Thermography, A New Approach in Food Science Studies: A Review

Samanian, Nargess; Mohebbi, Mohebbat

doi:10.15406/mojfpt.2016.02.00041

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Thermal imaging is an imaging method based on sensing infrared radiation emitted by an object into a visible image. This image captured with the aid of a thermal camera is a heat map illustrating the variation of the distribution of temperature in the scene captured (Ansari & Salankar, 2017; Samanian & Mohebbi, 2016). The underlying principle is due to the random thermal motion of particles which results when all objects emit thermal radiation above the absolute temperature of 0 K (−273°C) (Li et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging

Vetrekar

Prabhu

Naik

et al. 2022

Food Processing Preservation

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With the increasing demand for fruits in the consumer market and to fulfill the needs of consumers, fruits are deliberately resorted to ripen artificially using industrial‐grade Calcium Carbide (CaC2) which has carcinogenic properties. The preferences to employ imaging sensing technology across the electromagnetic spectrum have gained noteworthy attention in recent times. This work presents the study to distinguish between natural and artificially ripened bananas using a multispectral imaging approach. Specifically, we carry out the analysis using multispectral images collected for natural and artificially ripened bananas in eight narrow spectrum bands spanning from visible (VIS) to near‐infrared (NIR) wavelength. Further, to present the contribution of each individual spectral band, we propose a scheme that extracts the relevant features from “convolution 5 (conv5)” and “fully connected 6 (fc6)” layers of convolutional neural network (CNN) based on AlexNet architecture and processes these features independently with collaborative representation classifier (CRC) in a robust manner. Essentially, we demonstrate the experimental classification accuracy based on multispectral images comprised of 5760 sample images using 10‐fold cross‐validation. Based on the proposed scheme, the highest average classification accuracy of about 88.82 ± 1.65% is obtained using our proposed approach presenting the significance of our work. Practical applications The practical application of our proposed approach is to distinguish between natural and artificially ripened banana fruits in a market chain. Further, the approach is non‐invasive and non‐destructive based on multispectral imaging which has the potential to carry out real‐time quality analysis of bananas, especially to detect adulteration.

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Section: Literature Reviewmentioning

confidence: 99%

Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging

Vetrekar

Prabhu

Naik

et al. 2022

Food Processing Preservation

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“…A conventional thermography procedure is based on infrared (IR) radiation. The emitted radiation from the material under inspection is observed and captured using an infrared camera to represent the temperature distribution of the inspected material in a visible image [13]. Infrared thermography (IRT) is classified into passive and active thermography [14].…”

Section: Conventional Ndt Techniques a Thermography Ndtmentioning

confidence: 99%

Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

2019

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The development in materials technology has produced stronger, lighter, stiffer, and more durable electrically insulating composites which are replacing metals in many applications. These composites require alternative inspection techniques because the conventional nondestructive testing (NDT) techniques such as thermography, eddy currents, ultrasonic, X-ray and magnetic particles have limitations of inspecting them. Microwave NDT technique employing open-ended rectangular waveguides (OERW) has emerged as a promising approach to detect the defects in both metal and composite materials. Despite its promising results over conventional NDT techniques, OERW microwave NDT technique has shown numerous limitations in terms of poor spatial resolution due to the stand-off distance variations, inspection area irregularities and quantitative estimation in imaging the size of defects. Microwave NDT employing OERW in conjunction with robust artificial intelligence approaches have tremendous potential and viability for evaluating composite structures for the purpose mentioned here. Artificial intelligence techniques with signal processing techniques are highly possible to enhance the efficiency and resolution of microwave NDT technique because the impact of artificial intelligence approaches is proven in various conventional NDT techniques. This paper provides a comprehensive review of NDT techniques as well as the prospect of using artificial intelligence approaches in microwave NDT technique with regards to other conventional NDT techniques. INDEX TERMS Microwave nondestructive testing, open-ended rectangular waveguides, artificial intelligence.

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“…Computer vision systems can effectively detect defects in plant tissue through the analysis of images of fruits and vegetables obtained in the range of 8-13 microns [1]. In this case, the main conditions are significant differences in the thermophysical characteristics (TPCs) of defective and defect-free zones of the test object, as well as correctly selected operating parameters of the noncontact thermal non-destructive testing process, providing the required temperature contrast between zones of plant tissue of the object of different quality [2].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of the Heat Transfer Process in Spherical Objects With Flat, Cylindrical and Spherical Defects

Balabanov,

Egorov,

Divin

et al. 2024

Preprint

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This article discusses the application of thermal quality control methods for plant tissues. The purpose of the work is to determine the optimal parameters of thermal impact on a spherical-shaped plant control object, ensuring the detection of surface and subsurface defects. The authors of the work proposed mathematical models of the temperature field for a spherical body with defects and a flat sample when exposed to the thermal influence of a pulsed source. As a result of the use of mathematical models, the thermophysical characteristics of plant tissues of varying degrees of disease damage were obtained, which made it possible to simulate the temperature field of the control object and obtain an image of classified tissues. The developed recommendations made it possible to select the optimal parameters of the thermal effect on the test object.

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Thermography, A New Approach in Food Science Studies: A Review

Cited by 6 publications

References 33 publications

Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging

Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging

Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

Mathematical Modeling of the Heat Transfer Process in Spherical Objects With Flat, Cylindrical and Spherical Defects

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