Special Issue "Applications of Hyperspectral Imaging for Food and Agriculture"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (15 December 2016)

Special Issue Editor

Guest Editor
Dr. Kuanglin Kevin Chao

USDA, ARS, NEA, BARC, EMFSL, 10300 Baltimore Ave., Building 303 BARC-East, Room 012, Beltsville, MD 20705, USA
Website | E-Mail
Phone: +1 301 504 8450 (ext 260)
Fax: +1 301 504 9466
Interests: non-destructive sensing; food quality and safety evaluation; food authentication

Special Issue Information

Dear Colleagues,

Combining the advantages of conventional spectroscopy and imaging techniques, hyperspectral imaging can acquire highly-detailed spatial and spectral information across large (non-microscopic) areas. The many forms of hyperspectral implementation—e.g., visible, near- and mid-infrared, fluorescence, Raman scattering, etc.—produce high-resolution three-dimensional data suitable for non-destructive sample analysis for a vast array of purposes that may even be simultaneously performed. Technological advances have made hyperspectral imaging easier to implement for a variety of research and industry environments, with greater speeds, higher throughputs, and/or larger imaging areas than ever before possible. Consequently, there is tremendous interest in hyperspectral and multispectral imaging for non-destructive evaluation of foods and agricultural products for safety, quality, and authentication concerns.

The upcoming Special Issue of Applied Sciences will focus on recent developments in hyperspectral and multispectral imaging and analysis that target quality and safety issues for food and agricultural commodities, adulteration and authentication issues for foods and food ingredients, and advances in hardware and instrumentation, methodology, and practical implementation for hyperspectral analysis of food materials. We would like to invite you to submit or recommend original research papers for the “Applications of Hyperspectral Imaging for Food and Agriculture” Special Issue.

Dr. Kuanglin Chao (Kevin Chao)
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chemical imaging
  • contaminant detection
  • food quality
  • food safety
  • hyperspectral
  • ingredient authentication
  • line-scan
  • multispectral imaging
  • nondestructive sensing

Published Papers (15 papers)

