Detection of Meat Adulteration Using Spectroscopy-Based Sensors

The Effect of the Level of Goat Liver Addition to Goat Minced Meat on the Near-Infrared Spectra, Colour, and Shelf Life of Samples

This study aimed to evaluate the utilisation of near-infrared (NIR) spectroscopy combined with chemometric techniques to identify the addition of goat liver to goat minced meat and to monitor the shelf life of the samples up to 8 days of storage. Mix samples were created by adding goat liver to goat meat in different ratios (0%, 2%, 4%, 6%, and 8% w/w), and after mincing, the samples were stored under chilled (2-4 °C) conditions for 8 days. The NIR spectra, CIELab parameters, and pH of the mixture samples were collected at the start of the study and after 2, 4, 6, and 8 days of storage. The mince became darker with the increase in days of storage, while the pH value was not affected by days of storage. Partial least squares (PLS) regression was used to develop calibration models for the CIELab parameters to predict the level of liver addition to minced meat and to predict days of storage. The standard error in cross-validation (SECV) and the coefficient of determination in cross-validation (R2cv) were 0.10 (SECV: 3.3), 0.63 (SECV: 1.5), and 0.60 (SECV: 0.90) for L*, a*, and b*, respectively. The R2CV and SECV were 0.32 (SECV: 2.4%) and 0.92 (SECV: 0.98 days) to predict the level of liver addition to minced meat and days of storage, respectively. The NIR calibration models developed to predict the CIELab parameters and level of addition of liver to minced meat were inadequate for predicting new samples. On the other hand, the PLS models developed could predict the days of storage, R2cv 0.92 (SECV: 0.98 days). Compared with traditional methods such as CIELab or pH measurements, NIR spectroscopy can yield results more rapidly. However, the variability in the data set should be increased to allow the development of more reliable models.

Recent Advances in Biosensor Technologies for Meat Production Chain

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity and specificity for the rapid and accurate point-of-care (POC) quantitative detection of selected biomolecules. In the meat production chain, their application has gained attention due to the increasing demand for enhanced food safety, quality assurance, food fraud detection, and regulatory compliance. Biosensors can detect foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria and indicators, contaminants (pesticides, dioxins, and mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters and stress hormones), and metabolites (acute-phase proteins as inflammation markers) at different modules along the meat chain, from livestock farming to packaging in the farm-to-fork (F2F) continuum. By providing real-time data from the meat chain, biosensors enable early interventions, reducing the health risks (foodborne outbreaks) associated with contaminated meat/meat products or sub-standard meat products. Recent advancements in micro- and nanotechnology, microfluidics, and wireless communication have further enhanced the sensitivity, specificity, portability, and automation of biosensors, making them suitable for on-site field applications. The integration of biosensors with blockchain and Internet of Things (IoT) systems allows for acquired data integration and management, while their integration with artificial intelligence (AI) and machine learning (ML) enables rapid data processing, analytics, and input for risk assessment by competent authorities. This promotes transparency and traceability within the meat chain, fostering consumer trust and industry accountability. Despite biosensors' promising potential, challenges such as scalability, reliability associated with the complexity of meat matrices, and regulatory approval are still the main challenges. This review provides a broad overview of the most relevant aspects of current state-of-the-art biosensors' development, challenges, and opportunities for prospective applications and their regular use in meat safety and quality monitoring, clarifying further perspectives.

Toothpick DNA extraction combined with handheld LAMP microfluidic platform for simple and rapid meat authentication

Adulteration of meat is a global issue, necessitating rapid, inexpensive, and simple on-site testing methods. Therefore, the present study aimed to develop a one-minute toothpick-based DNA extraction method, a handheld microfluidic chip, and a smartphone-controlled portable analyzer for detecting multiple meat adulterations. A toothpick was inserted into the meat to promote DNA release and adsorption. Furthermore, a handheld microfluidic chip was designed for DNA elution on toothpicks and fluid distribution. Finally, a smartphone-actuated portable analyzer was developed to function as a heater, signal detector, and result reader. The portable device comprises a microcontroller, a fluorescence detection module, a step scanning unit, and a heating module. The proposed device is portable, and the app is user-friendly. This simple design, easy operation, and fast-response system could rapidly detect as little as 1% of simulated adulterated samples (following UK standards) within 40 min at a cost of less than USD 1 per test.

