A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

Detection and quantification of pork adulteration in beef meatballs with Raman spectroscopy and near infrared spectroscopy

One of the main halal concepts requires that food is free from pork substances. Muslim-majority countries establish halal regulations that require the screening of processed meat products, such as meatballs, are screened for adulteration with pork meat to guarantee appropriate halal certification for consumers. Currently, halal authorities rely on the analysis of DNA, protein, or fat with RT-PCR, LC-MS, or GC-FID, which are reliable but are not suitable for rapid screening of large numbers of samples. Hence, high throughout screening tools are demanded to identify suspected samples. Vibrational spectroscopy methods such as Raman spectroscopy (RS) and Near Infrared spectroscopy (NIRS) are widely studied as fast and non-destructive methods for compositional analysis of agrifood products. Therefore, the aim of this study was to evaluate their potential for screening of suspected meatball samples. To this end, different batches of pure beef meatballs and meatballs with different levels of adulteration (3, 5, 10, 50, and 100 % w/w) were prepared and scanned in backscattering (RS) and reflectance (NIRS) mode in intact and cut form. The acquired Raman spectra had dominant peaks at 1657 cm-1, 1443 cm-1 and 1299 cm-1, which were attributed to saturated and unsaturated fat, while the dominant peaks in the NIR spectra corresponded to O-H bonds of water (1457 nm and 1934 nm). The cross-sectioned configuration was found to provide more stable classification performance compared to measurements on intact meatballs for both RS and NIRS. The accuracy of the partial least squares-discriminant analysis (PLS-DA) models for cross-sectioned samples using four latent variables ranged from 52.50 % to 85.00 % for RS and from 58.97 % to 75.00 % for NIRS. The performance of RS and NIRS shows little difference, but RS provides better insights on primary component of meat. For further research, improving the quality of Raman signal with a higher excitation wavelength laser or RS techniques that minimize fluorescence interference may improve model performance.

Recognition of beef aging time using a miniaturized near-infrared spectrometer in tandem with support vector machine

Consumers increasingly demand sustainable production practices and high-quality standards. Near-infrared (NIR) spectroscopy presents a non-invasive and efficient tool for addressing these concerns. This study aimed to evaluate vacuum-aged beef across different aging periods (3, 10, 17, and 24-days) using NIR spectroscopy and chemometric methods, focusing on a classification model based on SVM. NIR spectra were collected from 356 samples, and PCA was performed. The first and third principal components explained 83.05 % of the variance, showing grouping tendencies for samples aged 3 and 10 days versus those aged 17 and 24 days. Chemical groups related to aging, such as proteins, water, lipids, acids, and alcohols, drove spectral differentiation. The spectral region between 1228 and 1337 nm was identified as the most relevant for model development, achieving an accuracy of 96.60 %. This study demonstrates that portable NIR spectrometer, in combination with SVM classification, offer a fast, cost-effective, and user-friendly method for classifying aged beef.

The development and application of SERS-based lateral flow immunochromatography in the field of food safety

Surface-Enhanced Raman Scattering-Lateral Flow Immunoassay (SERS-LFIA) inherits the advantages of simplicity, rapidness, and stability from Lateral Flow Immunoassay (LFIA), while integrating the sensitivity and accuracy of SERS, thereby attracting extensive attention in the field of food safety monitoring. This paper delves into the design strategies and principles underlying SERS-LFIA, introducing the detection formats based on SERS and contrasting the differences between traditional Raman molecules and those located in the Raman-silent region. It analyzes two immunoassay methods, namely sandwich and competitive, along with their respective applications. Importantly, by reviewing the applications of SERS-LFIA in food safety monitoring over the past 5 years, this paper summarizes the challenges faced by SERS-LFIA technology in practical applications and development. Furthermore, it provides a forward-looking perspective on the future development of SERS-LFIA. As a pivotal analytical method in the field of food safety monitoring, SERS-LFIA is demonstrating immense potential. It is hoped that this paper will offer valuable insights for the future development and application of SERS-LFIA.