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Research

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Open AccessArticle Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
Appl. Sci. 2017, 7(3), 268; doi:10.3390/app7030268
Received: 11 January 2017 / Revised: 21 February 2017 / Accepted: 1 March 2017 / Published: 8 March 2017
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Abstract
In South Africa, indigenous herbal teas are enjoyed due to their distinct taste and aroma. The acclaimed health benefits of herbal teas include the management of chronic diseases such as hypertension and diabetes. Quality control of herbal teas has become important due to
[...] Read more.
In South Africa, indigenous herbal teas are enjoyed due to their distinct taste and aroma. The acclaimed health benefits of herbal teas include the management of chronic diseases such as hypertension and diabetes. Quality control of herbal teas has become important due to the availability of different brands of varying quality and the production of tea blends. The potential of hyperspectral imaging as a rapid quality control method for herbal tea blends from rooibos (Aspalathus linearis), honeybush (Cyclopia intermedia), buchu (Agathosma Betulina) and cancerbush (Sutherlandia frutescens) was investigated. Hyperspectral images of raw materials and intact tea bags were acquired using a sisuChema shortwave infrared (SWIR) hyperspectral pushbroom imaging system (920–2514 nm). Principal component analysis (PCA) plots showed clear discrimination between raw materials. Partial least squares discriminant analysis (PLS-DA) models correctly predicted the raw material constituents of each blend and accurately determined the relative proportions. The results were corroborated independently using ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS). This study demonstrated the application of hyperspectral imaging coupled with chemometric modelling as a reliable, rapid and non-destructive quality control method for authenticating herbal tea blends and to determine relative proportions in a tea bag. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique
Appl. Sci. 2017, 7(3), 213; doi:10.3390/app7030213
Received: 10 November 2016 / Revised: 10 February 2017 / Accepted: 14 February 2017 / Published: 23 February 2017
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Abstract
A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400–1100 nm were acquired for fifty pork samples, and their
[...] Read more.
A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400–1100 nm were acquired for fifty pork samples, and their VIS/NIR diffuse reflectance spectra were extracted from the images. The reference values of TVC in pork samples were determined by classical microbiological plating method. Both partial least square regression (PLSR) model and support vector machine regression model (SVR) of TVC were built for comparative analysis to achieve better results. Different transformation methods and filtering methods were applied to improve the models. The results show that both the optimized PLSR model and SVR model can predict the TVC very well, while the SVR model based on second derivation was better, which achieved with RP (correlation coefficient of prediction set) = 0.94 and SEP (standard error of prediction set) = 0.4570 log CFU/g in the prediction set. An image processing algorithm was then developed to transfer the prediction model to every pixel of the image of the entire sample; the visualizing map of TVC would be displayed in real-time during the detection process due to the simplicity of the model. The results demonstrated that hyperspectral imaging is a potential reliable approach for non-destructive and real-time prediction of TVC in pork. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessFeature PaperArticle Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging
Appl. Sci. 2017, 7(1), 109; doi:10.3390/app7010109
Received: 3 December 2016 / Revised: 10 January 2017 / Accepted: 17 January 2017 / Published: 21 January 2017
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Abstract
The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm. The mean spectra of
[...] Read more.
The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky–Golay (S–G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
Appl. Sci. 2017, 7(1), 97; doi:10.3390/app7010097
Received: 23 November 2016 / Revised: 30 December 2016 / Accepted: 13 January 2017 / Published: 19 January 2017
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Abstract
Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%–100%).
[...] Read more.
Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%–100%). NIR spectra of meat mixtures were measured with dispersive (400–2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000–2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (Rcv2) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (Rcv2 = 0.99) and Rcv2 > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis
Appl. Sci. 2017, 7(1), 90; doi:10.3390/app7010090
Received: 27 November 2016 / Revised: 4 January 2017 / Accepted: 12 January 2017 / Published: 17 January 2017
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Abstract
Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day
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Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (D1, D2, D3, D4, D5, D6, D7) were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC). Hyperspectral images of all kernels were acquired within spectral range of 921–2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA) and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D1–2 (the group consisted of D1 and D2), D3–4 (D3 and D4), and D5–7 (D5, D6, and D7), were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing) and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction) were compared, according to the performance of the classification model built by support vector machines (SVM). The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected as the key wavelengths in the discrimination of fungal contamination levels. In general, near-infrared hyperspectral imaging can be used for early detection of fungal contamination in maize kernels. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Application of Near-Infrared Hyperspectral Imaging to Detect Sulfur Dioxide Residual in the Fritillaria thunbergii Bulbus Treated by Sulfur Fumigation
Appl. Sci. 2017, 7(1), 77; doi:10.3390/app7010077
Received: 8 November 2016 / Revised: 22 December 2016 / Accepted: 6 January 2017 / Published: 12 January 2017
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Abstract
Sulfur-fumigated Chinese medicine is a common issue in the process of Chinese medicines. Detection of sulfur dioxide (SO2) residual content in Fritillaria thunbergii Bulbus is important to evaluate the degree of sulfur fumigation and its harms. It helps to control the
[...] Read more.
Sulfur-fumigated Chinese medicine is a common issue in the process of Chinese medicines. Detection of sulfur dioxide (SO2) residual content in Fritillaria thunbergii Bulbus is important to evaluate the degree of sulfur fumigation and its harms. It helps to control the use of sulfur fumigation in Fritillaria thunbergii Bulbus. Near-infrared hyperspectral imaging (NIR-HSI) was explored as a rapid, non-destructive, and accurate technique to detect SO2 residual contents in Fritillaria thunbergii Bulbus. An HSI system covering the spectral range of 874–1734 nm was used. Partial least squares regression (PLSR) was applied to build calibration models for SO2 residual content detection. Successive projections algorithm (SPA), weighted regression coefficients (Bw), random frog (RF), and competitive adaptive reweighted sampling (CARS) were used to select optimal wavelengths. PLSR models using the full spectrum and the selected optimal wavelengths obtained good performance. The Bw-PLSR model was applied on a hyperspectral image to form a prediction map, and the results were satisfactory. The overall results in this study indicated that HSI could be used as a promising technique for on-line visualization and monitoring of SO2 residual content in Fritillaria thunbergii Bulbus. Detection and visualization of Chinese medicine quality by HSI provided a new rapid and visual method for Chinese medicine monitoring, showing great potential for real-world application. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
Appl. Sci. 2017, 7(1), 3; doi:10.3390/app7010003
Received: 29 September 2016 / Revised: 18 November 2016 / Accepted: 30 November 2016 / Published: 22 December 2016
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Abstract
A system to take high-resolution Visible/Near Infra-Red (VIS/NIR) hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm), respectively, for illumination purposes was designed, built, and tested. Components of the system include a semi-autonomous cart, a
[...] Read more.
