Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh

Wu, Di; Sun, Da‐Wen

doi:10.1016/j.talanta.2013.03.041

Cited by 194 publications

(63 citation statements)

References 47 publications

(52 reference statements)

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“…In each sampling run, some samples are first randomly chosen in a fixed ratio to build a calibration model. Then, exponentially decreasing function (EDF), competitive wavelength reduction by adaptive reweighted sampling (ARS), and root mean square error of cross validation (RMSECV) were adopted to select the key variables subset [28,29]. The procedure of CARS was performed in the Matlab 2013b (MathWorks, Natick, MA, USA).…”

Section: Model Development Methodsmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC 2 to PC 6 ) gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC 2 to PC 6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.

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Section: Model Development Methodsmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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“…Several studies have shown that characteristic variables selected using chemometric methods was helpful to improve the predictive capability of multivariate calibration models. [12][13][14] In this study, dielectric spectroscopy technique combined with characteristic variables selection approaches was applied to determine the SSC of persimmons during postharvest storage. The main objectives of this research were (1) to extract some characteristic dielectric variables that contain enough useful information by using different characteristic variables selection methods; (2) to build different models for determining SSC of persimmons originated from different orchards; and (3) to evaluate the potential of dielectric spectroscopy in nondestructively determining SSC of persimmons.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Liu

2017

International Journal of Food Properties

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To explore the feasibility of dielectric spectroscopy in predicting soluble solids content (SSC) of persimmons during postharvest storage period, the dielectric constant spectra and dielectric loss factor spectra of 105 'Shui' persimmons were measured from 20 MHz to 4500 MHz. Based on the joint x-y distances algorithm, the persimmon samples were divided into two sets: 70 samples in calibration set and 35 samples in prediction set. One hundred and seventyfour, 14, and 24 variables were extracted as characteristic variables from full dielectric spectra (FS) by uninformative variables elimination (UVE), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares (PLS) and least squares support vector machine (LSSVM) were applied to build SSC prediction models using FS and characteristic variables extracted by UVE, SPA, and CARS. The results indicated that LSSVM models offered better performance than PLS models at same input variables. CARS-LSSVM had the best SSC determination performance with the correlation coefficient and root-mean-square error of prediction set of 0.970 and 0.494°Brix. This study indicates that dielectric spectroscopy technique combined with characteristic variables selection methods is promising for determining SSC of persimmons. ARTICLE HISTORY

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“…[5][6][7] Optical detection works to identify bacteria based on a spectral profile that is inherently unique to the organism. Hyperspectral imaging (HSI) is a form of optical detection that differs from other methods by collecting both spatial data (x and y coordinates) and spectral data (l) in the form of a three-dimensional hypercube.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of Salmonella serotypes through hyperspectral microscopy with different lighting sources

Eady

Park

2016

J. Spectral Imaging

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The rapid detection of food-borne pathogenic bacteria is critical to the food industry for preventing the introduction of contaminated product into the marketplace and limiting the spread of outbreaks. Hyperspectral microscope images (HMI) are a form of optical detection, which classify bacteria by combining microscope images with a spectrophotometer. The objective of this study was to compare the spectra generated from dark-field HMIs of five live Salmonella serotypes from two lighting sources, metal halide (MH) and tungsten halogen (TH), assessing classification accuracy and robustness, between 450 nm and 800 nm. It was found that the MH spectra could be reduced to as few as 10 optimal bands between 594 nm and 630 nm, but TH band reduction decreased accuracy, due to the inherent broader peak structure generated by the TH light source. Collection of HMIs from the two light sources comparing the same cells shows slight differences in scatter intensity patterns. Principal component linear discriminate analysis classified serotype subsets (n = 1800), reporting both MH and TH accuracies at 100%, while the reduced key MH bands achieved 99.4-100% accuracy. Principal component regression calculated the root mean squared error of cross-validation < 0.014 and a R 2 > 0.948 for both full spectrum lamps. MH or TH lamps can be effectively used for discriminating bacteria HMIs on a cellular level by serotype, but reducing TH bands may lose crucial classification information.

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Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh

Cited by 194 publications

References 47 publications

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Rapid identification of Salmonella serotypes through hyperspectral microscopy with different lighting sources

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