Search

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]

Near-infrared (NIR) spectra contain abundant data, heterospectral two-dimensional correlation (H2D-CS) analysis offers a good way to interpret these data. For the first time, H2D-CS was used to correlate the NIR hyperspectral imaging (HSI) data with mid-infrared spectra and to identify feature-related wavebands for developing models for monitoring the oxidative damage of pork myofibrils during frozen storage. The HSI images were acquired at frozen state without thawing and the oxidative damage of myofibrils was assessed by carbonyl content. Results showed that the simplified PLSR model based on H2D-CS identified feature wavebands obtained determination coefficient in prediction (R 2 P ) of 0.896 and root mean square error in prediction (RMSEP) of 0.177 nmol/mg protein, which was better than the partial least square regression (PLSR) model based on full wavebands (R 2 P  = 0.856, RMSEP = 0.209 nmol/mg protein). Therefore, H2D-CS was effective in selecting feature-related wavebands of NIR HSI. [ABSTRACT FROM AUTHOR]

The study investigated the possibility of using hyperspectral imaging (HSI) in the spectral range of 1000–2200 nm to characterize myofibrils cold structural deformation degrees of frozen pork samples. The HSI images of pork samples frozen under different freezing rates were acquired in the frozen state without thawing. The myofibrils cold structural deformation degrees were evaluated by surface hydrophobicity (S 0 ANS) and Ca 2+ -ATPase activity. Spectral angle mapping (SAM) algorithm was used for the first time to extract the spectral information for regression. Compared with the optimized partial least square regression (PLSR) models based on selected wavebands by successive projections algorithm (SPA), the optimized PLSR models developed based on the spectral angles calculated by the SAM algorithm achieved comparable or even better performance with R 2 P of 0.896 for S 0 ANS and 0.879 for Ca 2+ -ATPase activity, respectively. The implications of the frozen meat spectrum were also analyzed in the current study. [ABSTRACT FROM AUTHOR]

The feasibility of hyperspectral imaging (HSI) (400–1000 nm) for tracing the chemical spoilage extent of the raw meat used for two kinds of processed meats was investigated. Calibration models established separately for salted and cooked meats using full wavebands showed good results with the determination coefficient in prediction (R 2 P ) of 0.887 and 0.832, respectively. For simplifying the calibration models, two variable selection methods were used and compared. The results showed that genetic algorithm-partial least squares (GA-PLS) with as much continuous wavebands selected as possible always had better performance. The potential of HSI to develop one multispectral system for simultaneously tracing the chemical spoilage extent of the two kinds of processed meats was also studied. Good result with an R 2 P of 0.854 was obtained using GA-PLS as the dimension reduction method, which was thus used to visualize total volatile base nitrogen (TVB-N) contents corresponding to each pixel of the image. [ABSTRACT FROM AUTHOR]

This study aimed to rapidly and nondestructively monitor 1-methylcyclopropene (1-MCP)-induced modulation of sugars accumulation in ripening ‘Hayward’ kiwifruit by hyperspectral imaging (HSI). Kiwifruit ( Actinidia deliciosa var. Hayward ) were treated with 0.5 μL L −1 1-MCP for 24 h at 23 °C and then stored for 20 d at room temperature for ripening. Hyperspectral images of 1-MCP treated and control fruit were recorded using a visible/near infrared (Vis/NIR) HSI system (400–1000 nm). The mean reflectance spectra of the inner cortex and the core were combined together to build a robust model for sugar contents in sliced samples. The best prediction accuracy for glucose, fructose, and sucrose in control fruit based on less than 10 selected features were: R 2 P of 0.934, 0.867, and 0.705, respectively. Moreover, the visualization maps showed a different sugar accumulation between treated and control fruit, with the sugar contents in the 1-MCP treated kiwifruit being significantly inhibited and the inhibitions were more effective in inner cortex than the core. The current study presented a rapid method for glucose, fructose, and sucrose detection in intact and fresh-cut kiwifruit as well as provided some insights for the effects of 1-MCP action on sugar accumulation through visualizing their distributions. [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]

