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As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C–H, N–H, and O–H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level. [ABSTRACT FROM AUTHOR]

Abstract Secondary structure of proteins is closely related to the overall quality of meat. The sequence of the changes in chemical bonds caused by secondary structure modification and rapid detection method of secondary structure fractions was investigated by near-infrared hyperspectral imaging combined with generalized (G2D-CS) and heterospectral (H2D-CS) two-dimensional correlation analysis in this study. Synchronous and asynchronous spectral results from G2D-CS analysis indicated that, as α-helix fraction decreasing, the spectral intensity of N H stretching changed before that of the C H and O H, and the spectral fluctuation of O H stretching occurred after the carbonyl related C H band and before those aliphatic and aromatic C H band. Feature wavebands identified by H2D-CS analysis obtained good results with R2 CV of 0.836 in predicting α-helix fractions. The results of this study are useful for interpreting the secondary structure modification process of proteins during frozen storage and monitoring the secondary structure fraction in a rapid way. Highlights • NIR HSI was employed by the first time to monitor α-helix fraction of actomyosin. • G2D-CS was used for understanding the process of secondary structure modification. • H2D-CS was applied for identifying α-helix related NIR wavebands. • PLSR model built by the selected wavebands showed good results. [ABSTRACT FROM AUTHOR]

Abstract Protein is an important nutrient for people diet, and protein content is one of the most vital properties in quality assessment of pork meat. The potential of line-scanning based hyperspectral imaging (HSI) has been proved to be able to assess the protein content of meat, however quality of images obtained from line-scanning HSI is affected by sample transformation conditions. A novel single shot (snapshot) HSI sensor was employed to evaluate the protein content of numerous processed pork meat by using back propagation - neural network (BP-NN) and partial least squares regression (PLSR) predictive models. The results of BP-NN were better than that of PLSR. The best spectral profile was selected from reflectance, absorbance, and Kubelka–Munk (K–M) spectra by comparing the performances of their BP-NN models. The BP-NN model combined with absorbance spectra showed the best performance for evaluating the protein content of various processed pork meats with determination coefficient of cross-validation set (R2 CV) = 0.8318, and root mean square error of cross-validation set (RMSECV) = 8.38 mg/g, respectively. Results indicated the feasibility of determining the protein content of pork meats by means of single shot HSI. Highlights • The single shot hyperspectral imaging (HSI) could capture an image in 1/150 s. • Protein content of pork decreased after wet salting – dehydrating. • Absorbance spectra provided better accuracy. • BP-NN model was used to predict protein contents of pork by snapshot HSI. • Protein contents distribution of processed pork were showed by snapshot HSI. [ABSTRACT FROM AUTHOR]

This study aimed to simultaneously predict different textural features in grass carps fillets ( Ctenopharyngodon idella ) during vacuum freeze drying using hyperspectral imaging models developed based on integrated group wavelengths in the range of 400 nm–1000 nm. Warner-Bratzler shear force (WBSF) is commonly used as an objective indicator for tenderness evaluation in fish products. First, partial least squares regression (PLSR) was employed to develop a quantitative function between the textural variations and the spectra extracted from the acquired hyperspectral images in the full spectral range at mean and median. Then the important wavelengths most related to WBSF, hardness, gumminess and chewiness were separately selected by regression coefficients (RC) from PLSR. Moreover, the integrated wavelengths for all the textural characteristics were determined by RC by considering all the individual group wavelengths. Finally, PLSR was conducted using different simplified group wavelengths and the performance of all the simplified models at mean or median were compared. The results showed that the integrated group wavelengths at median spectra proved to be the best for simultaneous prediction of WBSF ( R P 2 = 0.8774), hardness ( R P 2 = 0.8523), gumminess ( R P 2 = 0.7982) and chewiness ( R P 2 = 0.8453). The current study should widen the applications of hyperspectral imaging in the food industry. [ABSTRACT FROM AUTHOR]

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]