Fast non-destructive measurement of intramuscular fat in Australian beef and lamb using nuclear magnetic resonance (NMR) technologies

Nuclear magnetic resonance (NMR) is an excellent technique for non-destructive analysis of meat because it has high accuracy, a linear response, and insignificant drift over time, which removes the need for recalibration. Furthermore, single-side NMR devices have open geometries that enable measurements of subsections of larger samples without taking sub-samples. Here we demonstrated long-term reproducibility in a benchtop device and the utility of a single-sided NMR device. We validated long-term reproducibility of NMR measurements of lamb intramuscular fat (IMF%) in a commercial processor boning room years after the original model was created. It was hypothesised that the NMR IMF% model would retain precision and accuracy on independent validation. The root mean squared (RMS) error of prediction of lamb IMF was 0.79 %. The R2 between reference measurements, predicted IMF% was 0.74, the slope of the chemical IMF% vs NMR predictions was 0.989, and the bias was 0.53 % IMF%. In the second example, we showed that IMF% measurements of high value beef striploins could be measured off a commercial processing belt and returned without damaging the product. It was hypothesised that a commercial prototype single-sided NMR system would predict IMF% in beef M. longissimus thoracis et lumborum. Here the RMS error of the correlation was 1.58 % IMF% and R2 was 0.97. The long-term stability, high accuracy, and nondestructive nature make unilateral NMR devices ideal for applications in the red meat industry where IMF% contributes to product value.

Vis-NIRS as an auxiliary tool in the classification of bovine carcasses

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples.

Assessment of Tail-Cutting in Frozen Albacore (Thunnus alalunga) Through Ultrasound Inspection and Chemical Analysis

Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, t-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, t-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

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

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

Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb

The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.

Fast identification of the BmNPV infected silkworms by portable NIR spectroscopy and chemometrics

A convenient, low-cost, and rapid detection of BmNPV-infected silkworms is of great significance for the safety of the sericulture industry. In this study, a portable NIR system was used to collect the spectra of normal silkworms and the infected silkworms induced by the administration of Bombyx mori nuclear polyhedrosis virus (BmNPV). Different spectral pretreatment methods were applied, then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were used for the classification analysis. The results showed that PCA and LDA were unable to achieve the purpose. For the PLSDA calibration, after the pretreatment of SNV combining 2nd derivative, it had a high identification performance, and obtained low classification errors of 0.023, 0.033, and 0.030 for the calibration set, cross-validation set, and test set, respectively, with higher sensitivity and specificity. Therefore, the BmNPV-infected silkworms can be identified by portable NIR spectroscopy, which will effectively reduce losses for the sericulture industry.

Porphyrin fluorescence imaging for real-time monitoring and visualization of the freshness of beef stored at different temperatures

This study presents a novel fluorescence imaging method for the real-time monitoring of beef quality deterioration and freshness. The fluorescence property of porphyrin in the form of heme can be used to characterize quality changes in beef during storage. Therefore, a fluorescence imaging system with an excitation light source of 440 nm and a CCD camera with a specific wavelength filter of 595 nm was constructed, and the porphyrin fluorescence images of beef samples stored at different temperatures were then collected. The quantitative model for predicting the microbial freshness indicator (TVC) of beef was built with the support vector machine regression (SVR) algorithm and produced satisfactory results with Rc2 and Rp2 of 0.858 and 0.812, respectively. The classification model based on the support vector machine (SVM) algorithm classified beef freshness into "fresh" and "spoiled", with calibration and prediction accuracy of 100 % and 90.9 %, respectively.

