REVIEW

A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies

Yinyan Shi1,2https://orcid.org/0000-0002-1577-3511, Xiaochan Wang2https://orcid.org/0000-0003-0461-8417, Md Saidul Borhan1https://orcid.org/0000-0003-0381-7414, Jennifer Young3https://orcid.org/0000-0002-3171-9187, David Newman4https://orcid.org/0000-0002-4642-6432, Eric Berg3https://orcid.org/0000-0002-6173-6279, Xin Sun1,*https://orcid.org/0000-0002-0658-7522
Author Information & Copyright
1Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
2College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
3Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
4Department of Animal Science, Arkansas State University, Jonesboro, AR 72467, USA
*Corresponding author : Xin Sun, Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA, Tel: +1-701-231-5756, E-mail: xin.sun@ndsu.edu

© Korean Society for Food Science of Animal Resources. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Mar 22, 2021 ; Revised: May 04, 2021 ; Accepted: May 05, 2021

Published Online: Jul 01, 2021

Abstract

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers’ urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

Keywords: meat quality; non-destructive detection; key technology; grading assessment; industrial application

Introduction

Meat is the main source of protein and has great physiological value for people. Meat (beef, poultry, pork, and lamb) consumption keeps increasing every year around the world (Smet and Vossen, 2016; Zhang et al., 2017). According to the Organization for Economic Co-operation and Development (OECD)’s 2017 report, the average meat consumption per person is expected to increase to 35.5 kg (78.3 lb) globally by 2024 (OECD, 2017). With meat consumption growing, quality is becoming more and more important to consumer’s purchase decision (Wei et al., 2019). And research shows that meat quality is the most important purchase parameter affecting a consumer’s decision (Barbon et al., 2017; Kamruzzaman et al., 2016a).

Meat quality assessments have two major measurement methods, subjective and objective (Li et al., 2018). Subjective methods for meat quality assessment usually depend on sensory evaluation, which involve visual and eating experiences. The disadvantage of subjective assessment methods is that they are highly dependent on particular experience of evaluators, poor repeatability, and can be difficult to quantify (Andersen et al., 2018; Cheng et al., 2017a). Objective evaluation methods have historically been laboratory tests evaluating the physical and chemical properties of and the microorganisms present in meat (Kamruzzaman et al., 2015). Which produces accurate results, but the meat product is damaged or destroyed, and the detection procedure is cumbersome. Inherently, objective evaluation method is time-consuming and high-cost, resulting in difficulty meeting the demand of automated processing for modern meat production companies (Chen et al., 2016). Countries around the world urgently need a fast, accurate, and non-destructive online detection technology for consistently evaluating meat to promote the healthy and stable development of food safety and quality (Qu et al., 2012).

Artificial intelligence (AI) technology is one of the most popular topics and is becoming an essential part of industries all over the world. Many industries in our lives have been permeated by AI technology, including auto-pilot vehicles (Yan, 2017), medical science (Salman et al., 2017), agricultural science (Gan et al., 2011), and food engineering (El Barbri et al., 2014). Relevant to this review, AI technology has become important in the application of non-destructive prediction of meat quality, providing indispensable technical support for online meat grading and evaluation (Cheng et al., 2017b; Davies, 2009). In food science, AI technology that combines sensors, processors (computers), and other components allow for non-destructive evaluation of products, which result in the original shape, state, and nature of the sample being maintained (Wang et al., 2017). This technology uses the mechanics, optics, acoustics, electricity, and other pertinent information of the measured object to evaluate the physical characteristics, chemical composition, structural characteristics, and other data (Su et al., 2017), so as to achieve non-destructive and accurate evaluation of food quality.

In recent years, with the improvement of people’s awareness of food safety and the advancement of computer technology, non-destructive evaluation technology has been applied more and more widely in the field of meat quality testing (Chen et al., 2013), including ultrasonic technology (Liu et al., 2016), machine vision technology (Sun et al., 2016), spectral technology (Al-Sarayreh et al., 2018), and sensor technology (Li et al., 2016). At present, scholars around the world have done a lot of in-depth research on the application of AI technology in meat quality testing, such as sensory quality evaluation (meat freshness, tenderness, color and texture) (Cheng et al., 2018). The prediction of physical and chemical indicators of meat quality [meat pH, shear force (SF), water retention, moisture content (MC), protein] (Pang et al., 2014; Sun et al., 2014) and the analysis of meat varieties, metamorphism mechanisms, and adulteration identification (Feng et al., 2018) have also been researched fairly extensively.

This review introduces the common AI technologies used for non-destructive evaluation of meat quality attributes in recent years including computer vision system (CVS), near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), Raman spectrometry (RS), ultrasonic imaging, and electronic nose/tongue technologies. The principle characteristics and application status of AI technologies in meat quality testing, grading, and evaluation are explained. In addition, current challenges and future development directions are also discussed in this review, so as to create a comprehensive knowledge base including essential theoretical basis and technical references for AI technologies used to improve human food quality and safety.

Non-Destructive Detection Methods Used with Meat

With the increasing concern and attention of consumers, businesses and government departments, food quality and safety have been continuously studied in depth for long-term by domestic and foreign food scientists, and the non-destructive detection technology for meat quality has achieved a lot stage achievement (Chen et al., 2013). Commonly used non-destructive detection methods for meat quality are mainly focused on CVS, NIR, HSI, RS, ultrasonic monitoring, and electronic nose/tongue detection technologies.

Computer Vision System

Computer vision technology, also known as machine vision technology that obtains target image information through image sensors instead of human eyes, and applies computer technology to analyze and process bionic human brains to convert image into digital information, and then to identify, track, and detect target objects (Girolami et al., 2013). A common machine vision detection system is shown in Fig. 1 (Ma et al., 2016), which mainly includes a computer, an industry camera, an illumination system, and an image processing software system (Taheri-Garavand et al., 2019a).

kosfa-41-4-563-g1
Fig. 1. A typical computer vision system for meat quality non-destructive evaluation applications.
Download Original Figure

Due to the rapid advancement in computer technologies, the development of image processing technology and the machine vision based non-destructive detection systems have been widely used in extracting image-based features and feature recognition related to detecting meat quality. Sun et al. (2016; 2018) developed a CVS for objective measurement of pork loin quality. Color features (L*, a*, and b*) and marble patterns in the region of interest in an image of a meat cut were extracted. Subsequently, an AI prediction model [support vector machine (SVM)] was developed for determining pork color and marbling quality grades with a highest prediction accuracy of 92.5% and 75.0%, respectively. Liu et al. (2018b) investigated the ability of CVS to predict pork intramuscular fat percentage (IMF%) coupled with the development of stepwise regression and SVM models. Arsalane et al. (2018) applied an embedded machine vision system based on digital signal processing (DSP) to evaluate beef freshness. Results showed perfect prediction (classification and identification 100%) accuracies with new unknown samples using both principal component analysis (PCA) and SVM.

In addition, some studies have been attempted for the application of CVS to monitor meat defects. Chmiel et al. (2012) evaluated the potential of CVS to detect dark, firm, and dry (DFD) beef. A significant relationship was found among L*, a*, and b* color components with pH, which is an indicator to detect DFD beef. Chmiel and Słowiński (2016) determined the effectiveness of a CVS in measuring meat color to detect meat defects of m. longissimus lumborum (LL) in industrial settings. They reported that the CVS showed a strong promise to detect PSE (pale, soft, exudative) and DFD and to classify meat into quality groups.

Table 1 lists the typical applications of machine vision technologies as non-destructive detection methods for meat quality attributes during the recent years. These literatures have revealed that the current applications of CVS in meat quality inspection have been using the external features such as color or texture-based features extracted from the images acquired in visible region of the spectrum and combed with the stoichiometric methods for qualitative or quantitative analysis. However, the CVS method was found unable to express the characteristics of the internal components of the meat samples. The CVS is mainly used to detect external properties such as meat color, marbling pattern, tenderness, freshness, and fat content in one hand. On the other hand, CVS is unable to measure the internal characters such as MC, and protein content (Brosnan and Sun, 2004; Taheri-Garavand et al., 2019a).

Table 1. Recent studies on meat quality detection using computer vision system
Performance References
Accuracy of 92.5%, 75.0% (Sun et al., 2018)
Correlation coefficient of 0.98734 (Taheri et al., 2019b)
Correlation coefficient of 0.926 (Tappi et al., 2017)
Accuracy of 81.7% (Chmiel et al., 2016)
Error of 7.8% (Mortensen et al., 2016)
Download Excel Table

Near-Infrared Spectroscopy Technique

Near-infrared spectroscopy (NIR) is an electromagnetic radiation wave with a wavelength range of 780–2,526 nm between visible and mid-infrared light and referred as the first non-visible spectral region found in the absorption spectrum (Wang et al., 2015). And the spectral curves displayed by different chemicals in the near-infrared region are different (Cai et al., 2011; Wang, 2012). Therefore, the correlation between the original spectral data of the samples in the full wavelength range and the corresponding physical and chemical index values (function relationship) can be used to analyze (identify and quantify) the chemicals and their constituents (Alexandrakis et al., 2012; ElMasry et al., 2011). As a result, it can be perceived that based on the basic principles of NIR spectroscopy and the NIR detection system as shown in Fig. 2 (Xiong et al., 2015b), the degree of putrefaction in meat storage and the physical-chemical properties and parameters (such as moisture, protein, fat, water retention, gravy loss, etc.) during processing can be detected (Collell et al., 2011).

kosfa-41-4-563-g2
Fig. 2. Components of a meat spectral detection system.
Download Original Figure

Deterioration, spoilage, and decreased freshness of meat are closely related to moisture, protein, and fat content. The NIR spectroscopy can objectively reflect these changes of organic components such as fat and protein in fresh meat (Jiang et al., 2017a). Liu et al. (2009) detected fat, protein, and water by visible and NIR (Vis-NIR) transmittance spectroscopy in chilled pork. They found that the performance of evaluation model was hopeful, and the correlation coefficients were 0.95 and 0.92 for fat, 0.71 and 0.46 for protein, and 0.94 and 0.91 for water respectively. Liao et al. (2010) used Vis/NIR spectroscopy to predict quality attributes of fresh pork (content of IMF, protein and water, pH, and SF values) on-line. Results showed that the prediction models yielded high coefficient of determination (r2) of 0.757 or more for all traits except for the prediction of SF values. Guy et al. (2011) assessed the feasibility of NIR spectroscopy for predicting lamb meat fatty acid composition and demonstrated the accuracy of the prediction models through analyzing and comparing the measured reflectance spectrum of LL muscle. Tian et al. (2013b) studied the on-line detection and classification models of multi-quality parameters for fresh beef based on Vis/NIR reflectance spectroscopy. The prediction model showed a better performance with the correlation coefficient of 0.91 for beef tenderness, 0.89 for L*, 0.93 for a*, 0.85 for cooking loss with a highest classification accuracy of 93.5% for beef tenderness.

