Hyperspectral / multispectral imaging technology: application for pomegranate fruit internal quality evaluation and bruise detection
Date
2023-03
Authors
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Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: In recent years, consumer demand for fruit and vegetables are increasing due to a shift towards healthier and more sustainable diets. However, fruits and vegetables are highly perishable that providing the market with high-quality but affordable price is challenging. Also, fruit and vegetable diseases, due to fungal pathogens, are major causes of economic loss in agribusiness. There are multiple sources of contamination during preharvest and harvest–postharvest stages of production and particularly for pomegranate fruit. Pomegranate (Punica granatum L.) is undeniably one of the most ancient deciduous fruits in the world with growing increase in its demand due to its nutritional and health benefits. These quality issues have necessitated rapid and efficient quality and freshness monitoring and analysis tool in the postharvest. In the fruit and vegetable industries, quality inspections are mainly manual and mechanical, laborious, time-consuming, costly, and subjective. Hyperspectral imaging (HSI) has emerged as a powerful non-destructive inspection technique in the agricultural, biosecurity diagnostic and food domain recently. HSI is a non-invasive/ non-destructive technique that integrates spectroscopy as well imaging to form one system. This combined feature makes it a powerful tool for fruit\food quality assessment and defect detection, maturity indexing and physicochemical attributes in horticultural products. Therefore, the main objective of this study is to assess the application of hyperspectral/multispectral imaging for predicting the major quality attributes in fresh pomegranate fruit as well detect the presence of bruise or internal defect using artificial neural networks (ANNs). Section I (Chapter 1, 2 & 3) provides background information, discussing the general aim and objectives (General introduction) of the thesis study. It further provides a comprehensive review on recent applications of hyperspectral imaging technology for preharvest and postharvest analysis for biosecurity diagnostics in the fruit industry (Chapter 2) and narrowed down to applications on pomegranate fruit (Chapter 3). It explores hyperspectral imaging architecture, its equipment, image acquisition and data processing. This information is useful for those in the growers/ processing industries and food safety and quality control stakeholders and provides a review of literature on previous work done on different non-invasive techniques for evaluating different processed horticultural products over the last ten years. In Section II (Chapter 4, 5 and 6), hyperspectral imaging technique was investigated to evaluate maturity quality attributes which includes TSS, TA, pH, and colour components (a*, b*, L*, chrome and hue) of intact pomegranate fruit. The ANN prediction models for quality parameters performed well, with correlation coefficients from 0.421 to 0.951. three neural fitting algorithms were compared for prediction performance, LMG algorithm yielded better results for four of the 9 quality attributes accessed. BR gave the best prediction statistics for TA (R2=0.852, MSE=0.024), and b* (R2=0.951, MSE=3.923). The VNIR spectral was applied to build model using 6 effective wavelengths. This research study has demonstrated that hyperspectral imaging technique in combination with artificial neural network has the potential to predict maturity quality attributes of pomegranate fruit. Further in section II, two spectral ranges of VNIR and SWIR were deployed in the hyperspectral imaging technique to detect the presence of early bruise development of “Wonderful” pomegranate fruit, as well as classify bruit based on different levels of bruise severity. Scanned images were explored, and spectral data extracted for two surface area of interest (ROI and WF). ANN classification model showed model to be able to detect bruise immediately after occurrence to an accuracy of 90%. Both methods of data extraction are good enough to detect the early bruise damage which is invisible to the naked eye. The results confirm hyperspectral imaging technique combined with machine learning methods (ANN) to be an effective technique for early bruise detection. For bruise severity study, both SWIR and VNIR data yielded highly accurate classification results ranging from 80% - 96.7%. The overall average classification accuracy achieved was 93.3% for model to distinguish fruits dropped at 100cm and 90% for fruit dropped at 60cm height for the VNIR camera. Section III (Chapter 7) presents a general discussion on the results and key findings of the different chapters of the thesis. It integrates the results from previous chapters. It highlights the important practical contribution of this thesis towards successful non-destructive evaluation of intact pomegranate fruit.
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar.
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar.
Description
Thesis (PhD)--Stellenbosch University, 2023.