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

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

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

Predicting primal weight and primal yield in pork carcasses from a large-scale survey at major meat processing centers in Japan

Pork primal weight and primal yield are important indicators for pig breeding, feeding management, commercial distribution systems, and meat processing. Here, we aimed to determine whether primal weight and primal yield could be predicted through non-destructive measurements of pork carcass traits. A total of 4397 carcasses (1958 gilts and 2439 barrows) from eight major meat processing centers were used, and the mean primal weight and primal yield were 56.0 kg and 73.9%, respectively. Significant sex differences were observed for all primal and carcass traits (P < 0.001), except for carcass weight. A maximum of 12 variables were examined, and primal weight was predicted with very high accuracy (R = 0.95, RMSE = 1.7, RPD = 3.0) using four variables. Primal yield was predicted with relatively good accuracy (R = 0.71, RMSE = 2.3, RPD = 1.4) using three variables, and these same variables were also effective for predicting primal weight. These prediction formulas were sufficiently accurate without accounting for the effect of sex. Overall, our results demonstrate that primal weight and primal yield can be accurately predicted using four variables, "carcass weight," "backfat thickness above M. gluteus medius," "spinous process length of 13th thoracic vertebra," and "length from 1st thoracic vertebra to backfat," without accounting for the effect of sex.

Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?

This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616-0.9955) and low RMSE values (0.0400-0.1031). The prediction set's relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.

The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products

The following investigations describe the potential of handheld NIR spectroscopy and Raman imaging measurements for the identification and authentication of food products. On the one hand, during the last decade, handheld NIR spectroscopy has made the greatest progress among vibrational spectroscopic methods in terms of miniaturization and price/performance ratio, and on the other hand, the Raman spectroscopic imaging method can achieve the best lateral resolution when examining the heterogeneous composition of samples. The utilization of both methods is further enhanced via the combination with chemometric evaluation methods with respect to the detection, identification, and discrimination of illegal counterfeiting of food products. To demonstrate the solution to practical problems with these two spectroscopic techniques, the results of our recent investigations obtained for various industrial processes and customer-relevant product examples have been discussed in this article. Specifically, the monitoring of food extraction processes (e.g., ethanol extraction of clove and water extraction of wolfberry) and the identification of food quality (e.g., differentiation of cocoa nibs and cocoa beans) via handheld NIR spectroscopy, and the detection and quantification of adulterations in powdered dairy products via Raman imaging were outlined in some detail. Although the present work only demonstrates exemplary product and process examples, the applications provide a balanced overview of materials with different physical properties and manufacturing processes in order to be able to derive modified applications for other products or production processes.

Preliminary Feasibility of Near-Infrared Spectroscopy to Authenticate Grazing in Dairy Goats through Milk and Faeces Analysis

Consumers are increasingly prone to request information about the production systems of the food they buy. For this purpose, certification and authentication methodologies are necessary not only to protect the choices of consumers, but also to protect producers and production systems. The objective of this preliminary work was to authenticate the grazing system of dairy goats using Near-Infrared Spectroscopy (NIRS) analyses of milk and faeces of the animals. Spectral information and several mathematical pre-treatments were used for the development of six discriminant models based on different algorithms for milk and faeces samples. Results showed that the NIRS spectra of both types of samples had some differences when the two feeding regimes were compared. Therefore, good discrimination rates were obtained with both strategies (faeces and milk samples), with classification percentages of up to 100% effectiveness. Discrimination of feeding regime and grazing authentication based on NIRS analysis of milk samples and an alternative sample such as faeces is considered as a potential approach for dairy goats and small ruminant production.

Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy

The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.

