Non-destructive detection of bread staleness using hyperspectral images

Document Type : Research Article


1 Associate professor of Agricultural Sciences and Natural Resources University of Khuzestan

2 Department of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Ramin University of Agricultural & Natural Resources, Mollasani, Iran

3 2. MSc Student Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan


Hyperspectral imaging, a technology that combines imaging and spectroscopy, provides extensive spatial and spectral information, simultaneously. It is currently being developed as a non-destructive and rapid diagnostic tool for assessing food quality and safety. In this study, hyperspectral imaging was utilized to investigate the process of bread staleness and its effect on the behavior of the bread crumb within the wavelength range of 950-400 nm and with a resolution of 0.795 nm. Principal components were extracted and three modeling methods - PCR, PLSR, and GRNN - were employed to predict texture characteristics during six days of storage. Based on the findings of this study, it was observed that the General Regression Neural Network (GRNN) method demonstrated superior performance in terms of R2 values for both springiness and stiffness, with values of 0.96 and 0.94, respectively. Furthermore, the GRNN method also exhibited the lowest Root Mean Square Error (RMSE) values for cohesiveness and stiffness, with values of 0.11 and 0.32, respectively. This demonstrates the capability of the generalized regression neural network model to predict the textural characteristics of bread.

Graphical Abstract

Non-destructive detection of bread staleness using hyperspectral images


  • Hyperspectral imaging was used to study bread staleness.
  • Three modeling methods (PCR, PLSR, and GRNN) were employed to predict texture characteristics.
  • The GRNN method demonstrated its capability to predict the textural characteristics of bread.