Visible feature engineering to detect fraud in black and red peppers

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper.

Development of a real-time PCR protocol for the detection of chicken DNA in meat products

Food falsification is a pressing issue in today's food industry, with fraudulent substitution of costly ingredients with cheaper alternatives occurring globally. Consequently, developing straightforward and efficient diagnostic systems to detect such fraud is a top priority in scientific research. The aim of the work was to develop a test system and protocol for polymerase chain reaction (PCR) to detect in food products of animal origin the substitution of expensive meat raw materials for by-products of poultry processing. For this, real-time polymerase chain reaction (RT-PCR) was used, which allows determining the qualitative and quantitative substitution in raw and technologically prepared products. Other methods for detecting falsification - enzyme immunoassay (ELISA/ELISA) or express methods in the form of a lateral flow immunoassay are less informative. The extraction of nucleic acids for real-time polymerase chain reaction depends on the source matrix, with higher concentrations obtained from germ cells and parenchymal organs. Extraction from muscle and plant tissues is more challenging, but thorough grinding of these samples improves nucleic acid concentration by 1.5 times using DNA extraction kits. The selection of primers and fluorescent probes through GenBank and PCR Primer Design/DNASTAR software enables efficient amplification and identification of target chicken DNA fragments in various matrices.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Deep machine learning identified fish flesh using multispectral imaging

Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.

High-Oleic Sunflower Oil as a Potential Substitute for Palm Oil in Sugar Coatings-A Comparative Quality Determination Using Multispectral Imaging and an Electronic Nose

Palm oil has a bad reputation due to the exploitation of farmers and the destruction of endangered animal habitats. Therefore, many consumers wish to avoid the use of palm oil. Decorative sugar contains a small amount of palm oil to prevent the sugar from melting on hot bakery products. High-oleic sunflower oil used as a substitute for palm oil was analyzed in this study via multispectral imaging and an electronic nose, two methods suitable for potential large-batch analysis of sugar/oil coatings. Multispectral imaging is a nondestructive method for comparing the wavelength reflections of the surface of a sample. Reference samples enabled the estimation of the quality of unknown samples, which were confirmed via acid value measurements. Additionally, for quality determination, volatile compounds from decorative sugars were measured with an electronic nose. Both applications provide comparable data that provide information about the quality of decorative sugars.

Explorative study for the rapid detection of adulterated surimi using gas chromatography-ion mobility spectrometry

Driven by economic interests, surimi adulteration has become a high-frequency issue. This study aims to assess the feasibility of gas chromatography-ion mobility spectrometry (GC-IMS) in detecting surimi adulteration. In this work, three common adulterated surimi models were established by mixing with different fish species and ratios. The fingerprints enabled a clear discrimination among different tuna surimi, and other two surimi models with different mixing ratios also showed VOCs (volatile organic compounds) differences. Results of unsupervised principal component analysis (PCA) and supervised partial least-squares discrimination analysis (PLS-DA) revealed that different types of adulterated surimi models can be well separated from each other. A total of 12, 16, and 9 VOCs were selected as the potential markers in three simulated models by PLS-DA method, respectively. Therefore, GC-IMS coupled with certain chemometrics is expected to serve as an alternative analytical tool to directly and visually detect adulterated surimi.

Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration

The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for swiftly identifying counterfeit beef altered to appear fresh. The experiment involved 60 beef samples, half of which were artificially adulterated using a colouring solution. Despite meticulous analysis of the beef's colour attributes, no significant differences were observed between the fresh and adulterated samples. However, our method, utilising a 344-1040 nm spectral range, achieved a classification accuracy of 98.84%. To enhance practicality, we employed gradient-weighted class activation mapping and identified the 580-600 nm range as particularly influential for classification. Remarkably, even when we narrowed the input to the model to this spectral range, a high level of classification accuracy was maintained. To further validate the model's robustness and generalisability, we allocated 70 beef samples to an external validation set. Comparative performance analysis revealed that our model outperformed traditional machine learning algorithms, such as SVM and logistic regression, by 9.3% and 28.4%, respectively. Overall, this study offers invaluable insights for detecting counterfeited beef, thereby contributing to the preservation of meat product quality and integrity within the food industry.