This study investigated the feasibility of hyperspectral imaging (HSI) to predict moisture content (MC) values in heated - dehydrated (H-D) and cool - air - dehydrated (C-A-D) pork samples using the calibrated models established based on fresh (F), frozen - thawed (F-T), dry - salting - dehydrated (D-S-D) and wet - salting - dehydrated (W-S-D) pork samples. The full spectra were extracted from the region of interests (ROIs) in the spectral range of 400–1000 nm and the textural variables were extracted by gray-level gradient co-occurrence matrix (GLGCM) method from the first two PC images accounting for 98.73% of the total variance. Moreover, the optimal wavelengths were selected by regression coefficients (RC). Partial least-squares regression (PLSR) predictive model was developed based on the above extracted data and their mutual combination, in which the changes can be correlated with MC - related attributes. The results demonstrated that the PLSR model based on the incorporation of the optimal wavelengths and the textures (OW-T) from F, F-T, D- S -D and W-S-D samples were the best to predict MC in H-D and C-A-D samples with R p 2 of 0.9489 and RMSEP of 1.4736. Moreover, the generated visualization maps provided a rapid way to screen the MC values unequally distributed in the whole pork samples after diverse processing conditions. Therefore, it is feasible and promising to improve the applicability of the existing MC predictive models for MC prediction in the pork samples by supplementing more samples under different treatments as another prediction sets. [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]

Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products. [ABSTRACT FROM AUTHOR]

Biogenic amine index (BAI) is a sensitive indicator of meat freshness and quality. This study investigated the use of chemometric methods for analyzing hyperspectral imaging (HSI) data between 400 nm and 1000 nm to rapidly and non-destructively determine BAI values in pork. Partial least square regression (PLSR) model established using full wavelengths showed good results. In order to simplify the calibration model, four new PLSR and multiple linear regression (MLR) models based on the two sets of feature-related wavelengths selected by successive projections algorithm (SPA) and regression coefficients (RC) were built and compared. The optimized simplified model (RC-MLR) yielded excellent results with R 2 P of 0.957 and RMSEP of 4.866 mg/kg, which was thus used to visualize BAI value corresponding to each pixel of the image using pseudo color. In addition, the mechanisms of HSI for BAI determination were discussed. The established models used to determine BAI values were based on physiochemical changes associated with BAI generation in meat rather than direct detection of the BAI contents. The overall results of this study demonstrated that HSI data can be utilized to predict BAI values in pork based on chemometric analysis. [ABSTRACT FROM AUTHOR]

The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures $$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) $$ and freezing rates $$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) $$ . The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. [ABSTRACT FROM AUTHOR]

Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimension reduction methods in classifying water bodies with different algae concentration levels. [ABSTRACT FROM AUTHOR]

The total soluble solids play an important role in the fruit maturity process and determine the acceptance of rich nutrients as well as economic benefits in the fruit trade. Thus, development of rapid and nondestructive techniques for evaluating soluble solids content in fruits is important. This review focuses on recent advances in nondestructive techniques for soluble solids contents of fruits including hyperspectral imaging, laser light backscattering imaging, magnetic resonance imaging, X-ray computed tomography, near-infrared spectroscopy, and Raman spectroscopy. The advantages and disadvantages of these techniques are compared and discussed, and some viewpoints about future trends are also presented. [ABSTRACT FROM AUTHOR]

