Identification of turkey meat and processed products using near infrared spectroscopy

Processing of Larvae of Alphitobius diaperinus and Tenebrio molitor in Cooked Sausages: Effects on Physicochemical, Microbiological, and Sensory Parameters

Proteins from insect production represent an interesting (environmentally friendly) option or supplement to commercial livestock farming. At present, however, the larval stages of T. molitor (mealworm) and A. diaperinus (buffalo worm) have been authorized as food for human consumption EU-wide, as have the nymph and adult stages of Locusta (L.) migratoria (Locusta migratoria, Linnaeus, 1758) and Acheta (A.) domesticus (house cricket, Acheta domesticus, Linnaeus, 1758). However, there is the problem that insects that are recognizable as a whole tend to be avoided by consumers, especially in the European region, as they are reminiscent of living things and can cause aversion and disgust in consumers. Against this background, in the present study, five batches of two types of cooked sausages were produced: on the one hand, with turkey, and on the other hand, with pork lean meat as a base. In different formulations, 10% and 20% of the meat contents (turkey or pork) in these meat products were replaced by deep-frozen, pulverized T. molitor and A. diaperinus larvae. The effects of the addition of these insects in the products on the microbiological and physicochemical parameters of these cooked sausages, compared to a product without insect content, directly after heating, were investigated. After production, a storage trial was also carried out to determine whether possible insect ingredients could influence the growth of inoculated bacterial species (Bacillus (B.) cereus, Escherichia (E.) coli, Listeria (L.) monocytogenes, and Campylobacter (C.) jejuni) and how the addition of insect larvae affectsthe sensory and physicochemical properties during storage. The study showed that the products with insects had reduced lightness (turkey p C = 0.025), increased yellowness (pork p S = 0.0009, p C < 0.0001 and turkey p C = 0.0027) and a reduced red color (pork p S < 0.0001, p C = 0.0001) after heating when compared to the cooked sausages without insects. However, no significant differences between the various cooked sausages with or without insects in terms of cooking loss, firmness, and protein, ash, and fat or water contents were found. The microbiological tests showed, on the one hand, that the prior microbial reduction (e.g., in the form of blanching) of the insect larvae was essential in order to guarantee the flawless microbiological quality of the cooked sausages and, on the other hand, that the addition of insects to the cooked sausages did not significantly affect the growth of the inoculated bacterial species and that no sensory differences could be detected during storage. Despite the significant color effects on the product, A. diaperinus and T. molitor larvae would be suitable as protein or meat alternatives in cooked sausages, but they would have to undergo pre-treatment, primarily with regard to microbiological safety. The extent to which a complete replacement of meat is possible has to be investigated in further studies.

Beyond the Spectrum: Unleashing the Potential of Infrared Radiation in Poultry Industry Advancements

The poultry industry is dynamically advancing production by focusing on nutrition, management practices, and technology to enhance productivity by improving feed conversion ratios, disease control, lighting management, and exploring antibiotic alternatives. Infrared (IR) radiation is utilized to improve the well-being of humans, animals, and poultry through various operations. IR radiation occurs via electromagnetic waves with wavelengths ranging from 760 to 10,000 nm. The biological applications of IR radiation are gaining significant attention and its utilization is expanding rapidly across multiple sectors. Various IR applications, such as IR heating, IR spectroscopy, IR thermography, IR beak trimming, and IR in computer vision, have proven to be beneficial in enhancing the well-being of humans, animals, and birds within mechanical systems. IR radiation offers a wide array of health benefits, including improved skin health, therapeutic effects, anticancer properties, wound healing capabilities, enhanced digestive and endothelial function, and improved mitochondrial function and gene expression. In the realm of poultry production, IR radiation has demonstrated numerous positive impacts, including enhanced growth performance, gut health, blood profiles, immunological response, food safety measures, economic advantages, the mitigation of hazardous gases, and improved heating systems. Despite the exceptional benefits of IR radiation, its applications in poultry production are still limited. This comprehensive review provides compelling evidence supporting the advantages of IR radiation and advocates for its wider adoption in poultry production practices.