A system to take high-resolution Visible/Near Infra-Red (VIS/NIR) hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm), respectively, for illumination purposes was designed, built, and tested. Components of the system include a semi-autonomous cart, a gated-intensified camera, a spectral adapter, a frequency-triple Nd:YAG (Neodymium-doped Yttrium Aluminium Garnet) laser, and optics to convert the Gaussian laser beam into a line-illumination source. The front wheels of the cart are independently powered by stepper motors that support stepping or continuous motion. When stepping, a spreadsheet is used to program parameters of image sets to be acquired at each step. For example, the spreadsheet can be used to set delays before the start of image acquisitions, acquisition times, and laser attenuation. One possible use of this functionality would be to establish acquisition parameters to facilitate the measurement of fluorescence decay-curve characteristics. The laser and camera are mounted on an aluminum plate that allows the optics to be calibrated in a laboratory setting and then moved to the cart. The system was validated by acquiring images of fluorescence responses of spinach leaves and dairy manure. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
Appl. Sci. 2016, 6(12), 450; doi:10.3390/app6120450
Received: 5 October 2016 / Revised: 8 December 2016 / Accepted: 15 December 2016 / Published: 19 December 2016
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Abstract
Hyperspectral imaging technology was employed to detect slight bruises on Korla pears. The spectral data of 60 bruised samples and 60 normal samples were collected by a hyperspectral imaging system. To select the characteristic wavelengths for detection, several chemometrics methods were used on
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Hyperspectral imaging technology was employed to detect slight bruises on Korla pears. The spectral data of 60 bruised samples and 60 normal samples were collected by a hyperspectral imaging system. To select the characteristic wavelengths for detection, several chemometrics methods were used on the raw spectra. Firstly, principal component analysis (PCA) was conducted on the spectra ranging from 420 to 1000 nm of all samples. Considering that the reliability of the first two PCs was more than 90%, five characteristic wavelengths (472, 544, 655, 688 and 967 nm) were selected by the loading plot of PC1 and PC2. Then, each of the wavelength variables was considered as an independent classifier for bruised/normal classification, and all classifiers were evaluated by the receiver operating characteristic (ROC) analysis. Two wavelengths (472 and 967 nm) with the highest values under the curve (0.992 and 0.980) were finally selected for modeling. The classifying model was built by partial least squares discriminant analysis (PLS-DA) and the bruised/normal classification accuracy of the modeling set (45 damaged samples and 45 normal samples) and prediction set (15 damaged samples and 15 normal samples) was 98.9% and 100%, respectively, which is similar to that of the PLS-DA model based on the whole spectral range. The result shows that it is feasible to select characteristic wavelengths for the detection of slight bruises on pears by the methods combining the PCA and ROC analysis. This study can lay a foundation for the development of an online detection system for slight bruise detection on pears. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce
Appl. Sci. 2016, 6(12), 412; doi:10.3390/app6120412
Received: 23 October 2016 / Revised: 20 November 2016 / Accepted: 30 November 2016 / Published: 7 December 2016
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Abstract
Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 =
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Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky–Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm
Appl. Sci. 2016, 6(9), 249; doi:10.3390/app6090249
Received: 22 June 2016 / Revised: 28 July 2016 / Accepted: 1 September 2016 / Published: 6 September 2016
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Abstract
Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white
[...] Read more.
Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%–100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Distinguishing Bovine Fecal Matter on Spinach Leaves Using Field Spectroscopy
Appl. Sci. 2016, 6(9), 246; doi:10.3390/app6090246
Received: 6 May 2016 / Revised: 29 July 2016 / Accepted: 23 August 2016 / Published: 30 August 2016
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Abstract
Detection of fecal contaminants on leafy greens in the field will allow for decreasing cross-contamination of produce during and post-harvest. Fecal contamination of leafy greens has been associated with Escherichia coli (E. coli) O157:H7 outbreaks and foodborne illnesses. In this study,
[...] Read more.
Detection of fecal contaminants on leafy greens in the field will allow for decreasing cross-contamination of produce during and post-harvest. Fecal contamination of leafy greens has been associated with Escherichia coli (E. coli) O157:H7 outbreaks and foodborne illnesses. In this study, passive field spectroscopy measuring reflectance and fluorescence created by the sun’s light, coupled with numerical normalization techniques, are used to distinguish fecal contaminants on spinach leaves from soil on spinach leaves and uncontaminated spinach leaf portions. A Savitzky-Golay first derivative transformation and a waveband ratio of 710:688 nm as normalizing techniques were assessed. A soft independent modelling of class analogies (SIMCA) procedure with a 216 sample training set successfully predicted all 54 test set sample types using the spectral region of 600–800 nm. The ratio of 710:688 nm along with set thresholds separated all 270 samples by type. Application of these techniques in-field to avoid harvesting of fecal contaminated leafy greens may lead to a reduction in foodborne illnesses as well as reduced produce waste. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Potential Application of Fluorescence Imaging for Assessing Fecal Contamination of Soil and Compost Maturity
Appl. Sci. 2016, 6(9), 243; doi:10.3390/app6090243
Received: 19 May 2016 / Revised: 18 August 2016 / Accepted: 24 August 2016 / Published: 27 August 2016
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Abstract
Pathogenic microorganisms can lead to serious outbreaks of foodborne illnesses, particularly if fresh produce becomes contaminated and then happens to be inappropriately handled in a manner that can incubate pathogens. Pathogenic microbial contamination of produce can occur through a variety of pathways, such
[...] Read more.
Pathogenic microorganisms can lead to serious outbreaks of foodborne illnesses, particularly if fresh produce becomes contaminated and then happens to be inappropriately handled in a manner that can incubate pathogens. Pathogenic microbial contamination of produce can occur through a variety of pathways, such as from the excrement of domesticated and wild animals, biological soil amendment, agricultural water, worker health and hygiene, and field tools used during growth and harvest. The use of mature manure compost and preventative control of fecal contamination from wildlife and livestock are subject to safety standards to minimize the risk of foodborne illness associated with produce. However, in a field production environment, neither traces of animal feces nor the degree of maturity of manure compost can be identified by the naked eye. In this study, we investigated hyperspectral fluorescence imaging techniques to characterize fecal samples from bovine, swine, poultry, and sheep species, and to determine feasibilities for both detecting the presence of animal feces as well as identifying the species origin of the feces in mixtures of soil and feces. In addition, the imaging techniques were evaluated for assessing the maturity of manure compost. The animal feces exhibited dynamic and unique fluorescence emission features that allowed for the detection of the presence of feces and showed that identification of the species origin of fecal matter present in soil-feces mixtures is feasible. Furthermore, the results indicate that using simple single-band fluorescence imaging at the fluorescence emission maximum for animal feces, simpler than full-spectrum hyperspectral fluorescence imaging, can be used to assess the maturity of manure compost. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessArticle Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging
Appl. Sci. 2016, 6(6), 183; doi:10.3390/app6060183
Received: 18 May 2016 / Revised: 12 June 2016 / Accepted: 14 June 2016 / Published: 21 June 2016
Cited by 3 | PDF Full-text (2237 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were
[...] Read more.
Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares–support vector machines (LS–SVM) model. The LS–SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Review