The reliability and veracity of hyperspectral imaging integrated with multivariate analyses were investigated for authentication of sliced organic potato (OP) from non-organic tubers and rapid grading of tubers on the basis of different moisture levels. Hyperspectral images of all the tuber samples were obtained and their spectral data were extracted and pre-processed. Then, partial least squares discriminant analysis (PLSDA) model was established for recognition of the tested samples. Loading plots of the second derivative (SD) and principal component analysis (PCA) were used for selecting characteristic wavelengths. The OP samples were identified correctly (100% accuracy) from non-organic tubers by MC-PLSDA model using only characteristic wavelengths, with predicted sensitivities of 1.000, specificities of ⩾0.944, classification error of ⩽0.028, coefficient of determination ( R 2 P ) of no more than 0.979 and the root mean square error of prediction (RMSEP) of ⩽0.532 for each adulterate type. Another simplified PLSDA model was applied for grading tuber moisture levels, resulting in a correct classification of ⩾91.6%. The visualization results shown on classification maps achieved a rapid and convenient interpretation of tuber varieties and moisture levels. These results indicated that hyperspectral imaging has a great potential for discrimination of OP and identification of tuber moisture levels. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) system in tandem with chemometric methods is proposed as a rapid, efficient, cost-saving, and nondestructive detection technique, and multivariate data analysis is an indispensable part of this novel detection technique. In recent years, the rapid progress that we have made in using all kinds of chemometric methods to deal with hyperspectral data of meat products, however, cannot meet the practical needs very well. Thus, in order to give some suggestions on how to select an appropriate algorithm for hyperspectral data analysis, this review, first, briefly introduces the principle of the most widely used regression algorithms, and, more importantly, then focuses on the application of different algorithms in modeling the correlation between the quality attributes of the tested sample and their hyperspectral data. The advantages and limitations of these algorithms are compared and discussed. This review article will provide valuable guidelines for data analysis in the future progress of HSI detection technique. [ABSTRACT FROM AUTHOR]

Spectral absorption index was proposed to extract the morphological features of the spectral curves in pork meat samples ( longissimus dorsi ) under the conditions including fresh, frozen–thawed, heated–dehydrated and brined–dehydrated. Savitzky–Golay (SG) smoothing and multiplicative scatter correction (MSC) were used for calibrating both the spectral reflectance and absorbance values. The absorption values were better than the reflectance values and the calibrated spectra by MSC were better than the raw and SG smoothing corrected spectra in building moisture content predictive models. The optimized partial least square regression (PLSR) model attained good results with the MSC calibrated spectral absorption values based on the spectral absorption index features ( R 2 P = 0.952, RMSEP = 1.396) and the optimal wavelengths selected by regression coefficients ( R 2 P = 0.966, RMSEP = 0.855), respectively. The models proved spectral absorption index was promising in spectral analysis to predict moisture content in pork samples using HSI techniques for the first time. [ABSTRACT FROM AUTHOR]

Mango slices were dried by microwave-vacuum drying using a domestic microwave oven equipped with a vacuum desiccator inside. Two lab-scale hyperspectral imaging (HSI) systems were employed for moisture prediction. The Page and the Two-term thin-layer drying models were suitable to describe the current drying process with a fitting goodness of R 2 = 0.978. Partial least square (PLS) was applied to correlate the mean spectrum of each slice and reference moisture content. With three waveband selection strategies, optimal wavebands corresponding to moisture prediction were identified. The best model RC-PLS-2 ( R p 2 = 0.972 and RMSEP = 4.611%) was implemented into the moisture visualization procedure. Moisture distribution map clearly showed that the moisture content in the central part of the mango slices was lower than that of other parts. The present study demonstrated that hyperspectral imaging was a useful tool for non-destructively and rapidly measuring and visualizing the moisture content during drying process. [ABSTRACT FROM AUTHOR]

Background Most contamination in agricultural and food products (AFP) is caused by undesirable microorganisms and how to detect microbial contaminants in a rapid and reliable way has always been a topic of great interest for researchers. By integrating traditional spectroscopy and digital imaging function into one system, hyperspectral imaging simultaneously providing spectral and spatial information is proposed and could be used as a smart and promising technology for microbial evaluation in AFP. Scope and approach In this review, we summarized the recent progresses and applications of hyperspectral imaging in replacing conventional time-consuming, tedious, labor-intensive and destructive techniques by providing a rapid, real-time, non-destructive and efficient alternative for detecting various microbial contaminants in raw and processed AFP. The great potential and importance of hyperspectral imaging is emphasized and approved with its satisfactory performance in microbial evaluation of various AFP. Key findings and conclusions Although hyperspectral imaging shows some obvious advantages, it still faces challenges in terms of accurate reference value measurement, fast data mining and advanced system development. With the further research and development of optics, it would be expected that efficient multispectral imaging systems would likely be developed and applied for online microbial evaluation in agricultural and food industry. [ABSTRACT FROM AUTHOR]