A number of investigations have been conducted to explore non-contacting measuring techniques for predicting chemical components of agricultural product, but there is no research on non-destructive inspection and quantification of water binding capacity (WBC) and specific gravity (SG) in tuber. The purpose of this study was to exploit a rapid analytical technique using reflectance spectra (RS), generalised logarithm spectra (GLS), absorbance spectra (AS), and power spectra (PS) derived from spectral image data to develop partial least squares regression (PLSR) and locally weighted principal component regression (LWPCR) models that predicted tuber WBC and SG. Based upon the RS, GLS, AS, and PS, corresponding feature wavelengths were then respectively selected by using genetic algorithm (GA), first-derivative and mean centering iteration algorithm (FMCIA), and reverse variable algorithm (RVA). Compared to FMCIA and GA, the method of RVA achieved the highest accuracy based on the RVA-PS-LWPCR model for predicting WBC and SG. Then, all combinations of feature wavelengths were refined with the method of regression coefficient (RC). The simplified GA-RC-PS-LWPCR model obtained highest accuracy to measure WBC, resulting in a coefficient of determination in prediction (R2P) of 0.966 and root mean square error of prediction (RMSEP) of 0.199. Besides, the FMCIA-RC-GLS-LWPCR model showed the best performance to determine SG, with R2P of 0.978 and RMSEP of 0.009. The optimal models were then applied to each pixel of the spectral image to generate distribution maps of WBC and SG of tested samples. Furthermore, the overall performances of wavelength selection methods in terms of FMCIA-RC and RVA-RC were equivalent and slightly better than GA-RC. The results demonstrated that effective wavelength selection method can improve the performance of multispectral imaging system for detection of WBC and SG. [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]

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]

The investigation of visible and near infrared hyperspectral imaging (400–1000 nm) coupled with classifiers and spectral pre-processing techniques was conducted to discriminate fresh from cold-stored (4 °C for 7 days) and frozen-thawed (−20 °C and −40 °C for 30 days) grass carp fish fillets. Four classifiers with four spectral pretreatment methods were applied to establish the classification models. Compared with the original models established using the full wavelengths, the classification models with three classifiers of soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM) and probabilistic neural network (PNN) in tandem with the first derivative pretreatment showed the best classification performance and the highest correct classification rate (CCR) of 94.29%. In addition, in order to reduce the high dimensionality of hyperspectral images, seven optimal wavelengths were selected by successive projections algorithm (SPA) and used to simplify the classification models. The simplified model obtained by the LS-SVM classifier coupled with the first derivative pre-processing method also presented good prediction accuracy with the CCR of 91.43%. The results demonstrated that the integration of hyperspectral imaging and classifiers analysis had a great potential for on-line detection and was feasible to rapidly and non-invasively discriminate fresh and frozen-thawed fish fillets. [ABSTRACT FROM AUTHOR]

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]

A novel method of using hyperspectral imaging technique with the weighted combination of spectral data and image features by fuzzy neural network (FNN) was proposed for real-time prediction of polyphenol oxidase (PPO) activity in lychee pericarp. Lychee images were obtained by a hyperspectral reflectance imaging system operating in the range of 400–1000 nm. A support vector machine-recursive feature elimination (SVM-RFE) algorithm was applied to eliminating variables with no or little information for the prediction from all bands, resulting in a reduced set of optimal wavelengths. Spectral information at the optimal wavelengths and image color features were then used respectively to develop calibration models for the prediction of PPO in pericarp during storage, and the results of two models were compared. In order to improve the prediction accuracy, a decision strategy was developed based on weighted combination of spectral data and image features, in which the weights were determined by FNN for a better estimation of PPO activity. The results showed that the combined decision model was the best among all of the calibration models, with high R 2 values of 0.9117 and 0.9072 and low RMSEs of 0.45% and 0.459% for calibration and prediction, respectively. These results demonstrate that the proposed weighted combined decision method has great potential for improving model performance. The proposed technique could be used for a better prediction of other internal and external quality attributes of fruits. [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]

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]

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]