Performance of a Handheld Near-Infrared Spectroscopy Device to Predict Pork Primal Belly Fat Iodine Value and Loin Lean Intramuscular Fat Content

The increase in market demand and economic value of Canadian pork primal cuts has led to a need to assess advanced technologies capable of measuring quality traits. Fat and lean composition were measured using a Tellspec near-infrared (NIR) spectroscopy device to predict the pork belly fat iodine value (IV) and loin lean intramuscular fat (IMF) content in 158 pork belly primals and 419 loin chops. The calibration model revealed a 90.6% and 88.9% accuracy for the Tellspec NIR to predict saturated fatty acids (SFA) and IV, respectively, in the belly fat. The calibration model accuracy for the other belly fatty acids revealed an accuracy of 66.3-86.1%. Using the Tellspec NIR to predict loin lean IMF reported a lower accuracy for moisture (R² = 60) and fat % (R² = 40.4). This suggests that Tellspec NIR spectroscopy measures on the pork belly primal offers a cost-efficient, rapid, accurate, and non-invasive indicator of pork belly IV and could be used for the classification for specific markets.

In Vivo Analysis of a Biodegradable Magnesium Alloy Implant in an Animal Model Using Near-Infrared Spectroscopy

Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue's functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show that the optical probe can detect variations in vivo from biological tissues of rats with biodegradable magnesium alloy "WE43" implants, and the analysis identified a trend in the optical data over two weeks. The primary challenge of in vivo data analysis is the complexity of the implant interaction near the interface with the biological medium.

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R² in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

Approaches in Sustainable, Biobased Multilayer Packaging Solutions

The depletion of fossil resources and the growing demand for plastic waste reduction has put industries and academic researchers under pressure to develop increasingly sustainable packaging solutions that are both functional and circularly designed. In this review, we provide an overview of the fundamentals and recent advances in biobased packaging materials, including new materials and techniques for their modification as well as their end-of-life scenarios. We also discuss the composition and modification of biobased films and multilayer structures, with particular attention to readily available drop-in solutions, as well as coating techniques. Moreover, we discuss end-of-life factors, including sorting systems, detection methods, composting options, and recycling and upcycling possibilities. Finally, regulatory aspects are pointed out for each application scenario and end-of-life option. Moreover, we discuss the human factor in terms of consumer perception and acceptance of upcycling.

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

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

Adoption of analytical technologies for verification of authenticity of halal foods - a review

Halal authentication has become essential in the food industry to ensure food is free from any prohibited ingredients according to Islamic law. Diversification of food origin and adulteration issues have raised concerns among Muslim consumers. Therefore, verification of food constituents and their quality is paramount. From conventional methods based on physical and chemical properties, various diagnostic methods have emerged relying on protein or DNA measurements. Protein-based methods that have been used in halal detection including electrophoresis, chromatographic-based methods, molecular spectroscopy and immunoassays. Polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) are DNA-based techniques that possess better accuracy and sensitivity. Biosensors are miniatured devices that operate by converting biochemical signals into a measurable quantity. CRISPR-Cas is one of the latest novel emerging nucleic acid detection tools in halal food analysis as well as quantification of stable isotopes method for identification of animal species. Within this context, this review provides an overview of the various techniques in halal detection along with their advantages and limitations. The future trend and growth of detection technologies are also discussed in this review.

Adulteration detection technologies used for halal/kosher food products: an overview

In the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.

Development of a Portable Near-Infrared Spectroscopy Tool for Detecting Freshness of Commercial Packaged Pork

Real-time monitoring of meat quality requires fast, accurate, low-cost, and non-destructive analytical methods that can be used throughout the entire production chain, including the packaged product. The aim of this work was to evaluate the potential of a portable near-infrared (NIR) spectroscopy tool for the on-site detection of freshness of pork loin fillets in modified atmosphere packaging (MAP) stored on display counters. Pork loin slices were sealed in MAP trays under two proportions of O2/CO2/N2: High-Ox-MAP (30/40/30) and Low-Ox-MAP (5/20/75). Changes in pH, color, thiobarbituric acid reactive substances (TBARS), Warner−Bratzler shear force (WBSF), and microbiology (total viable counts, Enteriobacteriaceae, and lactic acid bacteria) were monitored over 15 days post-mortem at 4 °C. VIS-NIR spectra were collected from pork fillets before (through the film cover) and after opening the trays (directly on the meat surface) with a portable LABSPEC 5000 NIR system in diffuse reflectance mode (350−2500 nm). Quantitative NIR models by partial least squares regression (PLSR) showed a promising prediction ability for meat color (L*, a*, C*, and h*) and microbiological variables (R2VAL > 0.72 and RPDVAL > 2). In addition, qualitative models using PLS discriminant analysis obtained good accuracy (over 90%) for classifying pork samples as fresh (acceptable for consumption) or spoiled (not acceptable) based on their microbiological counts. VIS-NIR spectroscopy allows rapid evaluation of product quality and shelf life and could be used for on-site control of pork quality.