K value is an important freshness index widely used for indication of nucleotide degradation and assessment of chemical spoilage. The feasibility of hyperspectral imaging (400–1000 nm) for determination of K value in grass carp and silver carp fillets was investigated. Partial least square (PLS) regression and least square support vector machines (LS-SVM) models established using full wavelengths showed excellent performances and the PLS model was better with higher determination coefficients of prediction ( R 2 P = 0.936) and lower root mean square errors of prediction (RMSEP = 5.21%). The simplified PLS and LS-SVM models using the seven optimal wavelengths selected by successive projections algorithm (SPA) also presented good performances. The spatial distribution map of K value was generated by transferring the SPA-PLS model to each pixel of the images. The current study showed the suitability of using hyperspectral imaging to determine K value for evaluation of chemical spoilage and freshness of fish fillets. [ABSTRACT FROM AUTHOR]

Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction ( R p ) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA–PLSR model based on the selected wavelengths was built and showed good results with R p of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat. [ABSTRACT FROM AUTHOR]

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 1100 nm 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 ( R p 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 ( R p 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. [ABSTRACT FROM AUTHOR]

The feasibility of visible and near infrared hyperspectral imaging in the range of 400–1000 nm for determinating total viable counts (TVC) to evaluate microbial spoilage of fish fillets was investigated. Partial least square regression (PLSR) and least square support vector machines (LS-SVM) models established based on full wavelengths showed excellent performances and the LS-SVM model was better with higher residual predictive deviation (RPD) of 3.89, determination coefficients in prediction ( R 2 P ) of 0.93 and lower root mean square errors in prediction (RMSEP) of 0.49 log 10 CFU/g. Seven optimal wavelengths were selected by successive projections algorithm (SPA) and the simplified SPA-PLSR was better than SPA-LS-SVM models with RPD of 3.13, R 2 P of 0.90 and RMSEP of 0.57 log 10 CFU/g, and was transferred to each pixel of the hyperspectral images for generating the TVC distribution map. This study showed that hyperspectral imaging is suitable to determine TVC value for evaluating microbial spoilage of grass carp fillets in a rapid and non-invasive manner. [ABSTRACT FROM AUTHOR]

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]

Muscle food is one of the most perishable food products because of its vulnerability to microbial spoilage, which can result in critical food safety problems. Traditional techniques for detection and evaluation of microbial spoilage in muscle foods are tedious, laborious, destructive, and time-consuming. In recent years, spectroscopic and imaging technologies have shown great potentials for the assessment of food quality and safety due to their nondestructive, noninvasive, cost-effective, and rapid responsive nature. This review focuses on the applications of several valuable spectroscopic techniques including visible and near-infrared spectroscopy, Fourier transform infrared spectroscopy, fluorescence spectroscopy, Raman spectroscopy, and hyperspectral imaging for the rapid and nondestructive detection of microbial spoilage in common muscle foods such as meat, poultry, fish, and related products. Combined with chemometric analysis, such as spectral preprocessing and modeling methods, these potential technologies have been successfully developed for the determination of total viable count, aerobic plate count, Enterobacteriaceae, Pseudomonas, Escherichia coli, and lactic acid bacteria loads in muscle foods. Moreover, the advantages and disadvantages of these techniques are discussed and some perspectives about future trends are also presented. [ABSTRACT FROM AUTHOR]

