Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts

Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.

Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach

Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and near-infrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat). To comprehensively analyse the data from both techniques, the F1-score-based Model Reliability Estimation (F1-score-MRE) data fusion method was introduced. The obtained results demonstrate the superiority of the F1-score-MRE method, achieving an accuracy rate of 98.58% (F1-score: 0.9855) in detecting adulterated lamb meat. This surpasses the performance of the traditional data fusion and feature concatenation methods. Furthermore, the F1-score-MRE data fusion method exhibited enhanced stability and accuracy compared with the single electronic nose and near-infrared data processed by the self-adaptive BPNN model (accuracy: 94.36%, 93.66%; F1-score: 0.9435, 0.9368). This study offers a promising solution to address concerns regarding adulterated lamb meat.

Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics

Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95 % %w/w, 90 % %w/w, 50 % %w/w, 10 % %w/w and 5 % %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028 nm and 1224 nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R²) obtained for the determination of the per cent of species in a binary mixture was above 90 % with a standard error in cross validation (SECV) ranging between 12.6 and 15 %w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm^-1 with a resolution of 4 cm^-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R² ≥ 0.95) except for the RT variable (RMSE of 0.891% and R² of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R² of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories

South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze^® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

Fraud Detection in Meat Using Hyperspectral Imaging

Fraud detection in meat is a challenging task for researchers, consumers, industries, and regulatory agencies. Traditional approaches for fraud detection are time-consuming, complicated, laborious, and expensive; they require technical skills. Therefore, much effort has been devoted in academia and industry to developing rapid and nondestructive optical techniques for fraud detection in meat. Among them, hyperspectral imaging has gained enormous attention and curiosity throughout the world. Hyperspectral imaging is an emerging analytical technique that combines spectroscopy and imaging in one system to acquire spectra and spatial information from an object simultaneously. Hyperspectral imaging is the only analytical technology that answers commonly asked analytical questions such as what chemical species are in the samples, how much, and most importantly, where they are located. Therefore, the technology will undoubtedly play indispensable roles in research and industry for fraud detection in the coming days.

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400-1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn't influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork

Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (R_p²) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with R_p² of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.

Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.

NIR associated to PLS and SVM for fast and non-destructive determination of C, N, P, and K contents in poultry litter

Using near-infrared (NIR) spectroscopy for poultry litter characterization can be a rapid, non-destructive, and low-cost alternative. This study aims to estimate the C, N, P, and K content in poultry litter samples using for first time NIR spectroscopy. For these purposes, the building models were carried out using Partial Least Squares (PLS) and Support Vector Machines (SVM) methods. A total of 160 litter samples were analyzed in poultry houses of different rearing systems, seeking the highest possible variability in their chemical composition. NIR spectroscopy, combined with PLS and SVM methods, is an alternative method for non-destructive C, N, P, and K determination in poultry samples. The regression models using SVM provide better accuracy for all elements, laying the basis for the nonlinear regression approach's application. The K determination on poultry litter using NIR was possible only by the SVM model (R² = 0.8620 and RPD = 2.7330). Conclusively, the predictive ability was improved using the SVM method.

Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique

This study aimed to investigate the difference between ginger slices (vertically cut) and splits (horizontally cut) during microwave-vacuum drying (MVD) procedures. MVD ginger slices showed a higher shrinkage rate and a higher hardness value, with a more porous structure of the surface layer. MVD ginger splits had higher rehydration rates at the first 15 min of the rehydration. Nine optimal wavelengths were selected by regression coefficients (RC) from the partial least squares regression (PLSR) model based on the raw data. A simplified PLSR model based on optimal wavelengths showed a good performance with a coefficient of determination in prediction (Rp2) of 0.973 and a root mean square error in prediction (RMSEP) of 4.63%. Texture features of grey level co-occurrence matrix (GLCM) of moisture prediction maps demonstrated a more uniform moisture distribution in MVD ginger slices than that in splits in the original geometry.

