Protein and Oil Composition Predictions of Single Soybeans by Transmission Raman Spectroscopy

Schulmerich, Matthew V.; Gelber, Matthew K.; Kong, Rong; Kole, Matthew R.; Harrison, Sandra K.; McKinney, John D.; Thompson, D. Brian; Kull, Linda S.; Bhargava, Rohit

doi:10.1021/jf301247w

Cited by 28 publications

(31 citation statements)

References 25 publications

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“…The average reflectance obtained from cracked images was consistently higher than that obtained from the sound regions of the spectra. The decrease in reflectance was also associated with strong water absorption around 1190 nm and 1450 nm [20]. The intensity of the specular regions emanating due to surface and geometric properties of the tomato samples with respect to the illumination conditions was consistently higher than the reflectance intensity of other regions.…”

Section: Resultsmentioning

confidence: 99%

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

Lee

Kim

Jeong

et al. 2014

Sensors

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The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.

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Section: Resultsmentioning

confidence: 99%

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

Lee

Kim

Jeong

et al. 2014

Sensors

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“…Transmission Raman spectroscopy can overcome both subsurface presentation and bulk heterogeneity issues, allowing for retrieval of some Raman information from within a sample, by placing the sample between a laser and detector in order to acquire forward-scattered Raman signals that have passed through the sample. Transmission Raman spectroscopy has been effectively used for internal analysis of samples such as quantitative assessment of pharmaceutical capsules [9,10], evaluation of protein content in packed corn kernels [11], and evaluation of protein and oil composition in single soybeans [12]. Although this technique can overcome surface layer fluorescence (e.g., of a capsule, coating, or other packaging layer, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

Chao

Dhakal

Qin

et al. 2017

Sensors

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Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

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“…Transmission Raman spectroscopy is able to determine bulk composition of a sample, especially for small individual items with a diffusely scattering and weakly absorbing internal condition. The technique has been used for analysis of diffusing and translucent materials, such as chemical analysis of pharmaceutical tablets, composition analysis of single soybeans and corn kernels, and differentiation of the geographical origins of rice . Although the transmission method can provide overall internal information, it cannot separate the Raman information from individual layers of the sample.…”

Section: Introductionmentioning

confidence: 99%

A line‐scan hyperspectral Raman system for spatially offset Raman spectroscopy

Qin

Kim

Schmidt

et al. 2015

J Raman Spectroscopy

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Spatially offset Raman spectroscopy (SORS) is a technique that can obtain subsurface layered information by collecting Raman spectra from a series of surface positions laterally offset from the excitation laser. Currently optical fiber probes are used as major tools in SORS measurement, which are either slow (single fiber probe with mechanical movement) or restricted in selecting offset range and interval (fiber probe array). This study proposes a new method to conduct SORS measurement based on a newly developed line-scan hyperspectral Raman imaging system. A 785-nm point laser was used as an excitation source. A detection module consisting of an imaging spectrograph and a charge-coupled device camera was used to acquire line-shape SORS data in a spectral region of À592 to 3015 cm À1 . Using a single scan, the system allowed simultaneous collection of a series of Raman spectra in a broad offset range (e.g. 0-36 mm in two sides of the incident laser) with a narrow interval (e.g. 0.07 mm). Four layered samples were created by placing butter slices with thicknesses of 1, 4, 7, and 10 mm on top of melamine powder, providing different individual Raman characteristics to test the line-scan SORS technique. Self-modeling mixture analysis (SMA) was used to analyze the SORS data. Raman spectra from butter and melamine were successfully retrieved for all four butter-on-melamine samples using the SMA method. The line-scan SORS measurement technique provides a flexible and efficient method for subsurface evaluation, which has potential to be used for food safety and quality inspection.

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Protein and Oil Composition Predictions of Single Soybeans by Transmission Raman Spectroscopy

Cited by 28 publications

References 25 publications

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

A line‐scan hyperspectral Raman system for spatially offset Raman spectroscopy

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