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 ( R p ) 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 R p 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigated the potential of visible and near infrared (Vis/NIR) hyperspectral imaging (HSI) to differentiate between free-range and broiler chicken meats. 120 hyperspectral images of chicken fillets were acquired and then calibrated for reflectance. Spectral data were extracted from the region of interest (ROI), followed by multiple scatter correction (MSC) to reduce the noise. Successive projection algorithm (SPA) was used to select optimal wavelengths from the full spectra. On the other hand, principal component analysis (PCA) was applied to select optimum characteristic images, and the first two principal component (PC) images were selected because PC1 and PC2 explained over 95% of variances of all spectra. Then, gray-level gradient co-occurrence matrix (GLGCM) was implemented on PC1 and PC2 images to extract 30 textural variables in total. Based on data fusion, classification models were established, in which the radial basis function-support vector machine (RBF-SVM) model gave the best results with high correct classification rate (CCR) of 93.33% for the prediction samples, demonstrating that combining spectra with texture data were effective for differentiating between free-range and broiler chicken meats. [ABSTRACT FROM AUTHOR]

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Application of wavelet analysis to near-infrared (NIR) hyperspectral imaging data was exploited for categorization of lamb muscles in this study. A variety of common wavelet transforms was investigated to identify the best wavelet features for categorization of lamb muscles. The fifth-order Daubechies wavelet ('db5') was found to be the best wavelet function for decomposition of lamb spectral signal. Features of wavelet coefficients extracted from db5 wavelet at the fifth decomposition level were then used as the inputs of least-squares support vector machine (LS-SVM) for developing classification models. Principal component analysis (PCA) was used for dimensionality reduction. Classification performance of LS-SVM classifiers in tandem with wavelet transform and PCA was compared with the LS-SVM models based on original, first derivative, second derivative, smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC) spectral data; then, the overall correct classification performance for the training and test sets using combination with wavelet approximation and detail coefficients in fifth decomposition scale and PCA was 100 and 96.15 %, respectively. In addition, the developed classification models were successfully applied to the hyperspectral images for obtaining classification maps and the kappa coefficient of 0.83 was obtained for the visual classification. The results revealed that the application of wavelet analysis has a great potential for categorization of lamb muscles in tandem with multivariate analysis and image processing. [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]

This study was conducted to investigate the potential of hyperspectral imaging technique in a rapid and non-invasive manner for measuring colour distribution of grass carp fillets during cold storage. The quantitative calibration models were established between the spectral data extracted from the hyperspectral images and the measured colour reference values by partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The performance of two spectral ranges of 400-1,000 and 1,000-2,500 nm was compared to select the best spectral range for further colour analysis of grass carp fillets. The LS-SVM model using the whole spectral range possessed better performance than the PLSR model for predicting colour components of L* and a* with higher coefficients of determination ( R) of 0.916 and 0.905 and lower root-mean-square errors of prediction (RMSEPs) of 2.876 and 2.253, respectively. Seven (466, 525, 590, 620, 715, 850 and 955 nm) and five (465, 585, 660, 720 and 950 nm) optimal wavelengths carrying the most important and sensitive information were recognized and selected using successive projections algorithm (SPA) for predicting L* and a*, with R values of 0.912 and 0.891 being obtained from the optimized SPA-LS-SVM models established based on the selected valuable wavelengths. In addition, the visualization maps of colour distribution of the examined fish fillets were acquired. The overall results of this study demonstrated that hyperspectral imaging technique in the spectral range of 400-1,000 nm has the potential to be used as an objective and promising tool for rapid and non-destructive measurement of colour distribution of grass carp fillets. [ABSTRACT FROM AUTHOR]

