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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]

This study was conducted to assess the potential feasibility of using hyperspectral imaging (900–1700 nm) for rapid determination of the volatility of tuber compositions (VTC) and prediction of the tuber cooking degree (TCD) in low temperature baking (LTB). Tuber samples of six categories from different origins were imaged and calibrated. The partial least squares regression (PLSR) and three-layer back propagation artificial neural network (TBPANN) models were established to predict VTC and TCD using the entire spectral range and the feature wavelengths. The optimal combination of characteristic wavelengths (991, 1031, 1071, 1138, 1252, 1403, 1460 and 1641 nm) were selected by first derivative and mean centering iteration algorithm (FMCIA) rather than other conventional methods. Based on the qualified eight wavelengths, the FMCIA-TBPANN approach yielded greater overall performance for predicting both VTC and TCD. Furthermore, the distribution maps of VTC and TCD were generated using a resulting function to visualize each pixel of spectral image. This demonstrated the capability of spectral imaging technique for rapid and accurate evaluation of VTC and TCD during LTB. [ABSTRACT FROM AUTHOR]

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

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

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

This study investigated the potential of hyperspectral imaging (HSI) for quantitative determination of total pigments in red meats, including beef, goose, and duck. Partial least squares regression (PLSR) was applied to correlate the spectral data with the reference values of total pigments measured by a traditional method. In order to simplify the PLSR model based on the full spectra, eleven optimal wavelengths were selected using successive projections algorithm (SPA). The new SPA-PLSR model yielded good results with the coefficient of determination ( R 2 p ) of 0.953, root mean square error (RMSEP) of 9.896, and ratio of prediction to deviation (RPD) of 4.628. Finally, distribution maps of total pigments in red meats were developed using an image processing algorithm. The overall results from this study indicated HSI had the capability for predicting total pigments in red meats. [ABSTRACT FROM AUTHOR]

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]

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

Abstract: 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]

The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research. [ABSTRACT FROM AUTHOR]

Abstract: This study was carried out for post-mortem non-destructive prediction of water holding capacity (WHC) in fresh beef using near infrared (NIR) hyperspectral imaging. Hyperspectral images were acquired for different beef samples originated from different breeds and different muscles and their spectral signatures were extracted. Both principal component analysis (PCA) and partial least squares regression (PLSR) models were developed to obtain an overview of the systematic spectral variations and to correlate spectral data of beef samples to its real WHC estimated by drip loss method. Partial least squares modeling resulted in a coefficient of determination (R CV 2) of 0.89 and standard error estimated by cross validation (SECV) of 0.26%. The PLSR loadings showed that there are some important absorption peaks throughout the whole spectral range that had the greatest influence on the predictive models. Six wavelengths (940, 997, 1144, 1214, 1342, and 1443nm) were then chosen as important wavelengths to build a new PLS prediction model. The new model led to a coefficient of determination (R CV 2) of 0.87 and standard error estimated by cross validation (SECV) of 0.28%. Image processing algorithm was then developed to transfer the predicting model to each pixel in the image for visualizing drip loss in all portions of the sample. The results showed that hyperspectral imaging has the potential to predict drip loss non-destructively in a reasonable accuracy and the results could be visualised for identification and classification of beef muscles in a simple way. In addition to realize the difference in WHC within one sample, it was possible to accentuate the difference in samples having different drip loss values. [Copyright &y& Elsevier]

Highlights: [•] Hyperspectral imaging was used to determine drip loss and pH in salmon fillets. [•] The predictive abilities of PLSR models with two spectral ranges were compared. [•] Important wavelengths were selected to reduce redundancy of hyperspectral images. [•] Multivariate models were built for predicting drip loss and pH with good accuracy. [•] We developed image processing algorithm to obtain drip loss and pH distribution maps. [ABSTRACT FROM AUTHOR]

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

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]

Abstract: In this study, the reliability and accuracy of hyperspectral imaging technique in tandem with multivariate analyses were investigated for identification and authentication of different red meat species. Hyperspectral images were acquired from longissimus dorsi muscle of pork, beef and lamb and their spectral data were extracted and analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for recognition and authentication of the tested meat. The spectra were pre-treated by second derivative and six wavelengths (957, 1071, 1121, 1144, 1368 and 1394nm) were identified as important wavelengths from the 2nd derivative spectra. The resulting wavelengths were used in a pattern recognition algorithms for classification of meat samples with PLS-DA yielding 98.67% overall classification accuracy in the validation sets. The developed classification algorithms were then successfully applied in the independent testing set for the authentication of minced meat. The results clearly showed that the combination of hyperspectral imaging, multivariate analysis and image processing has a great potential as an objective and rapid method for identification and authentication of red meat species. Industrial Relevance: This study was carried out to investigate the potential of NIR hyperspectral imaging system for identification and authentication of red meat species for the meat industry. [Copyright &y& Elsevier]