A quantitative approach was proposed to evaluate anthocyanin content of lychee pericarp using hyperspectral imaging (HSI) technique. A HSI system working in the range of 350–1050 nm was used to acquire a 3-D lychee image. Successive projection algorithm (SPA) and stepwise regression (SWR) algorithm were utilized to reduce data dimensionality and search for optimal wavelengths related with anthocyanin content in pericarp. Radial basis function support vector regression (RBF-SVR) was adopted to establish quantitative relationship between hyperspectral image information in two sets of optimal wavelengths and anthocyanin content of pericarp. Finally, in order to improve prediction accuracy, SPA-RBF-SVR and SWR-RBF-SVR models were fused into a single model by radial basis function neural network (RBF-NN) algorithm. The results revealed that the fused model possessed a better performance than either SPA-RBF-SVR or SWR-RBF-SVR models alone, as the fused model showed higher coefficients of determination ( R 2 ) of 0.891 and 0.872, and lower root mean square errors (RMSEs) of 0.567% and 0.610% for the training and the testing sets, respectively. Visualization maps based on the fused model were generated to display the anthocyanin distribution within lychee pericarp. This study demonstrates that HSI is capable of predicting and visualizing anthocyanin evolution in the pericarp of lychee during storage. [ABSTRACT FROM AUTHOR]

Lychee is an important tropical and subtropical fruit. However, the quality of lychee fruit changes easily after harvest and it is difficult to control the process. One of the most significant factors impacting lychee quality seriously is enzymatic browning, which is commonly affected by moisture loss of pericarp during storage. As an emerging technique, hyperspectral imaging (HSI) carries many unique advantages compared to conventional detection methods, providing an innovative tool for quality evaluation of many fruits. The current study focused on exploring the relationship between browning levels of lychee and moisture contents (MC) of pericarp, and developing calibration models for determining browning degree of lychee based on the MC prediction of pericarp using HSI technique. Two sets of optimal wavelengths were selected using regression coefficients (RC) from partial least squares regression (PLSR) and successive projections algorithm (SPA), respectively. Calibration models for determining browning levels of lychee were developed using PLSR, back-propagation neural network (BP-NN) and radial basis function support vector regression (RBF-SVR) algorithms and their performances were compared. The results demonstrated that the RBF-SVR model based on the optimal wavelengths selected by RC had the best performance with coefficients of determination R 2 of 0.946 and 0.948, and root mean square error (RMSE) of 0.80% and 0.83% for training and testing sets, respectively, showing browning levels of lychee could be determined by this approach. Finally, the visualization map of lychee with different browning levels was created and distribution of browning degree in a lychee was observed by examining color variation among pixels in the map. [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]

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]

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]

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]

Quality analysis and evaluation for fish and other seafoods is playing an important role in providing products of superior quality in consideration of human health and international trade. Currently, it is necessary to look for effective and rapid techniques to monitor quality changes and safety indices due to the vulnerability and perishability of aquatic products. Hyperspectral imaging, as an emerging and innovative technique, combines typical computer vision and spectroscopy into one system. In recent years, hyperspectral imaging has been extensively studied and implemented as an alternative to traditional analytical methods and has proved its potential for simultaneously quantitative and qualitative determination of nematode contamination, measurement and visualization of physical and chemical constituents, recognition of fresh and treated samples as well as detection of microbial spoilage and economic adulteration in a rapid, objective and non-destructive way. In this review, the basic knowledge of hyperspectral imaging and its current research and potential industrial applications on quality inspection and evaluation of fish and other seafoods were discussed. In addition, some advantages, disadvantages and future trends of hyperspectral imaging applications were also presented. [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]

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]

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: 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 (900–1700nm) 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 0.80log10 CFUg−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 0.55log10 CFUg−1, 0.87 and 0.65log10 CFUg−1 as well as 0.88 and 0.64log10 CFUg−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 &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]

Abstract: Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka–Munck (K–M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K–M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40log10 CFU per gram as well as 0.94 and 0.50log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications. [Copyright &y& Elsevier]

Abstract: 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 (910–1700nm) 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 1217nm) 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 &y& Elsevier]

