Design, Characterization, and Application of a Continuously Tunable Wavelength Spatial Frequency Domain Imaging System for Measuring the Optical Properties of Fruits

Yuan Gao , Zhizhong Sun , Xuan Luo , Dong Hu , Benhui Dai , Yingjie Zheng , Yibin Ying , Lijuan Xie

Engineering ›› : 202601029

PDF
Engineering ›› :202601029 DOI: 10.1016/j.eng.2026.01.029
Research
research-article
Design, Characterization, and Application of a Continuously Tunable Wavelength Spatial Frequency Domain Imaging System for Measuring the Optical Properties of Fruits
Author information +
History +
PDF

Abstract

Spatial frequency domain imaging (SFDI) has been widely applied in fruit quality inspection because of its noncontact and wide-field advantages. However, conventional multispectral SFDI systems remain constrained by low transmission efficiency, limited spectral range, and reliance on mechanical scanning. To overcome these limitations, we developed a continuously tunable wavelength SFDI system (450-1040 nm) that enables both continuous-spectrum and selectable-band imaging through patterned monochromatic illumination. The system adopts a modular design that integrates a monochromatic light generation module, a projection module, an imaging module, and a motorized imaging platform. This configuration allows flexible coupling and replacement of light sources and projection modules, enabling automated measurement of optical properties across different wavelength ranges according to application needs. With its high tunability, the system supports customized measurements at specific wavelengths via dedicated acquisition software, and it also provides the potential for spectral extension into longer infrared bands by simply upgrading the light source and infrared-sensitive projection module. Leveraging its wavelength tunability, we further demonstrated the system’s capability for depth-resolved imaging by jointly regulating the spatial frequency and wavelength. The results showed that the system achieved an imaging depth of 3-4 mm. The optical property measurements of various fruits obtained using our system were in close agreement with the reference values provided by the integrating sphere (IS). The mean measurement error of the absorption coefficient was approximately 0.002 mm-1, while that of the reduced scattering coefficient was approximately 0.02 mm-1. In the application case of peach firmness prediction, the developed model achieved a coefficient of determination for prediction of 0.786. These results demonstrate that our system is more accurate than existing multiwavelength SFDI devices. This improvement indicates that the extended spectral range of the proposed SFDI system provides richer tissue information, thereby highlighting its potential for fruit quality evaluation. More importantly, this work establishes a new paradigm for SFDI instrumentation by transitioning from fixed multispectral sensing to customizable, spectrally continuous imaging, thereby broadening its applicability in the nondestructive evaluation of agricultural products and potentially other biological tissues.

Keywords

Spatial frequency domain imaging / Optical properties / Tunable wavelength system / Noncontact optical imaging / Fruit quality inspection

Cite this article

Download citation ▾
Yuan Gao, Zhizhong Sun, Xuan Luo, Dong Hu, Benhui Dai, Yingjie Zheng, Yibin Ying, Lijuan Xie. Design, Characterization, and Application of a Continuously Tunable Wavelength Spatial Frequency Domain Imaging System for Measuring the Optical Properties of Fruits. Engineering 202601029 DOI:10.1016/j.eng.2026.01.029

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Akter T, Bhattacharya T, Kim JH, Kim MS, Baek I, Chan DE, et al. A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies. J Agric Food Res 2024;15:101068.

[2]

He L, Sun Y, Chen L, Feng Q, Li Y, Lin J, et al. Advance on agricultural robot hand-eye coordination for agronomic task: a review. Engineering 2025;51:263-79.

[3]

Lu R, Van Beers R, Saeys W, Li C, Cen H. Measurement of optical properties of fruits and vegetables: a review. Postharvest Biol Technol 2020;159:111003.

[4]

Zhang X, Yang J. Advanced chemometrics toward robust spectral analysis for fruit quality evaluation. Trends Food Sci Technol 2024;150:104612.

[5]

Hu D, Jia T, Sun X, Zhou T, Huang Y, Sun Z, et al. Applications of optical property measurement for quality evaluation of agri-food products: a review. Crit Rev Food Sci Nutr 2024; 64(33):12599-619.

[6]

Cuccia DJ, Bevilacqua F, Durkin AJ, Tromberg BJ. Modulated imaging: quantitative analysis and tomography of turbid media in the spatial-frequency domain. Opt Lett 2005; 30(11):1354-6.