Use of Near-Infrared Spectroscopy to Discriminate DFD Beef and Predict Meat Quality Traits in Autochthonous Breeds

The potential of near-infrared reflectance spectroscopy (NIRS) to discriminate Normal and DFD (dark, firm, and dry) beef and predict quality traits in 129 Longissimus thoracis (LT) samples from three Spanish purebreeds, Asturiana de los Valles (AV; n = 50), Rubia Gallega (RG; n = 37), and Retinta (RE; n = 42) was assessed. The results obtained by partial least squares-discriminant analysis (PLS-DA) indicated successful discrimination between Normal and DFD samples of meat from AV and RG (with sensitivity over 93% for both and specificity of 100 and 72%, respectively), while RE and total sample sets showed poorer results. Soft independent modelling of class analogies (SIMCA) showed 100% sensitivity for DFD meat in total, AV, RG, and RE sample sets and over 90% specificity for AV, RG, and RE, while it was very low for the total sample set (19.8%). NIRS quantitative models by partial least squares regression (PLSR) allowed reliable prediction of color parameters (CIE L*, a*, b*, hue, chroma). Results from qualitative and quantitative assays are interesting in terms of early decision making in the meat production chain to avoid economic losses and food waste.

The Near-Infrared Spectroscopy of Ethanol-Fixed Tissues to Detect Illicit Treatments with Glucocorticoids in Bulls

This study aimed to set up indirect, rapid methods involving near infrared (NIR) spectroscopy analysis, to detect illicit treatments with glucocorticoids in bull. The ethanol fixation method (EtOH) was applied to 7 different tissues obtained from 20 Friesian bulls, 12 of which were experimentally administered with dexamethasone as part of a growth-promoting protocol for 60 days and slaughtered 26 days after the end of the treatment. A perfect discrimination was obtained for the 7 sampled tissues, considering a full UV-Vis-NIR range (350 ÷ 2500 nm), for both false positive and negative animals. The validated true positive and negative errors were zero for the longissimus thoracis muscle, 10% for the skin-dermis, 15% for the fat, 25% for the thymus gland and the semitendinosus muscle, 30% for the sternomandibularis muscle and 35% for the skin-hair. A multiple test on the most accessible tissues, that is, the thymus gland, the sternomandibularis muscle and fat, can be used as an alternative to provide indications about animals that have been subjected to illicit treatments. In the short space of three days from the slaughter, NIR spectroscopy of ETOH fixed tissues, would allow at least cost the detection of a probable illicit which could eventually be reported to health authorities for specific investigation in the frame of official controls.

Development of NIR-Based ANN Models for On-Line Monitoring of Glycerol Concentration during Biodiesel Production in a Microreactor

During the production process, a whole range of analytical methods must be developed to monitor the quality of production and the desired product(s). Most of those methods belong to the group of off-line monitoring methods and are usually recognized as costly and long-term. In contrast, on-line monitoring methods are fast, reliable, simple, and repeatable. The main objective of this study was to compare different methods for monitoring total glycerol concentration as one of the indicators of process efficiency during biodiesel production in a batch reactor and in a microreactor. During the biodiesel production process, the glycerol concentration was measured off-line using standard methods based on UV-VIS spectrophotometry and gas chromatography. Neither method provided satisfactory results, namely, both analyses showed significant deviations from the theoretical value of glycerol concentration. Therefore, near infrared spectroscopy (NIR) analysis was performed as an alternative analytical method. The analysis using NIR spectroscopy was performed in two ways: off-line, using a sample collected during the transesterification process, and on-line by the continuous measurement of glycerol concentration in a rector. Obtained results showed a great NIR application potential not only for off-line but also for on-line monitoring of the biodiesel production process.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm

Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQ_external values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.

