Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

Tschannerl, Julius; Ren, Jinchang; Jack, Frances; Krause, Julius; Zhao, Huimin; Huang, Wenjiang; Marshall, Stephen

doi:10.1016/j.foodchem.2018.07.089

Cited by 37 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…H YPERSPECTRAL images (HSI) contains spectral information in hundreds of contiguous bands. With the aid of a large number of spectral bands, hyperspectral image has been widely used in a range of applications [1]- [3], especially in the remote sensing area, such as precision agriculture [4]- [7], target detection [8], image enhancement [9], [10], object detection [11] and land cover analysis [12], [13], etc. Although numerous bands enable material identification and object detection, the processing of HSI suffers from the "curse of dimensionality" [14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection

Sun

Ren

Zhao

et al. 2022

IEEE Trans. Cybern.

Self Cite

View full text Add to dashboard Cite

Band selection has become a significant issue for the efficiency of hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted to the inconsistency in the outcome of UBS. Besides, most of UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this paper, an adaptive distance based band hierarchy (ADBH) clustering framework is proposed for unsupervised band selection in HSI, which can help to avoid the noisy bands whilst reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward whilst reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection

Sun

Ren

Zhao

et al. 2022

IEEE Trans. Cybern.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is in contrast to multispectral systems, such as red-green-blue (RGB) cameras, where only a limited number of wavebands are collected. 29,30 Most HSI applications have been focused on remote sensing systems, such as satellites or aircraft, to gather information for agricultural, geological inspections and military purposes. Nowadays, HSI is evolving into a standard for inline and online inspection in process analytics and quality control.…”

Section: Introductionmentioning

confidence: 99%

“…Prominent technical applications can be found in quality control for medicine, food and agricultural products. 29,31,32 In industrial applications, an HSI system is based on a combination of a pushbroom scanner and a conveyor belt. The pushbroom scanner is fixed over the conveyor belt as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

Ktash

Hauler

Ostertag

et al. 2020

J. Spectral Imaging

View full text Add to dashboard Cite

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

show abstract

“…Therefore, it is essential to explore the changes of phenolic content in Flos Lonicerae during storage for evaluating its pharmacodynamics. The current methods to determine total phenolic content include spectrophotometric techniques or high‐performance liquid chromatography (Tschannerl et al, ). However, the aforementioned methods are destructive, strenuous, and lengthy operation time.…”

Section: Introductionmentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

View full text Add to dashboard Cite

Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control of Flos Lonicerae as well as other agricultural products.

show abstract

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

Cited by 37 publications

References 33 publications

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection

Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Contact Info

Product

Resources

About