Discriminating geographical origins and determining active substances of water caltrop shells through near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the geographical origins of the water caltrop shells from different regions of China. Two active substances, the total phenolic content (TPC) and total flavonoid content (TFC) in the water caltrop shells were determined through the technique as well. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was adopted to build the geographical discriminant model. Quantitative analysis models of TPC and TFC were built using partial least squares (PLS) regression. 1st derivative and randomization test (RT) methods were used to optimize the quantitative analysis models. It was found that the geographical discriminant model can correctly recognize the water caltrop shells from different regions of China with a total accuracy of 93.33%. The values of TPC and TFC obtained by the optimized models and the standard method are close. The coefficient of determination (R2) and the ratio of prediction to deviation for the two substances were 0.91, 0.89 and 3.02, 3.02, respectively. The results demonstrated the feasibility of NIRS combined with chemometric methods for the geographical discrimination of water caltrop shells and the quantitative analysis of TPC and TFC in water caltrop shells.

Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness

BACKGROUND

Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies.

RESULTS

Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples.

CONCLUSION

The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging

Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs' manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process

This study examined the potential of hyperspectral techniques for the rapid detection of characteristic indicators of yak meat freshness during the oxidation of yak meat. TVB-N values were determined by significance analysis as the characteristic index of yak meat freshness. Reflectance spectral information of yak meat samples (400-1000 nm) was collected by hyperspectral technology. The raw spectral information was processed by 5 methods and then principal component regression (PCR), support vector machine regression (SVR) and partial least squares regression (PLSR) were used to build regression models. The results indicated that the full-wavelength based on PCR, SVR, and PLSR models were shown greater performance in the prediction of TVB-N content. In order to improve the computational efficiency of the model, 9 and 11 characteristic wavelengths were selected from 128 wavelengths by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The CARS-PLSR model exhibited excellent predictive power and model stability.

Heat-Treated Meat Origin Tracing and Authenticity through a Practical Multiplex Polymerase Chain Reaction Approach

Meat adulteration have become a global issue, which has increasingly raised concerns due to not only economic losses and religious issues, but also public safety and its negative effects on human health. Using optimal primers for seven target species, a multiplex PCR method was developed for the molecular authentication of camel, cattle, dog, pig, chicken, sheep and duck in one tube reaction. Species-specific amplification from the premixed total DNA of seven species was corroborated by DNA sequencing. The limit of detection (LOD) is as low as 0.025 ng DNA for the simultaneous identification of seven species in both raw and heat-processed meat or target meat: as little as 0.1% (w/w) of the total meat weight. This method is strongly reproducible even while exposed to intensively heat-processed meat and meat mixtures, which renders it able to trace meat origins in real-world foodstuffs based on the authenticity assessment of commercial meat samples. Therefore, this method is a powerful tool for the inspection of meat adulterants and has broad application prospects.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: “satisfactory”: 4−7 log CFU/g, “acceptable”: 7−8 log CFU/g, and “unacceptable”: >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Dry-cured loin characterization by ultrasound physicochemical and sensory parameters

The aim of this study was to evaluate the ability of ultrasound inspection and quality determinations to characterize two commercial categories of dry-cured pork loin, labelled as green (GL) and red (RL). For this objective, ultrasound inspection was carried out for two different frequencies (500 and 1000 kHz), considering parameters of ultrasonic pulse velocity (UPV), frequency components related to the fast Fourier transform (FFT), and variables related to the attenuation. Physicochemical (moisture and fat content, water activity, instrumental color), instrumental texture (TPA) and sensory analyses (QDA) were also carried out. Moreover, quality and ultrasonic parameters were subjected to a correlation analysis (Pearson). Several physicochemical, instrumental texture and sensory parameters allowed to discriminate the dry-cured loin category. Moreover, high significant correlations were found among quality and acoustics parameters. Thus, ultrasound inspection can determine quality parameters indirectly without the limitations of traditional methodologies, postulating as a tool for characterizing dry-cured loin samples of different category with a promising predictive nature. This work has showed new findings for dry-cured meat products that may be of interest to the meat industry.