In recent years, scholars worldwide have conducted many studies on the freshness detection of fresh meat using Vis/NIR spectroscopy technique particularly to predict total volatile basic nitrogen (TVB-N) and microbes as indicators. Wang et al. (2015) applied Vis/NIR spectroscopy to quantitatively evaluate pork TVB-N. The correlation coefficient was 0.98, which demonstrated the huge potential for Vis/NIR spectroscopy application to analyze pork freshness. Cai et al. (2009) applied NIR (1,100–2,500 nm) spectroscopy to detect the TVB-N content in pork and used synergy interval partial least squares (siPLS) algorithm for building the calibration model of TVB-N content. Guo et al. (2014) used NIR-HSI (900–1,700 nm) technology to detect the total viable count (TVC) on chilled mutton surface to indicate the degree of contamination and degradation of meat. The corresponding correlation coefficient and the root mean square error of prediction (RMSEP) were 0.99 and 0.25, respectively.

Table 2 shows the extensive application of NIR spectroscopy in the field of rapid non-destructive detection for meat quality in the past years. It can be seen that the current research on the detection of meat nutrient components based on NIR spectroscopy is relatively mature. Typically, the spectral data is a reflection of the internal chemical constituents in meat specimens, which is mostly used for meat identification, recognition and classification, while ignoring the influence of external attribute characteristics on meat quality changes (Zhu et al., 2019). The prediction accuracy of NIR technique for predicting sensory quality of meat is not high enough, which is in sharp contrast with machine vision technology (Dixit et al., 2017).

Table 2. Recent studies on meat quality detection using near-infrared spectroscopy
Category Measured attribute Analytical method Performance References
Chicken Identification and classification (moisture, lipid contents, protein contents, water holding capacity, and shear force) SVM Accuracy of 91.8% (Geronimo et al., 2019)
Pork Freshness BP-AdaBoost Correlation coefficient of 0.8325 (Huang et al., 2015)
Chicken Water-holding capacity PCA and PLSR Correlation coefficient of 0.91 (Barbin et al., 2015)
Mutton Discriminating the adulteration SVM Accuracy of 90.38%–99.07% (Zhang et al., 2015a)
Pork Moisture PLSR Correlation coefficient of 0.906 (Peng et al., 2018)
Chicken breast Protein LDA and PLSR Accuracy of 99.5%–100% (Wold et al., 2017)
Fish Microbial spoilage PLSR and LS-SVM Correlation coefficient of 0.93 (Cheng et al., 2015)
Rhubarb Identification PLS-DA, SIMCA, SVM and ANN Accuracy of 94.12% (Sun et al., 2017)
Beef Adulteration AF Correlation coefficient of 0.91 (Chen et al., 2018)
Beef, chicken and lard Authentication and classification SVM Accuracy of 98.33% (Alfar et al., 2016)
Turkey meat Identification PLS-DA Correlation coefficient >0.884 (Alamprese et al., 2016)

SVM, support vector machine; BP-AdaBoost, namely back propagation artificial neural network (BP-ANN) and adaptive boosting (AdaBoost); PCA, principal component analysis; PLSR, partial least squares regression; LDA, linear discriminant analysis; LS-SVM, least square support vector machine; PLS-DA, partial least squares-discriminant analysis; SIMCA, soft independent modeling of class analogies; LS-SVM, least square support vector machines; ANN, artificial neural network; AF, artificial fish swarm algorithm.

Download Excel Table

In addition, a single indicator can only describe one aspect of the characteristics of meat quality changes, which is another limitation of this method (Wiedemair et al., 2018). Therefore, it is necessary to find an innovative and advanced technology that can simultaneously possess the technical feature of NIR spectroscopy and CVS technology, taking into account the characteristics of internal components and external attributes of meat samples (He et al., 2019), so that make the meat quality detection become more comprehensive, accurate, stable, and sustainable.

Hyperspectral Imaging Technique

HSI technology is a derivative spectral detection technique based on hyperspectral remote sensing imaging technology. The spectral band of HSI covers all continuous bands in ultraviolet, visible, near-infrared, mid-infrared, far-infrared, and thermal infrared regions. HSI technology is an emerging and rapidly developing photoelectric detection fusion technology (Li et al., 2018; Xiong et al., 2015b) that combined the spectral detection technology with digital computer vision technology (two-dimensional imaging technology) and facilitated the integration of the spectral resolution and image resolution. Spectral information reflects the internal properties (mainly constituents) of the samples, and image information reflects the external features. When acquiring sample composition index retains its original physical and chemical properties, achieving rapid, accurate, and non-destructive detection of the samples (He and Sun, 2015; Liu et al., 2018a). HSI techniques can be divided into visible/near-infrared HSI techniques (Vis-NIR-HSI: 400–1,000 nm) and near-infrared HSI techniques (NIR-HSI: 900–1,700 nm) according to the covered wavelength range of the electromagnetic spectrum. Compared to NIR, HSI techniques integrates near-infrared spectroscopy and high-resolution imaging technology, which can acquire both spectral and image information in real time and simultaneously.

The technical principle of HSI is to use the traditional integrated hardware and software platform of two-dimensional imaging and spectroscopy to obtain both spatial and spectral information of each pixel of the object. Then, conduct qualitative and quantitative analysis on the obtained data through stoichiometry, so as to reflect the comprehensive properties and characteristics of the object to be measured (Cheng et al., 2015; Liu et al., 2017). HSI is a three-dimensional data cube in which spectral images composed of spectral data in hundreds of consecutive bands are arranged in a spectral order, called a hypercube or a spectral cube (x, y, λ), as shown in Fig. 3, where (x, y) is x, y coordinate value of the pixel in two-dimensional image, and the third dimension is the wavelength λ coordinate value, which representing the one-dimensional spectral dimension. Seeing from the one-dimensional dimension (λ), the HSI is a two-dimensional (x, y) image (Fig. 3a), and from the two-dimensional (x, y), the HSI is a strip of spectral lines (Fig. 3b) (Cheng et al., 2017a; Piqueras et al., 2012). Therefore, the two-dimensional image information of a certain wavelength point of the sample from the hyperspectral data cube can be extracted, and the absorbance value of a certain point or a certain region of the sample at each wavelength point can also be extracted, that is the spectral information at each point of the samples (Abasi et al., 2018; Elmasry et al., 2012).

kosfa-41-4-563-g3
Fig. 3. Hypercube information diagram of hyperspectral image for meat detection.
Download Original Figure

Generally, HSI technique combines the advantages of spectral analysis and image processing technology, and can rapidly and non-destructively extract the chemical composition, physical properties, and other related indicators of samples. Liu et al. (2014) investigated the utility of HSI techniques (400–1,000 nm) for predicting the color and pH of salted porcine meat. The model predicted L*, a*, and pH values with coefficients of determination of 0.72, 0.73, and 0.86, respectively, using small. Kamruzzaman et al. (2011; 2012) explored the potential of NIR-HSI in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. The partial least squares regression (PLSR) models performed well for predicting pH, color, and drip loss with the r2 of 0.65, 0.91, and 0.77, respectively. Furthermore, HSI technique is widely used in the field of food quality and safety, non-destructive testing, and has great potential for development of applications in the detection and classification of meat quality. Barbin et al. (2013) developed a push-broom NIR_HSI (900–1,700 nm) to determine the TVC and psychrotrophic plate count (PPC) in chilled pork during storage, and best regressions were obtained with r2 of 0.86 and 0.89 for TVC and PPC, respectively. Kamruzzaman et al. (2016b) investigated a hyperspectral real-time imaging system in the spectral range of 400–1,000 nm to monitor the changes of MC in red meat (beef, lamb, and pork). Xiong et al. (2015a) evaluated the potential HSI technology to predict hydroxyproline content in chicken meat. Their models yielded acceptable results with r2 0.87 in the prediction phase.

Table 3 lists the typical applications and achievements of HSI technology as non-destructive detection methods for meat quality determination in recent years. It was observed that the research based on HSI technology as the non-destructive detection methods for meat quality determination mainly includes: evaluation of safety indicators such as surface contamination and TVC; evaluation of sensory quality such as freshness, color and pH; detection of nutrient content such as meat moisture, protein and fat; as well as the real-time monitoring of processing train and classification of meat quality. Overall, HSI technology is recognized to be one of the fastest growing and most widely used techniques for non-destructive testing of meat quality and safety in recent years.

Table 3. Recent studies on meat quality detection using hyperspectral imaging (HSI) technique
Category Measured attribute Analytical method Performance References
Chicken meat Texture ACO-BPANN and PCA-BPANN Correlation coefficient of 0.754 (Khulal et al., 2016)
Prawn TVB-N (freshness) PLSR, LS-SVM, and BP-NN Correlation coefficient of 0.9547 (Dai et al., 2016)
Beef Total viable count (TVC) of bacteria (freshness) PLS and LS-SVM Accuracy of 97.14% (Yang et al., 2017a)
Pork meat Protein and TVB-N content PLSR and LS-SVM Correlation coefficient of 0.861 (Yang et al., 2017b)
Fish Freshness PCA and BP-ANN Accuracy of 94.17% (Huang et al., 2017)
Pork muscles Intramuscular fat contents SVM, SG, SNV, MSC, and PLSR Correlation coefficient of 0.9635 (Ma et al., 2018)
Frozen pork Myofibrils cold structural deformation degrees PLSR and SPA Correlation coefficient of 0.896 (Cheng et al., 2018)
Lamb, beef, and pork Adulteration SVM and CNN Accuracy of 94.4% (Al-Sarayreh et al., 2018)
Beef Adulteration PLSR and SVM Accuracy of 95.31% (Ropodi et al., 2017)
Fish (grass carp) Textural changes (Warner-Bratzler shear force, hardness, gumminess and chewiness) PLSR Correlation coefficient of 0.7982-Correlation coefficient of 0.8774 (Ma et al., 2017)
Lamb meat Adulteration SPA and SG Correlation coefficient above 0.99 (Zheng et al., 2019)
Pork Intramuscular fat content MLR Correlation coefficient of 0.96 (Huang et al., 2017)
Pork longissimus dorsi muscles Moisture content (MC) PLSR Correlation coefficient of 0.9489 (Ma et al., 2017)
Grass carp (Ctenopharyngodon idella) Moisture content PLSR Correlation coefficient of 0.9416 (Qu et al., 2017)
Lamb muscle Discrimination PCA, LMS, MLP-SCG, SVM, SMO, and LR Accuracy of 96.67% (Sanz et al., 2016)
Beef Adulteration PLSR, SVM, ELM, CARS, and GA Correlation coefficient of 0.97 (Zhao et al., 2019)

ACO, ant colony optimization; PCA, principle component analysis; BPANN, back propagation artificial neural network; PLSR, partial least squares regression; LS-SVM, least square support vector machines; BP-NN, back propagation neural network; PLS, partial least squares; SG, savitzky golay; SNV, smoothing, standard normal variate; MSC, multiplicative scatter correction; SPA, successive projections algorithm; CNN, convolution neural networks; LMS, linear least mean squares; MLP-SCG, multilayer perceptron with scaled conjugate gradient; SVM, support vector machine; SMO, sequential minimal optimization; LR, logistic regression; ELM, extreme learning machine; CARS, and competitive adaptive reweighted sampling; GA, Genetic algorithm.

