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Drying uniformity is one of the most important indicators in evaluating a drying technique as well as the final quality of dried products. In the current study, three drying approaches (hot-air drying (HAD), microwave-vacuum drying (MVD), and the combined method (HAD + MVD)) were applied to dehydrate mango slices. During the HAD + MVD process, the time required for hot-air drying was determined in terms of colour variations during hot-air drying. With the help of hyperspectral imaging in conjunction with multivariate data analysis and image processing, the moisture content distribution on mango slices subjected to different drying methods was visualised. Results showed a non-uniform drying property for mango slices dried by HAD or MVD individually, where HAD-dried samples had a higher moisture content in the centre but MVD-dried samples showed the opposite result. Drying uniformity was improved when HAD and MVD were combined, which produced dried products with an even moisture distribution. Mango slice samples dried by HAD + MVD showed a porous structure and a high percentage of colour retention. The current study led to the development of an effective combined HAD + MVD technique for enhancing drying uniformity for the industry. [ABSTRACT FROM AUTHOR]

With consumer concerns increasing over food quality and safety, the food industry has begun to pay much more attention to the development of rapid and reliable food-evaluation systems over the years. As a result, there is a great need for manufacturers and retailers to operate effective real-time assessments for food quality and safety during food production and processing. Computer vision, comprising a nondestructive assessment approach, has the aptitude to estimate the characteristics of food products with its advantages of fast speed, ease of use, and minimal sample preparation. Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting defects, and estimating properties such as color, shape, size, surface defects, and contamination. Therefore, in order to track the latest research developments of this technology in the agri-food industry, this review aims to present the fundamentals and instrumentation of computer vision systems with details of applications in quality assessment of agri-food products from 2007 to 2013 and also discuss its future trends in combination with spectroscopy. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

There are a large number of ham products produced and consumed throughout the world. The quality of ham is usually evaluated by human quality inspector based on grading standards. To maintain ham quality, processors need to carry out some destructive analysis of samples, which is usually laborious, time consuming and possesses the variability associated with human perception. Therefore, there is a need for the development of objective quality measurement or evaluation systems. Processors expect in-situ non-destructive measurements that can provide objective parameters with the incorporation of all the quality attributes and correlate them with the most demanding quality standards and consumer preferences. Hyperspectral imaging (HSI) is a promising technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from the sample. Although the hyperspectral imaging approach has not been widely used for the quality evaluation of hams, it possesses a great potential in attaining information about different physico-chemical properties of ham. With the application of this technology, quality of ham could be ensured in a non-destructive manner and with confidence. The aim of this review is to discuss about the opportunities relating to hyperspectral imaging techniques in the quality evaluation and control of ham. [ABSTRACT FROM AUTHOR]

Abstract: By integrating two classical optical sensing technologies of imaging and spectroscopy into one system, hyperspectral imaging can provide both spatial and spectral information, simultaneously. Therefore, hyperspectral imaging has the capability to rapidly and non-invasively monitor both physical and morphological characteristics and intrinsic chemical and molecular information of a food product in the purpose of quality and safety analysis and assessment. As the first part of this review, some fundamental knowledge about hyperspectral imaging is reviewed, which includes the relationship between spectroscopy, imaging, and hyperspectral imaging, principles of hyperspectral imaging, instruments for hyperspectral imaging, processing methods for data analysis, and discussion on advantages and disadvantages. 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]

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]

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. [ABSTRACT FROM PUBLISHER]

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]

Hyperspectral imaging technology was investigated to examine the changes of sarcoplasmatic and myofibrillar proteins contents in boiled pork. Different spectral pre-processing methods were employed to improve the partial least squares regression (PLSR) models. Seven optimal wavelengths in the region of 400–2000 nm without spectral pre-processing were used to build the best PLSR model for predicting the sarcoplasmatic proteins contents with R2 p of 0.9185 and RMSEP of 2.50 mg/g. Based on multiplicative scatter correction (MSC) method, eight wavelengths in the region of 400–1000 nm were also selected to construct the best predictive PLSR model for myofibrillar protein contents with R2 p of 0.9161 and RMSEP of 2.71 mg/g. The changes in sarcoplasmatic and myofibrillar proteins contents of boiled pork during different cooking periods were successfully visualized. • Sarcoplasmatic proteins content of pork was stabilized after boiling 9 min. • Myofibrillar proteins content of pork was stabilized after boiling 20 min. • Original reflectance spectra were suitable to predict sarcoplasmatic proteins. • MSC absorbance spectra were suitable to predict sarcoplasmatic proteins. • PLSR model of sarcoplasmatic and myofibrillar proteins having accuracy above 0.91. [ABSTRACT FROM AUTHOR]

Highlights: [•] Hyperspectral imaging was used to assess and predict tenderness in salmon fillets. [•] PLSR and LS-SVM models were developed to predict the WBSF in samples. [•] Optimal wavelengths were selected by means of RC and SPA methods. [•] Good accuracy of multivariate models was obtained for predicting WBSF. [•] An image processing algorithm was developed to create WBSF distribution map. [ABSTRACT FROM AUTHOR]

Abstract: 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 (900–1700nm) 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 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 &y& Elsevier]

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