Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

Portable and miniature sensors in supply chain for food authentication: a review

Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.

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.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Assessing food availability and healthier options in an urban Chinese university: a case study using the Chinese Nutrition Environment Measurement Survey for Stores (C-NEMS-S)

Young adults (18-24 years) in universities are frequently exposed to an environment that promotes unhealthy eating behaviors. Using a validated tool, the Chinese Nutrition Environment Measurement Survey for Stores (C-NEMS-S), we assess the food availability and healthier options in a large, urban Chinese university. We employed C-NEMS-S for scoring criteria and weighting. A total of 52 on-campus canteen outlets were audited in an urban university located in Shijiazhuang City, China. General food outlets (n 43) and self-served food outlets (n 7) were further categorized into eight subtypes. Beverage outlets (n 2) were discussed separately from food outlets. C-NEMS-S scores were significantly different across food outlet types (P = 0.0024), especially between noodle and rice outlets (P = 0.0415). Food availability scores for starchy tubers (P < 0.001), dry beans (P < 0.001), vegetables (P = 0.0225), and fruits (P < 0.001) were significantly different across food outlet subtypes. Healthier options were scarce and only appeared in "grains" (n 2) and "meat and poultry" (n 2) categories. Further research on improving the accustomed audit tool and assessing university student diet quality is warranted.

Bacteriological evaluation and advanced SYBR-green multiplex real-time PCR assay for detection of minced meat adulteration

Background

Minced meat is a valuable source of nutrients, but it is vulnerable to contamination by microorganisms commonly present in the environment. In addition, there is a risk of adulteration with cheaper meat sources, which can be harmful to consumers.

Aim

It is crucial to identify meat adulteration with distinct microbiological analysis for legal, economic, religious, and public health purposes.

Methods

A total of 100 minced meat samples were collected from several markets in Sharkia Governorate, Egypt. These samples were then subjected to bacteriological testing and an advanced multiplex PCR method. This method enables the detection of bovine, equine, porcine, and dog species in meat samples with just one step.

Results

The adulterated samples had a higher total bacterial count and pH values compared to pure bovine meat. These differences in bacterial count and pH values were statistically significant, with p-values of 0.843 (log10) and 0.233, respectively. The frequency of Escherichia coli occurrence was 13%, and the O111 serotype was predominant in the adulterated samples. Listeria monocytogenes and Staphylococcus aureus were isolated with prevalence rates of 3% and 29%, respectively. Besides, the SYBR-green multiplex real-time PCR assay used in this study detected adulteration with dog, equine, and porcine meats in the examined samples at rates of 9%, 5%, and 4%, respectively.

Conclusion

This method provides a sensitive and specific approach to detect issues related to well-being and safety.

A Highly Sensitive Indirect Enzyme-Linked Immunosorbent Assay (ELISA) Based on a Monoclonal Antibody Specific to Thermal Stable-Soluble Protein in Pork Fat for the Rapid Detection of Pork Fat Adulterated in Heat-Processed Beef Meatballs

Processed foods containing pork fat tissue to improve flavor and gain economic benefit may cause severe issues for Muslims, Jews, and vegetarians. This study aimed to develop an indirect enzyme-linked immunosorbent assay (iELISA) based on a monoclonal antibody specific to thermal stable-soluble protein in pork fat tissue and apply it to detect pork fat tissue in heat-processed (autoclave, steam, roast, and fry) beef meatballs. To develop a sensitive iELISA, the optimal sample pre-cooking time, coating conditions, primary and secondary dilution time, and various buffer systems were tested. The change in the iELISA sensitivity with different 96-well microtiter microplates was confirmed. The detection limit of iELISA performed with an appropriate microplate was 0.015% (w/w) pork fat in raw and heat-treated beef. No cross-reactions to other meats or fats were shown. These results mean that the iELISA can be used as an analytical method to detect trace amounts of pork fat mixed in beef.

