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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]

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]

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]

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]

• Pork samples were processed with 5 methods under 3 conditions. • Hyperspectral imaging (HSI) was applied to evaluate processed pork samples. • Monounsaturated and Polyunsaturated fatty acids in samples were predicted by HSI. • LS-SVM models were better than PLSR models. Freezing, heating, and pickling are common processes for pork meats. Unsaturated fatty acids including monounsaturated fatty acids and polyunsaturated fatty acids are indispensable nutrition beneficial to human's health and growth. However, Unsaturated fatty acids are affected by processing methods. Hyperspectral imaging is a novel technique widely used for food quality and safety evaluation. In the current study, the contents of monounsaturated and polyunsaturated fatty acids were assessed by Hyperspectral imaging. Optimal wavelengths were selected by the regression coefficients curves of partial least squares regression models. The least-squares support vector machine models established achieved a better coefficient of determination in the Monte Carlo validation set than the partial least squares regression models developed and the R2 MV values for the least squares - support vector machine models based on selected optimal wavelengths were higher than 0.81. Finally, colour maps of the contents of monounsaturated and polyunsaturated fatty acids were developed. [ABSTRACT FROM AUTHOR]

This study aimed to compare the moisture content (MC) uniformity of ginger slices subjected to microwave-vacuum drying (MVD) and hot-air drying (HAD) and discriminate MVD ginger slices from HAD ones (MC < 20 g/100 g) by NIR hyperspectral imaging (HSI). MC maps were visualized by a simplified partial least squares regression (PLSR) model with the determination coefficient in the prediction of 0.971. The hole effect of the semivariogram in MC maps indicated the moisture difference between the center and the edge. Furthermore, principal component analysis (PCA) of the semivariogram indicated a similar degree of moisture dissimilarity between MVD and HAD ginger slices with MC lower than 60 g/100 g. Partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) classifiers were respectively applied to the datasets of spectral reflectance at full wavelengths and nine optimum wavelengths selected by PCA, and variances in E-W and N-S directions to build classification models. The highest correct classification rate (CCR) of 95.1% was achieved by applying PLS-DA or SVM to the dataset of full wavelengths, while CCR of 90.3% was obtained for SVM model based on the dataset of nine selected wavelengths, confirming the potential of HSI spectra for MVD and HAD ginger slices discrimination. [ABSTRACT FROM AUTHOR]

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

This study aimed to investigate the impact of microwave-vacuum drying (MVD) on moisture uniformity in mushroom slices and whole mushrooms (Agaricus bisporus) by near infrared (NIR) hyperspectral imaging in combination with multivariate analysis. The partial least squares regression (PLSR) models based on standard normal variate (SNV) preprocessing spectra obtained good results for predicting moisture contents (MC) with the coefficient of determination in prediction ( R p 2 ) of 0.985 for mushroom slices and 0.963 for whole mushrooms. The MC of caps was 5–11 g/100 g higher than that of stipes for the first 12 min of drying and after 18 min of drying the MC of stipes was 6–22 g/100 g higher than that of caps. The MC of peripheral caps of whole mushrooms was 2–12 g/100 g higher than that of centers for the first 30 min of drying, but the MC of peripheral caps was 4–5 g/100 g lower than that of centers in the last 20 min of drying. In addition, the difference of MC between caps and stipes (6–22 g/100 g) in mushroom slices was more significant than that between the center and periphery (4–5 g/100 g) in whole mushrooms during the middle and later periods of drying procedure. • Rates of shrinkages, color changes and evaporation vary during drying. • Caps of mushroom slices tend to lose moisture faster than stipes. • Stipes under caps of whole mushrooms can cause uneven moisture distribution. • Moisture of sliced and whole mushrooms shows different patterns during drying. [ABSTRACT FROM AUTHOR]