Abstract: This study was carried out for rapid and non-invasive measurement of color distribution in salmon fillet using hyperspectral imaging in long-wave near infrared spectral range (LW-NIR). Successive projections algorithm (SPA) was used to select effective wavelengths. Instead of selecting different sets of effective wavelengths for each color component respectively, instrumental effective wavelengths (IEWs) were identified for the prediction of all three color components, leading to reducing the number of band-pass filters for designing the multispectral imaging system. Meanwhile, predictive effective wavelengths (PEW) were further chosen from IEW to optimize calibration models. The final prediction models were considered as the multiple linear regression (MLR) models with PEW, which resulted in correlation coefficients (r C ) of 0.876, 0.744, and 0.803 for L*, a*, and b*, respectively. Finally, the visualization of color distribution in salmon fillets was achieved. The results demonstrated that LW-NIR hyperspectral imaging is a potential technique to quantitatively measure color distribution of salmon fillet in a rapid and non-invasive way. Industrial relevance: This study was conducted to evaluate the feasibility of a hyperspectral imaging system in the LW-NIR spectral region (964 to 1631 nm) for rapid measurement of color in intact salmon fillets. On the basis of the results and accompanying illustrations presented in this work, the study demonstrated the ability of the method based on LW-NIR hyperspectral imaging to measure color distribution of salmon fillets. This method was a rapid, contact-free, and consistent evaluation, and can be used as a reliable and rapid alternative to traditional colorimeter for measuring color of salmon fillet for the food industry. [Copyright &y& Elsevier]

Sensory analysis is the evaluation of the signals received through senses of sight, sound, taste, smell, and touch. The traditional methods for the analysis of sensory properties including trained sensory panel, colourimeters and texture analysers are invasive, laborious, and small-scale procedures. Hyperspectral imaging systems (HSI) has emerged as a less time-consuming and non-destructive method to determine the sensory properties of a diverse range of food products. This review provides a comprehensive overview of recent application developments since 2010 for identifying sensory properties including colour, defects, texture, flavour, freshness, and maturity in various food products by HSI. The Vis-NIR (400–1000 nm) hyperspectral imaging is most used for the assessment of sensory properties. Moreover, the commonly applied multivariate analysis in the sensory evaluation by HSI is linear regression algorithms (PLSR and MLR), but non-linear analysis achieves better performance for the prediction of sensory features. Regression models are applied for determining the texture, colour, flavour, and freshness of food products while classification models are used to detect the defects and maturity by which the most accurate results are obtained. Furthermore, the combination of algorithms and the integration of spectral and spatial information yield more accurate results in the assessment of sensory features. Although there are still some limitations to overcome for the improvement of the HSI system, studies on the application of HSI in the real-time monitoring of sensory properties have proved a great potential for industrial applications. • Vis-NIR (400–1000 nm) hyperspectral imaging is most used in sensory evaluation. • The most used chemometric is linear regression algorithms in sensory evaluation. • The most accurate results are obtained in the detection of bruise and maturity. • Regression models are applied to predict texture, color, flavour and freshness. • Classification models are used to detect the defect and maturity. [ABSTRACT FROM AUTHOR]

This present work aims to provide new insights into the spectral variations associated with water and plasticizers in the protein matrix of casein based biopolymers by conducting time series hyperspectral chemical imaging (HCI). Near infrared (NIR) hyperspectral images (880–1720 nm) of sodium caseinate film were acquired at different periods during film forming process. Second derivative spectra revealed a red shift (from lower to higher wavelength) in the region of 1410–1492 nm during drying, indicating that hydrogen bond distribution converted from free toward associated hydroxyl groups. The introduction of plasticizers resulted in a strong hydrogen bonding matrix in the caseinate polymeric film as evidenced by analysis of second derivative spectra and curve fitting results. Moisture content (MC) of film samples were predicted with coefficient of determination in prediction ( R P r e d 2 ) of 0.96 and 0.97, and root mean square error of prediction (RMSEP) of 0.067 and 0.056 by conducting partial least squares (PLS) and support vector machine (SVM), respectively. SVM model was further used to visualize water distribution within the film samples over drying time and the time-evolution of water migration into a dried film. A novel time series spectral angle mapper (SAM) was also proposed to elucidate water penetration pattern in film material. Time series principal components analysis (PCA) and K-means analysis exhibited temporal variation of film samples during drying steps of film formation. The results clearly demonstrated the occurrence of strong hydrogen-bonded aggregates in the film samples, which was more likely due to the congregation of redundant plasticizers. The overall results confirmed that time series NIR HCI is a powerful and versatile tool to investigate water and plasticizer interactions in the protein-based polymeric film. [ABSTRACT FROM AUTHOR]

