An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts

Yuan, Deshuai; Jiang, Jinbao; Qiao, Xuejun; Qi, Xiaotong; Wang, Wenjia

doi:10.1016/j.jfoodeng.2020.109915

Cited by 20 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the above-published research analysis, it was con-cluded that 400-430 nm is the key wavelength range for aflatoxin B1 detection in peanuts. However, the detection accuracy was affected due to the irregular spherical shape of the nuts, which was solved by Yuan et al (2020). They noticed the spectral intensity of irregularly shaped peanut samples decreases gradually from the central region to the edges.…”

Section: Detection Of Mycotoxinsmentioning

confidence: 99%

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Mishra

Panda

Ramirez

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

show abstract

Section: Detection Of Mycotoxinsmentioning

confidence: 99%

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Mishra

Panda

Ramirez

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…And certainly there are other methods to make better use of the abundant information contained in spectral images. Yuan et al (2020) had studied a new method which provided another path to improve the classification of spectral images apart from averaging the images to correct the spectral reflectance.…”

Section: Discussionmentioning

confidence: 99%

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Zhang

Yang²,

Feng

et al. 2020

Front. Plant Sci.

View full text Add to dashboard Cite

Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.

show abstract

“…Most of the industrial applications of NIR-HSI are related to the agro-food industries. These industries have taken advantage of the chemical imaging capabilities of NIR-HSI, as well as the versatility of its instrumentation to be implemented in the production lines as a monitoring tool for the safety and quality assessment of agro-food products ( Badaró et al, 2020 ; Dale et al, 2013 ; Fox and Manley, 2014 ; Serranti et al, 2013 ; Yuan et al, 2020 ). Nevertheless, the pharmaceutical sector, minerals, plastics’ recycling and plastic converting industries, have found in the NIR-HSI a valuable monitoring technology ( Bonifazi et al, 2020 ; Bøtker et al, 2020 ; Dalm et al, 2015 ; Moroni and Mei, 2020 ; Schropp et al, 2013 ; Stiefenhöfer, 2020 ; Wu et al, 2020 ).…”

Section: Current Photonic Techniques Applied For Monitoring Of Plasticsmentioning

confidence: 99%

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

et al. 2021

View full text Add to dashboard Cite

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites’ value chains.

show abstract

An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts

Cited by 20 publications

References 34 publications

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

Contact Info

Product

Resources

About