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A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

With improvement in people's living standards, many people nowadays pay more attention to quality and safety of meat. However, traditional methods for meat quality and safety detection and evaluation, such as manual inspection, mechanical methods, and chemical methods, are tedious, time-consuming, and destructive, which cannot meet the requirements of modern meat industry. Therefore, seeking out rapid, non-destructive, and accurate inspection techniques is important for the meat industry. In recent years, a number of novel and noninvasive imaging techniques, such as optical imaging, ultrasound imaging, tomographic imaging, thermal imaging, and odor imaging, have emerged and shown great potential in quality and safety assessment. In this paper, a detailed overview of advanced applications of these emerging imaging techniques for quality and safety assessment of different types of meat (pork, beef, lamb, chicken, and fish) is presented. In addition, advantages and disadvantages of each imaging technique are also summarized. Finally, future trends for these emerging imaging techniques are discussed, including integration of multiple imaging techniques, cost reduction, and developing powerful image-processing algorithms.

Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing., (Copyright © 2015 Elsevier B.V. All rights reserved.)

This study investigated the potential of hyperspectral imaging (HSI) for quantitative determination of total pigments in red meats, including beef, goose, and duck. Partial least squares regression (PLSR) was applied to correlate the spectral data with the reference values of total pigments measured by a traditional method. In order to simplify the PLSR model based on the full spectra, eleven optimal wavelengths were selected using successive projections algorithm (SPA). The new SPA-PLSR model yielded good results with the coefficient of determination (R(2)p) of 0.953, root mean square error (RMSEP) of 9.896, and ratio of prediction to deviation (RPD) of 4.628. Finally, distribution maps of total pigments in red meats were developed using an image processing algorithm. The overall results from this study indicated HSI had the capability for predicting total pigments in red meats., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

In this study, the potential of hyperspectral imaging (HSI) for predicting hydroxyproline content in chicken meat was investigated. Spectral data contained in the hyperspectral images (400-1000 nm) of chicken meat was extracted, and a partial least square regression (PLSR) model was then developed for predicting hydroxyproline content. The model yielded acceptable results with regression coefficient in prediction (Rp) of 0.874 and root mean error squares in prediction (RMESP) of 0.046. Based on the eight optimal wavelengths selected by regression coefficients (RC) from the PLSR model, a new RC-PLSR model was built and good results were shown with high Rp of 0.854 and low RMSEP of 0.049. Finally, distribution maps of hydroxyproline were created by transferring the RC-PLSR model to each pixel in the hyperspectral images. The results demonstrated that HSI has the capability for rapid and non-destructive determination of hydroxyproline content in chicken meat., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The suitability of hyperspectral imaging technique (400-1000 nm) was investigated to determine the thiobarbituric acid (TBA) value for monitoring lipid oxidation in fish fillets during cold storage at 4°C for 0, 2, 5, and 8 days. The PLSR calibration model was established with full spectral region between the spectral data extracted from the hyperspectral images and the reference TBA values and showed good performance for predicting TBA value with determination coefficients (R(2)P) of 0.8325 and root-mean-square errors of prediction (RMSEP) of 0.1172 mg MDA/kg flesh. Two simplified PLSR and MLR models were built and compared using the selected ten most important wavelengths. The optimised MLR model yielded satisfactory results with R(2)P of 0.8395 and RMSEP of 0.1147 mg MDA/kg flesh, which was used to visualise the TBA values distribution in fish fillets. The whole results confirmed that using hyperspectral imaging technique as a rapid and non-destructive tool is suitable for the determination of TBA values for monitoring lipid oxidation and evaluation of fish freshness., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Currently, the issue of food safety and quality is a great public concern. In order to satisfy the demands of consumers and obtain superior food qualities, non-destructive and fast methods are required for quality evaluation. As one of these methods, hyperspectral imaging (HSI) technique has emerged as a smart and promising analytical tool for quality evaluation purposes and has attracted much interest in non-destructive analysis of different food products. With the main advantage of combining both spectroscopy technique and imaging technique, HSI technique shows a convinced attitude to detect and evaluate chicken meat quality objectively. Moreover, developing a quality evaluation system based on HSI technology would bring economic benefits to the chicken meat industry. Therefore, in recent years, many studies have been conducted on using HSI technology for the safety and quality detection and evaluation of chicken meat. The aim of this review is thus to give a detailed overview about HSI and focus on the recently developed methods exerted in HSI technology developed for microbiological spoilage detection and quality classification of chicken meat. Moreover, the usefulness of HSI technique for detecting fecal contamination and bone fragments of chicken carcasses are presented. Finally, some viewpoints on its future research and applicability in the modern poultry industry are proposed.

