Showing papers by "Umezuruike Linus Opara published in 2023"

Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning

Abstract: Spectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models that are not easily understood. Furthermore, collinearity between different wavelengths dictates that some of the data variables are redundant and may even contribute noise. The use of variable selection methods is one efficient way to obtain an optimal model, andthis was the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800–2500 nm were used to classify bruise damage in three apple cultivars, namely ‘Golden Delicious’, ‘Granny Smith’ and ‘Royal Gala’. Six prominent machine learning classification algorithms were employed, and two variable selection methods were used to determine the most relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were achieved using linear regression and support vector machine based on up to 40 wavelengths: these methods reached precision values in the range of 0.79–0.86, which were all comparable (within error bars) to a classifier based on the entire range of frequencies. The results also provided an open-source based framework that is useful towards the development of multi-spectral applications such as rapid grading of apples based on mechanical damage, and it can also be emulated and applied for other types of defects on fresh produce.

Determination of moisture loss of pomegranate cultivars under cold and shelf storage conditions and control strategies

Abstract: Pomegranates are prone to moisture loss, despite having a thick rind.

Recent advances in the potential of modeling and simulation to assess the performance of modified atmosphere packaging (MAP) systems for the fresh agricultural product: Challenges and development

Abstract: The metabolic process and physiological activities of fresh fruit and vegetables continue after harvesting and lead to quality reduction and waste enhancement. Modified atmosphere packaging (MAP) is a well-proven method to avoid anaerobiosis activities and extend the shelf life of agricultural products. As a result, researchers have proposed MAP technology as an appropriate and cost-effective method. The design of MAP systems depends on various factors, including the respiration and transpiration rates of the product, the temperature and humidity of the environment, and the permeability rate of the package. Therefore, the integration of mentioned information in the design of a successful MAP system is crucial. The modeling approach is a reliable method for understanding the incorporation of the effective parameters and achieving the mass balance equation to determine the optimal conditions of the MAP. This review investigates the developed models for achieving a MAP design for various agricultural products. Also, the existing challenges in the developed models are discussed. In addition, the strengths and shortcomings of the integrated models are described, and the prospects for the optimization of MAP are highlighted. Considering the interaction of effectual parameters and sub-models, mathematical modeling revealed acceptable accuracy to predict the shelf life of fresh produce under the actual supply chain conditions and MAP systems. Due to the difference in the optimum MAP requirements based on specific parameters such as gas concentration, research is needed to optimize the effective factors by taking into account the perm-selectivity of the packaging materials. More efforts should focus on developing the comprehensive mathematical model and simulation, including product characteristics, package properties, environmental conditions, and mass and heat transfer phenomena for optimal MAP design.

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

Abstract: Introduction Fresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique. Methods A total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification. Results and discussion The accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications.

Correction: Determination of moisture loss of pomegranate cultivars under cold and shelf storage conditions and control strategies

Abstract: Correction for ‘Determination of moisture loss of pomegranate cultivars under cold and shelf storage conditions and control strategies’ by Umezuruike Linus Opara et al., Sustainable Food Technol., 2023, 1, 79–91, https://doi.org/10.1039/D2FB00017B.