Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging

Hashim, Norhashila; Onwude, Daniel I.; Osman, Muhamad Syafiq

doi:10.1111/1750-3841.14127

Cited by 18 publications

(10 citation statements)

References 51 publications

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“…Multispectral imaging systems have been used in applications from various fields such as agriculture [47], microbiology [16], entomology [48], etc. In specific, adulteration and defects of agricultural produce have been analyzed using MIS for turmeric [49], fruits [50]- [52], beef [53], and rice [54], as well as in packed foods [55], and tomato paste [56]. Besides that, different imaging systems have been proposed in the literature with applications.…”

Section: Related Workmentioning

confidence: 99%

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

et al. 2022

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Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.

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Section: Related Workmentioning

confidence: 99%

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

et al. 2022

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“…Hyperspectral and multispectral imaging (HSI and MSI) are advancements of computer vision, which involves the capturing of image data at a different wavelength (e.g., continuous 400-1700 nm in steps of 1 nm for HSI and targeted 400-1100 nm in steps of 20 nm for MSI) across the electromagnetic spectrum [67][68][69][70]. Hyperspectral imaging particularly integrates both imaging and spectroscopy features to simultaneously gather spectral and spatial information from a product, thus making it a more powerful imaging technology compare to CV and multispectral imaging [71].…”

Section: Advanced Technologies For Quality Assessment In Postharvest Supply Chain: State-of-the-artmentioning

confidence: 99%

“…This imaging system coupled with PCA, CFS, GIA, SFS, SLR, LDA, kNN, and NN was able to give 89-98% accuracy (Table 1). However, not so much for multispectral imaging, as only two studies quantified the quality losses of mangoes during the cold storage, with a classification rate of ≈92% using PLS-DA (Table 1) [70,76].…”

Section: Advanced Technologies For Quality Assessment In Postharvest Supply Chain: State-of-the-artmentioning

confidence: 99%

“…To study the temperature distribution on a pallet of fruits during plastic boxes and cardboard packaging Apples ANN Thermal imaging showed the cardboard boxes to be a better packaging material for apples compared to plastic boxes [93] The statistical results demonstrated significant changes in the reference quality properties of samples before and after storage [70] 18.…”

Section: Imaging Technology Thermography Packagingmentioning

confidence: 99%

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Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

et al. 2020

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Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

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“…Multispectral imaging has been used previously to detect healthy and infected samples of citrus fruits [33] and raw french fries [34]. Adulteration and defects in food items and agricultural produce have been analyzed using multispectral imaging technology in packed food [35], fruits [36], [37], beef [38], rice [39] and tomato paste [40]. Crop monitoring and vegetation index calculation have been implemented using spectral imaging [41], where the authors talk about an RGB color imaging plus NIR imaging system that assess the scene for evaluating the health status of crops.…”

Section: Related Workmentioning

confidence: 99%

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

Raju

Sazonov

2020

IEEE Access

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There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energydense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system. INDEX TERMS Multispectral imaging, energy density, food detection, high calorie detection, dietary assessment.

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Evaluation of Chilling Injury in Mangoes Using Multispectral Imaging

Abstract: This paper demonstrates a fast, easy, and accurate method of identifying the effect of cold storage on mango, nondestructively. The method presented in this paper can be used industrially to efficiently differentiate different fruits from each other after low temperature storage.

Cited by 18 publications

References 51 publications

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging

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