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Open AccessReview Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review
Appl. Sci. 2017, 7(2), 189; doi:10.3390/app7020189
Received: 11 January 2017 / Accepted: 8 February 2017 / Published: 15 February 2017
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Abstract
New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new
[...] Read more.
New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Open AccessReview Line-Scan Hyperspectral Imaging Techniques for Food Safety and Quality Applications
Appl. Sci. 2017, 7(2), 125; doi:10.3390/app7020125
Received: 14 December 2016 / Revised: 12 January 2017 / Accepted: 18 January 2017 / Published: 26 January 2017
Cited by 1 | PDF Full-text (5697 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imaging technologies in the food and agricultural area have been evolving rapidly over the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed using
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Hyperspectral imaging technologies in the food and agricultural area have been evolving rapidly over the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed using different physical principles (e.g., reflectance, transmittance, fluorescence, Raman, and spatially resolved spectroscopy) and wavelength regions (e.g., visible (VIS), near infrared (NIR), and short-wavelength infrared (SWIR)). Line-scan hyperspectral imaging systems are mainly developed and used for surface inspection of food and agricultural products using area or line light sources. Some of these systems can also be configured to conduct spatially resolved spectroscopy measurements for internal or subsurface food inspection using point light sources. This paper reviews line-scan hyperspectral imaging techniques, with introduction, demonstration, and summarization of existing and emerging techniques for food and agricultural applications. The main topics include related spectroscopy techniques, line-scan measurement methods, hardware components and systems, system calibration methods, and spectral and image analysis techniques. Applications in food safety and quality are also presented to reveal current practices and future trends of line-scan hyperspectral imaging techniques. Full article
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Title: Line-Scan Hyperspectral Imaging Techniques for Food and Agricultural Applications
Author: Jianwei Qin
Affiliation: USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA; Phone: 1–301–504–8450 ext. 244; jianwei.qin@ars.usda.gov
Abstract: Hyperspectral imaging technologies have been evolved rapidly during the past decade owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched in different physical principles and wavelength regions, such as visible (VIS), near infrared (NIR), short-wavelength infrared (SWIR), fluorescence, and Raman. Line-scan hyperspectral imaging systems are mainly developed and used for surface inspection of food and agricultural products using area or line light sources. The line-scan systems can also be configured for subsurface food inspection using point light sources. This paper reviews line-scan hyperspectral imaging techniques, with introduction, demonstration, and summarization of existing techniques for food and agricultural applications. The main topics include image acquisition methods, imaging system components, system calibration methods, and image analysis techniques. The applications in the food and agricultural area are also presented to reflect current practices of the line-scan hyperspectral imaging techniques.

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