Download Excel Table

Raman Spectra Technique

Raman spectroscopy is a spectral analysis technique developed based on Raman scattering effect. It is generated by the change of polarizability, caused by the vibration of sample molecules, and can provide the vibration or rotation information of molecules (Li et al., 2019). Each functional group molecule in the meat has its own unique Raman spectral signal, which is mutually complementary with the infrared spectrum in the analysis of the molecular structure (Chen et al., 2012; Liu and Jin, 2015). Therefore, representative information in the Raman spectrum of meat can be extracted with the method of chemometrics, the relationship between the molecular structure and various radical groups in meat can be qualitatively analyzed, and then meat quality can be detected and evaluated.

In recent years, Raman spectroscopy has been increasingly applied in meat quality. Fowler et al. (2014) used a handheld Raman probe to predict the SF of fresh lamb, the correlation between tenderness and Raman data was established based on PLSR method, and the SF prediction model was found to have good accuracy. Bauer et al. (2016) applied a portable 671 nm Raman monitoring system to assess beef tenderness. SF measurements were performed and the results showed that tough and tender samples could be discriminated with 70%–88% and 59%–80% accuracy, respectively. Wang et al. (2012b) developed a Raman spectroscopic method to evaluate and predict the sensory attributes (tenderness, juiciness, and chewiness) of fresh, uncooked pork loins. The SVM method were able to differentiate and classify the pork loins into quality grades (“good” and “bad” in terms of tenderness and chewiness) with a prediction accuracy of >83% in comparison to sensory panel results.

In addition, semi-quantitative analysis can be performed according to the proportional relationship between the peak intensity of Raman spectrum and the concentration of measured substance. Han et al. (2014b) investigated the effect of NaCl concentration on the functional characteristics of pork myofibrillar protein (PMP) heat-induced gelation by textural analysis and Raman spectroscopy. Results indicated obvious changes of hardness and Raman spectroscopy of the PMP gel occurred with the increasing NaCl level. Xu et al. (2011) also appraised the use of Raman spectroscopy to study structural changes, textural properties and their relationships in PMP, combined with texture profile analysis (TPA) and PCA. With scholars’ deepening research on Raman spectroscopy, the application of Raman spectroscopy in meat processing and production is also gradually increasing. Zhang et al. (2015b) applied Raman spectroscopy to investigate the effects of high-pressure (100–500 MPa) on chemical forces and water holding capacity (WHC) of heat-induced myofibrillar protein (MP) gel. Pedersen et al. (2003) revealed a high correlation between the WHC of meat and the Raman spectrum using PLSR. They found that region 1,800–1,900 cm−1 contains the best predictive information that responded to WHC of the porcine meat. Scheier et al. (2014) performed a mobile Raman system to measure and predict important meat quality traits under real-life conditions of an abattoir using pig’s semimembranosus muscles. The traits of pH values, CIE L*a*b*, drip loss, and SF after 24 and 72 h were measured as reference and correlated with the Raman spectra using PLSR. Fowler et al. (2015) conducted the complementary studies to evaluate the potential for a Raman spectroscopic device to predict the quality traits of fresh lamb m. semimembranosus after ageing and freezing/thawing.

Also, the chemical structure of functional group molecules can be detected using Raman spectroscopy, and thus identifies meat quality. Boyaci et al. (2014) applied Raman spectroscopy and chemometric method (PCA) to rapidly differentiate the origin of the meat based on their extracted fat samples. Collected Raman data were analyzed with a four-stage PCA method, and seven meat species (cattle, sheep, pig, fish, poultry, goat, and buffalo) were successfully differentiated from each other according to their origin. Zając et al. (2014) proposed a new method based on FT-Raman measurements to determine the content of horse meat in its mixture with beef. The reasonable results showed good fitting between the spectroscopic parameters and chemical content of the studied samples, and analytical equations between these parameters have been proposed.

Recently, the application of Raman spectroscopy in the field of meat detection is more and more extensive and comprehensive. Table 4 lists the research on the detection of meat quality using Raman spectroscopy in the past 5 years. It can be seen that exploring quality change law in meat processing and evaluating meat safety mechanism are still the focus and direction of Raman spectroscopy in meat science research and industrial production applications.

Table 4. Recent studies on meat quality detection using Raman spectra technique
Category Measured attribute Raman frequency range Analytical method Performance References
Grass carp surimi Changes of protein structure and amino acid residue microenvironment 2,900 cm–1 / Effective (Gao et al., 2018)
Bull beef Sensory characteristics (flavour) 1,300–2,800 cm–1 PLSR Correlation coefficient of 0.80–0.96 (Zhao et al., 2018)
Beef tallow, pork lard, chicken fat, duck oil Adulteration (unsaturated fatty acids and total fatty acids) 700–1,800 cm–1 Correlated linear Correlation coefficient of 0.96674 and 0.97148 (Lee et al., 2018)
Chicken Sodium chloride or sodium bicarbonate 1,659±0.58 cm–1 to 1,661±0.58 cm–1 one-way ANOVA / (Zhu et al., 2018)
Bovine Tenderness (shear force) 800–1,550 cm–1 PLSR Accuracy of 70%–88% (Bauer et al., 2016)
Lamb Intramuscular fat content and major fatty acid groups 500–1,800 cm–1 PLSR and linear regression Correlation coefficient of 0.93 (Fowler et al., 2015)
Bovine serum albumin Orientation of Norfloxacin 300–1,800 cm–1 / / (Lian et al., 2019)
Cooked meat Endpoint temperature 1,800–2,000 cm–1 PLS-DA and PCA Accuracy of 97.87% (Berhe et al., 2015)
Beef lions Eating quality traits (juiciness and tenderness) 671 nm PLSR / (Fowler et al., 2018)
Porcine meat pH 323–2,105 cm–1 ACO Correlation coefficient of 0.90 (Nache et al., 2016)

PLSR, partial least squares regression; ANOVA, one-way analysis of variance; PLS-DA, partial least squares-discriminant analysis; PCA, principle component analysis; ACO, ant colony optimization.

Download Excel Table

Ultrasonic Imaging Technique

Ultrasonic can be divided into two types in practical applications, namely power ultrasonic waves and detection ultrasonic waves. The ultrasonic generated by power ultrasonic is of low-frequency and high-energy, which is usually used in food processing, such as food sterilization, thawing, drying, filtration, and homogenization. The ultrasonic produced by detection ultrasonic is of high-frequency and low-energy and commonly used to analyze and detect food quality (Wang et al., 2019). Ultrasonic techniques for detecting meat quality is based on the analysis of changes in acoustic characteristic parameters for predicting meat composition, muscle thickness, fat thickness, etc. Rapid and non-destructive detection and grading evaluation for meat quality are achieved without changing the internal traits of meat (Soria and Villamiel, 2010; Zhang et al., 2018).

Benedito et al. (2001) evaluated the changes in ultrasonic velocity to detect the composition of meat mixture. Fat, moisture, and protein can be determined by measuring the ultrasonic velocity in the mixtures using a semi-empirical equation. Li (2013) used ultrasonic imaging technique to identify the fat content of pork loin by analyzing B-mode ultrasound images. The SVM classifier combined with BPANN algorithm was designed to detect and classify the fat content with a classification accuracy of 94.9%. Fukuda et al. (2013) developed an image recognition method using a neural network to accurately estimate the beef marbling standard (BMS) number of live cattle using ultrasound echo imaging, and the results confirmed that the correlation coefficient between the actual and the estimated values was 0.70 (p<0.01). Prados et al. (2015) researched the feasibility of using low-intensity ultrasound (US) technology to predict the salt content in brined Biceps femoris (BF) and Longissimus dorsi (LD) pork muscles. Results obtained significant linear relationships between the US velocity and both factors (r2>0.77). Ayuso et al. (2013) assessed the use of ultrasound measurements in live animals to predict carcass composition, ham, foreleg weights, and lean meat yields of Iberian pigs. All the results showed high correlation coefficient (r2=0.84) between measured and predicted attributes.

The research on ultrasonic detection technology applied in the field of non-destructive testing for meat quality started earlier. It was mainly used to detect the content of moisture, fat, protein, and other components of meat as well as online detection and classification for pork carcasses. There have been some ultrasonic carcass grading systems for commercial applications abroad such as the UltraFom 300 and AutoFom in Denmark and CVT-2 in the USA (Fortin et al., 2004). However, ultrasonic detection is susceptible to the irregularities of the tested meat, the uneven distribution of fat and lean meat, the measurement site, the ultrasonic frequency, etc., will cause large measurement errors (Jiang et al., 2017b). In recent years, ultrasonic technology has been mainly applied in food processing, which is reflected in the sterilization, pickling, tenderization, thawing, freezing, etc. of meat, as well as the use of ultrasonic assisted extraction of components in food, improvement of meat quality, etc. (Fu et al., 2017; Ojha et al., 2017; Pérez et al., 2018; Suo et al., 2018; Zhang et al., 2019; Zou et al., 2018).

