Research

10 pages, 2626 KiB

Open AccessArticle

Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology

by Mohammed Raju Ahmed, DeMetris D. Reed, Jr., Jennifer M. Young, Sulaymon Eshkabilov, Eric P. Berg and Xin Sun

Appl. Sci. 2021, 11(10), 4588; https://doi.org/10.3390/app11104588 - 18 May 2021

Cited by 6 | Viewed by 3974

Abstract

Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, [...] Read more.

Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, and USDA choice, were used for HSI image acquisition in the spectral range of 400–1000 nm. Spectral information was extracted from the image by applying the partial least squares discriminant analysis (PLS-DA) for the three classifications. A total of eight different types of data pre-processing procedures were tested during PLS-DA to evaluate their individual performance, with the accepted pre-processing method selected based on the highest accuracy. Chemical and binary images were generated to visualize the fat mapping of the samples. Quantitative analysis of the samples was performed for the reference measurement of the dry matter and fat content. The highest overall accuracy, 86.5%, was found using the Savitzky–Golay second derivatives pre-processing method for PLS-DA analysis. The optimal wavelength values were found from the beta coefficient curve. The chemical and binary images showed significant differences in fat mapping among the three groups of samples, with AK having the greatest intramuscular fat content and USDA choice having the least. Similar results were observed during the proximate analysis. The findings of this study demonstrate that the HSI technique is a potential tool for the fast and non-destructive determination of beef grades based on fat mapping. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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13 pages, 679 KiB

Open AccessArticle

Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

by Stig Uteng, Thomas Haugland Johansen, Jose Ignacio Zaballos, Samuel Ortega, Lasse Holmström, Gustavo M. Callico, Himar Fabelo and Fred Godtliebsen

Appl. Sci. 2020, 10(7), 2298; https://doi.org/10.3390/app10072298 - 27 Mar 2020

Viewed by 1959

Abstract

Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples [...] Read more.

Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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16 pages, 3098 KiB

Open AccessArticle

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

by Fuding Xie, Cunkuan Lei, Cui Jin and Na An

Appl. Sci. 2020, 10(2), 463; https://doi.org/10.3390/app10020463 - 08 Jan 2020

Cited by 11 | Viewed by 2549

Abstract

Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with [...] Read more.

Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral–spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral–spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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12 pages, 2252 KiB

Open AccessArticle

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

by Yue Zhang, Hongzhe Jiang and Wei Wang

Appl. Sci. 2019, 9(18), 3926; https://doi.org/10.3390/app9183926 - 19 Sep 2019

Cited by 29 | Viewed by 3180

Abstract

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. [...] Read more.

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with R_p² = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the R_p², RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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14 pages, 2191 KiB

Open AccessArticle

Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging

by Juan He, Susu Zhu, Bingquan Chu, Xiulin Bai, Qinlin Xiao, Chu Zhang and Jinyan Gong

Appl. Sci. 2019, 9(9), 1959; https://doi.org/10.3390/app9091959 - 13 May 2019

Cited by 12 | Viewed by 2794

Abstract

Rapid and nondestructive determination of quality attributes in fresh and dry Chrysanthemum morifolium is of great importance for quality sorting and monitoring during harvest and trade. Near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to detect chlorogenic acid, luteolin-7- [...] Read more.

Rapid and nondestructive determination of quality attributes in fresh and dry Chrysanthemum morifolium is of great importance for quality sorting and monitoring during harvest and trade. Near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to detect chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid content in Chrysanthemum morifolium. Fresh and dry Chrysanthemum morifolium flowers were studied for harvest and trade. Pixelwise spectra were preprocessed by wavelet transform (WT) and area normalization, and calculated as average spectrum. Successive projections algorithm (SPA) was used to select optimal wavelengths. Partial least squares (PLS), extreme learning machine (ELM), and least-squares support vector machine (LS-SVM) were used to build calibration models based on full spectra and optimal wavelengths. Calibration models of fresh and dry flowers obtained good results. Calibration models for chlorogenic acid in fresh flowers obtained best performances, with coefficient of determination (R²) over 0.85 and residual predictive deviation (RPD) over 2.50. Visualization maps of chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid in single fresh and dry flowers were obtained. The overall results showed that hyperspectral imaging was feasible to determine chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid. Much more work should be done in the future to improve the prediction performance. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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15 pages, 3752 KiB

Open AccessArticle

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

by Insuck Baek, Moon S. Kim, Byoung-Kwan Cho, Changyeun Mo, Jinyoung Y. Barnaby, Anna M. McClung and Mirae Oh

Appl. Sci. 2019, 9(5), 1027; https://doi.org/10.3390/app9051027 - 12 Mar 2019

Cited by 35 | Viewed by 4419

Abstract

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) [...] Read more.

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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12 pages, 7169 KiB

Open AccessArticle

Optical Parameters for Using Visible-Wavelength Reflectance or Fluorescence Imaging to Detect Bird Excrements in Produce Fields

by Alan M. Lefcourt, Mark C. Siemens and Paula Rivadeneira

Appl. Sci. 2019, 9(4), 715; https://doi.org/10.3390/app9040715 - 19 Feb 2019

Cited by 3 | Viewed by 2626

Abstract

Consumption of produce contaminated with pathogens of fecal origin is the most common source of food borne illnesses. Current practice is to visually survey fields for evidence of fecal contamination, and to exclude problematic areas from harvest. Bird excrement is known to contain [...] Read more.

Consumption of produce contaminated with pathogens of fecal origin is the most common source of food borne illnesses. Current practice is to visually survey fields for evidence of fecal contamination, and to exclude problematic areas from harvest. Bird excrement is known to contain human pathogens, and is often not detectable in produce fields using current survey methods. The goal of this project was to identify parameters for optical detection of bird excrements to support development of instruments to be used to supplement existing visual surveys. Under daylight ambient conditions, results suggested that reflectance imaging at around 500–530 nm or 610–640 nm could be used to detect excrements from the three bird species tested. Images were acquired using ad hoc camera parameters; however, normalizing intensities for individual images at 525 nm and using a fixed detection threshold allowed detection of 100% of bird excrements with no false positives against the background that consisted of local soil and fresh romaine and spinach leaves. Similar results were obtained using fluorescence imaging. Fluorescent imaging was accomplished in a darkened room using 405-nm illumination. The largest consistent differences in intensity responses between excrements and the brightest non-excrement object in the background matrix occurred at around 550 nm. Results suggested that using reflectance or fluorescence imaging for detection of bird excrements could be a valuable tool for reducing risks of consuming contaminated produce. One possibility would be to incorporate appropriate reflectance imaging capabilities in drones under the control of the individuals currently conducting field surveys. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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14 pages, 1369 KiB

Open AccessArticle

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

by Peng Yu, Min Huang, Min Zhang and Bao Yang

Appl. Sci. 2019, 9(2), 331; https://doi.org/10.3390/app9020331 - 18 Jan 2019

Cited by 9 | Viewed by 3199

Abstract

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging [...] Read more.

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of

R_{p} = 0.966

and

R M S E P = 5.105 %

, which is better than those of successive projections algorithm (

R_{p} = 0.932

and

R M S E P = 7.329 %

), and the uninformative viable elimination algorithm (

R_{p} = 0.928

and

R M S E P = 7.416 %

). The results obtained by BFWA were more stable, with a smaller standard deviation of

R_{p}

and

R M S E P

than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying. Full article

(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)

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