[7]

Anderson ER, Cuccia DJ, Durkin AJ. 2007 Jan 20-25; Detection of bruises on golden delicious apples using spatial-frequency-domain imaging. Proceedings Volume 6430, Advanced Biomedical and Clinical Diagnostic Systems V; San Jose, CA, USA. Bellingham: SPIE; 2007. p. 308-18.

[8]

Hu D, Lu R, Ying Y, Fu X. A stepwise method for estimating optical properties of two-layer turbid media from spatial-frequency domain reflectance. Opt Express 2019; 27(2):1124-41.

[9]

Hu D, Lu R, Ying Y. Finite element simulation of light transfer in turbid media under structured illumination. Appl Opt 2017; 56(21):6035-42.

[10]

Hu D, Fu X, He X, Ying Y. Noncontact and wide-field characterization of the absorption and scattering properties of apple fruit using spatial-frequency domain imaging. Sci Rep 2016; 6(1):37920.

[11]

Lu Y, Li R, Lu R. Detection of fresh bruises in apples by structured-illumination reflectance imaging. In: Kim MS, Chao K, Chin BA, Baltimore, MD, USA. Proceedings Volume 9864, Sensing for Agriculture and Food Quality and Safety VIII; Bellingham: SPIE; 2016. p. 986406.

[12]

Lu Y, Lu R. Structured-illumination reflectance imaging for the detection of defects in fruit: analysis of resolution, contrast and depth-resolving features. Biosyst Eng 2019;180:1-15.

[13]

Dögnitz N, Wagnières G. Determination of tissue optical properties by steady-state spatial frequency-domain reflectometry. Lasers Med Sci 1998;13:55-65.

[14]

Lu Y, Li R, Lu R. Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples. Comput Electron Agric 2016;127:652-8.

[15]

Lu Y, Li R, Lu R. Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples. Postharvest Biol Technol 2016;117:89-93.

[16]

Lohner SA, Biegert K, Nothelfer S, Hohmann A, McCormick R, Kienle A. Determining the optical properties of apple tissue and their dependence on physiological and morphological characteristics during maturation. Part 1: spatial frequency domain imaging. Postharvest Biol Technol 2021;181:111647.

[17]

He X, Yang X, Fu X, Jiang X, Rao X. Assessing soluble solid content and texture of pear during shelf-life period by single snapshot spatial frequency domain imaging. Biosyst Eng 2021;212:252-63.

[18]

Yang Y, Fu X, Zhou Y. Hyperspectral spatial frequency domain imaging technique for soluble solids content and firmness assessment of pears. Horticulturae 2024; 10(8):853.

[19]

Sun Z, Hu D, Zhou T, Sun X, Xie L, Ying Y. Development of a multispectral spatial-frequency domain imaging system for property and quality assessment of fruits and vegetables. Comput Electron Agric 2023;214:108251.

[20]

Applegate M, Karrobi K, Angelo J, Austin W, Tabassum S, Aguénounon E, et al. OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system. J Biomed Opt 2020; 25(1):1-13.

[21]

Torabzadeh M, Stockton P, Kennedy G, Saager R, Durkin AJ, Bartels R, et al. Hyperspectral imaging in the spatial frequency domain with a supercontinuum source. J Biomed Opt 2019; 24(7):1-9.

[22]

Applegate MB, Spink SS, Roblyer D. Dual-DMD hyperspectral spatial frequency domain imaging (SFDI) using dispersed broadband illumination with a demonstration of blood stain spectral monitoring. Biomed Opt Express 2021; 12(1):676-88.

[23]

Singh-Moon RP, Roblyer DM, Bigio IJ, Joshi S. Spatial mapping of drug delivery to brain tissue using hyperspectral spatial frequency-domain imaging. J Biomed Opt 2014; 19(9):096003.

[24]

Luo Y, Dai L, Jiang X, Fu X. Measurement of absorption and scattering properties of milk using a hyperspectral spatial frequency domain imaging system. J Food Meas Charact 2022; 16(1):753-61.

[25]

Weber JR, Cuccia DJ, Johnson WR, Bearman GH, Durkin AJ, Hsu M, et al. Multispectral imaging of tissue absorption and scattering using spatial frequency domain imaging and a computed-tomography imaging spectrometer. J Biomed Opt 2011; 16(1):011015.

[26]

Lu Y, Lu R. Development of a multispectral structured illumination reflectance imaging (SIRI) system and its application to bruise detection of apples. Trans ASABE 2017; 60(4):1379-89.

