Non-Destructive Prediction of pH in Khormai and Khoni Plums Using a Hyperspectral Imaging System and Machine Learning Methods

Articles in Press

Document Type : Research Article

Authors

• M. Latifi-Amoghin
• Y. Abbaspour-Gilandeh

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

https://doi.org/10.22067/jam.2025.95325.1427

Abstract

Introduction
Traditional methods for evaluating fruit quality, such as pH measurement, are often destructive, time-consuming, and costly, leading to product loss and reduced efficiency in the supply chain. The growing need for rapid, accurate, and non-destructive methods makes the use of technologies like Hyperspectral Imaging (HSI) essential. HSI combines two-dimensional imaging with spectroscopy to simultaneously acquire spatial and spectral information from an object. Numerous studies have shown that this method is capable of accurately estimating internal fruit parameters in a non-destructive manner. The objective of this research was to develop a fast and reliable method for the non-destructive estimation of pH in two plum cultivars using HSI and machine learning algorithms such as Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANN). This study aims to overcome the limitations of conventional methods by leveraging the power of advanced imaging and computational techniques, providing a sustainable and efficient solution for the fruit industry.
Materials and Methods
In this study, 80 samples from each of the Khormaei and Khoni plum cultivars were used, which were purchased from local orchards. The samples were uniform in size, shape, and colour and were free from any physical damage. Hyperspectral images of the samples were acquired using a rotating hyperspectral imaging system in the range of 418 to 1072 nm. The pH of each fruit juice sample was measured using a digital pH meter. In the analysis of spectral data, the initial part of the spectrum was first removed due to high noise, and then the remaining data were processed with preprocessing methods such as a Gaussian filter and Multiplicative Scatter Correction (MSC). To select effective wavelengths (EWs), a hybrid approach using a Decision Tree (DT) and five metaheuristic algorithms was employed, with the Particle Swarm Optimisation (PSO) algorithm showing the best performance. Finally, pH modelling was performed on the selected wavelengths using PLSR and ANN. This comprehensive methodology ensures that the models are trained on high-quality data and are optimised for maximum accuracy.
Results and Discussion
Spectral analysis showed that the reflectance spectra of the Khoni and Khormaei plums had a high degree of variation, which is related to the differences in their chemical composition and structure. Descriptive statistics indicated that the average pH of Khormaei plum (3.909) was higher than that of Khoni plum (3.7375), and the pH range of Khoni plum (3.15 to 4.44) was wider than that of Khormaei plum (3.6 to 4.2). The results showed that modelling with ANN on the wavelengths selected by PSO, especially for Khoni plum, significantly increased prediction accuracy. The best ANN model for Khoni plum achieved an R²​ of 0.9834 and an RPD of 8.01, which indicates the outstanding accuracy of this method. For the Khormaei plum, the best ANN model also reached an R²​ of approximately 0.76 and a Ratio of Performance to Deviation (RPD) of 2.12, showing a considerable improvement over the PLSR model. The superior performance of the ANN models can be attributed to their ability to capture complex, non-linear relationships between spectral data and pH values, which linear models like PLSR may miss.
Conclusion
This research successfully demonstrated that hyperspectral imaging, in combination with machine learning algorithms, particularly ANN and PSO, can be an accurate and reliable method for the non-destructive prediction of pH in different plum cultivars. The hybrid approach used in this study, which combined DT for initial feature selection with PSO for optimal wavelength selection, enabled the models to predict pH values with very high accuracy, especially for the Khoni plum cultivar. This method can be used as an efficient tool in post-harvest quality control processes, helping to reduce waste and improve efficiency in the fruit supply chain. This work paves the way for the development of smart grading and sorting systems that can quickly and accurately assess fruit quality, benefiting both producers and consumers.

Keywords

• Hyperspectral imaging
• Metaheuristic algorithms
• Particle Swarm Optimisation
• pH
• Plum

Main Subjects

• Image Processing

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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