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Hyperspectral imaging (HSI) is a promising technique to evaluate food quality in a rapid and non-destructive way. Several types of HSI sensors are now commercially available. The aim of the current study was to compare one single shot and two line-scanning based HSI sensors in different spectral regions by using two wavelength dispersive devices – a prism-grating-prism monochromator and a mosaic wavelength dispersion unit. The research sample was cured pork meat, which is a popular meat product due to its flavor and long shelf life. Three main chemical compositions of cured pork meat – moisture, protein and fat content – were evaluated. The spectral properties of the three sensors were employed to establish partial least squares regression (PLSR) predictive models by applying different optimal wavelengths selection strategies. The results showed that optimal wavelength selection was more important than the selection of the wavelength dispersion device. • Three hyperspectral imaging (HSI) systems were compared. • Prediction models for compositions of dry and wet cured pork were established. • Nine wavelengths were sufficient for predicting chemical compositions. • Slight differences between the two wavelengths dispersion systems were observed. • Optimal wavelength selection was a key step in improving models of HSI. [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]

When considering food security and huge market interest, a high-efficiency method to ensure the authenticity of the food product is necessary. For this goal, spectral imaging was explored for quantitative detection of Irish organic wheat flour (OWF) adulterated with common wheat flour (WF), cassava flour (CaF) and corn flour (CoF). Hyperspectral images (900–1700 nm) of OWF samples with a series of adulteration percentages were collected. The acquired spectra were pre-processed by second derivative (2nd Der) and standard normal variate (SNV) before modelling. Then partial least squares regression (PLSR) and principal component regression (PCR) were employed for quantitative analysis of adulteration proportion of CoF, CaF and WF in OWF. To develop more effective simplified models, three groups of feature wavelengths were selected from the loading plots of principal component analysis (PCA), and first-derivative and mean centering iteration algorithm (FMCIA). The models developed using FMCIA were better than PCA. After, the corresponding feature wavelengths were further reduced based on model regression coefficients (RC). The optimal result of admixture detection was emerged by the RC-FMCIA-PLSR model, with a determination coefficient of prediction ( R 2 P ) of 0.973 and a root mean square error of prediction (RMSEP) of 0.036 for OWF adulterated with CoF, R 2 P of 0.986 and RMSEP of 0.026 for OWF adulterated with CaF, and R 2 P of 0.971 and RMSEP of 0.038 for OWF adulterated with WF. Visualization maps were generated by calculating the spectral response of each pixel on flour samples. This result indicates that spectral imaging integrated with multivariate analysis has the potential to authenticate the admixtures in specific wheat flour in the range of 3–75% (w/w). [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]

The total counts of Enterobacteriaceae and Pseudomonas spp. (EPC) appeared on edible salmon flesh were determined by using near-infrared (NIR) (900–1700 nm) hyperspectral imaging. Three-dimensional hyperspectral images ( x , y , λ ) of salmon samples were acquired at different storage time. Spectra ( λ ) extracted in reflectance ( R ) unit and two other transformed spectra units, absorbance ( A ) and Kubelka–Munck (KM), were prepared to relate to the measured EPC data by using partial least square (PLS) regression. Based on the three spectra parameters, three full wavelength PLS models defined as FR-PLS, FA-PLS and FKM-PLS were developed with all correlation coefficients of prediction ( R P ) over 0.900. To simplify these models, wavelengths holding the most important information were selected by executing competitive adaptive reweighted sampling (CARS) algorithm. Better performance was found in the resulting simplified R-PLS model (defined as FR S -PLS model) which was established with only nine important wavelengths (931, 1138, 1175, 1242, 1359, 1628, 1641, 1652 and 1655 nm) selected from R spectra. The absolute difference between root mean square errors of calibration (RMSEC) and prediction (RMSEP) in the FR S -PLS model was 0.063, less than half (44%) of that of the original FR-PLS model. By applying the FR S -PLS model to the 2-D images ( x , y ), EPC distribution maps were generated to visualize the spatial variation of EPC and the adaptability of the FR S -PLS model for EPC evaluation was further demonstrated with these distribution maps in which different colors indicated different degrees of EPC contamination. To sum up, NIR hyperspectral imaging technology shows a great potential to predict the EPC contamination in salmon flesh. In view of the results obtained from this study, a multi-spectral imaging system could be developed and further refined for online detection applications in fish industry. [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]

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

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]

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]

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]

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]

Highlights: [•] Hyperspectral imaging technique was firstly used to predict the texture of prawns. [•] Two prediction models based on PLSR and LS-SVM were established. [•] Successive projections algorithm was conducted to select the optimal wavelengths. [•] A non-linear relationship was found between the spectrum and texture property. [•] The distribution maps of textural features were created and visualized. [Copyright &y& Elsevier]

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

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

Highlights: [•] NIR imaging has potential to authenticate and classify Spanish cooked hams. [•] Multivariate analyses using PLSR and PLS-DA were applied to develop statistical models. [•] With a few numbers of wavelengths it is possible to identify ham qualities. [•] The PLS-DA model using 966, 1061, 1148, 1256, 1373 and 1628nm resulted successful. [Copyright &y& Elsevier]