Electronic Nose/Tongue Sensor Technique

Odor has always been an important indicator to judge the meat grade when consumers perceive the meat quality with their senses. During meat storage, with the decrease of freshness, the proteins, fats, and carbohydrates will be decomposed successively under the action of enzymes and bacteria, and thus, the smell of spoiled meat will become more and more intense (Kizil et al., 2015). Coincidentally, electronic nose is a kind of gas-sensitive sensor that is sensitive to various chemical substances and simulates the olfactory function of human nose, also known as artificial olfaction. It is an intelligent system that can sense and identify volatile gases, used to conduct odor detection and deterioration degree evaluation (Jia et al., 2018).

Many scholars at home and abroad have used the electronic nose technology to detect the change of meat odor, so as to judge the freshness of meat, and predict the shelf life of storage. Xiao and Xie (2010) and Li et al. (2016) both used electronic nose (E-nose) technology to detect changes in volatile components of chilled pork at different storage temperatures and periods, so as to assess the freshness of chilled pork. PCA method and discriminate factorial analysis (DFA) was used to analyze the E-nose signals by combining the changes of physical chemistry index such as TVC and TVB-N. Wang et al. (2012a) used an E-nose together with SVM to predict the TVC in chilled pork. The correlation between E-nose signal responses and bacterial numbers was established using the SVM combined with PLS. Jia et al. (2011) discussed the feasibility of meat adulteration recognition based on E-nose that used to analyze yak meat, beef, and pork, and the results indicated that E-nose could recognize yak meat, beef, and pork, and could recognize yak meat and beef samples at different growing locations.

At the same time, as the meat is spoiled, this condition changes the conductivity, and electronic tongue (E-tongue) which is an electronic circuit used to measure this conductivity (Wang et al., 2016). The E-tongue is an intelligent detection system composed of a taste sensor array and a pattern recognition system that can imitate the function of human taste system. In the application of meat quality detection, E-tongue sensor acquires the signal of the taste substance, and the computer uses the pattern recognition algorithm to analyze and identify the meat composition and metamorphic degree, as well as distinguish different meats (Tian et al., 2013a). Wang et al. (2012c) used the multi-frequency pulse E-tongue system to discriminate chicken meat quality. Results suggested that significantly different E-tongue sensor signals were observed for raw breast and leg samples from the same chicken breed. Similarly, Gil et al. (2011) also used E-tongues to describe the correlation found between potentiometric measurements and the variation in certain physicochemical, microbial, and biochemical parameters measured on a whole piece of pork loin stored in a refrigerator. Ultimately, they found a remarkable correlation between pH, so-called K-index, and the potentiometric data.

Generally, it was observed that the E-nose/E-tongue sensing technology mainly achieves the evaluation of meat freshness, the identification of meat varieties and quality, and the judgement of spoilage level and storage time based on the smell or taste. The test requirement of collection environment is relatively high, and the detection index is relatively simple and single, which cannot satisfy the requirement of multi-index comprehensive evaluation for meat quality (Han et al., 2014a).

Other Meat Quality Detection Techniques

With the rapid development of AI in the field of meat quality detection and in addition to the above-mentioned commonly used non-destructive testing technology, following non-destructive detection techniques for meat quality have emerged:

Nuclear magnetic resonance spectroscopy (NMR) is based on the principle of energy exchange between a magnetic nucleus and a radio frequency magnetic field to detect the structure of various organic or inorganic compounds. The technology has been widely used in medicine and achieved great success (Damez and Clerjon, 2013), afterwards, some scholars have applied it to the detection of internal ingredients in food. Shaarani et al. (2006) demonstrated the usage of a combination of bulk NMR and magnetic resonance imaging (MRI) measurements of the T2-values of water protons to determine the heat-induced changes in the structure and MC of fresh chicken meat. Graham et al. (2010) combined the data generated by NMR spectroscopy with chemometrics to determine the changes in polar metabolite concentrations in beef LD stored for different periods postmortem. Findings demonstrated the potential of this novel approach of using high resolution NMR spectrometry to be used as a suitable method for profiling meat samples. Liu et al. (2013) investigated the influence of age on the chemical composition of duck meat using the 1H NMR spectroscopy. Their results contribute to be used to help assess the quality of duck meat as a food. Xiao et al. (2019) characterized the effect of the process (washing, boiling 1 h with salt, deep frying, and boiling 2 h) on the water-soluble low molecular weight (WLOM) compound profiles of products using proton NMR spectroscopy, and the fatty acid composition of products was analyzed using gas chromatography-mass spectrometry. However, in terms of food science, NMR is mainly used for the analysis and detection of water, protein, fat, carbohydrate, and some trace elements, through analyzing the changes of chemical substances in meat to explore the change mechanism and causes of flavor, color, and tenderness (Yang et al., 2012).

Bioimpedance is a basic physical parameter of biological tissue, mainly reflects the complex dielectric properties of biological tissues, organs, cells or whole biological organisms. The measuring principle of this technique is to input tiny alternating current (or voltage) on the surface of the test object through electrodes, then obtaining the physiological or pathological information based on the changes in dielectric properties in terms of potential difference (Peng et al., 2011). Fang et al. (2008) investigated the variations and mutual relationships between bio-impedance values, pH value, and water loss rate of bovine muscles near freezing point. The results revealed that the correlation between bio-impedance and pH, and water loss rate are significant (p≥0.05). Yang et al. (2013) used bioelectrical impedance spectroscopy to measure MC in porcine meat, and forty-four pieces of porcine longissimus dorsi muscle (LDM) were evaluated with a four-terminal electrode in a portable bioimpedance spectroscopy system. Xie et al. (2016) established a method for rapidly detecting the freshness of chilled pork based on bioimpedance technology. The TVB-N content, impedance, and phase angle of 20 samples were measured and evaluated for their bioimpedance characteristics. Li et al. (2014) studied electric impedance magnitude and phase properties of unfrozen and frozen-thawed chicken breasts subjected to different thawing times to explore the impedance detection ability for frozen–thawed meat. Radial basis function (RBF) neural network was used to extract the impedance and amplitude information. It is observed that, in recent years, bioimpedance analysis has been widely used to predict the pH value, fat content, water activity, etc., as well as to determine the freshness and maturity of meat.

X-rays have the characteristics of penetrating power, diffractive action, and excitation fluorescence. This is done by capturing the difference of attenuation degree occurring after the interacting with atoms of different substances. When X-ray penetrating, the transmission images and tomographic images of samples can be obtained for further analysis of internal structure (Karoui and Blecker, 2011), so as to enable virtual segmentation of the carcass for grading. Nassy (2015) studied X-ray tomography to measure and evaluate porcine carcass composition and quality traits. The proportion of three main tissues, fat, lean, and bones were determined by X-ray computed tomography (CT), and then the carcass was well graded according to the thickness of the fat and lean. Tao and Ibarra (2000) proposed a new method to compensate for x-ray absorption variations to detect the bone fragments in poultry meat with uneven thickness. Experimental results demonstrated that the proposed imaging method eliminated the false patterns and enhanced the sensitivity of X-ray in bone fragment detection. Chen et al. (2017) analyzed the physical characteristics of Sanhuang chicken carcass based on CT image technique (X-ray scanning technique), and the experiment results showed that the relative position of the heart, lung, muscle, stomach, and kidney could be clearly determined based on the horizontal and vertical cross-sectional CT images of the carcass. Liu et al. (2015) studied the value of application in predicting the IMF content and other nutrition in sheep carcass with dual-energy X-rays, and the results proved the necessary basis for the application of dual-energy X-ray in the prediction and evaluation of meat quality. In addition, Furnols et al. (2009) used CT technique coupled with PLS regression to estimate the lean meat percentage (LMP) in pig carcass, indicated that for CT scanning data achieved a good prediction of the LMP of the whole carcass.

Applications

In recent years, with the increasing attention and continuous development of AI, additionally, the growing demand for high-quality and safe meat paired with increasing population, various non-destructive detection technologies have become more and more widely used in the field of meat quality testing (Chen et al., 2013). Throughout the existing research achievements on non-destructive detection for meat quality (Table 14), the studies on meat quality mainly focuses on the four categories of beef (Wei et al., 2019), pork (Sun et al., 2018), lamb (Zheng et al., 2019), and poultry (chicken) (Jiang et al., 2017a), including the evaluation of sensory characteristics, detection of nutrient components, identification of physical-chemical properties, discrimination of processing quality (quantitative analysis) and judgement of safety quality (qualitative analysis) (Taheri-Garavand et al., 2019a).

Sensory quality directly affects consumer’s desire to purchase, which reflects the commodity value of meat. It is generally evaluated from the aspects of meat color, marbling, freshness, tenderness, flavor, and juiciness. Among them, the meat flavor is closely related to the nutrients such as amino and fatty acids, and the juiciness is closely related to the fat and MC in meat. Sun et al. (2016) utilized a CVS to predict pork color attributes. A CVS developed for meat marbling classification resulted in accuracy values of 81.59% for bovine and 76.14% for swine. Wei et al. (2019) proposed a method for detecting beef freshness based on multi-spectral diffuse reflectance technique coupled with a LS-SVM for establishing a freshness prediction model, which yielded a correlation coefficient greater than 0.85. Bauer et al. (2016) evaluated a portable 671 nm Raman system to assess the tenderness of aged bovine gluteus medius muscles, and established a prediction model for beef tenderness by PLSR method that obtained 88% accuracy. Zhang et al. (2019) studied the effect of ultrasound technology on the tenderness of goose breast meat. Zhao et al. (2018) developed a rapid analytical technique to predict beef flavor using RS and to investigate correlations between sensory attributes of young dairy bull beef using chemometric method.

Nutritional components reflect the edible value of meat, which mainly refers to the monitoring and analysis of meat moisture, protein, fat, vitamins, and minerals. Peng et al. (2018) designed and developed an on-line detection and grading system for pork moisture based on NIR spectroscopy modeled with the PLSR technique for predicting and grading of pork moisture. Liu et al. (2009) determined the fat, protein, and water in chilled pork using Vis-NIR transmittance spectroscopy coupled with PLS model. This result showed that the Vis-NIR method could measure the fat and water contents in chilled pork well, however, not found suitable for protein. Liu et al. (2018b) investigated the ability of CVS to predict pork IMF%. The accuracy rates for regression models were 0.63 for stepwise and 0.75 for SVM. For better predicting IMF contents in pork muscles using HSI, Ma et al. (2018) employed a novel correlation-optimized warping (COW) technique with the first derivative on the full spectra and the feature wavelengths selected by successive projections algorithm.

