Non-intrusive studies of gas contents and gas diffusion in hen eggs

Li, Ying; Li, Wansha; Hu, Lingna; Svanberg, Katarina; Svanberg, Sune

doi:10.1364/boe.10.000083

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Different incident angles cause different information to be contained in the light collected by the camera, resulting in different accuracy of egg freshness. The freshness is closely related to the internal composition of an egg, yolk index [ 43 ], the pH of protein [ 1 ], and air chamber index [ 44 ]. The spectra collected about the more internal information of an egg is the precondition for establishing a model with higher accuracy.…”

Section: Discussionmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.

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Section: Discussionmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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show abstract

“…Light propagation inside an eggDifferent incident angles cause different information contained in the light collected by the camera, resulting in different accuracy of egg freshness. The freshness is closely related to the internal composition of an egg, yolk index[38], the pH of protein [1] and air chamber index[39]. The spectra collected about the more internal information of an egg is the precondition for establishing a model with higher accuracy.…”

mentioning

confidence: 99%

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Dai¹,

Jiang²,

Lu³

et al. 2020

Preprint

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00% and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence due that the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and selecting the incident angle of the light source, and have potential applications in online non-destructive testing.

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Gas in Scattering Media Absorption Spectroscopy

Svanberg

2019

Encyclopedia of Analytical Chemistry

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Gas in scattering media absorption spectroscopy (GASMAS) is a new variety of tunable diode laser spectroscopy (TDLS). It combines concepts from atmospheric trace‐gas monitoring with those pertinent to biological tissue optics. The former field deals with high‐resolution spectroscopy in nonscattering media, whereas the latter area is characterized by broad absorption structures in strongly scattering media. GASMAS provides novel applications in, for example, the material science and biophotonics fields. The absorptive imprints of free gases inside pores or cavities in surrounding solid or liquid matter are typically many orders of magnitude narrower than those of the host material, a fact that is critically utilized. The gas signals are detected in the weak, multiply scattered light emerging from the illuminated sample. Wavelength‐modulation and phase‐sensitive detection techniques are employed, typically in connection with single‐mode CW lasers. Any gas with useful absorption at wavelengths where the host material does not absorb strongly can be detected. For biological tissue, containing liquid water and blood, this limits the useful region to the ‘tissue optical window’ − 650–1400 nm – where, however, the interesting gases oxygen and water vapor absorb at around 760 and 950 nm, respectively. This limitation does not pertain to other materials, particularly not to those that do not contain liquid water. GASMAS experiments, which relate to basic physics, include studies of nanoporous ceramics, where wall collisions influence the line shape and provide information on pore‐size distribution. A small piece of strongly scattering ceramic can serve as an alignment‐free multipass cell with effective path length hundreds of times longer than the physical dimension. Porosity and gas transport studies in construction materials such as polystyrene foams, wood, ceramics, and paper are examples of applications in the material science field. The technique is further very powerful for studying gas in the human body and products that humans eat, such as packaged foods, fruits, and pharmaceutical preparations. A key aspect of food packaging is to prevent oxygen to influence the food. Frequently, modified atmospheres with nitrogen or carbon dioxide as filling gases are used. Applications include monitoring the performance of packaging machines and measurements of the product on the shelf. Porosity in pharmaceutical tablets is important to determine, as it has bearing on controlled release. Following initial work on healthy volunteers, a clinical trial concerning human sinus cavities has been performed. Gas filling and composition can be used as a diagnostic tool in connection with sinusitis, a very common disorder, also related to the problem with heavy overprescription of antibiotics and associated growing bacterial resistance. Following realistic phantom studies, it was shown that it is possible to detect gas in the lungs and intestines of a newborn baby, which could be of considerable interest for the future care of prematurely born children. Gas diffusion and transport can be dynamically studied in response to an abrupt change in gas composition in medicine as well as in material science. The GASMAS technique is fully nonintrusive and nondestructive.

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Non-intrusive studies of gas contents and gas diffusion in hen eggs

Cited by 5 publications

References 26 publications

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Gas in Scattering Media Absorption Spectroscopy

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