The feasibility of using hyperspectral imaging technique (1000-2500 nm) for predicting moisture content (MC) during the salting process of porcine meat was assessed. Different spectral profiles including reflectance spectra (RS), absorbance spectra (AS) and Kubelka-Munk spectra (KMS) were examined to investigate the influence of spectroscopic transformations on predicting moisture content of salted pork slice. The best full-wavelength partial least squares regression (PLSR) models were acquired based on reflectance spectra (Rc(2)=0.969, RMSEC=0.921%; Rc(2)=0.941, RMSEP=1.23%). On the basis of the optimal wavelengths identified using the regression coefficient, two calibration models of PLSR and multiple linear regression (MLR) were compared. The optimal RS-MLR model was considered to be the best for determining the moisture content of salted pork, with a Rc(2) of 0.917 and RMSEP of 1.48%. Visualisation of moisture distribution in each pixel of the hyperspectral image using the prediction model display moisture evolution and migration in pork slices., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Water-holding capacity (WHC) is a primary quality determinant of salmon flesh. One of the limiting factors for not having a direct measurement of WHC for salmon quality grading is that current WHC measurements are destructive, time-consuming, and inefficient. In this study, two hyperspectral image systems operated in the visible and short-wave near infrared range (400-1000 nm) and the long-wave near infrared range (897-1753 nm) were applied for non-invasive determination of four WHC indices, namely percentage liquid loss (PLL), percentage water loss (PWL), percentage fat loss (PFL), and percentage water remained (PWR) of salmon flesh. Two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) were applied, respectively, to establish calibration models of WHC indices based on the spectral signatures of salmon flesh, and the performances of these two methods were compared to determine the optimal spectral calibration strategy. The performances were also compared between two hyperspectral image systems, when full range spectra were considered. Out of 121 wavelength variables, only thirteen (PLL), twelve (PWL), nine (PFL), and twelve variables (PWR) were selected as important variables by using competitive adaptive reweighted sampling (CARS) algorithm to reduce redundancy and collinearity of hyperspectral images. The CARS-PLSR combination was identified as the optimal method to calibrate the prediction models for WHC determination, resulting in good correlation coefficient of prediction (rP) of 0.941, 0.937, 0.815, and 0.970 for PLL, PWL, PFL, and PWR analysis, respectively. CARS-PLSR equations were obtained according to the regression coefficients of the CARS-PLSR models and were transferred to each pixel in the image for visualizing WHC indices in all portions of the salmon fillet. The overall results show that the laborious, time-consuming, and destructive traditional techniques could be replaced by hyperspectral imaging to provide a rapid and non-invasive measurement of WHC distribution in salmon flesh., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The purpose of this study was to develop and test a hyperspectral imaging system (900-1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner-Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (Rcv=0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. Successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The effects of agitation (1002 rpm), different pressure reduction rates (60 and 100 mbar/min), as well as employing cold water with different initial temperatures (IWT: 7 and 20°C) on immersion vacuum cooling (IVC) of cooked pork hams were experimentally investigated. Final pork ham core temperature, cooling time, cooling loss, texture properties, colour and chemical composition were evaluated. The application for the first time of agitation during IVC substantially reduced the cooling time (47.39%) to 4.6°C, compared to IVC without agitation. For the different pressure drop rates, there was a trend that shorter IVC cooling times were achieved with lower cooling rate, although results were not statistically significant (P>0.05). For both IWTs tested, the same trend was observed: shorter cooling time and lower cooling loss were obtained under lower linear pressure drop rate of 60 mbar/min (not statistically significant, P>0.05). Compared to the reference cooling method (air blast cooling), IVC achieved higher cooling rates and better meat quality., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

In this study a near-infrared (NIR) hyperspectral imaging technique was investigated for non-destructive determination of chemical composition of intact and minced pork. Hyperspectral images (900-1700 nm) were acquired for both intact and minced pork samples and the mean spectra were extracted by automatic segmentation. Protein, moisture and fat contents were determined by traditional methods and then related with the spectral information by partial least-squares (PLS) regression models. The coefficient of determination obtained by cross-validated PLS models indicated that the NIR spectral range had an excellent ability to predict the content of protein (R(2)(cv)=0.88), moisture (R(2)(cv)=0.87) and fat (R(2)(cv)=0.95) in pork. Regression models using a few selected feature-related wavelengths showed that chemical composition could be predicted with coefficients of determination of 0.92, 0.87 and 0.95 for protein, moisture and fat, respectively. Prediction of chemical contents in each pixel of the hyperspectral image using these prediction models yielded spatially distributed visualisations of the sample composition., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Bacterial pathogens are the main culprits for outbreaks of food-borne illnesses. This study aimed to use the hyperspectral imaging technique as a non-destructive tool for quantitative and direct determination of Enterobacteriaceae loads on chicken fillets. Partial least squares regression (PLSR) models were established and the best model using full wavelengths was obtained in the spectral range 930-1450 nm with coefficients of determination R(2)≥ 0.82 and root mean squared errors (RMSEs) ≤ 0.47 log(10)CFUg(-1). In further development of simplified models, second derivative spectra and weighted PLS regression coefficients (BW) were utilised to select important wavelengths. However, the three wavelengths (930, 1121 and 1345 nm) selected from BW were competent and more preferred for predicting Enterobacteriaceae loads with R(2) of 0.89, 0.86 and 0.87 and RMSEs of 0.33, 0.40 and 0.45 log(10)CFUg(-1) for calibration, cross-validation and prediction, respectively. Besides, the constructed prediction map provided the distribution of Enterobacteriaceae bacteria on chicken fillets, which cannot be achieved by conventional methods. It was demonstrated that hyperspectral imaging is a potential tool for determining food sanitation and detecting bacterial pathogens on food matrix without using complicated laboratory regimes., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Hyperspectral imaging was exploited for its potential in direct and fast determination of Pseudomonas loads in raw chicken breast fillets. A line-scan hyperspectral imaging system (900-1700 nm) was employed to obtain sample images, which were then further corrected, modified and processed. The prepared images were correlated with the true Pseudomonas counts of these samples using partial least squares (PLS) regression. To enhance model performance, different spectral extraction approaches, spectral preprocessing methods as well as wavelength selection schemes based on genetic algorithm were investigated. The results revealed that extraction of mean spectra is more efficient for representation of sample spectra than computation of median spectra. The best full wavelength model was attained based on spectral images preprocessed with standard normal variate, and the correlation coefficients (R) and root mean squared errors (RMSEs) for the model were above 0.81 and below 0.80 log10 CFU g(-1), respectively. In development of simplified models, wavelengths were selected by using a proposed two-step method based on genetic algorithm. The best model utilized only 14 bands in five segments and produced R and RMSEs of 0.91 and 0.55 log10 CFU g(-1), 0.87 and 0.65 log10 CFU g(-1) as well as 0.88 and 0.64 log10 CFU g(-1) for calibration, cross-validation and prediction, respectively. Moreover, the prediction maps offered a novel way for visualizing the gradient of Pseudomonas loads on meat surface. Hyperspectral imaging is demonstrated to be an effective tool for nondestructive measurement of Pseudomonas in raw chicken breast fillets., (Copyright © 2013 Elsevier B.V. All rights reserved.)