Physical-chemical properties are the inherent characteristics of meat. Therefore, the non-destructive detection technology is primarily applied for the prediction and evaluation of the microbial, pH, TVC, and TVB-N content of meat (He and Sun, 2015). Barbin et al. (2013) exploited another push-broom NIR-HSI (900–1,700 nm) to study the undesirable microbial growths (TVC and PPC) caused by temperature fluctuation during chilled pork storage. Results were encouraging and showed the promise of hyperspectral technology for detecting bacterial spoilage in pork. Nache et al. (2016) presented a new approach to predict the pH values as quality indicator to assess porcine meat quality by combining Raman spectroscopy with the ACO metaheuristics. Yang et al. (2017a) investigated the feasibility of an HSI technique to determine the (TVC) of cooked beef during storage for evaluating the freshness state. The developed LS-SVM classification models yielded a high overall classification accuracy of 97.14%. Li et al. (2016) used E-nose to predict the TVC and TVB-N in pork and assessed the freshness of chilled pork during refrigerated storage under different packaging methods. Cheng et al. (2016) measured the biogenic amine index (BAI) in pork based on HSI data combined with stoichiometric analysis to evaluate meat freshness and quality. The PLSR technique showed an excellent prediction with a r2 of 0.96.

Processing quality is an important reference for evaluating the meat processability. The commonly used indicator for characterizing meat processing is hydraulic power, also referred as drip loss or water retention, which is used to evaluate the ability of meat muscle tissue to retain water. ElMasry et al. (2011) carried out the post-mortem non-destructive prediction of WHC in fresh beef using NIR-HSI. The modeling of spectral data of beef samples to its real WHC estimated by drip loss method resulted in a r2 of 0.89. An 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 meat sample. Barbin et al. (2015) tested the NIR reflectance as a potential technique for predicting the WHC of chicken breast (Pectoralis major). Spectra in the wavelengths between 400 and 2,500 nm were analyzed using the PCA method and quality attributes were predicted using the PLSR. Results showed that the WHC was the most challenging attribute to determine with r2 of 0.70 and SECV of 2.40%.

Safety quality is an important content of meat safety testing including the identification of meat varieties and origin, the recognition of components adulteration, and the qualification of corruption degree or shelf life. Chmiel and Słowiński (2016) determined the effectiveness of a CVS to detect meat defects of m. LL in industrial settings. It was found that it is possible to employ the CVS to detect PSE and DFD (dark, firm, dry) and to classify meat into quality groups. Geronimo et al. (2019) studied to identify and classify chicken with wooden breast (WB) using a CVS and spectral information from the NIR region by linear and nonlinear algorithms. A 91.8% of chicken breasts were correctly classified as WB or Normal (N), and NIR spectral information showed an accuracy of 97.5%. Ropodi et al. (2017) investigated the potential of multispectral imaging coupled with data analysis methods for the detection of minced beef adulteration with horsemeat, as well as to explore model performance during storage in refrigerated conditions, and the results showed that all pure and freshly-ground samples were classified correctly. Zheng et al. (2019) described a rapid and non-destructive method based on Vis-NIR-HSI system (400–1,000 nm) for detecting adulteration with duck meat in minced lamb. The results indicated that the PLSR model with selected wavelengths achieved better results than others with a r2 0.98. Xiao and Xie (2010) used E-nose technology to determine the freshness and shelf life of chilled pork. Studies had shown that the shelf life of chilled pork stored at temperatures of 283 K and 277 K was 2 d and 5 d, respectively.

Challenges and Trends

It can be seen that with the popularization and development of AI technology, through the unremitting efforts and pursuit of food scientists at home and abroad, non-destructive testing technology has achieved relatively desired research achievements in meat quality testing. However, most of the testing techniques adopt a single detection method for some specific detection index with acceptable predicting results, which cannot obtain multiple information to comprehensively evaluate the samples (Xiong et al., 2017).

Yet, meat quality is affected by many external factors, its contamination and deterioration are complex change process, which are the result of joint action between its internal components and external attributes. A single limited indicator can only describe one of the characteristics of quality changes limiting to achieve a comprehensive evaluation of meat quality as a whole, and the test results ultimately lack comprehensiveness, applicability and accuracy. Therefore, it is necessary to synthesize multiple detection methods and indicators, and utilize fusion of data information to study the comprehensive evaluation method for meat quality (Rosa et al., 2017). Geronimo et al. (2019) combined both CVS and NIR spectroscopy to identify and classify chicken freshness, respectively, and performed physical and technical characterization. Huang et al. (2014) attempted to use multi-source information fusion technology to further improve the accuracy of non-destructive testing, and effectively integrated NIRS, CVS, and E-nose techniques to evaluate pork freshness. Compared with single technique, integrating three techniques has its own superiority in improving the accuracy and stability of the freshness prediction performance significantly. Lu et al. (2011) have studied the complementary technologies of mid-infrared and Raman spectroscopy to rapidly differentiate and quantify the bacteria and microorganisms in meat with determinations taking less than an hour. Pérez-Palacios et al. (2014) combined MRI and CVS to forecast quality traits of Iberian hams by using non-destructive analysis and data mining methods.

The fusion of multi-source information will certainly bring great difficulties and challenges to data processing and analysis. Additionally, a large number of redundant images and added data information will call for higher requirements on the hardware performance of detection system. Therefore, it warrants the necessity to extract the useful information for inspection indicators as few and accurate as possible. Moreover, non-destructive testing is mostly indirect measurement that uses the stoichiometric method to establish relationship models between detection data and quality indicators through a certain number of test samples. The accuracy and reliability of the prediction models depend on effective modeling methods and original samples. Therefore, on the basis of ensuring the hardware performance (such as computing performance, camera resolution, and sharpness, etc.) of the detection system, it is urgent to optimize the statistical analysis methods to reduce unnecessary and irrelevant data information (Chen et al., 2013). Therefore, to speed up the system operation process, it is critical to establish more reasonable and improved regression algorithm models (PLSR, SVM, ANN, etc.) and machine learning for further mining data information to facilitate improvement of the prediction accuracy, efficiency, and overall performance (adaptability and robustness) of the meat quality detection system.

Furthermore, most of the current non-destructive testing methods for meat quality remain at the experimental research stage, although it has been proved that the detection system can meet certain testing speed and precision. However, working performance of non-destructive technologies and their effects have not been verified in the real-world meat processing production line. Therefore, while strengthening the research intensity on detection methods, prediction models, and system equipment, it is critically necessary to validate the performance of the testing equipment in the actual production process and thus, to promote the demonstration application of detection system for meat quality and intelligent development of meat processing industry. An appropriate automatic commercial inspection system for meat quality testing can only be realized when a feedback on the performance of non-destructive technologies in industrial settings (real-time meat processing production line) are available.

Conclusion

In the event of continuously increasing people’s demand for high-quality meat coupled with development of AI including non-destructive testing technologies have been more and more widely applied in meat quality detection. Machine vision, near-infrared spectroscopy, hyperspectral, Raman spectroscopy, electronic nose/tongue, and ultrasonic imaging technologies have shown their respective unique technical characteristics when exposed to meat. Overtime, these technologies have achieved gratifying research achievements for the detection, evaluation and grading of sensory quality, nutritional quality, physical-chemical quality, processing and safe quality on the meat (beef, pork, lamb, poultry, and aquatic).

Nonetheless, machine vision technology is useful to obtain the appearance characteristics of meat such as color, surface morphology, etc., but, it is difficult to acquire the internal quality of meat using CVS. In contrast, NIR can detect the changes in internal composition of meat, but incapable of recognizing the external information such as meat color and odor. Unlike CVS and NIR, E-nose technology is mainly used to monitor the volatile gases released from meat and cannot determine the appearance color and internal composition changes of meat. The HSI technology integrates the advantages of both CVS and NIR methods, which facilitates predicting both internal characteristic information of the samples along with detecting the external basic spatial information. However, most of the studies only make use of the single spectral information or image information in hyperspectral data for modeling purpose. The characteristics of ‘combination of spectrum and image’ of the HSI technology are not fully utilized yet in conducting quantitative analysis and qualitative discrimination on comprehensive determination of meat quality parameters.

Therefore, multiple non-destructive testing technologies are organically integrated fully to obtain the multivariate data information of integrated sample that combined with the optimized and improved chemometric methods. Additionally, the digital image processing technology paired with AI learning algorithms were used to construct quantitative prediction models and qualitative discrimination methods for meat quality. Furthermore, performing the comprehensive and entire evaluation of fresh meat from sensory characteristics, internal constituents and external factors, and applying the developed high-performance quality detection systems to actual meat processing production lines are all still the research focuses and development trends in meat quality nondestructive testing, so as to strictly ensure the quality and safety of commercial market meat.

So, this review provided a comprehensive summary of the current challenges and future research directions for meat quality detection tools based on the analysis, critical reviews, and synthesizing the findings of the recent articles on non-destructive technologies.

Conflicts of Interest

The authors declare no potential conflicts of interest.

Author Contributions

Conceptualization: Sun X. Investigation: Shi Y, Borhan MS. Writing - original draft: Shi Y, Borhan MS. Writing - review & editing: Shi Y, Wang X, Borhan MS, Young J, Newman D, Berg E, Sun X.

Ethics Approval

This article does not require IRB/IACUC approval because there are no human and animal participants.

References

1.

Abasi S, Minaei S, Jamshidi B, Fathi D. 2018; Dedicated non-destructive devices for food quality measurement: A review. Trends Food Sci Technol. 78:197-205

2.

Al-Sarayreh M, Reis MM, Yan QW, Klette R. 2018; Deep spectral-spatial features of snap shot hyperspectral images for red-meat classitication. In: 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealandpp p. 1-6

3.

Alamprese C, Amigo JM, Casiraghi E, Engelsen SB. 2016; Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics. Meat Sci. 121:175-181

4.

Alexandrakis D, Downey G, Scannell AGM. 2012; Rapid non-destructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier transform mid-infrared spectroscopy and multivariate statistics. Food Bioprocess Technol. 5:338-347

5.

Alfar IJ, Khorshidtalab A, Akmeliawati R, Ahmad S, Jaswir I. 2016; Towards authentication of beef, chicken and lard using micro near-infrared spectrometer based on support vector machine classification. ARPN J Eng Appl Sci. 11:4130-4136.

6.

Andersen PV, Wold JP, Enger EG, Veiseth-Kent E. 2018; Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy. Meat Sci. 145:94-100

7.