Rapid Nondestructive Prediction of Multiple Quality Attributes for Different Commercial Meat Cut Types Using Optical System

There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R c) and prediction set (R p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L*, 0.83 and 0.78 for a*, 0.83 and 0.82 for b*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.

A feasibility study on nondestructive classification of frozen Atlantic salmon (Salmo salar) fillets based on temperature history at the logistics using NIR spectroscopy

Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near-infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS-DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four-class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two-class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein- and water-related changes were found important to distinguish the fillets. Based on these findings, the four-class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two-class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. PRACTICAL APPLICATION: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output.

Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400-900 nm) and short-wave infrared (900-1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.

Infrared Predictions Are a Valuable Alternative to Actual Measures of Dry-Cured Ham Weight Loss in the Training of Genome-Enabled Prediction Models

Selection to reduce ham weight losses during dry-curing (WL) requires individual traceability of hams throughout dry-curing, with high phenotyping costs and long generation intervals. Infrared spectroscopy enables cost-effective, high-throughput phenotyping for WL 24 h after slaughter. Direct genomic values (DGV) of crossbred pigs and their purebred sires were estimated, for observed (OB) and infrared-predicted WL (IR), through models developed from 640 and 956 crossbred pigs, respectively. Five Bayesian models and two pseudo-phenotypes (estimated breeding value, EBV, and adjusted phenotype) were tested in random cross-validation and leave-one-family-out validation. The use of EBV as pseudo-phenotypes resulted in the highest accuracies. Accuracies in leave-one-family-out validation were much lower than those obtained in random cross-validation but still satisfactory and very similar for both traits. For sires in the leave-one-family-out validation scenario, the correlation between the DGV for IR and EBV for OB was slightly lower (0.32) than the correlation between the DGV for OB and EBV for OB (0.38). While genomic prediction of OB and IR can be equally suggested to be incorporated in future selection programs aiming at reducing WL, the use of IR enables an early, cost-effective phenotyping, favoring the construction of larger reference populations, with accuracies comparable to those achievable using OB phenotype.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400-1000 nm) and a handheld NIR (900-1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95-100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.

Feasibility of Application of Near Infrared Reflectance (NIR) Spectroscopy for the Prediction of the Chemical Composition of Traditional Sausages

In the present study, the potential of application of near infrared reflectance (NIR) spectroscopy for the estimation of the chemical composition of traditional (village style) sausages was examined. The chemical composition (moisture, ash, protein and, fat content) was determined by standard reference methods. For the development of the calibration model, 39 samples of traditional fresh sausages were used, while for external validation, 10 samples of sausages were used. The correlation coefficients of calibration (RMSEC) and standard errors (SEC) were 0.92 and 1.58 (moisture), 0.77 and 0.18 (ash), 0.87 and 0.89 (protein) and 0.93 and 1.73 (fat). The cross-validation correlation coefficients (RMSECV) and standard errors (SECV) were 0.86 and 2.13 (moisture), 0.56 and 0.26 (ash), 0.78 and 1.17 (protein), and 0.88 and 2.17 (fat). The results of the calibration model showed that NIR spectroscopy can be applied to estimate with very good precision the fat content of traditional village-style sausages, whereas moisture and protein content can be estimated with good accuracy. The external validation confirmed the ability of NIR spectroscopy to predict the chemical composition of sausages.