In this study, wavelet textural analysis was applied to hyperspectral images in the visible and near-infrared (VIS/NIR) region (400-1,000 nm) for differentiation between fresh and frozen-thawed pork. The spectral data of acquired hyperspectral images were analyzed using partial least squares (PLS) regression and five wavelengths (462, 488, 611, 629, and 678 nm) were selected as the feature wavelengths by the regression coefficients from the PLS model. The fourth-order daubechies wavelet ('db4') was used to serve as the wavelet mother function for wavelet textural extraction of the feature images at the above selected feature wavelengths with the wavelet decomposition level from 1 to 4. Four textural features were calculated in the horizontal, vertical, and diagonal orientations at each level. Forty-eight textural features were extracted from each feature image and used to differentiate between fresh and frozen-thawed pork samples by least-squares support vector machine (LS-SVM) model. Wavelet texture extracted from all five feature images at first decomposition level was identified as optimal wavelet texture combination, resulting in the highest classification accuracy for the LS-SVM models (98.48 % for the training set and 93.18 % for the testing set). Based on the texture combination, the quality attributes of pork meat could be predicted with correlation coefficients of calibration ( r) of 0.982 and 0.913, and correlation coefficients of prediction ( r) of 0.845 and 0.711 for pH and thawing loss, respectively. The results showed the possibility of developing a fast and reliable hyperspectral system for discrimination between fresh and frozen-thawed pork samples based on wavelet texture in the VIS/NIR wavelength range. [ABSTRACT FROM AUTHOR]

There is great interest in developing hyperspectral imaging (HSI) techniques for rapid and nondestructive inspection of food quality, safety, and authenticity. In recent years, image quality has been constantly improved through advances in instrumentation, particularly in more powerful detectors. Nevertheless, pretreatment of data by de-noising is a necessary step to insure clean HSI datasets for further analysis. This review first introduces the typical and commonly used de-noising methods in HSI that correct for undesirable variations and remove noisy variables. Their advantages, disadvantages, and implementation are also discussed by giving examples of recent applications in the food industry. Finally, some advice is given for selecting the de-noising methods that are best suited for a particular application. This review offers an overview of the most frequently applied methods and the latest progress made in HSI de-noising in food applications. It provides systematic insight into future trends for generating high-accuracy predictions regarding food safety and quality. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) facilitates better characterization of intrinsic and extrinsic properties of foods by integrating traditional spectral and image techniques, in which careful and sophisticated data processing plays an important role. In the past decade, much progress has been made on applying various algorithms to deal with hyperspectral images. This review first introduces the general procedure of hyperspectral data analysis and then illustrates the most typically and commonly used algorithms for denoising, feature selection, model establishment, and evaluation, as well as their applications for assessing food quality, safety, and authenticity. Finally, brief summaries for regression and classification methods are presented. This article will provide a guideline for data mining in the future development of HSI in the food field. [ABSTRACT FROM AUTHOR]

In this study, hyperspectral imaging working in near-infrared (NIR) region (900-1700nm) was applied to evaluate surface lactic acid bacteria (LAB) spoilage of farmed salmon flesh during cold storage. Hyperspectral images of salmon samples were acquired at different storage times. Spectral information within regions of interest (ROIs) of images were extracted to relate to reference LAB values measured by standard pour plate method. Least-squares support vector machine (LS-SVM) algorithm was used to calibrate the full NIR range spectral data, resulting in regression coefficients of prediction (RP) of 0.929 with root mean square error of prediction (RMSEP) of 0.515. Competitive adaptive reweighted sampling (CARS) algorithm was employed to reduce the spectral redundancy and identify the most informative wavelengths (MIWs) most related with LAB prediction across the whole wavelength range. Eight individual MIWs at 1155nm, 1255nm, 1373nm, 1376nm, 1436nm, 1641nm, 1665nm and 1689nm were finally selected from the full 239 wavelengths. Based on the selected MIWs, a new optimised model named CARS-LS-SVM was established, leading to RP of 0.925 with RMSEP of 0.531. At last, the CARS-LS-SVM model was transferred to each pixel of hyperspectral images of samples and colour maps were generated for visualising the LAB spoilage process in salmon flesh. The overall results indicated that NIR hyperspectral imaging is very potential and could be used as a rapid, non-destructive and efficient technique for LAB evaluation in salmon flesh. [ABSTRACT FROM AUTHOR]

Highlights: [•] We introduced recent advances in hyperspectral imaging (HSI) systems. [•] We reviewed applications of detecting quality attributes of red meats using HSI. [•] We discussed challenges in using HSI such as dimensionality reduction. [•] Improving hardware and exploiting new algorithms are future trends of HSI. [Copyright &y& Elsevier]