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical-chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

A voltammetric immunosensor based on a nanoporous alumina millirod for detection of porcine serum albumin

A voltammetric immunosensor was developed for detection of porcine serum albumin (PSA) to identify raw meat products adulterated with pork. A novel strategy to fabricate multiple individual nanoporous alumina (NPA) millirods (length, 5.0 mm; diameter, 1.0 mm) as the biorecognition platform is described. Each NPA millirod was covalently bioconjugated with anti-PSA capturing antibodies (α-PSAC). Following immunocapture, the PSA bound to the α-PSAC/NPA millirod bioconjugate were tagged with gold nanoparticles (AuNPs) functionalized with anti-PSA detection antibodies as the signaling probe. Subsequently, the AuNPs were voltammetrically analyzed to quantify the target PSA. The immunosensor exhibited 100 % specificity and high sensitivity to PSA with a limit of detection (LoD) of 50 (range, 0-1000) pg/mL (R² = 0.9907). Real-world applicability was successfully validated using pork/beef adulterated mixtures with a LoD of 0.05 % (w/w). Overall, the detection performance of the proposed immunosensor was excellent and, thus, is suitable for surveillance of food safety and quality.

Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk.

Rapid and sensitive detection of box turtles using an electrochemical DNA biosensor based on a gold/graphene nanocomposite

The Southeast Asian box turtle, Cuora amboinensis, is an ecologically important endangered species which needs an onsite monitoring device to protect it from extinction. An electrochemical DNA biosensor was developed to detect the C. amboinensis mitochondrial cytochrome b gene based on an in silico designed probe using bioinformatics tools, and it was also validated in wet-lab experiments. As a detection platform, a screen-printed carbon electrode (SPCE) enhanced with a nanocomposite containing gold nanoparticles and graphene was used. The morphology of the nanoparticles was analysed by field-emission scanning electron microscopy and structural characteristics were analysed by using energy-dispersive X-ray, UV-vis, and Fourier-transform infrared spectroscopy. The electrochemical characteristics of the modified electrodes were studied by cyclic voltammetry, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy. The thiol-modified synthetic DNA probe was immobilised on modified SPCEs to facilitate hybridisation with the reverse complementary DNA. The turtle DNA was distinguished based on hybridisation-induced electrochemical change in the presence of methylene blue compared to their mismatches, noncomplementary, and nontarget species DNA measured by DPV. The developed biosensor exhibited a selective response towards reverse complementary DNAs and was able to discriminate turtles from other species. The modified electrode displayed good linearity for reverse complementary DNAs in the range of 1 × 10-11-5 × 10-6 M with a limit of detection of 0.85 × 10-12 M. This indicates that the proposed biosensor has the potential to be applied for the detection of real turtle species.

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Food adulteration is the most serious problem found in the food industry as it harms people's healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.

Machine-learning assisted modelling of multiple elements for authenticating edible animal blood food

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The critical elements for identifying species of the animal blood food were selected.

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Elemental fingerprint coupled with ELM were proposed for species identification of the animal blood food.

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The optimal ELM model for identifying the species of the animal blood food was constructed.

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The absolute and relative content of 25 elements in animal blood food were reported for the first time.

Elemental fingerprint coupled with machine learning modelling was proposed for species authentication of the edible animal blood gel (EABG). A total of 25 elements were determined by inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS) in 150 EABG samples prepared from five species of animals, namely duck, chicken, bovine, pig, and sheep. Extreme learning machine (ELM) models were constructed and optimized. Principal component analysis and Fisher linear discriminant analysis were comparatively utilized for dimension reduction of the crucial input elements selected via stepwise discriminant analysis and one-way ANOVA. The optimal ELM model was obtained with the crucial elements selected by one-way ANOVA from the relative content of the measured elements, which afforded accuracies of 98.0% and 96.0% for the training and test set, respectively. All findings suggest that elemental fingerprint accompanied by ELM have great potential in authenticating the edible animal blood food.

