Monitoring the Aging and Edible Safety of Pork in Postmortem Storage Based on HSI and Wavelet Transform.

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.

Visualized detection of quality change of cooked beef with condiments by hyperspectral imaging technique.

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.

Real-time evaluation of polyphenol oxidase (PPO) activity in lychee pericarp based on weighted combination of spectral data and image features as determined by fuzzy neural network.

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 400-1000nm. 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.

Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique.

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 (Rp(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 (Rp(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.

Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging.

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.

Quantitative determination of total pigments in red meats using hyperspectral imaging and multivariate analysis.

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.

Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat.

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 (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.

Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: a review.

Currently, the issue of food safety and quality is a great public concern. In order to satisfy the demands of consumers and obtain superior food qualities, non-destructive and fast methods are required for quality evaluation. As one of these methods, hyperspectral imaging (HSI) technique has emerged as a smart and promising analytical tool for quality evaluation purposes and has attracted much interest in non-destructive analysis of different food products. With the main advantage of combining both spectroscopy technique and imaging technique, HSI technique shows a convinced attitude to detect and evaluate chicken meat quality objectively. Moreover, developing a quality evaluation system based on HSI technology would bring economic benefits to the chicken meat industry. Therefore, in recent years, many studies have been conducted on using HSI technology for the safety and quality detection and evaluation of chicken meat. The aim of this review is thus to give a detailed overview about HSI and focus on the recently developed methods exerted in HSI technology developed for microbiological spoilage detection and quality classification of chicken meat. Moreover, the usefulness of HSI technique for detecting fecal contamination and bone fragments of chicken carcasses are presented. Finally, some viewpoints on its future research and applicability in the modern poultry industry are proposed.