Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> A neuro-fuzzy based image classification system that utilizes color-imaging features of poultry viscera in the ::: spectral and spatial domains was developed in this study. Color images of 320 livers and hearts from normal, ::: airsacculitis, cadaver, and septicemia chickens were collected in the poultry process plant. These images in red, green, ::: and blue (RGB) color space were segmented and statistical analysis was performed for feature selection. A neuro-fuzzy ::: system utilizing hybrid paradigms of fuzzy inference system and neural networks was used to enhance the robustness of ::: the classification processes. The accuracy for separation of normal from abnormal livers ranged 87.5 to 92.5%, when two ::: classes of validation data were used. For classification of normal and abnormal chicken hearts, the accuracies were ::: 92.5 to 97.5%. When neuro-fuzzy models were employed to separate chicken livers into normal, airsacculitis, and ::: cadaver, the accuracy was 88.3% for the training data and 83.3% for the validation data. Combining features of chicken ::: liver and heart, a generalized neuro-fuzzy model was designed to classify poultry viscera into four classes (normal, ::: airsacculitis, cadaver, and septicemia). The classification accuracy was 86.3% for training and 82.5% for validation. <s> BIB001 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract The potential of predicting beef quality attributes after ageing from early post mortem near infrared (NIR) reflectance spectroscopy (1100–2500 nm) has been studied. Altogether, 127 hot boned, Longissimus dorsi muscles from both bulls and cows were investigated in two separate studies. 36 of these carcasses were low voltage electrically stimulated. NIR recordings were obtained 2–30 h post mortem on fresh, sliced loin, while the quality assessments were performed after 2 or 7 days ageing at 4°C on frozen/thawed beef. Spectral changes during rigor mortis were not related to the ageing potential of the individual loin samples. Predicting final tenderness from NIR spectra recorded at different post mortem times yielded predictive models. However, the multivariate correlation coefficients of the models were relatively low, for example, Warner–Bratzler (WB) shear press measurements ranged from 0.47 to 0.55. Making separate prediction models based on genders yielded models for WB shear press with correlation coefficients up to 0.68. Prediction from sensory tenderness gave prediction models with lower correlation coefficients. Intramuscular fat content in intact meat was predicted with correlation coefficients of 0.78-0.85, and prediction errors (RMSEP) of 1.2–1.4%. The results obtained in this study do not support that early post mortem NIR spectroscopy can be used as precise predictor of final tenderness. <s> BIB002 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> This article presents a method for detecting skin tumors on chicken carcasses using hyperspectral fluorescence ::: imaging data, which provide fluorescence information in both spectral and spatial dimensions. Since these two kinds of information ::: are complementary to each other, it is necessary to exploit them in a synergistic manner. Chicken carcasses are examined ::: first using spectral information, and the results are used to determine candidate regions for skin tumors. Next, a spatial ::: classifier selects the real tumor spots from the candidate regions. It was shown that the method detected chicken carcasses ::: with tumors, but failed to detect some tumors that were smaller than 3 mm in diameter. This study uncovered meaningful spectral ::: bands for detecting tumors using hyperspectral imaging data. A detection system based on this method can improve the ::: detection rate, and processing time can also be reduced, because the detection procedure is simplified by using a limited number ::: of features, which reduces computational complexity. The resultant detection rate, false positive rate, and missing rate ::: of the proposed method are 76%, 28%, and 24%, respectively. <s> BIB003 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Prediction ability of near infrared (NIR) spectroscopy for intramuscular fat content (IMF) determination was studied. The material comprised 126 muscle samples; 46 pig longissimus dorsi and semitendinosus and 34 beef longissimus dorsi muscle samples. The IMF content was chemically determined in duplicate using two different chemical methods; fat extraction according to Folch et al. and Soxhlet extraction with hydrolysis according to SIST ISO 1443. Folch extraction underestimated IMF content compared to Soxhlet extraction with hydrolysis (-0.32%, P < 0.0001). Similar repeatability was obtained for Folch and Soxhlet extraction with hydrolysis (0.17% and 0.18%, respectively, P < 0.0001). Sample spectra were scanned from 400–2500 nm by the NIR Systems model 6500 spectrophotometer (Silver Spring, MD, USA) and analysed by WinISI II on minced and intact (pork only) samples. Modified partial least squares regression was used to develop models and to obtain calibration statistics: coefficient of determination in c... <s> BIB004 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> In this research, the potential of using a multi-spectral imaging system was investigated for detecting bruises on ‘Golden Delicious’ apples. For this purpose, a hyperspectral imaging system has been built in the spectral region between 400 and 1000 nm. Based on the principal components analysis for the hyperspectral images, four wavebands were selected for a multi-spectral imaging test. The principal components analysis on the multi-spectral images gave very similar results as on the hyperspectral images. The second and third principal components scores images were found to be suitable for identifying the presence of bruises. An