greensill C (2004) Sorting of fruit using near infrared spectroscopy: application to a range of fruit and vegetables for soluble solids and dry matter content. J Near Infrared Spectrosc 12:141–148

    Article  CAS  Google Scholar 

  • Wang H, Peng J, Xie C, Bao Y, He Y (2015) Fruit quality evaluation using spectroscopy technology: a review. Sensors 15(5):11889–11927

    Article  Google Scholar 

  • Williams PC, Saranwong S, Kawano S, Isaksson T, Segtnan VH (2006) Applications to agricultural and marine products. In: Ozaki Y, McClure WF, Christy AA (eds) Near-infrared spectroscopy in food science and technology. Wiley, Hoboken, New Jersey, pp 163–277

    Chapter  Google Scholar 

  • Zhang S, Zhang H, Zhao Y, Zhao H (2012) Comparison of modeling methods of fresh jujube soluble solids measurement by NIR spectroscopy. Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery 43(3):108–112

    Google Scholar 

  • Zude-Sasse M, Truppel I, Herold B (2002) An approach to non-destructive apple fruit chlorophyll determination. Postharvest Biol Technol 25:123–133. https://doi.org/10.1016/S0925-5214(01)00173-9

    Article  CAS  Google Scholar 

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Acknowledgments

S.S.S. would like to acknowledge Prof. G. Paliyat from the University of Guelph for teaching how to measure SSC and DM in fruit samples.

Funding

The research was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant program.

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Authors and Affiliations

  1. Centennial Public School, 141 Amos Ave, Waterloo, ON, N2L 2 W8, Canada

    Hardit Singh

  2. Department of Electrical and Computer Engineering, University of Waterloo, 200, University Ave West, Waterloo, ON, N2L 3G1, Canada

    Aneesh Sridhar & Simarjeet S. Saini

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  1. Hardit Singh
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  2. Aneesh Sridhar
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  3. Simarjeet S. Saini
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Contributions

H.S. and S.S.S. conceptualized the design of the multispectral detector. H.S. and A.S. designed and fabricated the prototype. S.S.S. designed the experimental study for validation. The study was conducted by H.S. and S.S.S. Data analysis was done by H.S., A.S., and S.S.S. All authors contributed to the writing of the paper.

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Correspondence to Simarjeet S. Saini.

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Singh, H., Sridhar, A. & Saini, S.S. Ultra-Low-Cost Self-Referencing Multispectral Detector for Non-Destructive Measurement of Fruit Quality. Food Anal. Methods 13, 1879–1893 (2020). https://doi.org/10.1007/s12161-020-01810-7

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