Detection of chicken and fat adulteration in minced lamb meat by VIS/NIR spectroscopy and chemometrics methods

Meat fraud has been changed to an important challenge to both industry and governments because of the public health issue. The main purpose of this research was to inspect the possibility of using VIS/NIR spectroscopy, combined with chemometric techniques to detect the adulteration of chicken meat and fat in minced lamb meat. 180 samples of pure lamb, chicken and fat and adulterated samples at different levels: 5, 10, 15 and 20% (w/w) were prepared and analyzed after pre-processing techniques. In order to remove additive and multiplicative effects in spectral data, derivatives and scatter-correction preprocessing methods were applied. Principle Component Analysis (PCA) as unsupervised method was applied to compress data. Moreover, Support Vector Machine (SVM) and Soft Independent Modeling Class Analogies (SIMCA) as supervised methods was applied to estimate the discrimination power of these models for nine and three class datasets. The best classification results were 56.15 and 80.70% for classification of nine class and three class datasets respectively with SVM model. This study shows the applicability of VIS/NIR combined with chemometrics to detect the type of fraud in minced lamb meat.

A Heptaplex PCR Assay for Molecular Traceability of Species Origin With High Efficiency and Practicality in Both Raw and Heat Processing Meat Materials

Frequent meat frauds have become a global issue because adulteration risks the food safety, breaches market rules, and even threatens public health. Multiplex PCR is considered to be a simple, fast, and inexpensive technique that can be applied for the identification of meat products in food industries. However, relatively less is known about a multiplex PCR method authenticating seven animal species simultaneously in one reaction due to technological challenge. Through screening new species-specific primers and optimizing PCR system, a heptaplex PCR method was established, which could simultaneously detect seven meat ingredients of camel (128 bp), pigeon (157 bp), chicken (220 bp), duck (272 bp), horse (314 bp), beef (434 bp), and pork (502 bp) in a single-tube reaction. DNA sequencing solidly validated that each set of primers specifically amplified target species from total DNA mixtures of seven meat species. The developed multiplex assay was stable and sensitive enough to detect 0.01–0.025 ng DNA from various meat treatments including raw, boiled, and autoclaved meat samples or target meat content of 0.1% total meat weight, suggesting the suitability of the heptaplex PCR technique for tracing target meats with high accuracy and precision. Most importantly, a market survey validated the availability of this multiplex PCR technique in real-world meat products with a good application foreground.

Coal identification based on a deep network and reflectance spectroscopy

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Rapid detection of mussels contaminated by heavy metals using near-infrared reflectance spectroscopy and a constrained difference extreme learning machine

The consumption of mussels contaminated with heavy metals can cause toxicity in humans. To realize quick, accurate, and non-destructive detection of heavy metals in mussels, a new method based on near-infrared reflection spectroscopy was developed in this study. Spectral data from 900 nm to 1700 nm of non-contaminated mussels and mussels contaminated with Cd, Zn, Pb, and Cu were collected using a near-infrared spectrometer. After pre-processing spectral data with multiplicative scatter correction, wavelength selection algorithms based on consistency measures of neighborhood rough sets were used to extract wavelengths for distinguishing non-contaminated and contaminated mussels. A constrained difference extreme learning machine was established as a classification model to detect contaminated mussels. In the proposed model, the weight and bias of the hidden layers are calculated by the difference vectors of samples between classes instead of being randomly selected. The results indicate that the proposed model performs significantly well in differentiating between non-contaminated and contaminated mussels. The average classification accuracy of 50 randomly generated test datasets reaches 97.53%, 95.67%, 99.00%, and 98.80% for the detection of Zn, Pb, Cd, and Cu contamination, respectively. This study demonstrates that near-infrared spectroscopy coupled with a constrained difference extreme learning can be used to rapidly and accurately detect mussels contaminated with heavy metals. This is of great significance for the evaluation of the quality and safety of mussels.

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.