Arsalane A, El Barbri N, Tabyaoui A, Klilou A, Rhofir K, Halimi A. 2018; An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification. Comput Electron Agric. 152:385-392

8.

Ayuso D, González A, Hernández F, Corral JM, Izquierdo M. 2013; Prediction of carcass composition, ham and foreleg weights, and lean meat yields of Iberian pigs using ultrasound measurements in live animals. J Anim Sci. 91:1884-1892

9.

Barbin DF, ElMasry G, Sun DW, Allen P, Morsy N. 2013; Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging. Innov Food Sci Emerg Technol. 17:180-191

10.

Barbin DF, Kaminishikawahara CM, Soares AL, Mizubuti IY, Grespan M, Shimokomaki M, Hirooka EY. 2015; Prediction of chicken quality attributes by near infrared spectroscopy. Food Chem. 168:554-560

11.

Barbon APADC, Barbon S, Campos GFC, Seixas JL, Peres LM, Mastelini SM, Andreo N, Ulrici A, Bridi AM. 2017; Development of a flexible computer vision system for marbling classification. Comput Electron Agric. 142:536-544

12.

Bauer A, Scheier R, Eberle T, Schmidt H. 2016; Assessment of tenderness of aged bovine gluteus medius muscles using Raman spectroscopy. Meat Sci. 115:27-33

13.

Benedito J, Carcel JA, Rossello C, Mulet A. 2001; Composition assessment of raw meat mixtures using ultrasonics. Meat Sci. 57:365-370

14.

Berhe DT, Lawaetz AJ, Engelsen SB, Hviid MS, Lametsch R. 2015; Accurate determination of endpoint temperature of cooked meat after storage by Raman spectroscopy and chemometrics. Food Control. 52:119-125

15.

Boyaci İH, Uysal RS, Temiz T, Shendi EG, Yadegari RJ, Rishkan MM, Velioglu HM, Tamer U, Ozay DS, Vural H. 2014; A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method. Eur Food Res Technol. 238:845-852

16.

Brosnan T, Sun DW. 2004; Improving quality inspection of food products by computer vision: A review. J Food Eng. 61:3-16

17.

Cai J, Chen Q, Wan X, Zhao J. 2011; Determination of total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy. Food Chem. 126:1354-1360

18.

Cai JR, Wan XM, Chen QS. 2009; Feasibility study for the use of near-infrared spectroscopy in the quantitative analysis of TVB-N content in pork. Acta Opt Sin. 29:2808-2812

19.

Chen KJ, Liu HL, Yu HM, Zhang DC. 2017; Analysis on physical characteristics of Sanhuang chicken carcasses based on CT image technology. Trans Chin Soc Agric Mach. 48:294-300.

20.

Chen Q, Li PJ, Kong BH. 2012; Application of Raman spectroscopy technique in meat science: A review. Food Sci. 33:307-313.

21.

Chen QS, Zhang CJ, Zhao JW, Ouyang Q. 2013; Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety. Trends Anal Chem. 52:261-274

22.

Chen W, Feng YZ, Jia GF, Zhao HT. 2018; Application of artificial fish swarm algorithm for synchronous selection of wavelengths and spectral pretreatment methods in spectrometric analysis of beef adulteration. Food Analyt Methods. 11:2229-2236

23.

Chen YN, Sun DW, Cheng JH, Gao WH. 2016; Recent advances for rapid identification of chemical information of muscle foods by hyperspectral imaging analysis. Food Eng Rev. 8:336-350

24.

Cheng JH, Nicolai B, Sun DW. 2017a; Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Sci. 123:182-191

25.

Cheng L, Sun DW, Zhu Z, Zhang Z. 2017b; Emerging techniques for assisting and accelerating food freezing processes: A review of recent research progresses. Crit Rev Food Sci Nutr. 57:769-781

26.

Cheng W, Sun DW, Pu H, Wei Q. 2018; Characterization of myofibrils cold structural deformation degrees of frozen pork using hyperspectral imaging coupled with spectral angle mapping algorithm. Food Chem. 239:1001-1008

27.

Cheng WW, Cheng JH, Sun DW, Pu HB. 2015; Marbling analysis for evaluating meat quality: Methods and techniques. Compr Rev Food Sci Food Saf. 14:523-535

28.

Cheng WW, Sun DW, Cheng JH. 2016; Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. LWT-Food Sci Technol. 73:13-19

29.

Chmiel M, Słowiński M. 2016; The use of computer vision system to detect pork defects. LWT-Food Sci Technol. 73:473-480

30.

Chmiel M, Słowiński M, Dasiewicz K, Florowski T. 2012; Application of a computer vision system to classify beef as normal or dark, firm, and dry. J Anim Sci. 90:4126-4130

31.

Chmiel M, Słowiński M, Dasiewicz K, Florowski T. 2016; Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. LWT-Food Sci Technol. 65:532-536

32.

Collell C, Gou P, Arnau J, Comaposada J. 2011; Non-destructive estimation of moisture, water activity and NaCl at ham surface during resting and drying using NIR spectroscopy. Food Chem. 129:601-607

33.

Dai Q, Cheng JH, Sun DW, Zhu Z, Pu H. 2016; Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chem. 197(Part A):257-265

34.

Damez JL, Clerjon S. 2013; Quantifying and predicting meat and meat products quality attributes using electromagnetic waves: An overview. Meat Sci. 95:879-896

35.

Davies ER. 2009; The application of machine vision to food and agriculture: A review. Imaging Sci J. 57:197-217

36.

Dixit Y, Casado-Gavalda MP, Cama-Moncunill R, Cama-Moncunill X, Markiewicz-Keszycka M, Cullen PJ, Sullivan C. 2017; Developments and challenges in online NIR spectroscopy for meat processing. Compr Rev Food Sci Food Saf. 16:1172-1187

37.

El Barbri N, Halimi A, Rhofir K. 2014; A nondestructive method based on an artificial vision for beef meat quality assessement. Int J Innov Res Electr Electron Instrum Control Eng. 2:2060-2063

38.

ElMasry G, Barbin DF, Sun DW, Allen P. 2012; Meat quality evaluation by hyperspectral imaging technique: An overview. Crit Rev Food Sci Nutr. 52:689-711

39.

ElMasry G, Sun DW, Allen P. 2011; Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res Int. 44:2624-2633

40.

Fang HT, Liu HY, De L. 2008; Variations and mutual relationships of bio-impedance value, pH value and water loss rate of bovine muscles. Food Sci. 29:116-119.

41.

Feng CH, Makino Y, Oshita S, Martín JFG. 2018; Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances. Food Control. 84:165-176

42.

Fortin A, Tong AKW, Robertson WM. 2004; Evaluation of three ultrasound instruments, CVT-2, UltraFom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses. Meat Sci. 68:537-549

43.

Fowler SM, Schmidt H, van de Ven R, Hopkins DL. 2018; Preliminary investigation of the use of Raman spectroscopy to predict meat and eating quality traits of beef loins. Meat Sci. 138:53-58

44.

Fowler SM, Schmidt H, van de Ven R, Wynn P, Hopkins DL. 2014; Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy. Meat Sci. 97:597-601

45.

Fowler SM, Schmidt H, van de Ven R, Wynn P, Hopkins DL. 2015; Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device. Meat Sci. 108:138-144

46.

Fu L, Zheng BL, Gao XQ, Yang BJ, Huangpu YY. 2017; Optimization of rapid ultrasonic-assisted curing and tenderization of beef. Meat Res. 31:23-29.

47.

Fukuda O, Nabeoka N, Miyajima T. 2013; Estimation of marbling score in live cattle based on ICA and a neural network. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UKpp p. 1622-1627

48.

Furnols MF, Teran MF, Gispert M. 2009; Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression. Chemom Intell Lab Syst. 98:31-37

49.

Gan YY, Hou CS, Zhou T, Xu SF. 2011; Plant identification based on artificial intelligence. Adv Mat Res. 255-260:2286-2290

50.

Gao WH, Ye RS, Pan TT, Zeng XA. 2018; Analysis of structural changes of surimi proteins during frozen storage by Raman spectroscopy. Food Sci. 39:71-77.

51.

Geronimo BC, Mastelini SM, Carvalho RH, Júnior SB, Barbin DF, Shimokomaki M, Ida EI. 2019; Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization. Infrared Phys Technol. 96:303-310

52.

Gil L, Barat JM, Baigts D, Martínez-Máñez R, Soto J, Garcia-Breijo E, Aristoy MC, Toldrá F, Llobet E. 2011; Monitoring of physical-chemical and microbiological changes in fresh pork meat under cold storage by means of a potentiometric electronic tongue. Food Chem. 126:1261-1268

53.

Girolami A, Napolitano F, Faraone D, Braghieri A. 2013; Measurement of meat color using a computer vision system. Meat Sci. 93:111-118

54.

Graham SF, Kennedy T, Chevallier O, Gordon A, Farmer L, Elliott C, Moss B. 2010; The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem. Metabolomics. 6:395-404

55.

Guo ZH, Zheng CY, Jin L. 2014; Measurement of total viable count on chilled mutton surface based on near-infrared hyperspectral imaging technique. Sci Technol Food Ind. 35:66-68 81.

56.

Guy F, Prache ST, Bauchart D, Andueza D. 2011; Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chem. 127:1280-1286

57.

Han FK, Huang XY, Teye E, Gu FF, Gu HY. 2014a; Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis. Anal Methods. 6:529-536

58.

Han MY, Li WF, Wang P, Xu XL, Zhou GH. 2014b; Hardness of myofibrillar proteins heat-induced gelation as affected by NaCl concentration: A Raman spectroscopy study. J Nucl Agric Sci. 28:2193-2199.

59.

He HJ, Sun DW. 2015; Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products. Trends Food Sci Technol. 46:99-109

60.

He HJ, Wang W, Ma HJ, Liu SH, Chen FS, Kang ZL, Pan RS, Zhu MM, Zhao SM, Wang ZR. 2019; Recent progresses on near-infrared spectroscopy for meat adulteration. Sci Technol Food Ind. 41:345-356.

61.

Huang H, Liu L, Ngadi MO. 2017; Assessment of intramuscular fat content of pork using nir hyperspectral images of rib end. J Food Eng. 193:29-41

62.

Huang L, Zhao JW, Chen QS, Zhang YH. 2014; Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem. 145:228-236

63.

