Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes

Nansen, Christian; Singh, Keshav Dev; Mian, Ajmal; Allison, Brittany; Simmons, Christopher W.

doi:10.1016/j.jfoodeng.2016.06.010

Cited by 31 publications

(13 citation statements)

References 40 publications

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“…For imaging spectroscopy, Nansen and others () found that the VIS/NIR HSI technique could be applied to measure protein content in coffee powder accurately with PLSR model estimation. Nevertheless, no preprocessing and wavelength selection methods were adopted to further optimize the developed models in their research.…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

“…Nevertheless, no preprocessing and wavelength selection methods were adopted to further optimize the developed models in their research. In addition, other studies provided evidence that different brands and species of powdered coffee products could be identified accurately based on HSI technology (Nansen and others ).…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

See 1 more Smart Citation

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

131

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Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

131

View full text Add to dashboard Cite

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“…That is, the exact same 20 adult mosquitoes were imaged four times (placed on different sheets of paper. After hyperspectral imaging, radiometric filtering procedures described in previously published studies [ 20 , 22 – 25 ] were deployed, so that background (paper) was excluded. Analyses of variance (Proc glm) of 20 replications were performed, in which average reflectance from adult mosquitoes with white paper as background was compared with average reflectance when colored paper was used as background.…”

Section: Methodsmentioning

confidence: 99%

Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications

Nansen

2018

PLoS ONE

Self Cite

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Proximal remote sensing is being used across a very wide range of research fields and by scientists, who are often without deep theoretical knowledge optical physics; the author of this article falls squarely in that category! This article highlights two optical phenomena, which may greatly influence the quality and robustness of proximal remote sensing: penetration and scattering. Penetration implies that acquired reflectance signals are associated with both physical and chemical properties of target objects from both the surface and internal tissues/structures. Scattering implies that reflectance signals acquired from one point or object are influenced by scattered radiometric energy from neighboring points or objects. Based on a series of laboratory experiments, penetration and scattering were discussed in the context of “robustness” (repeatability) of hyperspectral reflectance data. High robustness implies that it is possible to control imaging conditions and therefore: 1) obtain very similar radiometric signals from inert objects (objects that do not change) over time, and 2) be able to consistently distinguish objects that are otherwise highly similar in appearance (size, shape, and color) and in terms of biochemical composition. It was demonstrated that robustness of hyperspectral reflectance data (40 spectral bands from 385 to 1024 nm) were significantly influenced by penetration and scattering of radiometric energy. In addition, it was demonstrated that the influence of penetration and scattering varied across the examined spectrum. Characterization of how optical phenomena may affect the robustness of reflectance data is important when using proximal remote sensing technologies as tools used to classify engineering and biological objects.

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“…Each pixel has a spectrum in the hyperspectral image together with a gray-scale image at each wavelength. Hyperspectral imaging has been reported to detect coffee quality 8 – 13 , and the use of pixel-wise spectra have not been discussed in coffee quality determination.…”

Section: Introductionmentioning

confidence: 99%

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

Zhang

Liu

2018

Sci Rep

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Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF).Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.Coffee is one of the most popular beverage in the world. Coffee variety is among the key factors influencing the coffee quality and price. According to International Coffee Organization (ICO), the estimated average number of global coffee consumption in the past 4 years was higher than 8 × 10 6 tons 1 . Identification of coffee beans has been studied by traditional reagent-based laboratory chemical methods 2,3 , spectroscopy techniques 4,5 and digital imaging techniques 6,7 . Reagent-based chemical methods are time consuming, reagent dependent and complex to operate. Spectroscopy and imaging techniques have been widely adopted as rapid, non-destructive and accurate techniques. Hyperspectral imaging (HSI), a technique integrating both spectroscopy and imaging techniques, has drawn raising attentions from researchers of different fields. HSI acquires spectral and spatial information simultaneously. The hyperspectral image is a three-dimensional (3D) data cube (x, y, λ) with the two-dimensional spatial information (x, y) and the third dimension of spectral information (λ). Each pixel has a spectrum in the hyperspectral image together with a gray-scale image at each wavelength. Hyperspectral imaging has been reported to detect coffee quality [8][9][10][11][12][13] , and the use of pixel-wise spectra have not been discussed in coffee quality determination.One of the main advantages of hyperspectral imaging is to form and visualize the distribution maps of the samples. It reveals not only the physical attributes but also the chemical compositions within or between samples. Theoretically, visualization of the physical attributes and the chemical...

show abstract

Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes

Cited by 31 publications

References 40 publications

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

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