Background The quality of products depends on their processing. Effective way of monitoring and controlling these processes will ensure the quality and safety of products. Since traditional measurement methods cannot achieve on-line monitoring, imaging spectroscopy, as a fast, accurate and non-destructive detection tool, has been widely used to evaluate quality and safety attributes of foods undergoing various processes. Scope and Approach In the current review, detailed applications of hyperspectral imaging (HSI) system in various food processes are outlined, including cooking, drying, chilling, freezing and storage, and salt curing. The study emphasized the ability of HSI technique to detect internal and external quality parameters in different food processes. Also, the advantages and disadvantages of HSI applications on these food processes are discussed. Key Findings and Conclusions The literature presented in this review clearly demonstrate that HSI has the ability to inspect and monitor different food manufacturing processes and has the potential to control the quality and safety of the processed foods. Although still with some barriers, it can be expected the HSI systems will find more useful and valuable applications in the future evaluation of food processes. [ABSTRACT FROM AUTHOR]

This study was carried out to explore the potential of computer vision system (CVS) and two hyperspectral imaging (HSI) systems covering the visible and short-wave near infrared range (400–1000 nm) and the long-wave near infrared range (897–1753 nm), respectively, for differentiation of organic and conventional farm-raised salmon fillets in fresh and chill-stored conditions. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and random forests (RF) classifiers were used to build classification models for recognition and authentication of the tested samples. The results suggested that hyperspectral discrimination performed much better than CVS. For the same validation set, the highest correct classification rate (CCR) equivalent to 98.2% was presented in two SVM models, one was built from full spectral variables in the 400–1000 nm and the other from four optimal wavelengths in the same spectral region. The best prediction for CVS was obtained by using PLS-DA with CCR of 83.6% for validation, while the most satisfying results for HSI in 897–1753 nm were achieved by applying SVM algorithm on full spectral region as well as 10 important wavelengths with both CCRs of 92.7% for validation. In a nutshell, hyperspectral imaging in the 400–1000 nm has presented the best predictive ability to differentiate between organic and conventional salmon fillets, while SVM classifier has been confirmed to be very powerful for multivariable analysis in our case. Overall, the outcome achieved from this research strongly suggested the capability of the hyperspectral imaging for objective and rapid categorization of the two salmon varieties under circumstances of fresh and chill-stored conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aimed to develop an alternative technique for rapid, accurate and non-invasive determination of gross energy density values of salmon fillets based on hyperspectral imaging (900–1700 nm). Spectral data were extracted from the hyperspectral images with outlier pixels removed. Good performances were achieved with r P of 0.906 and 0.909, and normalized RMSEP of 6.768% and 7.064% for partial least squares regression (PLSR) and epsilon-support vector regression (epsilon-SVR) analysis, respectively. The optimized stepwise-PLS model built with four wavelengths (931, 1001, 1135 and 1168 nm) yielded good results with r P of 0.908 and normalized RMSEP of 6.871%. The distribution maps for visualizing energy density indices in all portions of the salmon fillet were subsequently generated. The overall results confirmed the successful development of a rapid and non-destructive technique using hyperspectral imaging for determining energy value of salmon fillets, and the provision of an alternative method for caloric density measurement for the food industry. [ABSTRACT FROM AUTHOR]

Chlorophyll a (Chl-a) is regarded as one of the important components to estimate water quality and sustainability of freshwater aquaculture operations. In the current study, a hyperspectral imaging (HSI) system was used to determine the effect of season models on the accuracy of Chl-a estimation in outdoor aquaculture ponds. A visible and near infrared hyperspectral imaging system (400–1000 nm) was used to measure surface spectral reflectance ( R ) of water collected from outdoor ponds in four different seasons. Firstly, values of surface spectral reflectance ( R ) were amplified by a baseline correction (740 nm). Two-band, three-band and four-band spectral reflectance were used to compute Chl-a concentration and a new cross band ratio algorithm with six wavelengths was proposed in the study. Results indicated that two-band model established based on reflectance ratio ( R 702 /R 666 ) had better performances for Chl-a prediction with determination coefficients ( r 2 ) of 0.908 than those by ( R 675 - 1 − R 691 - 1 )* R 743 and ( R 675 - 1 − R 691 - 1 )/( R 743 - 1 − R 691 - 1 ) models with r 2 of 0.902 and 0.896, respectively. Six optimal wavelengths (410, 682, 691, 966, 972, and 997) were identified using successive projections algorithm (SPA). The optimized regression model ( R 410 - 1 − R 966 - 1 )/( R 682 - 1 − R 972 - 1 )/( R 691 - 1 − R 997 - 1 ) showed best result with r 2 of 0.961 for Chl-a prediction. Model of cross band ratio algorithm with six wavelengths was mapped to each pixel in the image to display Chl-a component in outdoor ponds under four different seasons. The current study showed that it was feasible to use the HSI system for monitoring the influence of seasons for outdoor aquaculture water quality. [ABSTRACT FROM AUTHOR]

