The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the ...integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures
R
c
2
=
0.898
R
p
2
=
0.844
and freezing rates
R
c
2
=
0.879
R
p
2
=
0.829
. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods.
The heat treatment and seasoning of meat are indispensable before its consumption. In this work, the spectral characteristics of cooked meat and condiments were analysed by hyperspectral imaging ...(HSI) technology. The spectral reflectance of spices was significantly lower than that of meat protein, and that the spectral reflectance of protein regularly increased upon heating at 800–956 nm range. PCA pre-process and SVM models were used to predict beef moisture (
R
2
= 0.912) and tenderness (
R
2
= 0.771) based on 100 beef data. Mapping technology clearly showed the dynamic change of meat tenderness during heating, and the performance of 3D mapping was better than that of 2D mapping. Based on 750 nm/900 nm ratio image and machine-vision method, spice uniformity was accurately calculated. Thus, the quality of cooked meat and condiments distribution can be simultaneously evaluated by HSI. This technology can be used in the intelligent production of complex meat products in the future.
The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal ...postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.
Application of wavelet analysis to near-infrared (NIR) hyperspectral imaging data was exploited for categorization of lamb muscles in this study. A variety of common wavelet transforms was ...investigated to identify the best wavelet features for categorization of lamb muscles. The fifth-order Daubechies wavelet (“db5”) was found to be the best wavelet function for decomposition of lamb spectral signal. Features of wavelet coefficients extracted from db5 wavelet at the fifth decomposition level were then used as the inputs of least-squares support vector machine (LS-SVM) for developing classification models. Principal component analysis (PCA) was used for dimensionality reduction. Classification performance of LS-SVM classifiers in tandem with wavelet transform and PCA was compared with the LS-SVM models based on original, first derivative, second derivative, smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC) spectral data; then, the overall correct classification performance for the training and test sets using combination with wavelet approximation and detail coefficients in fifth decomposition scale and PCA was 100 and 96.15 %, respectively. In addition, the developed classification models were successfully applied to the hyperspectral images for obtaining classification maps and the kappa coefficient of 0.83 was obtained for the visual classification. The results revealed that the application of wavelet analysis has a great potential for categorization of lamb muscles in tandem with multivariate analysis and image processing.
•HSI was developed for predicting TBARS content in chicken meat.•Prediction models was built using the PLS algorithm and yielded good results.•14 Optimal wavelengths were identified by weighted ...regression coefficients.•Image visualization of TBARS was achieved by an image processing algorithm.
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 (Rp) 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 Rp 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.
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 1100nm 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 (Rp2) 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 (Rp2) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.
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•Spectral characteristics of frozen pork meat were studied.•Relationship of spectral reflectance with freezing temperatures was revealed.•Frozen pork quality without thawing was evaluated directly by HIS.•The improved ROI method was able to withstand interference on spectral modeling.•Model performances for different quality indicators were compared.
•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.
Red meats, such as pork, beef, and lamb meats, play an important role in people’s daily diet as they can provide good protein, vitamins, and minerals to promote human health. Either the meat processing industry or consumers usually evaluate meat quality with some common quality characteristics, which generally encompass microbiological attributes (freshness, spoilage), chemical attributes (fat, protein, moisture), sensory attributes (color, tenderness, flavor) as well as technological attributes (pH, water-holding capability). Manual inspection and chemical detection methods are tedious, time-consuming, and destructive. Consequently, fast and nondestructive methods are required for detecting these attributes in the modern meat industry. Hyperspectral imaging is one of the promising methods, which integrates the merits of imaging and spectroscopy techniques. This paper provides a comprehensive review on the recent development of hyperspectral imaging systems and their applications in detecting some important quality attributes of pork (color, drip loss, pH, marbling, tenderness, chemical compositions), beef (color, pH, tenderness, water-holding capacity, microbial spoilage), as well as lamb (color, drip loss, pH, tenderness, chemical composition). Finally, the future potential of hyperspectral imaging is also discussed.
•HSI was applied to predict hydroxyproline content in chicken meat.•PLSR was used to build a prediction model and yielded good results.•Optimal wavelengths were selected to develop multispectral ...imaging system.•An image processing algorithm was developed to generate distribution maps.
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 (400–1000nm) 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 (Rp) 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 Rp 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.
•HSI technique was applied for predicting total pigments in red meats.•PLSR was used to build a prediction model and yielded good results.•Ten optimal wavelengths were selected by weighted regression ...coefficients.•An image processing algorithm was developed to generate distribution maps.
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 (R2p) 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.
This study aimed to investigate the potential of hyperspectral imaging technique in tandem with chemometrics analysis for rapid and nondestructive determination of anthocyanin content within ...purple-fleshed sweet potato (PFSP) during drying process. Hyperspectral images of PFSP in the spectral range of 371–1023 nm were obtained during contact ultrasound-assisted hot air drying (CUHAD) process, and the reference anthocyanin contents of PFSP were measured by a traditional method. Partial least square regression (PLSR) and least-square support vector machine (LS-SVM) were applied to establish the calibration models based on raw extracted spectrum and spectrum preprocessed by four different methods. In order to simplify the calibration model, three algorithms including PLSR, LS-SVM, and multiple linear regression (MLR) were used to build models based on ten optimal wavelengths selected by regression coefficients (RC) method. The results showed that the RC-MLR yielded best results with the coefficient of determination for calibration (
R
C
2
) of 0.868 and coefficient of determination for prediction (
R
P
2
) of 0.866. Finally, distribution maps were developed based on an image processing algorithm to visualize anthocyanin content of PFSP at different drying periods which cannot be achieved by conventional methods. The overall results demonstrated that hyperspectral imaging technique is a useful tool for rapid and nondestructive determination of the anthocyanin content during drying process.