image processing and classification algorithm based on moments thresholding was developed. The classification results indicated that, for the sample population used in this experiment, about 93% of the non-bruised apples were recognised as sound. An accuracy of about 86% was achieved for detecting bruises, although all the bruised regions were found to remain in the images after moments thresholding. <s> BIB005 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Partial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration ( R 2 ) and standard error of cross validation (SECV) for IMF ( R 2 =0·87; SECV=1·8 g/kg), for moisture ( R 2 =0·90; SECV=1·1 g/kg) and for WBSF ( R 2 =0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture ( R 2 R 2 =0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples. <s> BIB006 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Bruising in fruits and its detection is a great concern for food safety to the consumers, and for incurrence of economic losses to industry. This research was aimed at developing techniques for detection of compression bruises in ‘Akihime’ variety strawberries (Fragaria × ananassa Duch.) using near infrared (NIR) hyperspectral imaging. Using 120 fruit samples at 70-80% ripe and full-ripe levels of maturity that had received six levels of bruising force (0 N, 0.5 N, 1.0 N, 1.5 N, 2.0 N and 3.0 N), spectral images were taken from 650 to 1, 000 nm at 5 nm wavelength intervals and from 0 to 4 days after bruising. Using stepwise linear discriminant analysis, optimal wavelengths of 825 nm and 980 nm were identified. The three judgment methods (linear discriminant analysis, normalized difference and artificial neural network) had performed equally well, while the normalized difference method was found to be more useful. Additionally, the extent of bruising damage detected in strawberries by the judgment method had markedly decreased with time, which elucidated temporal changes in bruising pattern while in storage. <s> BIB007 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract Pork quality is usually evaluated subjectively based on color, texture and exudation characteristics of the meat. In this study, a hyperspectral imaging-based technique was evaluated for rapid, accurate and objective assessment of pork quality. In addition, marbling level was also automatically determined. The system was able to extract spectral characteristics of pork samples. Appropriate spatial features were obtained for marbling distribution in pork meat. Existing marbling standards were scanned, and indices of the marbling scores were formulated by co-occurrence matrix. The principal component analysis (PCA) method was used to compress the entire spectral wavelengths (430–1000 nm) into 5, 10 and 20 principal components (PCs), which were then clustered into quality groups. Artificial neural network was used to classify these groups. Results showed that reddish, firm and non-exudative (RFN) and reddish, soft and exudative (RSE) samples were successfully grouped; the total corrected ratio was 75–80%. The feed-forward neural network model yielded corrected classification as 69% by 5 PCs and 85% by 10 PCs. Angular second moment was successfully used to determine marbling scores excepting the score at 10.0. Forty samples were sorted and the result showed that the samples’ marbling score ranged from 3.0 to 5.0. <s> BIB008 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Over the past three decades, near infrared reflectance (NIR) spectroscopy has been proved to be one of the most efficient and advanced tools for the estimation of quality attributes in meat and meat products. This review focuses on the use of NIR spectroscopy to predict different meat properties, considering the literature published mainly in the last decade. Firstly, the potential of NIR to predict chemical composition (crude protein, intramuscular fat, moisture/dry matter, ash, gross energy, myoglobin and collagen), technological parameters (pH value; L*, a*, b* colour values; water holding capacity; Warner-Bratzler and slice shear force) and sensory attributes (colour, shape, marbling, odour, flavour, juiciness, tenderness or firmness) are reviewed. Secondly, the usefulness of NIR for classification into meat quality grades is presented and thirdly its potential application in the industry is shown. The review indicates that NIR showed high potential to predict chemical meat properties and to categorize meat into quality classes. In contrast, NIR showed limited ability for estimating technological and sensory attributes, which may be mainly due to the heterogeneity of the meat samples and their preparation, the low precision of the reference methods and the subjectivity of assessors in taste panels. Hence, future work to standardize sample preparation and increase the accuracy of reference methods is recommended to improve NIR ability to predict those technological and sensory characteristics. In conclusion, the review shows that NIR has a considerable potential to predict simultaneously numerous meat quality criteria. <s> BIB009 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Hyperspectral scattering images between 600nm and 1000nm were acquired for 580 'Delicious' apples for mealiness classification. A locally linear embedding (LLE) algorithm was developed to extract features directly from the hyperspectral scattering image data. Partial least squares discriminant analysis (PLSDA) and support vector machine (SVM) were applied to develop classification models based on the LLE, mean-LLE and mean spectra algorithms. The model based on the LLE algorithm achieved an overall classification accuracy of 80.4%, compared with 76.2% by the mean-LLE algorithm and 73.0% by the mean spectra method for two-class classification (i.e., mealy and nonmealy) coupled with PLSDA. For the SVM models, the LLE algorithm had