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(P)(2)) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R(P)(2) of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Many studies have been carried out in developing non-destructive technologies for predicting meat adulteration, but there is still no endeavor for non-destructive detection and quantification of adulteration in minced lamb meat. The main goal of this study was to develop and optimize a rapid analytical technique based on near-infrared (NIR) hyperspectral imaging to detect the level of adulteration in minced lamb. Initial investigation was carried out using principal component analysis (PCA) to identify the most potential adulterate in minced lamb. Minced lamb meat samples were then adulterated with minced pork in the range 2-40% (w/w) at approximately 2% increments. Spectral data were used to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range (910-1700 nm) with a coefficient of determination (R(2)(cv)) of 0.99 and root-mean-square errors estimated by cross validation (RMSECV) of 1.37%. Four important wavelengths (940, 1067, 1144 and 1217 nm) were selected using weighted regression coefficients (Bw) and a multiple linear regression (MLR) model was then established using these important wavelengths to predict adulteration. The MLR model resulted in a coefficient of determination (R(2)(cv)) of 0.98 and RMSECV of 1.45%. The developed MLR model was then applied to each pixel in the image to obtain prediction maps to visualize the distribution of adulteration of the tested samples. The results demonstrated that the laborious and time-consuming tradition analytical techniques could be replaced by spectral data in order to provide rapid, low cost and non-destructive testing technique for adulterate detection in minced lamb meat., (Copyright © 2012 Elsevier B.V. All rights reserved.)

A new algorithm for the conversion of device dependent RGB colour data into device independent L*a*b* colour data without introducing noticeable error has been developed. By combining a linear colour space transform and advanced multiple regression methodologies it was possible to predict L*a*b* colour data with less than 2.2 colour units of error (CIE 1976). By transforming the red, green and blue colour components into new variables that better reflect the structure of the L*a*b* colour space, a low colour calibration error was immediately achieved (ΔE(CAL) = 14.1). Application of a range of regression models on the data further reduced the colour calibration error substantially (multilinear regression ΔE(CAL) = 5.4; response surface ΔE(CAL) = 2.9; PLSR ΔE(CAL) = 2.6; LASSO regression ΔE(CAL) = 2.1). Only the PLSR models deteriorated substantially under cross validation. The algorithm is adaptable and can be easily recalibrated to any working computer vision system. The algorithm was tested on a typical working laboratory computer vision system and delivered only a very marginal loss of colour information ΔE(CAL) = 2.35. Colour features derived on this system were able to safely discriminate between three classes of ham with 100% correct classification whereas colour features measured on a conventional colourimeter were not., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Many subjective assessment methods for fresh meat quality are still widely used in the meat industry, making the development of an objective and non-destructive technique for assessing meat quality traits a vital need. In this study, a hyperspectral imaging technique was investigated for objective determination of pork quality attributes. Hyperspectral images in the near infrared region (900-1700 nm) were acquired for pork samples from the longissimus dorsi muscle, and the representative spectral information was extracted from the loin eye area. Several mathematical pre-treatments including first and second derivatives, standard normal variate (SNV) and multiplicative scatter correction (MSC) were applied to examine the influence of spectral variations in predicting pork quality characteristics. Spectral information was used for predicting color features (L, a, b, chroma and hue angle), drip loss, pH and sensory characteristics by partial least-squares regression (PLS-R) models. Independent sets of feature-related wavelengths were selected for predicting each quality attribute. The results showed that color reflectance (L), pH and drip loss of pork meat could be predicted with determination coefficients (R(CV)(2)) of 0.93, 0.87 and 0.83, respectively. The regression coefficients from the PLS-R models at the selected optimal wavelengths were applied in a pixel-wise manner to convert spectral images to prediction maps that display the distribution of attributes within the sample. Results indicated that this technique is a potential tool for rapid assessment of pork quality., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900-1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R(2)) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

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., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread deployment.

Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images., (Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.)

The visual texture of pork ham slices reveals information about the different qualities and perceived image heterogeneity, which is encapsulated as spatial variations in geometry and spectral characteristics. Detrended Fluctuation Analysis (DFA) detects long-range correlations in nonstationary spatial sequences, by a self-similarity scaling exponent alpha. In the current work, the aim is to investigate the usefulness of alpha, using different colour channels (R, G, B, L*, a*, b*, H, S, V, and Grey), as a quantitative descriptor of visual texture in sliced ham surface patterns for the detection of long-range correlations in unidimensional spatial series of greyscale intensity pixel values at 0 degrees , 30 degrees , 45 degrees , 60 degrees , and 90 degrees rotations. Images were acquired from three qualities of pre-sliced pork ham, typically consumed in Ireland (200 slices per quality). Results indicated that the DFA approach can be used to characterize and quantify the textural appearance of the three ham qualities, for different image orientations, with a global scaling exponent. The spatial series extracted from the ham images display long-range dependence, indicating an average behaviour around 1/f-noise. Results indicate that alpha has a universal character in quantifying the visual texture of ham surface intensity patterns, with no considerable crossovers that alter the behaviour of the fluctuations. Fractal correlation properties can thus be a useful metric for capturing information embedded in the visual texture of hams., (Copyright (c) 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.)

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values., (Copyright 2009 Elsevier Ltd. All rights reserved.)

A new study was conducted to apply computer vision methods successfully developed using trained sensory panel palatability data to new samples with consumer panel palatability data. The computer vision methodology utilized the traditional approach of using beef muscle colour, marbling and surface texture as palatability indicators. These features were linked to corresponding consumer panel palatability data with the traditional approach of partial least squares regression (PLSR). Best subsets were selected by genetic algorithms. Results indicate that accurate modelling of likeability with regression models was possible (r(2)=0.86). Modelling of other important palatability attributes proved encouraging (tenderness r(2)=0.76, juiciness r(2)=0.69, flavour r(2)=0.78). Therefore, the current study provides a basis for further expanding computer vision methodology to correlate with consumer panel palatability data., (Copyright 2009 Elsevier Ltd. All rights reserved.)

Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of b*, which indicates blue to yellow in L*a*b* colour space] and three textural features [entropy of b*, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R, mean of H, std. deviation of a*, which indicates green to red in L*a*b* colour space) and two textural features [contrast of B, contrast of L* (luminance or lightness in L*a*b* colour space)] for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-value<0.4) for sensory grading (surface colour, colour uniformity, bitonality, texture appearance and acceptability), a dichotomous logistic regression model using the best image features was able to explain the variability of consumers' responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams., (Copyright 2009 Elsevier Ltd. All rights reserved.)

The water holding capacity of meat products is a very important quality attribute which has an influence on product yield, which in turn has economic implications, but is also important in terms of eating quality. A number of pre-and post-mortem factors influence the water holding capacity (WHC) of meat. During the growth and development of meat animals, genotype and animal diet are important due to their direct influence on muscle characteristics. In the immediate pre-slaughter period, stresses on the animal such as fasting, and different stunning methods are likely to influence meat WHC. In the post-slaughter period chilling, ageing, injecting non-meat ingredients, as well as tumbling have important influences on WHC. Furthermore, cooking and cooling procedures for the final meat products can also affect the WHC of the product, in particular the cooking and the cooling methods, the heating and the cooling rate, the cooking temperature, and the endpoint temperature. This paper provides an overview of recent research on important intrinsic and extrinsic factors that affect the WHC of beef, pork, and lamb products, and reveals explanations and solutions to some of the critical problems related to WHC and product quality.

Meat and seafood products present a viable medium for microbial propagation, which contributes to foodborne illnesses and quality losses. The development of novel and effective techniques for microbial decontamination is therefore vital to the food industry. Water presents a unique advantage for large-scale applications, which can be functionalized to inactivate microbial growth, ensuring the safety and quality of meat and seafood products. By taking into account the increased popularity of functionalized water utilization through electrolysis, ozonation and cold plasma technology, relevant literature regarding their applications in meat and seafood safety and quality are reviewed. In addition, the principles of generating functionalized water are presented, and the safety issues associated with their uses are also discussed. Functionalization of water is a promising approach for the microbiological safety and quality of meat and seafood products and possesses synergistic effects when combined with other decontamination approaches. However, functionalized water is often misused since the active antimicrobial component is applied at a much higher concentration, despite the availability of applicable regulations. Functionalized water also shows reduced antimicrobial efficiency and may produce disinfection by-products (DBPs) in the presence of organic matter, especially at a higher concentration of active microbial component. Utilization should be encouraged within regulated guidelines, especially as hurdle technology, while plasma functionalized water which emerges with great potentials should be exploited for future applications. It is hoped that this review should encourage the industry to adopt the functionalized water as an effective alternative technique for the food industry. [ABSTRACT FROM AUTHOR]

