Embedded bone fragment detection in chicken fillets using transmittance image enhancement and hyperspectral reflectance imaging

This paper is concerned with the detection of bone fragments embedded in compressed de-boned skinless chicken breast fillets by enhancing single-band transmittance images generated by back-lighting and exploiting spectral information from hyperspectral reflectance images. Optical imaging of chicken fillets is often dominated by multiple scattering properties of the fillets. Thus, resulting images from multiple scattering are diffused, scattered and low contrast. In this study, a fusion of hyperspectral transmittance and reflectance imaging, which is a non-ionized and non-destructive imaging modality, was investigated as an alternative method to the conventional transmittance X-ray imaging technique which is an ionizing imaging modality. An image formation model, called an illumination–transmittance model, was applied for correcting non-uniform illumination effects so that embedded bones are more easily detectable by a simple segmentation method using a single threshold value. Predicted bones from the segmentation were classified by the nearest neighbor classifier that was trained by the spectral library of mean reflectance of chicken tissues like fat, meat and embedded bones. Experimental results with chicken breast fillets and bone fragments are provided.     

Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method

Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. Four optimal wavelengths (553, 677, 718, and 858 nm) were identified in visible and short-wavelength near-infrared region that could be adopted by a future multispectral imaging solution for detecting citrus canker on a sorting machine. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.     

Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging

Insect damage in wheat adversely affects its quality and is considered one of the most important degrading factors in Canada. The potential of near-infrared (NIR) hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated. Healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000–1600 nm wavelength range using an NIR hyperspectral imaging system. Dimensionality of the acquired hyperspectral data was reduced using multivariate image analysis. Six statistical image features (maximum, minimum, mean, median, standard deviation, and variance) and 10 histogram features were extracted from images at 1101.69 and 1305.05 nm and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) for classification. Linear discriminant analysis and quadratic discriminant analysis classifiers correctly classified 85–100% healthy and insect-damaged wheat kernels.     

高光谱成像技术在果蔬品质安全无损检测中的应用

近年来,人们对果蔬品质关注度提高。随着成像技术和光谱技术的快速发展,高光谱成像技术广泛应用于各种果蔬产品的快速和非破坏性检测中。高光谱成像技术具有"图谱合一"的优点,可同时检测果蔬的内部、外部品质信息。本文介绍了高光谱成像技术的基本原理,综述了高光谱成像技术在果蔬内外部品质检测方面的应用进展,并简要分析了高光谱成像技术在果蔬品质检测中的发展趋势和面临的挑战,以期对我国相关研究人员的研究工作提供参考。     

Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packing houses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500–1000 nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance the product quality and marketability.     

Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging

Hyperspectral imaging operated under simultaneous reflectance (400–675 nm) and transmittance (675–1000 nm) modes was studied for non-destructive and non-contact sensing of surface color and bloater damage in whole pickles. Good and defective pickles were collected from a commercial pickle processing plant. Hyperspectral images of these pickles were obtained using a prototype of on-line hyperspectral imaging system, operating in the wavelength range of 400–1000 nm. Principal component analysis was applied to the hyperspectral images of the pickle samples for bloater damage detection. Color of the pickles was modeled using tristimulus values calculated based on the hyperspectral images. There were no differences in chroma and hue angle of good and defective pickles. The average chroma of good and defective pickles was 15.5 and 15.0, respectively, and the hue angle 94.0° and 93.8°, respectively. Transmittance images at 675–1000 nm were much more effective for internal defect detection compared to reflectance images for the visible region of 500–675 nm. An overall defect classification accuracy of 86% was achieved, compared with an accuracy of 70% by the human inspectors. With further improvement, the hyperspectral imaging system could meet the need of bloated pickles detection in a commercial plant setting.     

Detection of early bruises in apples using hyperspectral data and thermal imaging

The early detection of bruises in apples was studied using a system that included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400–1000 nm), short wavelength infrared (1000–2500 nm) and thermal imaging camera in mid-wavelength infrared (3500–5000 nm) ranges. The principal components analysis (PCA) and minimum noise fraction (MNF) analyses of the images that were captured in particular ranges made it possible to distinguish between areas with defects in the tissue and the sound ones. The fast Fourier analysis of the image sequences after pulse heating of the fruit surface provided additional information not only about the position of the area of damaged tissue but also about its depth. The comparison of the results obtained with supervised classification methods, including soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and support vector machines (SVM) confirmed that broad spectrum range (400–5000 nm) of fruit surface imaging can improve the detection of early bruises with varying depths.     

Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to assess the quality of cooked turkey hams of different ingredients and processing parameters. Hyperspectral images were acquired for ham slices originated from each quality grade and then their spectral data were extracted. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 241 wavelengths, only eight wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436 and 1641 nm) were selected as the optimum wavelengths for the classification and characterization of turkey hams. The data analysis showed that it is possible to separate different quality turkey hams with few numbers of wavelengths on the basis of their chemical composition. The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for the authentication and classification of cooked turkey ham slices.     

Near-infrared hyperspectral imaging for grading and classification of pork

In this study, a hyperspectral imaging technique was developed to achieve fast, accurate, and objective determination of pork quality grades. Hyperspectral images were acquired in the near-infrared (NIR) range from 900 to 1700 nm for 75 pork cuts of longissimus dorsi muscle from three quality grades (PSE, RFN and DFD). Spectral information was extracted from each sample and six significant wavelengths that explain most of the variation among pork classes were identified from 2nd derivative spectra. There were obvious reflectance differences among the three quality grades mainly at wavelengths 960, 1074, 1124, 1147, 1207 and 1341 nm. Principal component analysis (PCA) was carried out using these particular wavelengths and the results indicated that pork classes could be precisely discriminated with overall accuracy of 96%. Algorithm was developed to produce classification maps of the tested samples based on score images resulting from PCA and the results were compared with the ordinary classification method. Investigation of the misclassified samples was performed and showed that hyperspectral based classification can aid in class determination by showing spatial location of classes within the samples.     

Development of a portable hyperspectral imaging system for monitoring the efficacy of sanitation procedures in food processing facilities

Cleaning and sanitation in food processing facilities is a critical step in reducing the risk of transfer of pathogenic organisms to food consumed by the public. Current methods to check the effectiveness of sanitation procedures rely on visual observation and sub-sampling tests such as ATP bioluminescence assays and culturing methods. To augment existing verification methods, a hand-held visible hyperspectral imaging device was developed. The device is capable of acquiring reflectance images using ambient lighting, and fluorescence responses to supplemental violet (405 nm) excitation. To enhance the ability of detecting relatively low intensity fluorescence responses in the presence of ambient lighting, the device includes the ability to identify wavebands where the intensity of ambient lighting is relatively low. Valleys in ambient illumination intensity when using fluorescent lighting were found at around 475, 520, 570, and 675 nm. A principal goal is to acquire data to support development of a commercially-viable, hand-held, imaging system.     

冷鲜羊肉品质的高光谱成像无损检测

利用400¹000 nm可见近红外高光谱成像系统对冷鲜羊肉蛋白质含量、嫩度、p H进行无损检测研究。采集冷鲜羊肉表面的高光谱散射图像,提取样本感兴趣区域的反射光谱曲线获得原始数据。先对原始光谱预处理并建立偏最小二乘回归（PLSR）模型,优选最佳预处理方法,后采用正自适应加权算法（CARS）和连续投影算法（SPA）提取特征波长,建立不同特征波长下各品质参数的PLSR预测模型。结果表明:利用原始光谱建立的冷鲜羊肉蛋白质、嫩度和p H的PLSR模型均优于经过光谱预处理所建PLSR模型;在不同波长下建立预测模型,OS-PLSR光谱模型对冷鲜羊肉蛋白质含量预测效果最佳,Rp=0.869,RMSEP=0.097;建立的SPA-PLSR光谱预测模型对p H预测效果理想,Rp=0.958,RMSEP=0.067;CARS-PLSR光谱预测模型对嫩度的预测能力较高,Rp=0.862,RMSEP=0.706。研究表明:利用可见近红外高光谱技术对冷鲜羊肉品质进行快速无损检测是可行的。      

Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras

The potential of RGB digital imaging and hyperspectral imaging (900–1700 nm) was evaluated for discriminating maturity level in apples under different storage conditions along the shelf-life. Segmentation, preprocessing and partial least squares-discriminant analysis (PLS-DA) were used for hyperspectral data analysis, while illumination correction, dimensionality reduction and linear discriminant analysis (LDA) were used for RGB data analysis. The results showed that hyperspectral discrimination classified different storage regimes better than RGB, with an overall success rate of 95.83%. In addition, color evolution of apples during shelf-life under different storage regimes was modeled using RGB zero and first order regression models, fitting better to a first order kinetic model.     

Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks

This paper reports a novel application of a type of neural network committee, called AdaBoost, to the estimation of grape anthocyanin concentration using hyperspectral data. The inputs from the neural networks were the principal components of the grapes’ spectra. Hyperspectral data were collected in the reflectance mode for 46 individual whole grapes of the Cabernet Sauvignon variety, using a hyperspectral camera that operates with wavelengths ranging from 400 to 1000 nm at an approximate 0.6 nm resolution. The hyperspectral camera was positioned a few tens of centimetres away from the grapes. The grapes were harvested on five dates between August 28th and September 23rd in 2009 and presented average sugar content values between 14.6 and 20.2 Brix. They were kept frozen until January 2010, when they were thawed and the hyperspectral data collected at ambient temperature. The anthocyanin concentration values obtained by our calibrations exhibited a squared correlation coefficient value of 0.65 compared to the values measured using conventional laboratory techniques. This correlation value is better than the value reported in another recent scientific work which estimated anthocyanin values in individual whole grapes of Cabernet Sauvignon.     

高光谱图像法对稻谷贮藏中五种常见真菌生长拟合及区分

利用高光谱成像系统（HIS）获取稻谷贮藏中常见真菌（黑曲霉、米曲霉、杂色曲霉、构巢曲霉、桔青霉）在马铃薯葡萄糖琼脂板上培养期间的高光谱图像,波峰709 nm处的光谱值和全波段光谱值的第一主成分得分两种方法构建真菌Gompertz函数的生长模拟模型。Gompertz函数拟合结果显示,五种真菌基于全波段光谱值PCA分析后的第一主成分得分建立的生长拟合模型R2为0.1781⁰.9501,基于波峰709 nm光谱值建立的拟合模型R2为0.9095⁰.9679,效果明显优于第一主成分得分的建模效果。另外,主成分分析（PCA）结合偏最小二乘法判别分析（PLS-DA）可以区分五种不同菌种。其中,训练集和测试集中,PLS-DA模型对培养48 h的黑曲霉、米曲霉、构巢曲霉、桔青霉四种真菌及对照组的区分准确率为100%;而对杂色曲霉,训练集区分准确率为100%,测试集的区分率为33.33%。结果表明高光谱图像技术能够用来对真菌种类进行区分。      

Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging

This study was carried out to investigate the ability of hyperspectral imaging technique in the NIR spectral region of 900–1700 nm for the prediction of water and protein contents in Spanish cooked hams. Multivariate analyses using partial least-squares regression (PLSR) and partial least squares-discriminant analysis (PLS-DA) were applied to the spectral data extracted from the images to develop statistical models for predicting chemical attributes and classify the different qualities. Feature-related wavelengths were identified for protein (930, 971, 1051, 1137, 1165, 1212, 1295, 1400, 1645 and 1682 nm) and water (930, 971, 1084, 1212, 1645 and 1682 nm) and used for regression models with fewer predictors. The PLS-DA model using optimal wavelengths (966, 1061, 1148, 1256, 1373 and 1628 nm) successfully classified the examined hams in different quality categories. The results revealed the potentiality of NIR hyperspectral imaging technique as an objective and non-destructive method for the authentication and classification of cooked hams.     

Prediction of moisture content in steamed and dried purple sweet potato using hyperspectral imaging analysis

Food Science and Biotechnology - Partial least squares regression (PLSR) modeling was performed to predict the moisture content in steamed, dried purple sweet potato based on spectral data obtained...     

Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging

Hyperspectral fluorescence imaging (HSFI) is potentially useful for assessing food and agricultural products, because it combines the merits of both hyperspectral imaging and fluorescence spectroscopy. This paper provides an introduction to HSFI: the principle and components of HSFI, calibration and image processing are described. In addition, recent advances in the application of HSFI to food and agricultural product assessment are reviewed, such as contaminant detection, constituent analysis and quality evaluation. Finally, current limitations and likely future development trends are discussed. Copyright © 2012 Society of Chemical Industry     

油料作物与产品品质近红外光谱及高光谱成像检测研究进展

油料作物及产品含有丰富的营养成分,为人类健康提供必要的能量供给和营养物质,其品质优劣直接影响企业的经济效益以及人民的身体健康。近红外光谱及高光谱成像技术具有无损、便捷、高效以及绿色环保等优点,在油料作物与产品品质检测领域有较广泛的研究探索。本文简述了近红外光谱及高光谱成像技术的基本原理和常规分析步骤,重点综述了近年来该技术在油料作物理化成分的测定、品种识别和产地鉴定以及食用油真实性的鉴别等方面的具体应用,并最后展望了其在油料作物与产品品质检测领域中的应用前景。