A review of Canadian and international food safety systems: Issues and recommendations for the future

In January 2019, the Safe Food for Canadians Act/Safe Food for Canadians regulations (heretofore identified as SFCR) came into force across Canada and brought a more streamlined process to food safety practice in Canada. Food trade and production processes have evolved rapidly in recent decades, as Canada imports and exports food products; therefore it is critically important to remain aware of the latest advances responding to a range of challenges and opportunities in the food safety value chain. Looking through the optics of the recent SFCR framework, this paper places the spotlight on leading domestic and international research and practices to help strengthen food safety policies of the future. By shedding some light on new research, we also draw attention to international developments that are noteworthy, and place those in context as to how new Canadian food safety policy and regulation can be further advanced. The paper will benchmark Canada through a review study of food safety best practices by juxtaposing (i) stated aspirations with, (ii) actual performance in leading Organization for Economic Cooperation and Development (OECD) jurisdictions.

A Simple and Reliable Single Tube Septuple PCR Assay for Simultaneous Identification of Seven Meat Species

Multiplex PCR methods have been frequently used for authentication of meat product adulteration. Through screening of new species-specific primers designed based on the mitochondrial DNA sequences, a septuple PCR method is ultimately developed and optimized to simultaneously detect seven species including turkey (110 bp), goose (194 bp), pig (254 bp), sheep (329 bp), beef (473 bp), chicken (612 bp) and duck (718 bp) in one reaction. The proposed method has been validated to be specific, sensitive, robust and inexpensive. Taken together, the developed septuple PCR assay is reliable and efficient, not only to authenticate animal species in commercial meat products, but also easily feasible in a general laboratory without special infrastructures.

Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra

In this study, liquid egg, albumen, and egg yolk were artificially inoculated with E. coli. Ultrasound equipment (20/40 kHz, 180/300 W; 30/45/60 min) with a circulation cooling system was used to lower the colony forming units (CFU) of E. coli samples. Frequency, absorbed power, energy dose, and duration of sonication showed a significant impact on E. coli with 0.5 log CFU/mL in albumen, 0.7 log CFU/mL in yolk and 0.5 log CFU/mL decrease at 40 kHz and 6.9 W absorbed power level. Significant linear correlation (p < 0.001) was observed between the energy dose of sonication and the decrease of E. coli. The results showed that sonication can be a useful tool as a supplementary method to reduce the number of microorganism in egg products. With near-infrared (NIR) spectra analysis we were able to detect the structural changes of the egg samples, due to ultrasonic treatment. Principal component analysis (PCA) showed that sonication can alter C-H, C-N, -OH and N-H bonds in egg. The aquagrams showed that sonication can alter the properties of H₂O structure in egg products. The observed data showed that the absorbance of free water (1412 nm), water molecules with one (1440 nm), two (1462 nm), three (1472 nm) and four (1488 nm) hydrogen bonds, water solvation shell (1452 nm) and strongly bonded water (1512 nm) of the egg samples have been changed during ultrasonic treatment.

Application of terahertz spectrum and interval partial least squares method in the identification of genetically modified soybeans

Genetically modified soybeans are the world's most important genetically modified agricultural product. At present, the traditional methods for identifying genetically modified and non-transgenic soybeans are time-consuming, costly, and complicated to operate, which cannot meet the needs of practical applications. Therefore, it is necessary to discover a fast and accurate method for identifying transgenic soybeans. Terahertz (THz) time domain spectra were collected in sequence from 225 transgenic and non-transgenic soybean samples. Fourier transform was used to convert the terahertz time domain spectrum into a THz frequency domain spectrum with a frequency range of 0.1-2.5 THz. Firstly, the interval partial least squares (iPLS) method was used to remove interference spectral bands and select appropriate spectral intervals. Secondly, 168 samples were selected as the calibration set. Discriminant partial least squares (DPLS), Grid Search support vector machine (Grid Search-SVM) and principal component analysis back propagation neural network (PCA-BPNN) were used to establish a qualitative identification model. Afterwards, 57 test set samples were predicted. By comparing the experimental results, it was found that iPLS could effectively screen and remove the interference THz band, which was more helpful to improve the efficiency and accuracy of the identification model. After the iPLS and mean center pre-treatment technology, the Grid Search-SVM identification model had the best identification effect, with a total accuracy rate of 98.25% (transgenic identification rate was 96.15%, non-transgenic identification rate was 100%). This study shows that after selecting spectra from iPLS, THz spectroscopy combined with chemometrics can more accurately, quickly, and efficiently identify transgenic and non-transgenic soybeans.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.