Near infrared hyperspectral imaging for non-destructive determination of pH value in red delicious apple fruit during shelf life

Document Type : Research Article


1 Associate Professor, Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

2 PhD candidate, Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran


Quality assessment and fruits’ sorting are important activities in postharvest process that are attracting notices interestingly because of increasing demand for healthy products with better quality. In the recent decades, various non-invasive and non-destructive techniques for fruits and vegetables evaluations have been employed. Among these methods, hyperspectral imaging as a non-destructive, fast and ecofriendly technique is getting researchers’ attraction increasingly in order to crops properties assessment. Regarding this fact that consumption of organic acids and consequent changes pH is considered as one of the important qualitative parameters of apple fruits, this study aimed to investigate the effect of shelf life time on pH value of Red Delicious apples during 60 days. Hyperspectral reflecting imaging in range of 400-1000 nm has been applied while the pH of samples was measured destructively. After discarding noises using principal component analysis (PCA) analysis, to improve spectrum, different primary pre-processing had been applied and their effects were investigated. The suitable model was obtained via Partial Least Square method (PLS). Important wavelengths were selected based on regression coefficient of the best model includes large absolute values of weighted regression coefficients (BW) and using various techniques were modeled. Concerning the PLS analysis, the best results were obtained through smoothing Savitzky-Golay pre-processing with mean square root error (RMSE) of 0.02 and 0.018 and coefficient of determination (R2) both 0.980 for calibration and validation data, respectively. According to regression coefficient of the best model, 9 wavelengths were determined as the best. In modeling by efficient wavelength, artificial neural network (ANN) gave the best result. Hence, it was obvious that non-destructive method of hyperspectral imaging was capable to predict pH values of apple fruits with high accuracy during the shelf life.

Graphical Abstract

Near infrared hyperspectral imaging for non-destructive determination of pH value in red delicious apple fruit during shelf life


  • Near-infrared hyperspectral imaging was used to evaluate pH in ‘Red Delicious’ apples during cold storage.
  • The influence of different Pre-processing method on PLS model were studied.
  • Effective wavelengths for pH discrimination of apples were selected based on BW of best PLS model.
  • Different regression models created with effective wavelengths effective wavelengths and their performance was compared.