an overall classification accuracy of 82.5%, compared with 79.4% by the mean-LLE algorithm and 78.3% by the mean spectra method. Hence, the LLE algorithm provided an effective means to extract hyperspectral scattering features for mealiness classification. <s> BIB010 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract This study was carried out for rapid and non-invasive measurement of color distribution in salmon fillet using hyperspectral imaging in long-wave near infrared spectral range (LW-NIR). Successive projections algorithm (SPA) was used to select effective wavelengths. Instead of selecting different sets of effective wavelengths for each color component respectively, instrumental effective wavelengths (IEWs) were identified for the prediction of all three color components, leading to reducing the number of band-pass filters for designing the multispectral imaging system. Meanwhile, predictive effective wavelengths (PEW) were further chosen from IEW to optimize calibration models. The final prediction models were considered as the multiple linear regression (MLR) models with PEW, which resulted in correlation coefficients ( r C ) of 0.876, 0.744, and 0.803 for L *, a *, and b *, respectively. Finally, the visualization of color distribution in salmon fillets was achieved. The results demonstrated that LW-NIR hyperspectral imaging is a potential technique to quantitatively measure color distribution of salmon fillet in a rapid and non-invasive way. Industrial relevance : This study was conducted to evaluate the feasibility of a hyperspectral imaging system in the LW-NIR spectral region (964 to 1631 nm) for rapid measurement of color in intact salmon fillets. On the basis of the results and accompanying illustrations presented in this work, the study demonstrated the ability of the method based on LW-NIR hyperspectral imaging to measure color distribution of salmon fillets. This method was a rapid, contact-free, and consistent evaluation, and can be used as a reliable and rapid alternative to traditional colorimeter for measuring color of salmon fillet for the food industry. <s> BIB011 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract Marbling is an important technical quality attribute of pork. Its assessment usually corresponds to a subjective score being given by trained panelists based on the marbling standards charts. The purpose of this study was to investigate objective determination of pork marbling using pattern analysis techniques. A line pattern recognition technique called the wide line detector (WLD) and a texture extraction technique based on an improved grey-level co-occurrence matrix (GLCM) were employed and compared. Fifty three fresh pork loin chops from the longissimus dorsi (LD) muscle were collected and their marbling scores were assessed in a plant. Red-Green-Blue (RGB) images of these chops were acquired using a digital camera. The loin eye area was selected as the region of interest (ROI) of the pork images. Marbling was extracted from the ROI by either GLCM or WLD. Proportion of marbling ( PM ) obtained from WLD or image texture measurement from GLCM ( GI ) was formulated as indices of the marbling score. Linear regressions based on the PM and GI were carried out at the red, green, and blue channels as well as the combined RGB channels. The results of WLD and GLCM based models showed the effectiveness of pattern analysis techniques for pork marbling assessment. The comparison indicated that the WLD based models had stronger predictive ability for pork marbling score determination than GLCM. The green channel was demonstrated to have the best explanatory for pork marbling assessment no matter which pattern analysis technique used. High correlation coefficients of calibration and validation ( Rc = 0.94, Rv = 0.94) of the WLD based linear model at the green channel strongly indicated the great potential of pattern analysis techniques especially the line pattern recognition methods for the accurate and real-time evaluation of pork marbling. <s> BIB012 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce aflatoxin or not. The objective was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between maize kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Maize ears were inoculated with AF13, a toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8 weeks after inoculation. After harvest, single kernels were divided into three groups prior to imaging: control, adjacent, and glowing. Both sides of the kernel, germ and endosperm, were imaged separately using a fluorescence hyperspectral imaging system. After imaging each single kernel was processed with affinity column fluorimetry to determine aflatoxin level. Results from discriminant analysis of the imaging data found that the classification accuracies of the three visually designated groups were not promising. The separation of maize kernels based on different fungal inoculums yielded better results. The best results were achieved with the germ side of the maize kernels. The kernels were later grouped into ‘contaminated’ and ‘healthy’ with 20 ppb and 100 ppb thresholds. The contaminated kernels all had longer peak wavelength than did the healthy ones. Results from the discriminant analysis classification indicated overall higher classification accuracy for the 100 ppb threshold on the germ side (94.4%). The germ side was also more useful at discriminating healthy from contaminated kernels for the 20 ppb threshold. <s> BIB013 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract 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. <s> BIB014 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract Consumers want fresh food with a long shelf-life, which in 2010, resulted in an important increase in packaged and processed food. This is especially important for fishery products due to their highly perishable nature. One