Vacuum cooling is a rapid cooling method widely used in cooling some food products. Simulating the vacuum cooling process with mathematical models helps to acquire a more intuitive understanding and optimize the whole cooling process. However, there is no review summarizing the mathematical models of vacuum cooling. In this review, heat and mass transfer process during vacuum cooling, types of mathematical models for vacuum cooling, and numerical methods including finite difference method, finite element method and finite volume method used for process simulation are introduced in details. The food products used in numerical simulation study of vacuum cooling generally include liquid food, vegetables and cooked meat. The ranges of application of various numerical methods are also discussed. Moreover, heat and mass transfer coefficients have a great influence on the accuracy of the model, and are generally provided by the literature. The investigations presented in this review invariably demonstrate that mathematical modeling can provide good prediction of key information of vacuum cooling process, and has a great potential to improve vacuum cooling process in the food industry. However, more efforts are still needed to realize the industrial translation of laboratory results. [ABSTRACT FROM AUTHOR]

Freezing is one of the most widespread used preservation methods for meats including fish meat. Traditional freezing methods such as air blast freezing and cryogenic freezing could induce some quality deterioration such as damage to cell structure, increased drip loss, and poor sensory value. Therefore, novel freezing methods have been developed to minimize the disadvantages of traditional freezing methods. This review describes the enhancement of quality attributes of muscle tissues frozen by novel freezing technologies, including high pressure freezing, electrically and magnetically assisted freezing, ultrasound assisted freezing and antifreeze protein. These quality attributes include microstructure, moisture loss, color, tenderness, protein denaturation, lipid and protein oxidation, and microbial counts. In this review, the principles of these emerging freezing technologies are introduced, and the impacts of these technologies on controlling the formation and growth of ice crystals and on complex changes of protein are also discussed. The current review shows that the novel freezing methods have positive effects on promoting the quality of frozen muscle. At a micro level, the majority of the novel methods have some certain ability on controlling the formation and growth of ice crystals, thus creating smaller, and more homogeneous and regular distribution of ice crystals, leading to better microstructure and enhanced quality attributes of frozen meats. Meanwhile, complex changes of protein take place under some of these novel freezing processes, and therefore the possible negative effect of the changes of protein should also be considered for commercial applications of these technologies in the frozen food industry. [ABSTRACT FROM AUTHOR]

Background Microwave processing techniques have been extensively used in the food industry due to its significant reduction in cooking time and energy consumption. Microwave processing technologies such as microwave drying, heating and sterilizing play a significant role in food quality and safety control. However, few reviews have been published in recent years summarizing the latest developments in the application of microwave technology in the food industry. Scope and approach This review focuses on recent applications of microwave processing technologies including microwave drying, heating, and sterilizing in fruit (banana, apple, olive, sour cherries, pomegranate arils, blueberries, kiwifruit, aronia, strawberry, and grape tomato), vegetables (potato, bamboo shoot, purslane leaves, onion, green bean, pumpkin, eggplant, edamame, sea tangle, garlic, kale, red cabbage, tomato, cassava, lentils, chickpea, broccoli, Brussels sprouts, cauliflower, jalapeño peppers, and coriander foliage), and meat products (sardine fish, restructured silver carp slices, sea cucumber, beef semitendinosus muscle, bovine supraspinatus muscle, camel longissimus dorsi muscle, foal meat, bovine gluteus medium muscle, chicken steak, mature cows semimembranosus and semitendinosus muscles, kavurma (a ready-to-eat meat product), salmon, cod, drumettes, and beef slices), changes in product quality as affected with microwave processing are discussed in details, and future directions of research are presented. Key findings and conclusions Microwave drying has the advantages of low energy consumption and high efficiency as compared to conventional drying, while producing more porous structure of foods. Microwave drying usually combines with other conventional drying to enhance the quality of a food product. Compared with the traditional method, microwave heating or cooking can generally retain higher levels of bioactive components, antioxidant activity and attractive color of vegetables, while microwave cooking with water can cause a serious drop in nutrients due to leaching and thermal liability. Microwave sterilization has the capacity to completely inactivate microorganisms and effectively destroy enzyme activity, and less effect on antioxidant activity, texture and color of food products compared with conventional pasteurization. [ABSTRACT FROM AUTHOR]

This study aimed to develop simplified models for rapid and nondestructive monitoring TVB-N contents during drying of cured meats based on a hyperspectral imaging system (HSI) in the spectral range of 400–1000 nm. Multivariate calibration models were first developed using partial least-squares regression (PLSR) and least-squares support vector machines (LS-SVM) in the full spectral range. In order to simplify the calibration model, a set of 9 feature wavelengths was then selected using the regression coefficient and two simplified models using PLSR and multiple linear regression (MLR) were subsequently established. The best simplified model obtained was based on MLR, with an R 2 p of 0.861 and RMSEP of 4.73. Mapping of TVB-N contents was realized by transferring the quantitative model to each pixel in the image to display protein degradation in cured pork slices at different drying periods. The results provided a possibility of realizing a multispectral imaging technique for on-line monitoring the TVB-N contents of cured meats during drying processes. [ABSTRACT FROM AUTHOR]