Design and development of a rapid meat detection system based on RPA-CRISPR/Cas12a-LFD

In recent years, meat adulteration safety incidents have occurred frequently, triggering widespread attention and discussion. Although there are a variety of meat quality identification methods, conventional assays require high standards for personnel and experimental conditions and are not suitable for on-site testing. Therefore, there is an urgent need for a rapid, sensitive, high specificity and high sensitivity on-site meat detection method. This study is the first to apply RPA combined with CRISPR/Cas12a technology to the field of multiple meat identification. The system developed by parameter optimization can achieve specific detection of chicken, duck, beef, pork and lamb with a minimum target sequence copy number as low as 1 × 100 copies/μL for 60 min at a constant temperature. LFD test results can be directly observed with the naked eye, with the characteristics of fast, portable and simple operation, which is extremely in line with current needs. In conclusion, the meat identification RPA-CRISPR/Cas12a-LFD system established in this study has shown promising applications in the field of meat detection, with a profound impact on meat quality, and provides a model for other food safety control programs.

Adulteration detection in minced beef using low-cost color imaging system coupled with deep neural network

Major processed meat products, including minced beef, are one of the favorite ingredients of most people because they are high in protein, vitamins, and minerals. The high demand and high prices make processed meat products vulnerable to adulteration. In addition, eliminating morphological attributes makes the authenticity of minced beef challenging to identify with the naked eye. This paper aims to describe the feasibility study of adulteration detection in minced beef using a low-cost imaging system coupled with a deep neural network. The proposed method was expected to be able to detect minced beef adulteration. There were 500 captured images of minced beef samples. Then, there were 24 color and textural features retrieved from the image. The samples were then labeled and evaluated. A deep neural network (DNN) was developed and investigated to support classification. The proposed DNN was also compared to six machine learning algorithms in the form of accuracy, precision, and sensitivity of classification. The feature importance analysis was also performed to obtain the most impacted features to classification results. The DNN model classification accuracy was 98.00% without feature selection and 99.33% with feature selection. The proposed DNN has the best performance with individual accuracy of up to 99.33%, a precision of up to 98.68%, and a sensitivity of up to 98.67%. This work shows the enormous potential application of a low-cost imaging system coupled with DNN to rapidly detect adulterants in minced beef with high performance.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Visual Detection of Chicken Adulteration Based on a Lateral Flow Strip-PCR Strategy

The aim of this study was to develop an accurate, easy-to-use, and cost-effective method for the detection of chicken adulteration based on polymerase chain reaction (PCR) and lateral flow strip (LFS). We compared six DNA extraction methods, namely the cetyltrimethylammonium bromide (CTAB) method, salt method, urea method, SDS method, guanidine isothiocyanate method, and commercial kit method. The chicken cytb gene was used as a target to design specific primers. The specificity and sensitivity of the PCR-LFS system were tested using a self-assembled lateral flow measurement sensor. The results showed that the DNA concentration obtained by salt methods is up to 533 ± 84 ng µL⁻¹, is a suitable replacement for commercial kits. The PCR-LFS method exhibits high specificity at an annealing temperature of 62 °C and does not cross-react with other animal sources. This strategy is also highly sensitive, being able to detect 0.1% of chicken in artificial adulterated meat. The results of the test strips can be observed with the naked eye within 5 min, and this result is consistent with the electrophoresis result, demonstrating its high accuracy. Moreover, the detection system has already been successfully used to detect chicken in commercial samples. Hence, this PCR-LFS strategy provides a potential tool to verify the authenticity of chicken.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

Detection of food fraud of meat products from the different brands by application of histological methods

In Sohag City, 400 samples were collected from different food markets of different meat products from two companies with high and low prices (e.g., minced meat, kofta sausage, beef burger, and luncheon meat) for determining food fraud. Light, fluorescence, and scanning electron microscopy (SEM) were used to examine the samples. "Special histochemical stains" permit the microscopic examination of different cell types, structures, and/or microorganisms. Histological examination revealed variant tissue types, besides skeletal muscles. Nuchal ligaments, bones, hyaline cartilages, white fibrocartilages, large and medium arteries, cardiac muscles, tendons, and collagenous connective tissues comprised the capsule of a parenchymatous organ. Additionally, a crystal of food additives was recognized using light microscopy and SEM. SEM allows the visualization of bacterial contamination. Using different microscopic anatomy techniques is an efficient methodology for qualitative evaluations of various meat products. No difference in quality was observed between low- and high-priced meat products.

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm². Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm², respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.