Abstract: This study was conducted to explore the potential of hyperspectral imaging (HSI) technique in visible and near-infrared (VIS–NIR) region (400–1700nm) for determining the spatial distribution of moisture content (MC) in farmed Atlantic salmon fillets rapidly and non-destructively. The quantitative relationship between spectral data and the reference MC values was successfully established by partial least squares regression (PLSR). Three spectral ranges of 400–1000nm (Spectral Range I), 900–1700nm (Spectral Range II) and 400–1700nm (Spectral Range III) were considered, and their results were compared to choose the best spectral range. The established PLSR models had coefficients of determination (R P 2) of 0.893, 0.902 and 0.849, and root-mean-square errors of prediction (RMSEP) of 1.513%, 1.450% and 1.800% for three spectral ranges, respectively. Important wavelengths were then selected by using regression coefficients of PLSR models for three spectral ranges, and optimised PLSR models were built using only the important wavelengths, resulting in R P 2 of 0.893, 0.888 and 0.884 with RMSEP of 1.517%, 1.553% and 1.578% for three spectral ranges, respectively. PLSR model with eight important wavelengths (420, 445, 545, 585, 635, 870, 925 and 955nm) selected from Spectral Range I was considered as the best model for MC determination and was transferred to each pixel within the image for visualising MC in all locations of salmon fillets with an aid of a developed image processing algorithm. The results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately. In addition to realising the MC difference within salmon fillets, it could be possible for hyperspectral imaging to classify and grade salmon fillets based on different MC levels. Industrial Relevance: The results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately for the food industry. [Copyright &y& Elsevier]

Abstract: Currently, the time of grape harvest is normally determined according to the sugar level in the pulp of the berry. Nonetheless, the stage of maturation in grape seeds should be taken into account more frequently to decide the appropriate harvest period. There are chemical and sensory analyses available to assess stage of maturation of grape seeds but they are destructive and time-consuming. Hyperspectral imaging is an alternative technology to characterize the grape seeds according to their chemical attributes, and the current work aimed to non-destructively characterize grape seeds in regard of the variety and stage of maturation. For this purpose, 56 samples of seeds from two red grape varieties (Tempranillo and Syrah) and one white variety (Zalema) in two kinds of soil were selected to assess their features based on the reflectance in the near-infrared (NIR) spectra by using prediction models (partial least squares regression) and multivariate analysis methods (principal component analysis and general discriminant analysis). In this study, a reliable methodology for predicting the stage of maturation was developed, and it was shown that it was possible to distinguish the variety of grape and even the type of soil from hyperspectral images of grape seeds. [Copyright &y& Elsevier]

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

[Display omitted] • Hyperspectral imaging (HSI) was first time used to study vacuum cooling (VC) process. • Quality attributes of mushroom were dynamically evaluated during (VC) by HSI. • Partial least squares regression models realized good prediction for the attributes. • Pseudocolour maps were developed to visualize the distribution of the attributes. • The study provides a rapid and real-time nondestructive detection strategy. Visible near-infrared (400–1000 nm) hyperspectral imaging technology (HSI) was first time used to evaluate changes in various quality attributes including moisture contents (MC), lightness (L*), yellowness (b*), hardness (H) and chewiness (ch) of Agaricus bisporus as affected by vacuum cooling (VC). Partial least squares regression (PLSR) models based on Savitzky-Golay (SG) smoothing combined with the first derivative (SG-1st der) spectral preprocessing achieved good results for predicting MC with R2 P of 0.823 and RMSEP of 0.341%. Similarly, PLSR models based on full wavelengths achieved good results for predicting L*, b* and H, and ch with R2 P of 0.796, 0.848 and 0.810, and 0.903, with RMSEP of 1.253, 0.808 and 9.254, and 2.717. Finally, pseudocolour maps were developed to visualize the distribution of these attributes during VC. The current study provides a rapid, real-time and non-destructive detection strategy for evaluating the VC process. [ABSTRACT FROM AUTHOR]