Main Subjects

[[1]] Aguirre, J. F., Osella, C. A., Carrara, C. R., Sanchez, H. D., & Buera, M. D. P. (2011). Effect of storage temperature on starch retrogradation of bread staling. Starch/Staerke, 63(9), 587–593.
[[1]] Gray, J. A., & Bemiller, J. N. (2003). Bread staling: Molecular basis and control. CRFSFS., 2(1), 1–21.
[[1]] Ribotta, P.D., & Le-Bail, A. (2007). Thermophysical assessment of bread during staling. LWT., 40(5), 879-884.
[[1]] Edel León, A., Durán, E., & De Barber, C. (2002). Utilization of Enzyme Mixtures to Retard Bread Crumb Firming. Agric. Food Chem., 50(6), 1416-9.
[[1]] Ribotta, P. D., Leon, A. E., & Anon, M. C. (2003). Effect of freezing and frozen storage on the gelatinization and retrogradation of amylopectin in dough baked in a differential scanning calorimeter. Food Res. Int. 36, 357–363.
[[1]]Aguirre, J.F., Osella, C.A., Carrara, C.R., Sanchez, H.D., & Buera, M.P. (2011). Effect of storage temperature on starch retrogradation of bread staling. Starch – Starke, 63(9), 587-593.  
[[1]] Gray, J. A., & Bemiller, J. N. (2003). Bread Staling: Molecular Basis and Control.CRFSFS., 2(1), 1−21.
[[1]] Marinopoulou, A. Petridis, D., & Raphaelides, S.N. (2019). Assessment of texture changes in sliced pan bread on aging using sensory and instrumental method. Food Process. Preserv., 43(2), 13982.
[[1]] Chen, Y., Eder, S., Schubert, S., Gorgerat, S., Boschet, E., Baltensperger, L., Boschet, E., Städeli, Ch., Kuster, S., Fischer, P., & Windhab, E.J. (2021). Influence of Amylase Addition on Bread Quality and Bread Staling. ACS Food Sci. Technol., 1(6), 1143–1150.
[[1]] Xie, F., Dowell, F., & Sun, X. (2003). Compuarison of Near-Infrared Reflectance Spectroscopy and Texture Analyzer for Measuring Wheat Bread Changes in Storage. Cereal Chem., 80(1), 25-29.
[[1]] Al-Mahasneh, M., Aljarrah, M., Rababah, T., & Aludatt, M. (2018). Using MR-STIR and Texture Profile to Track the Effect of Storage Time and Temperature on Pita Bread Staling. Food Qual., 18(1), 1-9.
[[1]] Nouri, M., Nasehi, B., Goudarzi, M., & Abdanan Mehdizadeh., S. (2018). Non-destructive Evaluation of bread Staling Using Gray Level Co-occurrence Matrices. Food Anal. Methods, 11(2).
[[1]] Rusinek, R., Gancaraz, M., & Agieszka, N. (2020). Application of an electronic with novel method for generation of smellprints for testing the suitability for consumption of wheat bread during 4-day storage. Food Sci. Technol., 117, 108665.
[[1]] Abdanan Mehdizadeh, S., & Nouri, S. F. (2022). Development of a non-destructive method to determine the textural characteristics of baguette bread using a Doppler laser vibrometer sensor. Innovative Food Technol., 10(1), 69-85. [In Persian]
[[1]] Scotter, C. (1990). Use of near infrared spectroscopy in the food industry with particular reference to its applications to on/in-line food processes. Food Control, 1(3), 142-149.
[[1]] Ariana, D., Lu, R., & Guyer, D. E. (2006). Hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Comput. Electron.  Agric., 53(1), 60-70.
[[1]] Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision e a review. Food Eng., 61(1), 3-16.
[[1]] Du, C. J., & Sun, D. W. (2004). Recent developments in the applications of image processing techniques for food quality evaluations. Trends in Food Sci. Technol., 15, 230-249.
[[1]] Chao, K., Chen, Y. R., Early, H., & Park, B. (1999). Color image classification systems for poultry viscera inspection. Appl. Eng. Agric., 15(4), 363-369
[[1]] Liu, Y., Chen, Y. R., Kim, M. S., Chan, D. E., & Lefcourt, A. M. (2007). Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging. Food Eng., 81(2), 412-418.
[[1]] Park, B., Lawrence, K. C., Windham, W. R., & Smith, D. (2006). Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. Food Eng., 75(3), 340-348.
[[1]] Lu, B., Dao, Ph., Liu, J., He, Y., & Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12(16), 2659.
[[1]] Gowen, A.A., O’Donnell,P.J, C.P., Downey, C.G., & Frias, J.M. (2007). Hyperspectral imaging an emerging process analytical tool for food quality and safety control. Trends in Food Sci. Technol., 18, 590-598.
[[1]] Lucieer, A., Malenovský, Z.; Veness, T., & Wallace, L. (2014). HyperUAS-imaging spectroscopy from a multirotor unmanned aircraft system. Field Robot, 31, 571–590.
[[1]] Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sens.,7, 13586–13605.
[[1]] Lee, K., Cohen, W.B., Kennedy, R.E., Maiersperger, T.K., & Gower, S.T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four di-erent biomes. Remote Sens. Environ., 91, 508–520.
[[1]] Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T., & Platonov, A. (2013). Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ., 139, 291–305.
[[1]] Marshall, M., & Thenkabail, P. (2015). Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS J. Photogramm., 108, 205–218.
[[1]] Sun, J., Yang, J., Shi, S., Chen, B., Du, L., Gong, W., & Song, S. (2017). Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance. Remote Sens., 9, 951.
[[1]] Mahlein, A.K., Steiner, U., Hillnhütter, Ch., Dehne, H.W., & Oerke, E.C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8(1), 3.
[[1]] Balasundaram, D., Burks, TF., Bulanon, D.M., Schubert, T., & Lee, W. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biol. & Tec., 51, 220-226.
[[1]] Qin, J., Burks, T.F., Ritenour, M.A., & Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Food Eng., 93, 183-191.
[[1]] Chen, Z., Wang, Q., Zhang, H., & Nie, P. (2021). Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions. Sensors, 21(2), 583.
[[1]] Saleem, Z., Hussain Khan, M., Ahmad, M., Sohaib, A., Ayaz, H., & Mazzara, M. (2020). Prediction of Microbial Spoilage and Shelf-Life of Bakery Products Through Hyperspectral Imaging. IEEE Access, 8.