Huang QP, Chen QS, Li HH, Huang GP, Ouyang Q, Zhao JW. 2015; Non-destructively sensing pork’s freshness indicator using near infrared multispectral imaging technique. J Food Eng. 154:69-75

64.

Jia HF, Lu Y, He JH, Pan T, Xiao L, Zhang ZY, Zhu LM. 2011; Recognition of yak meat, beef and pork by electronic nose. Trans Chin Soc Agric Eng. 27:358-363.

65.

Jia WS, Liang G, Wang YL, Wang JH. 2018; Electronic noses as a powerful tool for assessing meat quality: A mini review. Food Anal Methods. 11:2916-2924

66.

Jiang HZ, Wang W, Yang Y, Chu X, Zhao X. 2017a; Research progress on analysis of chicken meat quality traits using near-infrared spectroscopy. J Food Saf Qual. 8:4298-4304.

67.

Jiang Y, Cheng TF, Wang JR, Yu LH. 2017b; Effect of ultrasonic thawing on the quality characteristics of frozen pork. Meat Res. 31:14-19.

68.

Kamruzzaman M, ElMasry G, Sun DW, Allen P. 2011; Application of NIR hyperspectral imaging for discrimination of lamb muscles. J Food Eng. 104:332-340

69.

Kamruzzaman M, ElMasry G, Sun DW, Allen P. 2012; Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta. 714:57-67

70.

Kamruzzaman M, Makino Y, Oshita S. 2015; Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta. 853:19-29

71.

Kamruzzaman M, Makino Y, Oshita S. 2016a; Online monitoring of red meat color using hyperspectral imaging. Meat Sci. 116:110-117

72.

Kamruzzaman M, Makino Y, Oshita S. 2016b; Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chem. 196:1084-1091

73.

Karoui R, Blecker C. 2011; Fluorescence spectroscopy measurement for quality assessment of food systems: A review. Food Bioprocess Technol. 4:364-386

74.

Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. 2018; Modern trends in hyperspectral image analysis: A review. IEEE Access. 6:14118-14129

75.

Khulal U, Zhao JW, Hu WW, Chen QS. 2016; Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem. 197:1191-1199

76.

Kizil Ü, Genç L, Rahman SL, Khaitsa ML, Turgut T. 2015; Design and test of a low-cost electronic nose system for identification of Salmonella enterica in poultry manure. Trans ASABE. 58:819-826

77.

Lee JY, Park JH, Mun H, Shim WB, Lim SH, Kim MG. 2018; Quantitative analysis of lard in animal fat mixture using visible Raman spectroscopy. Food Chem. 254:109-114

78.

Li DZ, Wu GF, Liu HT, Yang M, Xing K. 2018; Recent progress in evaluation of red meat quality using hyperspectral imaging technology. Meat Res. 32:59-63.

79.

Li K, Yan L, Zhao Y, Zhao D, Bai Y. 2019; Research progress on application of Raman spectroscopy on meat processing and quality control. Food Sci. 40:298-304.

80.

Li MY, Wang HB, Sun LX, Zhao GM, Huang XQ. 2016; Application of electronic nose for measuring total volatile basic nitrogen and total viable counts in packaged pork during refrigerated storage. J Food Sci. 81; :M906-M912

81.

Li T. 2013 Research on pig fat content base on ultrasound image. Ph.D. dissertationChongqing University of Technology. Chongqing, China: .

82.

Li WM, Wang P, Chen TH, Xu XL, Zhou GH. 2014; Classification of chicken breasts with different freezing-thawing cycles by impedance properties and artificial neural networks. Trans Chin Soc Agric Eng. 58:250-257.

83.

Lian WH, Liu YW, Yang HM, Ma H, Su R, Han XX, Zhao B, Niu L. 2019; Investigation of the binding sites and orientation of Norfloxacin on bovine serum albumin by surface enhanced Raman scattering and molecular docking. Spectrochim Acta A Mol Biomol Spectrosc. 207:307-312

84.

Liao YT, Fan YX, Cheng F. 2010; On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy. Meat Sci. 86:901-907

85.

Liu B, Ji XY, Fu JK, Jin T, Duan H, Zhang WG. 2015; Study on predicting intramuscular fat content of sheep carcass using dual energy X-ray. China Anim Husb Vet Med. 42:2144-2149.

86.

Liu CL, Pan DD, Ye YF, Cao JX. 2013; 1H NMR and multivariate data analysis of the relationship between the age and quality of duck meat. Food Chem. 141:1281-1286

87.

Liu D, Ma J, Sun DW, Pu HB, Gao WH, Qu JH, Zeng XA. 2014; Prediction of color and pH of salted porcine meats using visible and near-infrared hyperspectral imaging. Food Bioproc Technol. 7:3100-3108

88.

Liu H, Zheng FP, Xiong ZH, Liu Y. 2018a; Application of hyperspectral imaging technology in meat quality evaluation. Food Sci. 39:276-283.

89.

Liu JH, Sun X, Young JM, Bachmeier LA, Newman DJ. 2018b; Predicting pork loin intramuscular fat using computer vision system. Meat Sci. 143:18-23

90.

Liu KW, Cheng F, Lin HJ, Sun T, Xu K, Hu LX, Ying YB, Xu HR. 2009; Visible/NIR analysis of fat, protein and water in chilled pork. Guang Pu Xue Yu Guang Pu Fen Xi. 29:102-105.

91.

Liu PX, Li YY, Kong BH. 2016; Effect of low-frequency and high-intensity ultrasonic on the quality and processing properties of meat products: A review. Meat Res. 30:37-42.

92.

Liu YD, Jin TT. 2015; Application of Raman spectroscopy technique to agricultural products quality and safety determination. Guang Pu Xue Yu Guang Pu Fen Xi. 35:2567-2572.

93.

Liu YW, Pu HB, Sun DW. 2017; Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci Technol. 69:25-35

94.

Lu XN, Al-Qadiri HM, Lin MS, Rasco BA. 2011; Application of mid-infrared and raman spectroscopy to the study of bacteria. Food Bioproc Technol. 4:919-935

95.

Ma J, Pu HB, Sun DW. 2018; Predicting intramuscular fat content variations in boiled pork muscles by hyperspectral imaging using a novel spectral pre-processing technique. LWT-Food Sci Technol. 94:119-128

96.

Ma J, Sun DW, Pu HB. 2017; Model improvement for predicting moisture content (MC) in pork longissimus dorsi muscles under diverse processing conditions by hyperspectral imaging. J Food Eng. 196:65-72

97.

Ma J, Sun DW, Qu JH, Liu D, Pu HB, Gao WH, Zeng XA. 2016; Applications of computer vision for assessing quality of agri-food products: A review of recent research advances. Criti Rev Food Sci Nutr. 56:113-127

98.

Mortensen AK, Lisouski P, Ahrendt P. 2016; Weight prediction of broiler chickens using 3D computer vision. Comput Electron Agric. 123:319-326

99.

Nache M, Hinrichs J, Scheier R, Schmidt H, Hitzmann B. 2016; Prediction of the pH as indicator of porcine meat quality using Raman spectroscopy and metaheuristics. Chemom Intell Lab Syst. 154:45-51

100.

Nassy G. 2015; New captors for use in controlling pork quality: Evaluation of porcine carcass composition and quality traits during slaughter and processing. Meat Res. 29:21-24.

101.

OECD [Organisation for Economic Cooperation and Development]. 2017 Meat consumption. Retrieved fromhttps://doi.org/10.1787/fa290fd0-enAccessed at Jan 30, 2021

102.

Ojha KS, Harrison SM, Brunton NP, Kerry JP, Tiwari BK. 2017; Statistical approaches to access the effect of Lactobacillus sakei culture and ultrasound frequency on fatty acid profile of beef jerky. J Food Compos Anal. 57:1-7

103.

Pang B, Sun X, Liu D, Chen K. 2014; Beef marbling image segmentation based on homomorphic filtering. J Multimed. 9:189-195

104.

Pedersen DK, Morel S, Andersen HJ, Engelsen SB. 2003; Early prediction of water-holding capacity in meat by multivariate vibrational spectroscopy. Meat Sci. 65:581-592

105.

Peng JL, Li HQ, Gao RP, Zhao GH. 2011; A review on applications of electrical impedance technology in quality control of meat. Food Sci. 32:326-330.

106.

Peng YK, Yang QH, Wang WX. 2018; On-line detection and classification of pork moisture based on near-infrared spectra. Trans Chin Soc Agric Mach. 49:347-353.

107.

Pérez-Palacios T, Caballero D, Caro A, Rodríguez PG, Antequera T. 2014; Applying data mining and computer vision techniques to MRI to estimate quality traits in Iberian hams. J Food Eng. 131:82-88

108.

Pérez-Santaescolástica C, Carballo J, Fulladosa E, Garcia-Perez JV, Benedito J, Lorenzo JM. 2018; Application of temperature and ultrasound as corrective measures to decrease the adhesiveness in dry-cured ham. Influence on free amino acid and volatile compound profile. Food Res Int. 114:140-150

109.

Piqueras S, Burger J, Tauler R, Juan A. 2012; Relevant aspects of quantification and sample heterogeneity in hyperspectral image resolution. Chemom Intell Lab Syst. 117:169-182

110.

Prados M, García-Pérez JV, Benedito J. 2015; Non-destructive salt content prediction in brined pork meat using ultrasound technology. J Food Eng. 154:39-48

111.

Qu JH, Ma J, Sun DW, Wu D, Zeng XA. 2012; Researches and applications of hyperspectral imaging in food industry (I). Meat Res. 26:40-45.

112.

Qu JH, Sun DW, Cheng JH, Pu HB. 2017; Mapping moisture contents in grass carp (Ctenopharyngodon idella) slices under different freeze drying periods by Vis-NIR hyperspectral imaging. LWT-Food Sci Technol. 75:529-536

113.

Ropodi AI, Panagou EZ, Nychas GE. 2017; Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat. Food Control. 73:57-63

114.

Rosa ARD, Leone F, Cheli F, Chiofalo V. 2017; Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment: A review. J Food Eng. 210:62-75

115.

Salman M, Ahmed AW, Khan OA, Raza B, Latif K. 2017; Artificial intelligence in bio-medical domain, an overview of AI based innovations in medical. Int J Adv Comput Sci Appl. 8:319-327

116.

Sanz JA, Fernandes AM, Barrenechea E, Silva S, Santos V, Gonçalves N, Paternain D, Jurio A, Melo-Pinto P. 2016; Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms. J Food Eng. 174:92-100

117.