Application of hyperspectral technology in detection of agricultural products and food: A Review

Food is the foundation of human survival. With the development and progress of society, people increasingly focus on the problems of food quality and safety, which is closely related to human's health. Thus, the whole industrial chain from farmland to dining table need to be strictly controlled. Traditional detection methods are time-consuming, laborious, and destructive. In recent years, hyperspectral technology has been more and more applied to food safety and quality detection, because the technology can achieve rapid and nondestructive detection of food, and the requirement to experimental condition is low; operability is strong. In this paper, hyperspectral imaging technology was briefly introduced, and its application in agricultural products and food detection in recent years was systematically summarized, and the key points in the research process were deeply discussed. This work lays a solid foundation for the peers to the following in-depth research and application of this technology.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.

Preliminary investigation for the prediction of intramuscular fat content of lamb in-situ using a hand- held NIR spectroscopic device

Intramuscular fat (IMF) content is critical in the determination of eating quality. At present the Australian lamb industry has no ability to measure IMF as carcases are not split and processing speeds of up to 15 animals per minute prohibit the use of traditional methods. Consequently, the potential for a hand-held Near- Infrared (NIR) device to predict the IMF content of lamb topside in-situ was investigated. Models demonstrated that there is an ability to predict the IMF content of topside (R² = 0.58, RMSEP = 0.85) using NIR spectra collected at 24 h post-mortem and loin (R² = 0.50, RMSEP = 0.91). However, the models were limited by the range and distribution of the lamb population measured. Thus, further research is required to determine whether these models can be improved by increasing the range of data in the calibration models and considering alternate methods of analysis which are suitable for skewed populations.

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods

High economic returns induce the continuous occurrence of meat adulteration. In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy with multivariate methods was used for the rapid detection of adulteration in minced beef. First, the reflectance spectra of different adulterated minced beef samples were measured at 350-2500 nm. Standardization and Savitzky-Golay (SG) smoothing were applied to reduce spectral interference and noise. Then, support vector machine (SVM), random forest (RF), partial least squares regression (PLSR), and deep convolutional neural network (DCNN) were adopted for adulteration type identification and level prediction. Moreover, principal component analysis (PCA), locally linear embedding (LLE), subwindow permutation analysis (SPA), and competitive adaptive reweighted sampling (CARS) were performed to eliminate redundant information. SG smoothing performed better on interference reduction. DCNN and PCA identified adulteration type with the accuracy above 99%. In adulteration level prediction, the RF with spectra of important wavelengths selected by CARS provided optimal performance for beef adulterated with pork, and coefficient of determination of prediction (R²_P) and the root mean square error of prediction (RMSEP) were 0.973 and 2.145. The best prediction for beef adulterated with beef heart was obtained using PLSR and CARS with R²_P of 0.960 and RMSEP of 2.758. Accordingly, Vis/NIR reflectance spectroscopy coupled with multivariate methods can provide the rapid and accurate detection of adulterated minced beef.

Prediction of monounsaturated and polyunsaturated fatty acids of various processed pork meats using improved hyperspectral imaging technique

Freezing, heating, and pickling are common processes for pork meats. Unsaturated fatty acids including monounsaturated fatty acids and polyunsaturated fatty acids are indispensable nutrition beneficial to human's health and growth. However, Unsaturated fatty acids are affected by processing methods. Hyperspectral imaging is a novel technique widely used for food quality and safety evaluation. In the current study, the contents of monounsaturated and polyunsaturated fatty acids were assessed by Hyperspectral imaging. Optimal wavelengths were selected by the regression coefficients curves of partial least squares regression models. The least-squares support vector machine models established achieved a better coefficient of determination in the Monte Carlo validation set than the partial least squares regression models developed and the R²_MV values for the least squares - support vector machine models based on selected optimal wavelengths were higher than 0.81. Finally, colour maps of the contents of monounsaturated and polyunsaturated fatty acids were developed.

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with R_p² = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that R_p² was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique

A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x-y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.