[27]

Zhou T, Hu D, Qiu D, Yu S, Huang Y, Sun Z, et al. Analysis of light penetration depth in apple tissues by depth-resolved spatial-frequency domain imaging. Foods 2023; 12(9):1783.

[28]

Cuccia DJ, Bevilacqua F, Durkin AJ, Ayers FR, Tromberg BJ. Quantitation and mapping of tissue optical properties using modulated imaging. J Biomed Opt 2009; 14(2):024012.

[29]

Gao Y, Sun Z, Hu D, Xie L, Ying Y. GMOPNet: A GAN-MLP two-stage network for optical properties measurement of kiwifruit and peaches with spatial frequency domain imaging. Food Chem 2025; 465(Pt 1):141944.

[30]

Gebhart SC, Thompson RC, Mahadevan-Jansen A. Liquid-crystal tunable filter spectral imaging for brain tumor demarcation. Appl Opt 2007; 46(10):1896-910.

[31]

Wang W, Li C, Tollner EW, Rains GC, Gitaitis RD. A liquid crystal tunable filter based shortwave infrared spectral imaging system: calibration and characterization. Comput Electron Agric 2012;80:135-44.

[32]

Jia J, Wang Y, Zheng X, Yuan L, Li C, Cen Y, et al. Design, performance, and applications of AMMIS: a novel airborne multimodular imaging spectrometer for high-resolution earth observations. Engineering 2025;47:38-56.

[33]

Luo Y, Jiang X, Fu X. Spatial frequency domain imaging system calibration, correction and application for pear surface damage detection. Foods 2021; 10(9):2151.

[34]

Bouchard JP, Veilleux I, Jedidi R, Noiseux I, Fortin M, Mermut O. Reference optical phantoms for diffuse optical spectroscopy. Part 1—error analysis of a time resolved transmittance characterization method. Opt Express 2010; 18(11):11495-507.

[35]

Tian S, Tian H, Yang Q, Xu H. Internal quality assessment of kiwifruit by bulk optical properties and online transmission spectra. Food Control 2022;141:109191.

[36]

Prahl SA, van Gemert MJC, Welch AJ. Determining the optical properties of turbid media by using the adding-doubling method. Appl Opt 1993; 32(4):559-68.

[37]

Aernouts B, Zamora-Rojas E, Van Beers R, Watté R, Wang L, Tsuta M, et al. Supercontinuum laser based optical characterization of Intralipid® phantoms in the 500-2250 nm range. Opt Express 2013; 21(26):32450-67.

[38]

Deng R, He Y, Qin Y, Chen Q, Chen L. Pure water absorption coefficient measurement after eliminating the impact of suspended substance in spectrum from 400 nm to 900 nm. Nat Remote Sens Bull 2012; 16(1):174-91. Chinese.

[39]

Deng R, He Y, Qin Y, Chen Q, Chen L. Measuring pure water absorption coefficient in the near-infrared spectrum (900-2500 nm). Nat Remote Sens Bull 2012; 16(1):192-206. Chinese.

[40]

Goldfain AM, Lemaillet P, Allen DW, Briggman KA, Hwang J. Polydimethylsiloxane tissue-mimicking phantoms with tunable optical properties. J Biomed Opt 2021; 27(7):074706.

[41]

Hwang J, Kim HJ, Lemaillet P, Wabnitz H, Grosenick D, Yang L, et al. Polydimethylsiloxane tissue-mimicking phantoms for quantitative optical medical imaging standards. Proceedings Volume 10056, Design and Quality for Biomedical Technologies X Design and Quality for Biomedical Technologies X; 2017 Jan 28-Feb 2; San Francisco, CA, USA. Bellingham: SPIE; 2017. p. 15-20.

[42]

Zhao Y, Pilvar A, Tank A, Peterson H, Jiang J, Aster JC, et al.Shortwave-infrared meso-patterned imaging enables label-free mapping of tissue water and lipid content. Nat Commun 2020; 11(1):5355.

[43]

Van Beers R, Aernouts B, Watté R, Schenk A, Nicolaï B, Saeys W. Effect of maturation on the bulk optical properties of apple skin and cortex in the 500- 1850 nm wavelength range. J Food Eng 2017;214:79-89.

[44]

Sheng R, Cheng W, Li H, Ali S, Akomeah Agyekum A, Chen Q. Model development for soluble solids and lycopene contents of cherry tomato at different temperatures using near-infrared spectroscopy. Postharvest Biol Technol 2019;156:110952.