Scheier R, Bauer A, Schmidt H. 2014; Early postmortem prediction of meat quality traits of porcine semimembranosus muscles using a portable Raman system. Food Bioprocess Technol. 7:2732-2741

118.

Shaarani SM, Nott KP, Hall LD. 2006; Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Sci. 72:398-403

119.

Smet SD, Vossen E. 2016; Meat: The balance between nutrition and health. A review. Meat Sci. 120:145-156

120.

Soria AC, Villamiel M. 2010; Effect of ultrasound on the technological properties and bioactivity of food: A review. Trends Food Sci Technol. 21:323-331

121.

Su WH, He HJ, Sun DW. 2017; Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review. Crit Rev Food Sci Nutr. 57:1039-1051

122.

Sun WJ, Zhang X, Zhang ZY, Zhu RH. 2017; Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb. Spectrochim Acta A Mol Biomol Spectrosc. 171:72-79

123.

Sun X, Chen KJ, Berg EP, Newman DJ, Schwartz CA, Keller WL, Maddock Carlin KR. 2014; Prediction of troponin-T degradation using color image texture features in 10 d aged beef longissimus steaks. Meat Sci. 96:837-842

124.

Sun X, Young J, Liu JH, Bachmeier L, Somers RM, Chen KJ, Newman D. 2016; Prediction of pork color attributes using computer vision system. Meat Sci. 113:62-64

125.

Sun X, Young J, Liu JH, Newman D. 2018; Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Sci. 140:72-77

126.

Suo YJ, Xuan XT, Cui Y, Ding T, Ling JG. 2018; Application of ultrasonic-assisted freezing and thawing technique for aquatic and meat products. Chin J Bioproc Eng. 16:78-83.

127.

Taheri-Garavand A, Fatahi S, Omid M, Makino Y. 2019a; Meat quality evaluation based on computer vision technique: A review. Meat Sci. 156:183-195

128.

Taheri-Garavand A, Fatahi S, Shahbazi F, de la Guardia M. 2019b; A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique. J Food Process Eng. 42; :e13039

129.

Tao Y, Ibarra JG. 2000; Thickness-compensated X-ray imaging detection of bone fragments in deboned poultry-model analysis. Transactions of the ASAE. 43:453-459

130.

Tappi S, Rocculi P, Ciampa A, Romani S, Balestra F, Capozzi F, Rosa MD. 2017; Computer vision system (CVS): A powerful non-destructive technique for the assessment of red mullet (Mullus barbatus) freshness. European Food Res Technol. 243:2225-2233

131.

Tian XJ, Liu LX, Wang J. 2013a; Application of electronic tongue in meat and meat products. Sci Technol Food Ind. 34:397-400.

132.

Tian XY, Xu Y, Peng YK, Tang XY, Guo H, Lin W. 2013b; Rapid detection model of beef quality based on spectroscopy. Trans Chin Soc Agric Mach. 44:171-176.

133.

Wang DF, Wang XC, Liu T, Liu Y. 2012a; Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. Meat Sci. 90:373-377

134.

Wang JJ, Zhou CY, Wang C, Zheng YY, Ye KP, Tang CB, Zhou GH. 2019; The application of ultrasonic technology in meat processing and its influence on flavor precursors. Sci Technol Food Ind. 40:320-323 335.

135.

Wang PP. 2012 Study on the breed discrimination and quality determination of lamb by near infrared spectroscopy. Master dissertation. Chinese Academy of Agricultural Sciences. Beijing, China: .

136.

Wang Q, Lonergan SM, Yu CX. 2012b; Rapid determination of pork sensory quality using Raman spectroscopy. Meat Sci. 91:232-239

137.

Wang WX, Peng YK, Liu YY. 2015; Quantitative evaluation of pork freshness based on near-infrared spectroscopy. J Food Saf Qual. 6:3007-3013.

138.

Wang WX, Peng YK, Zheng XH, Sun HW, Tian F, Bai J. 2017; Portable nondestructive detection device for nutrient components of pork. Trans Chin Soc Agric Mach. 48:303-311.

139.

Wang X, Xu XL, Wang P. 2012c; Discrimination of chicken meat quality by electronic tongue. Food Sci. 33:100-103.

140.

Wang XY, Pang GC, Li Y. 2016; Research progress of difference between electronic tongue and real taste evaluation. Food Mach. 32:213-216 220.

141.

Wei WS, Peng YK, Zheng XC, Wang WX. 2019; Research on detection of beef freshness parameters based on multi spectral diffuse reflectance method. Guang Pu Xue Yu Guang Pu Fen Xi. 39:1177-1185.

142.

Wiedemair V, Biasio MD, Leitner R, Balthasar D, Huck C. 2018; Application of design of experiment for detection of meat fraud with a portable near-infrared spectrometer. Curr Anal Chem. 14:58-67

143.

Wold JP, Veiseth-Kent E, Høst V, Løvland A. 2017; Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy. PLOS ONE. 12:e0173384

144.

Xiao H, Xie J. 2010; Identification chilled pork freshness by electronic nose. Food Ferment Ind. 36:169-172.

145.

Xiao ZC, Luo YT, Wang GY, Ge CR, Zhou GH, Zhang WG, Liao GZ. 2019; 1H-NMR-based water-soluble low molecular weight compound characterization and fatty acid composition of boiled Wuding chicken during processing. J Sci Food Agric. 99:429-435

146.

Xie YD, Xiao WM, Kang DC, Zhou GH, Xu XL, Zhang WG. 2016; A method for detection of chilled pork freshness using bioimpedance. J Nanjing Agric Univ. 39:845-851.

147.

Xiong ZJ, Sun DW, Pu HB, Gao WH, Dai Q. 2017; Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Crit Rev Food Sci Nutr. 57:755-768

148.

Xiong ZJ, Sun DW, Xie AG, Han Z, Wang L. 2015a; Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem. 175:417-422

149.

Xiong ZJ, Xie AG, Sun DW, Zeng XA, Liu D. 2015b; Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: A review. Crit Rev Food Sci Nutr. 55:1287-1301

150.

Xu XL, Han MY, Fei Y, Zhou GH. 2011; Raman spectroscopic study of heat-induced gelation of pork myofibrillar proteins and its relationship with textural characteristic. Meat Sci. 87:159-164

151.

Yan ST. 2017; Application of electronic control based on artificial intelligence in the automobile production and commissioning. Agro Food Ind Hi Technol. 28:1098-1102.

152.

Yang D, He DD, Lu AX, Ren D, Wang JH. 2017a; Detection of the freshness state of cooked beef during storage using hyperspectral imaging. Appl Spectrosc. 71:2286-2301

153.

Yang HH, Li PJ, Kong BH, Liu Q, Li J. 2012; Application of low field nuclear magnetic resonance in meat science. Sci Technol Food Ind. 33:400-405.

154.

Yang Q, Sun DW, Cheng WW. 2017b; Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. J Food Eng. 192:53-60

155.

Yang Y, Wang ZY, Ding Q, Huang L, Wang C, Zhu DZ. 2013; Moisture content prediction of porcine meat by bioelectrical impedance spectroscopy. Math Comput Model. 58:819-825

156.

Zając A, Hanuza J, Dymińska L. 2014; Raman spectroscopy in determination of horse meat content in the mixture with other meats. Food Chem. 156:333-338

157.

Zhang K, Yang H, Bian H, Zou Y, Wang DY, Xu WM, Zhao BL. 2019; Effect of ultrasound combined with 5’-adenosine monophosphate (AMP) on the tenderness of goose breast meat. Sci Technol Food Ind. 40:1-7.

158.

Zhang LH, Hao LH, Li SF, Zong W. 2015a; Fast discriminating the adulteration of minced mutton with near infrared spectroscopy based on support vector machine. Sci Technol Food Ind. 3:289-293.

159.

Zhang X, Song L, Gao T, Zhang L, Jiang Y, Li JL, Gao F, Zhou GH. 2018; Effect of ultrasonic thawing method on quality characteristics of chicken breast meat. Food Sci. 39:135-140.

160.

Zhang ZQ, Zang MW, Zhang KH, Li D, Chen WH, Li XM. 2017; State of the art in worldwide studies on mechanism of meat quality changes. Meat Res. 31:57-63.

161.

Zhang ZY, Yang YL, Tang XZ, Chen YJ, You Y. 2015b; Chemical forces and water holding capacity study of heat-induced myofibrillar protein gel as affected by high pressure. Food Chem. 188:111-118

162.

Zhao HT, Feng YZ, Chen W, Jia GF. 2019; Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging. Meat Sci. 151:75-81

163.

Zhao M, Nian YQ, Allen P, Downey G, Kerry JP, O’Donnell CP. 2018; Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. Food Res Int. 107:27-40

164.

Zheng XC, Li YY, Wei WS, Peng YK. 2019; Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging. Meat Sci. 149:55-62

165.

Zhu DY, Kang ZL, Ma HJ, Xu XL, Zhou GH. 2018; Effect of sodium chloride or sodium bicarbonate in the chicken batters: A physico-chemical and Raman spectroscopy study. Food Hydrocoll. 83:222-228

166.

Zhu YD, He HJ, Wang H, Ma HJ, Chen FS, Liu X, Jia FF, Kang ZL, Liu H, Zhu MM, Zhao SM, Wang ZR, Liu SH. 2019; Research progresses on application of near-infrared spectroscopy technology for quality detection in fresh poultry meat. Food Sci. 40:317-323.

167.

Zou Y, Zhang K, Bian H, Zhang M, Sun C, Xu WM, Wang DY. 2018; Rapid tenderizing of goose breast muscle based on actomyosin dissociation by low-frequency ultrasonication. Process Biochem. 65:115-122

Change of publication charge


As day of June 1, 2021 (based on date of article submission), article processing charges (APC) will be applied to papers accepted after peer review as follows:
 

Author APC Remark
Member 1,000,000 KR won First and corresponding authors should pay membership fee to Korean Society for Food Science of Animal Resources.
Non-member in Korea 1,200,000 KR won
Other countries except Korea 500 US $ Affiliation of corresponding author

I don't want to open this window for a day.

Special Issue: 67th ICoMST 2021


The 67th International Congress of Meat Science and Technology (ICoMST) is currently accepting abstracts.

The deadline for submitting abstracts has been extended to April 15th, 2021, and FSAR will apply a 50% publishing fee discount to this special issue.

For more information, please check the website below.

https://www.icomst2021.com/

 


I don't want to open this window for a day.