Highlights: [•] First time hyperspectral imaging was used to monitor meat salting process. [•] Spectral profiles were compared to improve model performance. [•] Moisture contents of porcine meat was predicted with good accuracy. [•] Optimised PLSR and MLR models were developed. [•] Chemical images were developed to display moisture evolution and migration. [Copyright &y& Elsevier]

Highlights: [•] First time, the texture distribution of salmon fillet was assessed non-invasively. [•] Both image texture and spectral features were extracted from hyperspectral images. [•] Spectra at 400–1000nm and 967–1634nm were compared for assess texture of salmon. [•] The selection of IOW and POW was proposed instead of traditional variable selection. [•] The distribution maps of TPA parameters were visualised in a pixel-wise manner. [ABSTRACT FROM AUTHOR]

During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems. [ABSTRACT FROM AUTHOR]

Abstract: Hyperspectral imaging technique in the spectral wavelength range of 400–1000nm was implemented in this study to determine the total volatile basic nitrogen (TVB-N) contents of grass carp fillets during the frozen storage. The quantitative calibration models were built between the spectral data extracted from the hyperspectral images and the reference measured TVB-N values by using partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The LS-SVM model using full spectral range had a better performance than the PLSR model for prediction of TVB-N value with the corresponding coefficients of determination (R2 P) of 0.916 and 0.905, and root-mean-square errors of prediction (RMSEP) of 2.346% and 2.749%, respectively. Nine optimal wavelengths (420, 466, 523, 552, 595, 615, 717, 850 and 955nm) were selected using successive projections algorithm (SPA), and R2 P values of 0.902 and 0.891 with the corresponding RMSEP of 2.782% and 2.807% were obtained from the new optimized models established based on the selected valuable wavelengths. The best SPA-LS-SVM model was used to achieve the visualization map of TVB-N content distribution of the tested fish fillet samples. The results of this study indicated that hyperspectral imaging technique as an objective and promising tool is capable of determining TVB-N values for evaluation of fish freshness quality in a rapid and non-destructive way. Industrial relevance: The study showed that VIS–NIR hyperspectral imaging technique was an effective and powerful tool for rapid and non-destructive determination and assessment of fish fillet freshness for the fish industry. [Copyright &y& Elsevier]

Abstract: 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 (400–1000nm) and the long-wave near infrared range (897–1753nm) 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 ( ) 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 &y& Elsevier]

The potential of visible and near infrared (VIS/NIR) hyperspectral imaging was investigated as a rapid and nondestructive technique to determine whether fish has been frozen-thawed. A total of 108 halibut ( Psetta maxima) fillets were studied, including 48 fresh and 60 frozen-thawed (F-T) samples. Regarding the F-T samples, two speeds of freezing (fast and slow) were tested. The hyperspectral images of fillets were captured using a pushbroom hyperspectral imaging system in the spectral region of 380 to 1,030 nm. All images were calibrated for reflectance, followed by the minimum noise fraction rotation to reduce the noise. A region-of-interest (ROI) at the image center was selected, and the average spectral data were generated from the ROI image. Dimension reduction was carried out on the ROI image by principal component analysis. The first three principal components (PCs) explained over 98 % of variances of all spectral bands. Gray-level co-occurrence matrix analysis was implemented on the three PC images to extract 36 textural feature variables in total. Least squares-support vector machine classification models were developed to differentiate between fresh and F-T fish based on (1) spectral variables; (2) textural variables; (3) combined spectral and textural variables, respectively. Satisfactory average correct classification rate of 97.22 % for the prediction samples based on (3) was achieved, which was superior to the results based on (1) or (2). The results turned worse when different freezing rates were taken into consideration to classify three groups of fish. The overall results indicate that VIS/NIR hyperspectral imaging technique is promising for the reliable differentiation between fresh and F-T fish. [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: 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]

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]

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. [ABSTRACT FROM AUTHOR]

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]