[[1]] Sricharoonratana, M., Thompson, A., & Teerachaichayut, S. (2021). Use of near infrared hyperspectral imaging as a nondestructive method of determining and classifying shelf life of cakes. LWT, 136(2), 110369.
[[1]] Kim, G., Lee, H., Baek, I., Cho, B., & Kim, M. (2022). Short-Wave Infrared Hyperspectral Imaging System for Nondestructive Evaluation of Powdered Food. Biosyst. Eng., 47(2), 223-232.
[[1]] Savage, S. H., Levy, T. E., & Jones, I. W. (2012). Prospects and problems in the use of hyperspectral imagery for archaeological remote sensing: A case study from the Faynan copper mining district. Jordan. Archaeologic Sci., 39(2), 407–420.
[[1]] Vidal, M., & Amigo, J. M. (2012). Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom. Intell. Lab. Syst., 117, 138–148.
[[1]] Joleini, M., GhiafeDawoodi, M., & Sheikholeslami, Z. (2016). Effect of flaxseed addition on nutritional properties and shelf life of Berber bread. innovation in Food sci, Technol., 9(3), 1-11. [In Persian]
[[1]] AACC (American Association of Cereal Chemists). (2000). Method 44-19, Moisture, Approved Methods. MN, USA: St Paul.
[[1]] Sahraiyan, B., Mazaheri Tehrani, M., Naghipour, F., Ghiafeh Davoodi, M., & Soleimani, M. (2013). The effect of mixing wheat flour with rice bran and soybean flour on physicochemical and sensory properties of baguettes. Iranian J. Nutrition Sci. Food Technol., 8(3), 229-240. [In persian]
[[1]] Burger, J., & Geladi, P. (2005). Hyperspectral NIR image regression part I: Calibration and correction. Chemometrics, 19, 355–363.  
[[1]] Burger, J., & Geladi, P. (2006). Hyperspectral NIR image regression part II: Dataset preprocessing diagnostics.
Chemometrics, 20(3-4), 106–119.
[[1]] Aviara, N. A., Liberty, J. T., Olatunbosun, O. S., Shoyombo, H. A., & Oyeniyi, S. K. (2022). Potential application of hyperspectral imaging in food grain quality inspection, evaluation and control during bulk storage. Agric. Food Res., 100288.
[[1]] Wold, S., Esbensen, K., & Geladi, P. (1987). Priciple component analysis. Chemom. Intell. Lab. Syst., 2: 37-52.جزئیات بیشتر؟؟
[[1]] Bro, R., & Smilde, A. K. (2014). Principal component analysis (Tutorial Review). Analytical Methods. 6(9), 2812–2831.
[[1]] De Juan, A., Piqueras, S., Maeder, M., Hancewicz, T., Duponchel, L., & Tauler, R. (2014). Chemometric Tools for Image Analysis. R, Salzer, H.W. Siesler (Eds). In: Infrared and Raman Spectroscopic Imaging (2nd ed., pp. 57–110). Publisher: Wiley-VCH.
[[1]] Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemom.Intell. Lab. Syst., 58(2), 109–130.
[[1]] Soltani Kazemi, M., Abdanan Mehdizadeh, S., Heidari, M., & Faregh, S. M. (2017). Predict changes of some quality parameters of black mulberry juice (Morusnigra L.) during ripening using machine vision and fractal analysis. Iranian Food Sci. Technol. Res. J.13(5), 730-743.
[[1]] Salehi, M., Farhadi, S., Moieni, A., Safaie, N., & Hesami, M. (2021). A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture. Plant Methods17, 1-13.
[[1]] Xu, X., Chen, S., Ren, L., Han, C., Lv, D., Zhang, Y., & Ai, F. (2021). Estimation of heavy metals in agricultural soils using vis-NIR spectroscopy with fractional-order derivative and generalized regression neural network. Remote Sens.13(14), 2718.
[[1]] Melini, V., & Melini, F. (2018). Strategies to Extend Bread and GF Bread Shelf-Life: From Sourdough to Antimicrobial Active Packaging and Nanotechnology. Fermentation, 4(1).
[[1]]  Pateras, I.M.C. (1998). Bread spoilage and staling. Technology of Breadmaking,240–261.
[[1]] Nhouchi, Z., & Karoui, R. (2018). Application of Fourier-transform mid infrared for the monitoring of pound cakes quality during storage. Food Chem., 252, 327-334.
[[1]] Botosoa, E.P., Christine, C., & Karoui, R. (2013). Monitoring Changes in Sponge Cakes during Aging by Front Face Fluorescence Spectroscopy and Instrumental Techniques. Agric. Food Chem., 61(11), 2687-2695.
[[1]] AzarBad, H., Mzaheri, M., & Rashidi, H. (2016). Determination of chemical, sensory and mechanical texture characteristics of reduced gluten Barbari bread made from Wheat flour and Millet flour blend. Food Res. (Agric. sci.), 26(1), 139-149. [In persian].
[[1]] Mehdizadeh, S. A., Minaei, S., Hancock, N. H., & Torshizi, M. A. K. (2014). An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy. Inf. Process. Agric.1(2), 105-114.
[[1]] Guo, S., Rösch, P., Popp, J., & Bocklitz, T. (2020). Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications. Chemometrics34(4), 3202.
[[1]] Ozaki, Y., Huck, C., Tsuchikawa, S., & Engelsen, S.B. (2021). Near-infrared spectroscopy: theory, spectral analysis, instrumentation, and applications (1st ed. ). Berlin-Heidelberg, Germany: Springer.
[[1]] Raypah, M. E., Faris, A. N., Mohd Azlan, M., Yusof, N. Y., Suhailin, F. H., Shueb, R. H., & Mustafa, F. H. (2022). Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective. Sensors22(12), 4391.
[[1]] Raypah, M. E., Omar, A. F., Muncan, J., Zulkurnain, M., & Abdul Najib, A. R. (2022). Identification of stingless bee honey adulteration using visible-near infrared spectroscopy combined with aquaphotomics. Molecules27(7), 2324.
[[1]] Fagan, C. C., Everard, C., O’Donnell, C. P., Downey, G., Sheehan, E. M., Delahunty, C. M., O’Callaghan, D. J., & Howard, V. (2007). Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools. Food Eng., 80(4), 1068-107.
[[1]] Chakravartula, S., Cevoli, Ch., Balestra, F., Fabbri, A., & Rosa, M. (2019). Evaluation of drying of edible coating on bread using NIR spectroscopy. Food Eng., 240, 29-37.