This study investigated the potential of using hyperspectral imaging technology (900–1700 nm) to determine the thiobarbituric acid (TBA) value and pH for evaluation of lipid oxidation in Atlantic salmon ( Salmo salar ) fillets during cold storage for 0, 3, 6, 9 and 12 days at 1 ± 1 °C. Good results were achieved for both parameters by using partial least square regression (PLSR) calibration models with full spectral region. Two simplified models were then built by using forward stepwise-multiple linear regression (MLR) variable selection method to select 18 and 10 most important wavelengths for TBA value and pH, respectively. The optimised stepwise-MLR model for TBA value yielded satisfactory results with correlation coefficient ( r C ) of 0.921 and root mean square error of calibration (RMSEC) of 1.840 μmol MDA/kg fish. This model was used to visualise the TBA value distributions during different storage days. Further improvements were achieved by applying particle analysis on the images to extract only the spectra from white stripes in salmon fillet. When in tandem with detrend pre-processing technique, the calibration model based on particle analysis demonstrated the best performance for TBA value prediction ( r C = 0.957 and RMSEC = 1.449 μmol MDA/kg). In addition, a novel chemometric strategy accomplished by the use of hyperspectrograms was also proposed in this work and satisfactory results were obtained. The overall results confirmed the capability of near-infrared hyperspectral imaging as a rapid and non-invasive technique to monitor lipid oxidation in salmon fillets. [ABSTRACT FROM AUTHOR]

There is a great interest in developing optical techniques that have the capability of predicting quality attributes, safety parameters and authenticity in real-time assessment. Recently, hyperspectral imaging technique has been widely used for rapid and non-destructive inspection of various food products. Although the technique is currently in an early development stage, its potential is promising. Due to the extensive time needed for the processing of the large volumes of data, hyperspectral imaging technique cannot be directly implemented in an online system. However, selecting some feature wavelengths from hyperspectral images can be useful to develop a multispectral imaging system, which can meet the speed requirement of industrial production. Indeed, the success of multispectral imaging heavily depends on the effectiveness of hyperspectral imaging (HSI) for providing the feature wavelengths. If the high dimensionality of hyperspectral data can be reduced properly in order to design/form a low-cost multispectral imaging sensor based on some selected feature wavelengths for certain applications, the technique would certainly be incomparable for process monitoring and real-time inspection. This review first introduces the fundamental steps for selecting feature wavelengths from hyperspectral data and then describes the feature wavelengths derived from hyperspectral imaging applications to make a more effective and efficient multispectral real-time imaging system. It is anticipated that this review can act as a basis for researchers and industry for further development of online multispectral inspection system for quality, safety and authenticity of muscles food. [ABSTRACT FROM AUTHOR]

The potential of visible and near infrared (400–1000 nm) hyperspectral imaging as a rapid and non-invasive method was investigated to differentiate freshness of prawns. In both unfrozen and frozen groups (a total of 280 prawns), two different freshness levels were used for classification, respectively. Mean spectral data from the full surface of prawns were extracted automatically as the hyperspectral cubes. Both the first and second derivative spectra were performed for waveform analysis. Successive projections algorithm (SPA) was conducted to select the individual feature wavelengths for classification. Least squares-support vector machine (LS-SVM), adaptive boosting (AdaBoost) algorithm and back-propagation neutral networks (BP-NN) were carried out for classification using the derivative spectrums based on both full wavelengths and selected feature wavelengths. The results demonstrated that SPA–LS-SVM achieved satisfactory average correct classification rate of 98.33% and 95% for prediction samples in unfrozen and frozen groups, respectively. Visualization map of classification of eight prawns (two groups) was also presented. The overall results revealed that hyperspectral imaging technique is promising for freshness classification of prawns rapidly and non-invasively. [ABSTRACT FROM AUTHOR]