• Effects of isochoric freezing on quality attributes of chicken meat were studied. • Samples frozen in pure water and 1.5% NaCl solution were significantly affected. • Samples frozen in 2.5% NaCl solution at −4 and −8 °C had no significant change. • Pressure negative effect was minimized with increasing NaCl concentration. This study investigated the potential of isochoric freezing for chicken breast meat for the first time. Chicken breast samples were immersed in isochoric NaCl solutions with various concentrations of 0 (pure water, PW), 1.5 and 2.5 % at −4 and −8 °C, respectively, and effects of process parameters such as temperature, pressure, and solution concentration on quality characteristics of the sample including colour, water holding capacity, weight loss, texture, microstructure, and water mobility were evaluated. Results showed that increasing NaCl concentrations depressed freezing temperature and pressure and samples treated in PW and 1.5 % NaCl solution at −4 and −8 °C exhibited a significant decrease (P < 0.05) in the quality characteristics, while those treated in 2.5 % solution at −4 and −8 °C showed no significant difference compared with the control. These results indicated the potential for enhancing the quality of meats preserved in isochoric systems. [ABSTRACT FROM AUTHOR]

Marbling is one of the most important traits determining the quality of meat, and the richness of marbling is often considered by consumers as the primary factor when buying meat in view of its contribution to sensory characteristics of meat. In the market, there has been a constant demand for meat products with an assured degree of marbling. Conventionally, marbling of meat is assessed by visual appraisal or chemical analysis, which has the disadvantages of being subjective and time-consuming. In order to improve the detection accuracy and production efficiency, a variety of modern instrumental techniques, including spectroscopic techniques, imaging techniques, and hyperspectral imaging, have been developed for marbling analysis. This paper gives a comprehensive overview on the recent advances in marbling analysis. What's more, the advantages, limitations and some perspectives on the future trends of these techniques are also presented. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the impact of two convection-microwave heating (M100 and M30) on texture and colour properties, cooking yield and thermal shrinkage of bovine product ( gluteus medius muscle) as compared with conventional roasting at constant temperature (P180 and P80) and roasting with ΔT program (Δ80 and Δ40). The product processed by the M100 method was characterised by high shear force ( WBSF) and high compression force ( TPA). The reduction of microwave intensity to 30% (roasting M30) could improve the tenderness of final product. In addition, microwave heating at the intensity of 30% shortened the roasting time, and the produced sample exhibited comparable process loss and heat shrinkage extent compared to those treated by the high-temperature conventional roasting method (P180). The study indicated that combining of convection and low-intensity microwave heating could be successfully used in catering business for beef roasting due to the shorter cooking time, reduced cooking loss and higher tenderness of meat. [ABSTRACT FROM AUTHOR]

Abstract: Developing a rapid and non-destructive method for food safety and quality monitoring has become a crucial request from the meat industry. Hyperspectral imaging technique provides extraordinary advantages over the traditional imaging and spectroscopy techniques in food quality evaluation due to the spatial and spectral information that it can offer. In this study, a laboratory-based pushbroom hyperspectral imaging system in reflectance mode was developed in the near infrared (NIR) range (900–1700nm) for non-invasive determination of the major chemical compositions of beef. Beef samples collected from different breeds were scanned by the system followed by traditional assessment of their chemical composition by using the ordinary wet-chemical methods. The extracted spectral data and the measured quality parameters were modeled by partial least squares regression (PLSR) for predicting water, fat and protein contents yielding a reasonable accuracy with determination coefficients of 0.89, 0.84 and 0.86 concomitant with standard error of prediction (SEP) of 0.46%, 0.65% and 0.29%, respectively. Some image processing algorithms were developed and the most relevant wavelengths were selected to visualize the predicted chemical constituents in each pixel of the hyperspectral image yielding the spatially distributed visualizations of the sample contents. The results were promising and implied that hyperspectral imaging technique associated with appropriate chemometric multivariate analyses has a great potential for simultaneous assessment of various chemical constituents without using hazardous chemical reagents. [Copyright &y& Elsevier]

Abstract: In recent years, hyperspectral imaging has gained a wide recognition as a non-destructive and fast quality and safety analysis and assessment method for a wide range of food products. As the second part of this review, applications in quality and safety determination for food products are presented to illustrate the capability of this technique in the food industry for classification and grading, defect and disease detection, distribution visualization of chemical attributes, and evaluations of overall quality of meat, fish, fruits, vegetables, and other food products. The state of the art of hyperspectral imaging for each of the categories was summarized in the aspects of the investigated quality and safety attributes, the used systems (wavelength range, acquisition mode), the data analysis methods (feature extraction, multivariate calibration, variables selection), and the performance (correlation, error, visualization). With its success in different applications of food quality and safety analysis and assessment, it is evident that hyperspectral imaging can automate a variety of routine inspection tasks. Industrial relevance: It is anticipated that real-time food monitoring systems with this technique can be expected to meet the requirements of the modern industrial control and sorting systems in the near future. [Copyright &y& Elsevier]