Main Subjects

[1]Thovhogi, F. (2009). Consumer reference (of red-fleshed apples) an quantification of quality related traits, particularly skin and flesh colour, in apple breeding families. Stellenbosch, South Africa: University of Stellenbosch, Department of Horticulture.
[2]Hosseinpour, R., Ahmadi, K., Ebadzadeh, H., Mohammadnia S., Afroozi & Abbasteghani, R. (2014). Export and import of agricultural sector. Tehran, I.R. Iran: Ministry of Jihad Keshvarzi Publisher. [In Persian]
[3]FAO (Food and Agriculture Organization). Crops and livestock products, 2019. URL Accessed 08.08.21.
[4]Rai, M., Ribeiro, C., Mattoso, L., & Duran, N. (2015). Nanotechnologies in food and agriculture. New York: Springer.
[5]Du, C. J., & Sun, D. W. (2006). Learning techniques used in computer vision for food quality evaluation: a review. J. Food Eng., 72(1), 39-55.
[6]Park, B., & Lu, R. (Eds.). (2015). Hyperspectral imaging technology in food and agriculture. New York: Springer.
[7]ElMasry, G., Kamruzzaman, M., Sun, D. W., & Allen, P. (2012). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci. Nutr., 52(11), 999-1023.
[8]Gowen, A. A., Taghizadeh, M., & O’Donnell, C. P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. J. Food Eng., 93(1), 7-12.
[9]Munera, S., Amigo, J. M., Blasco, J., Cubero, S., Talens, P., & Aleixos, N. (2017). Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. J. Food Eng., 214, 29-39.
[10]Ekramirad, N., Mohtasebi, S. S., & Eyvani, A. (2017). Non-destructive Detection of Codling Moth (.Cydia pomonella L) Damage in Apple Fruit Using Hyperspectral Imaging Method. Iran. J. Biosyst. Eng., 48(2), 241-249. [In Persian]
[11]Zhu, H., Chu, B., Fan, Y., Tao, X., Yin, W., & He, Y. (2017). Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Sci. Rep., 7(1), 1-13.
[12]Rahi, S., Mobli, H., Jamshidi, B., Azizi, A., & Sharifi, M. (2020). Microbial Contamination Assessment of Lettuce using NIR Hyperspectral Imaging: Case Study on Escherichia coli. Iran. J. Biosyst. Eng., 51(3), 599-610. [In Persian]
[13]Wei, X., He, J., Zheng, S., & Ye, D. (2020). Modeling for SSC and firmness detection of persimmon based on NIR hyperspectral imaging by sample partitioning and variables selection. Infrared Phys. Technol., 105, 99-103.
[14]Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Nikkhah, M. J., Digvir, J., Siliveru, K., Malihipour, A. (2021). Processing the Hyperspectral Images for Detecting Infection of Pistachio Kernel by R5 and KK11 Isolates of Aspergillus flavus Fungus. Iran. J. Biosyst. Eng., 52(1), 13-25. [In Persian]
[15]Roopa, N., Chauhan, O. P., Madhukar, N., Ravi, N., Kumar, S., Raju, P. S., & Dasgupta, D. K. (2015). Minimal processing and passive modified atmosphere packaging of bread fruit (Artocarpus altilis) sticks for shelf life extension at refrigerated temperature. J. Food Sci. Technol, 52(11), 7479-7485.
[16]Xu, K., Wang, A., & Brown, S. (2012). Genetic characterization of the Ma locus with pH and titratable acidity in apple. Mol. Breed., 30(2), 899-912.
[17]Polder, G., van der Heijden, G. W., Keizer, L. P., & Young, I. T. (2003). Calibration and characterisation of imaging spectrographs. J. Near Infrared Spectrosc, 11(3), 193-210.
[18]Ding, J., Zhang, R., Ahmed, S., Liu, Y., and Qin, W. 2019. Effect of Sonication Duration in the Performance of Polyvinyl Alcohol/Chitosan Bilayer Films and Their Effect on Strawberry Preservation. Molecules, 24(7), 1408-1414.‌
[19]Rossel, R. A. V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemom. Intell. Lab. Syst., 90(1), 72-83.
[20]Tahmasebi, M., Golmohammadi, A., & Tabatabaei-kolor, R. (2017). Measuring of Paddy mass flow using capacitive sensor and modeling with using multiple regression, ANN, and ANFIS models. Iran. J. Biosyst, Eng., 48 (2), 221-227. [In Persian]
[21]Tamuno, E. N. J., & Onyedikachi, E. C. (2015). Effect of packaging materials, storage conditions on the vitamin C and pH value of cashew apple (Anacardium occidentale L.) juice. J. Food. Nutr Sci., 3(4), 160-165.
[22]Alenazi, M. M., Shafiq, M., Alsadon, A. A., Alhelal, I. M., Alhamdan, A. M., Solieman, T. H., ... & Al-Selwey, W. A. (2020). Improved functional and nutritional properties of tomato fruit during cold storage. Saudi J. Biol. Sci., 27(6), 1467-1474.
[23]Gelly, M., Recasens, I., Girona, J., Mata, M., Arbones, A., Rufat, J., & Marsal, J. (2004). Effects of stage II and postharvest deficit irrigation on peach quality during maturation and after cold storage. J. Sci. Food Agric., 84(6), 561-568.
[24]Etemadipoor, R., Ramezanian, A., Dastjerdi, A. M., & Shamili, M. (2019). The potential of gum arabic enriched with cinnamon essential oil for improving the qualitative characteristics and storability of guava (Psidium guajava L.) fruit. Sci. Hortic., 251, 101-107.
[25]Cozzolino, D., Cynkar, W. U., Shah, N., & Smith, P. (2011). Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality. Food Res. Int., 44(7), 1888-1896.
[26]Jamshidi, B., Minaei, S., Mohajerani, E., & Ghassemian, H. (2012). Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges. Comput. Electron. Agric., 85, 64-69.
[27]Heidari, P., Rezaei, M., Sahebi, M., & Khadivi, A. (2019). Phenotypic variability of Pyrus boissieriana Buhse: Implications for conservation and breeding. Sci. Hortic., 247, 1-8.
[28]Rahman, A., Kandpal, L. M., Lohumi, S., Kim, M. S., Lee, H., Mo, C., & Cho, B. K. (2017). Nondestructive estimation of moisture content, pH and soluble solid contents in intact tomatoes using hyperspectral imaging. Appl. Sci., 7(1), 109.
[29]Zhang, N., Zhang, C., Han, C., & Shen, T. (2018). The Handling Approach of Near-Infrared Spectroscopy for Apple Quality Prediction Based on Digital Signal Processing. In: 37th Chinese Control Conference (CCC) (pp. 4136-4140). Wuhan, China.
[30]Kim, S. Y., Hong, S. J., Kim, E., Lee, C. H., & Kim, G. (2021). Neural Network based Prediction of Soluble Solids Concentrationin Oriental Melon using VIS/NIR spectroscopy. Appl. Eng. Agric ., (in press). doi:10.13031/aea.14332.
[31]Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Foo Bioproc, Tech, 5(2), 425-444.
[32]Li, X., Wei, Y., Xu, J., Feng, X., Wu, F., Zhou, R., ... & He, Y. (2018). SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology. Postharvest Biol. Technol., 143, 112-118.
[33]Zhiming, G., Wenqian, H., Liping, C., Yankun, P., & Xiu, W. (2014). Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple. Int. J. Agric. Biol., 7(2), 130-137.
[34]Razavi, M. S., Golmohammadi, A., Sedghi, R., & Asghari, A. (2020). Prediction of bruise volume propagation of pear during the storage using soft computing methods. Food Sci. Nutr., 8(2), 884-893.
[35]Mousavi-Avval, S. H., Rafiee, S., Sharifi, M., Hosseinpour, S., & Shah, A. (2017). Combined application of Life Cycle Assessment and Adaptive Neuro-Fuzzy Inference System for modeling energy and environmental emissions of oilseed production. Renew. Sustain. Energy Rev., 78, 807-820.
[36]Sabzi, S., Pourdarbani, R., Rohban, M. H., Fuentes-Penna, A., Hernández-Hernández, J. L., & Hernández-Hernández, M. (2021). Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting. Plants, 10(5), 898-911.