[45]

Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 2001; 58(2):109-30.

[46]

Magwaza LS, Landahl S, Cronje PJR, Nieuwoudt HH, Mouazen AM, Nicolaï BM, et al. The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of ‘Nules Clementine’ mandarin fruit. Food Chem 2014;163:267-74.

[47]

Tian K, Zhu W, Wang M, Chen T, Li F, Xie J, et al. Qualitative and quantitative assessment of apple quality using bulk optical properties in combination with machine learning and chemometrics techniques. Lebensm Wiss Technol 2024;211:116894.

[48]

Bashkatov A, Genina E, Kochubey V, Tuchin V. Optical properties of the subcutaneous adipose tissue in the spectral range 400-2500 nm. Opt Spectrosc 2005; 99(5):836-42.

[49]

Cen H, Lu R, Mendoza FA, Ariana DP. Assessing multiple quality attributes of peaches using optical absorption and scattering properties. Trans ASABE 2012;55:647-57.

[50]

Hu D, Fu X, Ying Y. Characterizing pear tissue with optical absorption and scattering properties using spatially-resolved diffuse reflectance. J Food Meas Charact 2017; 11(2):930-6.

[51]

Sun Y, Lu R, Pan L, Wang X, Tu K. Assessment of the optical properties of peaches with fungal infection using spatially-resolved diffuse reflectance technique and their relationships with tissue structural and biochemical properties. Food Chem 2020;321:126704.

[52]

Wei K, Ma C, Sun K, Liu Q, Zhao N, Sun Y, et al. Relationship between optical properties and soluble sugar contents of apple flesh during storage. Postharvest Biol Technol 2020;159:111021.

[53]

Ma C, Feng L, Pan L, Wei K, Liu Q, Tu K, et al. Relationships between optical properties of peach flesh with firmness and tissue structure during storage. Postharvest Biol Technol 2020;163:111134.

[54]

Rodriguez CE, Bustamante CA, Budde CO, Müller GL, Drincovich MF, Lara MV. Peach fruit development: a comparative proteomic study between endocarp and mesocarp at very early stages underpins the main differential biochemical processes between these tissues. Front Plant Sci 2019;10:715.

[55]

Musse M, Bidault K, Quellec S, Brunel B, Collewet G, Cambert M, et al. Spatial and temporal evolution of quantitative magnetic resonance imaging parameters of peach and apple fruit-relationship with biophysical and metabolic traits. Plant J 2021; 105(1):62-78.

[56]

Ding C, Shi S, Chen J, Wei W, Tan Z. Analysis of light transport features in stone fruits using Monte Carlo simulation. PLoS One 2015; 10(10):e0140582.

[57]

Wang Z, Zuo C, Wang M, Song S, Hu Y, Song J, et al. Optical properties related to cell wall pectin contribute to determine the firmness and microstructural changes during apple softening. Postharvest Biol Technol 2024;218:113150.

[58]

Fang L, Jiang M, Lan W, You S, Ding F, Tu K, et al. Assessing sugar composition and tissue structure indices of ‘Korla’ pear cortex using bulk optical properties in the 500-1500 nm. Postharvest Biol Technol 2023;206:112571.

[59]

Peng H, Zhang C, Sun Z, Sun T, Hu D, Yang Z, et al. Optical property mapping of apples and the relationship with quality properties. Front Plant Sci 2022;13:873065.

[60]

Lin AJ, Ponticorvo A, Konecky SD, Cui H, Rice TB, Choi B, et al. Visible spatial frequency domain imaging with a digital light microprojector. J Biomed Opt 2013; 18(9):096007.

[61]

Gao Y, Sun Z, Xie L, Ying Y. Spatial frequency domain imaging for fruit quality assessment: a comprehensive review. Trends Food Sci Technol 2025;162:105110.

[62]

Tian X, Li J, Wang Q, Fan S, Huang W. A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments. Food Chem 2018;239:1055-63.

[63]

Yu S, Liu D, Li D, Hao H, Yang M, Chen G. Exploring the effects of impact damage on optical properties of ‘Korla’ pears and Monte Carlo simulation of light propagation in pear tissue. Postharvest Biol Technol 2026;231:113870.

[64]

Urban BE, Subhash HM. Multimodal hyperspectral fluorescence and spatial frequency domain imaging for tissue health diagnostics of the oral cavity. Biomed Opt Express 2021; 12(11):6954-68.

PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

/