Special Issue "Hyperspectral Chemical Imaging for Food Authentication"

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

Deadline for manuscript submissions: closed (31 August 2018).

Special Issue Editor

Dr. Daniel Cozzolino
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Guest Editor

Special Issue Information

Dear Colleagues,

Compared with conventional spectroscopy, hyperspectral imaging can acquire highly-detailed spatial and spectral information across large surface areas of a sample. The many available systems of hyperspectral imaging, e.g., visible, near- and mid-infrared, fluorescence, Raman-provide high-resolution three-dimensional data suitable for non-destructive analysis for a vast range of samples and applications. In recent years, technological advances have made hyperspectral imaging available for both research and industry applications. Consequently, there is growing interest in hyperspectral and multispectral imaging for non-destructive evaluation of foods and agricultural products targeting authentication issues (e.g. geographical origin, provenance).

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

Assoc. Prof. Dr. Daniel Cozzolino
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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
  • hyperspectral
  • authentication
  • multispectral imaging

Published Papers (11 papers)

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Research

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Open AccessArticle
Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour
Appl. Sci. 2018, 8(7), 1076; https://doi.org/10.3390/app8071076 - 03 Jul 2018
Cited by 11 | Viewed by 1665
Abstract
The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility [...] Read more.
The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950–1700 nm were acquired. A standard normal variate combined with the Savitzky–Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (Rp2) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets
Appl. Sci. 2018, 8(4), 640; https://doi.org/10.3390/app8040640 - 20 Apr 2018
Cited by 9 | Viewed by 1610
Abstract
This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000–2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast filets. Three physical and chemical indicators—drip loss, expressible fluid, and salt-induced water gain—were measured to be [...] Read more.
This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000–2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast filets. Three physical and chemical indicators—drip loss, expressible fluid, and salt-induced water gain—were measured to be different WHC references of chicken meat. Different partial least squares regression (PLSR) models were established with corresponding input variables including the full spectra, key wavelengths, and texture variables, as well as the fusion data of key wavelengths and the corresponding texture variables, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While the best model to predict salt-induced water gain was based on key wavelengths (Rp = 0.69, RMSEp = 18.04), this was mainly because salt-induced water gain was measured on mince samples, which lacked the important physical structure to represent the texture information of meat. Our results of this study demonstrated the potential to further improve the evaluation of the WHC of chicken meat by HSI. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessFeature PaperArticle
Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
Appl. Sci. 2018, 8(4), 564; https://doi.org/10.3390/app8040564 - 05 Apr 2018
Cited by 10 | Viewed by 1937
Abstract
Curry powder is extensively used in Southeast Asian dishes. It has been subject to adulteration by azo dyes. This study used a newly developed 1064 nm dispersive point-scan Raman system for detection of metanil yellow and Sudan-I contamination in curry powder. Curry powder [...] Read more.
Curry powder is extensively used in Southeast Asian dishes. It has been subject to adulteration by azo dyes. This study used a newly developed 1064 nm dispersive point-scan Raman system for detection of metanil yellow and Sudan-I contamination in curry powder. Curry powder was mixed with metanil yellow and (separately) with Sudan-I, at concentration levels of 1%, 3%, 5%, 7%, and 10% (w/w). Each sample was packed into a nickel-plated sample container (25 mm × 25 mm × 1 mm). One Raman spectral image of each sample was acquired across the 25 mm × 25 mm surface area. Intensity threshold value was applied to the spectral images of Sudan-I mixtures (at 1593 cm−1) and metanil yellow mixtures (at 1147 cm−1) to obtain binary detection images. The results show that the number of detected adulterant pixels is linearly correlated with the sample concentration (R2 = 0.99). The Raman system was further used to obtain a Raman spectral image of a curry powder sample mixed together with Sudan-I and metanil yellow, with each contaminant at equal concentration of 5% (w/w). The multi-component spectra of the mixture sample were decomposed using self-modeling mixture analysis (SMA) to extract pure component spectra, which were then identified as matching those of Sudan-I and metanil yellow using spectral information divergence (SID) values. The results show that the 1064 nm dispersive Raman system is a potential tool for rapid and nondestructive detection of multiple chemical contaminants in the complex food matrix. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Selection of Spectral Resolution and Scanning Speed for Detecting Green Jujubes Chilling Injury Based on Hyperspectral Reflectance Imaging
Appl. Sci. 2018, 8(4), 523; https://doi.org/10.3390/app8040523 - 29 Mar 2018
Cited by 5 | Viewed by 1153
Abstract
Hyperspectral imaging is a non-destructive method for the detection of chilling injury in fruit. However, the limitation of this technique is the lacking of an appropriate working parameters and a feasible discriminating model for chilling on-line sorting. This research was aimed to select [...] Read more.