Preparation of Mesoporous Silica Nanosphere-Doped Color-Sensitive Materials and Application in Monitoring the TVB-N of Oysters

In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the color-sensitive materials were doped with MSNs. The “before image” and the “after image” of the colorimetric senor array, which was composed of nanocolorimetric-sensitive materials by a charge-coupled device (CCD) camera were then collected. The different values of the before and after image were analyzed by principal component analysis (PCA). Moreover, the error back-propagation artificial neural network (BP-ANN) was used to quantitatively predict the TVB-N values of the oysters. The correlation coefficient of the colorimetric sensor array after being doped with MSNs was greatly improved; the Rc and Rp of BP-ANN were 0.9971 and 0.9628, respectively when the principal components (PCs) were 10. Finally, a paired sample t-test was used to verify the accuracy and applicability of the BP-ANN model. The result shows that the colorimetric-sensitive materials doped with MSNs could improve the sensitivity of the colorimetric sensor array, and this research provides a fast and accurate method to detect the TVB-N values in oysters.

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.

Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef-duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.

DNA/Nano based advanced genetic detection tools for authentication of species: Strategies, prospects and limitations

Authentication, detection and quantification of ingredients, and adulterants in food, meat, and meat products are of high importance these days. The conventional techniques for the detection of meat species based on lipid, protein and DNA biomarkers are facing challenges due to the poor selectivity, sensitivity and unsuitability for processed food products or complex food matrices. On the other hand, DNA based molecular techniques and nanoparticle based DNA biosensing strategies are gathering huge attention from the scientific communities, researchers and are considered as one of the best alternatives to the conventional strategies. Though nucleic acid based molecular techniques such as PCR and DNA sequencing are getting greater successes in species detection, they are still facing problems from its point-of-care applications. In this context, nanoparticle based DNA biosensors have gathered successes in some extent but not to a satisfactory stage to mark with. In recent years, many articles have been published in the area of progressive nucleic acid-based technologies, however there are very few review articles on DNA nanobiosensors in food science and technology. In this review, we present the fundamentals of DNA based molecular techniques such as PCR, DNA sequencing and their applications in food science. Moreover, the in-depth discussions of different DNA biosensing strategies or more specifically electrochemical and optical DNA nanobiosensors are presented. In addition, the significance of DNA nanobiosensors over other advanced detection technologies is discussed, focusing on the deficiencies, advantages as well as current challenges to ameliorate with the direction for future development.

Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration

The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty application in the meat industry. This study aimed to determine the optimal dilution level of meat extract for e-tongue evaluations and also to develop three standardized meat extraction methods. For practicality, the developed methods were applied to detect low levels of meat adulteration using beef and pork mixtures and turkey and chicken mixtures as case studies. Dilution factor of 1% w/v of liquid meat extract was determined to be the optimum for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w chicken in turkey and pork in beef with linear discriminant analysis accuracies (LDA) of 78.13% (recognition) and 64.73% (validation). Even higher LDA accuracies of 89.62% (recognition) and 68.77% (validation) were achieved for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w of pork in beef. Partial least square models could predict both sets of meat mixtures with good accuracies. Extraction by cooking was the best method for discriminating meat mixtures and can be applied for meat quality evaluations with the e-tongue.

Application of volatile and spectral profiling together with multimode data fusion strategy for the discrimination of preserved eggs

The maturity level of eggs during pickling is conventionally assessed by choosing few eggs from each curing batch to crack open. Yet, this method is destructive, creates waste and has consequences for financial losses. In this work, the feasibility of integrating electronic nose (EN) with reflectance hyperspectral (RH) and transmittance hyperspectral (TH) data for accurate classification of preserved eggs (PEs) at different maturation periods was investigated. Classifier models based solely on RH and TH with EN achieved a training accuracy (93.33%, 97.78%) and prediction accuracy (88.89%; 93.33%) respectively. The fusion of the three datasets, (EN + RH + TH) as a single classifier model yielded an overall training accuracy of 98.89% and prediction accuracy of 95.56%. Also, 52 volatile compounds were obtained from the PE headspace, of which 32 belonged to seven functional groups. This study demonstrates the ability to integrate EN with RH and TH data to effectively identify PEs during processing.

Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi-sensor fusion technology based on a novel cost-effective electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) was proposed. The E-nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E-Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E-nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost-effective E-nose created and voltammetric E-tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

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.