Abstract: Identification of fresh and frozen–thawed meat is an important authenticity issue, although a challenging task. The potential application of a pushbroom hyperspectral imaging system in the near-infrared (NIR) range (900–1700nm) was explored as a rapid and non-destructive technique for the investigation of meat freshness. The effect of standard freeze–thaw routines was studied in pork samples from the longissimus dorsi muscle. Partial least squares discriminant analysis (PLS-DA) models were used to distinguish between fresh and frozen–thawed samples. Optimal wavelengths were selected and used for sample discrimination with reduced spectral data and image processing. Classification models with reduced spectral data achieved an overall correct classification of 100% for an independent set of samples. An image processing algorithm was also developed for visualizing the classification results. The best classification model obtained was successfully applied to the images to produce classification maps with high overall accuracy. Results confirmed the possibility of developing a fast and reliable system for discrimination between fresh and frozen–thawed pork based on reflectance in the NIR wavelength range. Industrial relevance: The application of NIR hyperspectral analysis could enable the development of a rapid, reliable and non-destructive method for authentication of fresh meat samples for the benefit of the retail sector and the consumer. [Copyright &y& Elsevier]

The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research. [ABSTRACT FROM AUTHOR]

Abstract: A detailed evaluation of the influence of variations in product (beef size and porosity) and process parameters (vacuum chamber pressure reduction rate) on cooling time, total mass loss, pressure and temperature distributions during the immersion vacuum cooling of large cooked beef joint samples was performed using a previously developed and validated mathematical model. Beef size variations were done by changing radius values (between 5.5 and 8cm) and porosity levels (between 2 and 5%). Pressure reductions rates varied between circa 5 to 25mbar/min. The simulation indicated that using the maximum pressure drop rate tested (circa 25mbars/min) beef samples (3% porosity) of up to 6.5 and 7cm radius can be cooled within Irish and American official safety guidelines, respectively. An increase in porosity could further reduce cooling time but incur higher mass losses, which can be detrimental to product quality. The identified effects and relevant trends due to variations on the different parameters studied on the cooling process performance lead to recommendations in relation to optimal pressure drop rate, in relation to size and porosity ranges. Necessary modifications to the equipment design, such as the introduction of agitation in the cooling medium, are also emphasised. Industrial relevance: Meat is typically cooked before consumption, and if it is not for immediate consumption, it should be cooled as soon as possible thereafter. Commercial products such as large cured and precooked meat pieces undergo heat treatment, and hence need to be cooled prior to packing, storage and distribution. Product temperature should be reduced as quickly as possible to its final target, to minimise quality loss and prevent or minimise microbial growth or spore germination. After relatively mild heat treatments such as pasteurisation (heat processes in the range of 60–80°C) the microorganisms of concern are often heat resistant micro flora such as mesophilic spores of Bacillus and Clostridium species that under adequate conditions might germinate and grow, posing a safety risk. The difficulties of cooling large commercial meat pieces or joints have been reported, and the responsibility is on manufacturers to ensure that procedures are adequate and safety risks are minimised. Recent investigations into vacuum cooling have shown advances for the meat industry. [Copyright &y& Elsevier]

Abstract: Hyperspectral imaging system operated in the near infrared (NIR) region (900–1700nm) was developed for non-contact measurement of surface colour, pH and tenderness of fresh beef. Hyperspectral images were acquired for beef samples and their spectral signatures were extracted. The real colour (expressed as L ∗ a ∗ b ∗), pH and tenderness of the same samples were recorded using traditional contact methods and then modelled with their corresponding spectral data using partial least square regression (PLSR). The L ∗, b ∗, pH and tenderness values were predicted with coefficients of determination () of 0.88, 0.81, 0.73 and 0.83 and root mean square errors estimated by cross validation (RMSECV) of 1.21, 0.57, 0.06 and 40.75, respectively. The weighted regression coefficients of the resulting PLSR models were used to identify the most important wavelengths and to reduce the high dimensionality of the hyperspectral data. By using these important wavelengths, image processing algorithm was developed to transfer the predicting models to every pixel in the image for visualizing colour and pH in all portions of the sample. The results demonstrated that NIR hyperspectral imaging system is a potential technique for non-destructive prediction of beef quality attributes, thus facilitating identification and classification of beef meat in a simple and fast way. With more improvement in terms of speed and processing, the hyperspectral imaging system could be effectively implemented in commercial meat product processing plants for non-destructive and rapid quality measurements. [Copyright &y& Elsevier]

Abstract: This study was carried out for post-mortem non-destructive prediction of water holding capacity (WHC) in fresh beef using near infrared (NIR) hyperspectral imaging. Hyperspectral images were acquired for different beef samples originated from different breeds and different muscles and their spectral signatures were extracted. Both principal component analysis (PCA) and partial least squares regression (PLSR) models were developed to obtain an overview of the systematic spectral variations and to correlate spectral data of beef samples to its real WHC estimated by drip loss method. Partial least squares modeling resulted in a coefficient of determination (R CV 2) of 0.89 and standard error estimated by cross validation (SECV) of 0.26%. The PLSR loadings showed that there are some important absorption peaks throughout the whole spectral range that had the greatest influence on the predictive models. Six wavelengths (940, 997, 1144, 1214, 1342, and 1443nm) were then chosen as important wavelengths to build a new PLS prediction model. The new model led to a coefficient of determination (R CV 2) of 0.87 and standard error estimated by cross validation (SECV) of 0.28%. Image processing algorithm was then developed to transfer the predicting model to each pixel in the image for visualizing drip loss in all portions of the sample. The results showed that hyperspectral imaging has the potential to predict drip loss non-destructively in a reasonable accuracy and the results could be visualised for identification and classification of beef muscles in a simple way. In addition to realize the difference in WHC within one sample, it was possible to accentuate the difference in samples having different drip loss values. [Copyright &y& Elsevier]