Hyperspectral imaging is a non-destructive method for the detection of chilling injury in fruit. However, the limitation of this technique is the lacking of an appropriate working parameters and a feasible discriminating model for chilling on-line sorting. This research was aimed to select the optimal spectral resolution, scanning speed, and classification model for green jujube chilling injury detection based on hyperspectral reflectance imaging. Criminisi algorithm was firstly carried out to reconstruct the specular reflection region in spectral images before deriving mean spectra, and thus the optimal wavelengths were selected by random frog. Results showed that the Criminisi algorithm presented a desirable ability of spectral image inpainting. The linear discriminant analysis (LDA) achieved overall accuracies of 98.3% and 93.3% for two-class and three-class classification, respectively, at the speed of 20 mm/s with the spectral resolution of 5.03 nm based on selected spectral features. The results demonstrated that 20 mm/s with the spectral resolution of 5.03 nm was more feasible for the detection of green jujube chilling injury in hyperspectral imaging system due to a higher scanning efficiency, but a less data size. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging
Appl. Sci. 2018, 8(4), 513; https://doi.org/10.3390/app8040513 - 28 Mar 2018
Cited by 4 | Viewed by 1371
Abstract
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 [...] Read more.
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 (PC2 to PC6) 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 PC2 to PC6 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. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods
Appl. Sci. 2018, 8(4), 485; https://doi.org/10.3390/app8040485 - 23 Mar 2018
Cited by 3 | Viewed by 1489
Abstract
Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses [...] Read more.
Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Using a Combination of Spectral and Textural Data to Measure Water-Holding Capacity in Fresh Chicken Breast Fillets
Appl. Sci. 2018, 8(3), 343; https://doi.org/10.3390/app8030343 - 28 Feb 2018
Cited by 4 | Viewed by 1241
Abstract
The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400–1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data [...] Read more.
The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400–1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA) classification models. Smoothing, a first derivative process, and principle component analysis (PCA) were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR) value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA) were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM) was implemented on the first principle component image (with 98.13% of variance) of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity). The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets
Appl. Sci. 2018, 8(2), 256; https://doi.org/10.3390/app8020256 - 09 Feb 2018
Cited by 5 | Viewed by 1918
Abstract
Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast [...] Read more.
Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400–1000 nm) range. Mean spectra were extracted from regions of interest (ROIs) determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1) of VNIR full spectra, performed best with correct classification rates (CCRs) of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS). Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network
Appl. Sci. 2018, 8(2), 212; https://doi.org/10.3390/app8020212 - 31 Jan 2018
Cited by 65 | Viewed by 2742
Abstract
The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges [...] Read more.
The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Open AccessArticle
Interactions of Insolation and Shading on Ability to Use Fluorescence Imaging to Detect Fecal Contaminated Spinach
Appl. Sci. 2017, 7(10), 1041; https://doi.org/10.3390/app7101041 - 12 Oct 2017
Cited by 2 | Viewed by 1409
Abstract
Fecal contamination of produce in fields is a recognized food safety risk, and it is a requirement that fields be surveyed for evidence of fecal contamination. It may be possible to increase the efficacy of such surveys using imaging techniques that rely on [...] Read more.
Fecal contamination of produce in fields is a recognized food safety risk, and it is a requirement that fields be surveyed for evidence of fecal contamination. It may be possible to increase the efficacy of such surveys using imaging techniques that rely on detection of fluorescence responses of fecal material to UV excitation. However, fluorescence responses are easily masked by ambient illumination. This study investigated the potential of using a shroud to reduce the impact of ambient illumination on responses measured using relatively inexpensive optical components. During periods of near peak insolation, even with full shrouding, results indicate that reliable detection would be problematic. Towards dusk, effective imaging could be accomplished even with a gap of 250 cm at the bottom of the shroud. Results suggest that imaging using relatively inexpensive components could provide the basis for detection of fecal contamination in produce fields if surveys were conducted during dawn or dusk, or at night. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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Review

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Open AccessReview
A Short Update on the Advantages, Applications and Limitations of Hyperspectral and Chemical Imaging in Food Authentication
Appl. Sci. 2018, 8(4), 505; https://doi.org/10.3390/app8040505 - 27 Mar 2018
Cited by 7 | Viewed by 1191
Abstract
Around the world, the food industry needs to maintain high quality and safety standards in order to satisfy consumers demand for healthy foods and to trace the origin of raw materials and products that are used during food manufacture. These objectives can be [...] Read more.
Around the world, the food industry needs to maintain high quality and safety standards in order to satisfy consumers demand for healthy foods and to trace the origin of raw materials and products that are used during food manufacture. These objectives can be achieved by applying analytical methods and techniques that are able to provide information about composition, structure, physicochemical properties, and sensory characteristics of foods. Modern techniques and methods based on spectroscopy (near infrared (NIR), mid infrared (MIR), Raman) are highly desirable due to their low cost and easy to implement, and often requiring minimal sample preparation. This paper reviews some of the advantages and recent applications of hyperspectral and chemical imaging to discriminate and authenticate foods. Full article
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
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