problem is that it is not possible to measure freshness in packaged food only using the visible spectrum. Moreover, the detection of freshness is a complex problem as fish has different tissues with different biodegradation processes. Therefore, it would be especially interesting to have a non-destructive method to evaluate the shelf-life of packed processed fish. This paper proposes a method for detecting expired packaged salmon. Firstly, this method uses hyperspectral imaging spectroscopy (HIS) using visible and SW-NIR wavelengths. Secondly, a classification of different salmon tissues is carried out by image segmentation. Finally, classifications of expired or non expired salmon are performed with the PLS-DA statistical multivariate method due to the large amount of captured data. In a similar way, spectral data and the physicochemical, biochemical and microbiological properties of salmon are correlated using partial least squares (PLSs). The result obtained has a classification success rate of 82.7% in cross-validation from real commercial samples of salmon. Therefore, this is a promising technique for the non-destructive detection of expired packaged smoked salmon. <s> BIB015 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Abstract An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006–1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains. <s> BIB016 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> A. FOOD QUALITY AND SAFETY ASSESSMENT <s> Having acquired near infrared (NIR) hyperspectral images of intact pork loin samples through an NIR hyperspectral imaging system, the efficiency of a variety of image processing techniques including texture pattern analysis techniques were applied to process hyperspectral images so as to determine the intramuscular fat (IMF) content non-destructively. After the segmentation of region of interest (ROI), the raw spectral, texture-based spectral and textural characteristics of pork images were extracted by spectral averaging and pattern recognition techniques namely Gabor filter and improved gray level co-occurrence matrix (GLCM), respectively. First derivatives of the non-filtered and the Gabor filtered spectra were also investigated. Full waveband partial least squares regression (PLSR) was employed to determine the optimal parameters of Gabor filter and GLCM, and to select optimal wavelengths for IMF prediction. A stepwise procedure was applied to the optimal wavelengths to further optimize them to key wavelengths. Multiple linear regression (MLR) models were built based on the key wavelengths. Mean spectra and the Gabor filtered spectra outperformed GLCM. The best result, represented by correlation coefficients of calibration (Rc), cross validation (Rcv) and prediction (Rp) of 0.89, 0.89, and 0.86, respectively, was achieved using the first derivative of Gabor filtered spectra at 1193 and 1217 nm. To visualize the IMF content in pork, the distribution maps of IMF content in pork were drawn using a mean spectra-based MLR model. These promising results highlight the great potential of NIR hyperspectral imaging for non-destructive prediction of IMF content of intact pork. <s> BIB017
Due to the growing need for high efficiency and low production costs of food products, the food industry is facing numerous challenges such as ensuring the quality and safety of food products while avoiding liability issues. Food quality and safety is assessed by the examination of various physical, chemical and biological attributes of food. The traditional methods based on visual inspection and chemical and biological inspection of food are destructive, time consuming and also environmentally unfriendly in some cases. Technological advancements in instrumentation engineering and computer technology enabled efficient and faster assessment of food. Computer vision and machine learning based methods using color image processing have been successfully applied for assessment of external attributes of food BIB001 - BIB012 . These methods are unable to examine the internal characteristics of food because they lack the ability to capture broad spectral information. Near Infrared (NIR) spectroscopy helped overcome this limitation of machine vision based methods due to close relationship between food components and NIR spectra BIB002 , BIB004 . However, NIR spectroscopy could not help in examination of heterogeneous materials due to the lack of ability to capture spatial information BIB006 , BIB009 . Hyperspectral imagery contains rich amount of spectral as well as spatial information which makes HSI based methods well suited for assessment of food quality and safety BIB008 . Hyperspectral image analysis has been used for identification of defects BIB005 , BIB007 and detection of contaminations BIB013 , BIB003 in food items. Valenzuela et al. BIB014 employed visual and infrared (VIR) hyperspectral imaging to examine the firmness and solid content of blueberries. Prediction accuracy of 87% and 79% was obtained for firmness and solid content respectively. Huang et al. BIB010 determined mealiness of apple using VIR hyperspectral imaging, achieving classification accuracy of 82.5%. Huang et al. BIB017 employed Grey Level Co-occurrence Matrix (GLCM) and Gabor Filter to determine fat content between muscles in pork achieving a classification accuracy of 89%. HSI is also used to determine color distribution in salmon fillet BIB011 . Ivorra et al. BIB015 used NIR hyperspectral imaging to detect expired vacuum packed salmon, reaching a classification accuracy of 82.7%. In BIB016 , Principal Component Analysis (PCA) and PLS-DA based classification model is presented for classification of oat and grout kernels in NIR hyperspectral images. The proposed method BIB016 achieved a high classification rate of almost 100% and thus, showed the efficacy of HSI as an industrial tool in food analysis.