Abstract: A common assumption in heat and mass transfer modelling of processes such as chilling and freezing is to consider water activity (a w) being constant or equal to 1. However, this constant value assumption does not represent the actual variation of water activity during the process. In the present work, the effect of different values of water activity on the freezing/thawing times and weight loss prediction of cured cooked pork and beef, and chicken meat was studied. Experimental sorption data of these meats were previously obtained, and the results were incorporated to the developed computer simulation program. An explicit finite difference scheme, which incorporates the mass transfer and variation of a w was used in the analysis. Experimental data on freezing/thawing times and weight loss were also obtained to verify and compare with the model predictions. The high water activity range (a w =0.85, 0.9, 0.95 and 1) was analysed. In general and for all the meats considered, the lowest error was obtained when variable a w was used. Results indicated that if data on the sorption isotherm are not available a constant water activity value of 0.875 could be used instead to predict the freezing time and weight loss for cooked cured pork and beef, and a value of 0.9 for chicken meat; while for the modelling of the thawing process a w values of 0.95 and 1 are recommended for cured cooked pork and beef and chicken meat, respectively. Hence, water activity is an important factor and should be incorporated in the modelling in order to improve the accuracy of the predictions. [Copyright &y& Elsevier]

Abstract: A new structural method was developed to characterise texture of colour meat images. Structural primitives or region-primitives were constructed using a region-growing method with a global and automatically calculated threshold. A total of eight textural features under the consideration of size, orientation, and contrast, which are the primary factors of human perception of texture pattern, were obtained from the region-primitives. An experiment was set up to classify the texture pattern of a set of 60 cooked meat colour images consisting of 20 beef images, 20 lamb images, and 20 pork images using texture features obtained from the proposed region-primitive method and run-length matrix method, respectively. The proposed region-primitive method (error rate: 18.3%) was found to perform better than the run-length matrix method (error rate: 35.0%). Results also indicated that the texture features obtained by the proposed method carried adequate texture information for classification and that the proposed region-primitive method could be potentially used for the characterisation of irregular textural pattern in cooked meat images, which was inapproachable by conventional structural texture analysis techniques. [Copyright &y& Elsevier]

Abstract: Experiments were carried out to test two combined cooling methods for their suitability in minimising the cooling loss and cooling time to 10 and 4°C of large cooked beef joints. The combined cooling methods were: vacuum cooling of the cooked joint to an intermediate temperature of 35, 30, 25, or 20°C and then air blast cooling to the final temperature of 4°C; and air blast cooling to an intermediate temperature of 35, 30, 25 or 20°C and then vacuum cooling to the final temperature of 4°C. It was found that the latter method was more effective at minimising the cooling loss with similar cooling loss to air blast cooling. Both methods were similar in optimising the cooling time to 10°C and could produce cooling times similar to the Irish and British recommended cooling times. Neither method could give a cooling time similar to vacuum cooling. [Copyright &y& Elsevier]

Abstract: As a preliminary test for combining water cooking with vacuum cooling in soup of pork ham, three package treatments were designed to study the effect of cooking bag and netting on the quality of water cooked ham, i.e. ham cooked with a cooking bag and without a cooking bag (single netting and double netting). For treatments without a cooking bag, the results indicated that there was no significant superiority of ham cooked with double netting compared with ham cooked with single netting on the processing efficiency and quality characteristics. Although the hams cooked with a bag performed better in some chemical retentions and pigment, the water contents were significantly lower than those hams cooked in single netting (P <0.05), and there was a higher shrinkage tendency compared with the hams cooked without a bag. For the processing characteristics and texture properties of pork ham, there were no significant differences observed among the treatments with and without a cooking bag in terms of the combined effect of cooking and cooling (P >0.05). By considering the safety, convenience, cost, and the recovery effect on the quality changes of ham during vacuum cooling in soup, cooking with single netting is a better choice for future research. [Copyright &y& Elsevier]

Abstract: In the production of cooked meats, rapid cooling is required to cool the meats immediately after the cooking process is finished in order to minimise the growth of surviving organisms. Traditional air blast, water immersion or slow air cooling cannot achieve the required rapid cooling effect for large cooked meats. Vacuum cooling has shown its high cooling efficiency for cooked meats. In this study, vacuum cooling is used to cool large block joints of cooked meats, which have abundant of water and porosity. A mathematical model of simultaneous transient heat and mass transfer with inner heat and mass generation is developed for analysing the performance of vacuum cooling cooked meats. The variable physical properties of cooked meats during cooling process were incorporated into the model. The predictions from the model are compared with the experimental results with good agreement. [Copyright &y& Elsevier]