Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS)

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.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

New research development on trans fatty acids in food: Biological effects, analytical methods, formation mechanism, and mitigating measures

The trans fatty acids (TFAs) in food are mainly generated from the ruminant animals (meat and milk) and processed oil or oil products. Excessive intake of TFAs (>1% of total energy intake) caused more than 500,000 deaths from coronary heart disease and increased heart disease risk by 21% and mortality by 28% around the world annually, which will be eliminated in industrially-produced trans fat from the global food supply by 2023. Herein, we aim to provide a comprehensive overview of the biological effects, analytical methods, formation and mitigation measures of TFAs in food. Especially, the research progress on the rapid, easy-to-use, and newly validated analytical methods, new formation mechanism, kinetics, possible mitigation mechanism, and new or improved mitigation measures are highlighted. We also offer perspectives on the challenges, opportunities, and new directions for future development, which will contribute to the advances in TFAs research.

Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics

Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.

Review: Quality of animal-source foods

This article critically reviews the current state of knowledge on the quality of animal-source foods according to animal production and food processing conditions, including consumer expectations-behaviours and the effects of consumption of animal-source foods on human health. Quality has been defined through seven core attributes: safety, commercial, sensory, nutritional, technological, convenience, and image. Image covers ethical, cultural and environmental dimensions associated with the origin of the food and the way it is produced and processed. This framework enabled to highlight the priorities given to the different quality attributes. It also helped to identify potential antagonisms and synergies among quality attributes, between production and processing stages, and among stakeholders. Primacy is essentially given to commercial quality attributes, especially for standard commodity animal-source foods. This primacy has strongly influenced genetic selection and farming practices in all livestock commodity chains and enabled substantial quantitative gains, although at the expense of other quality traits. Focal issues are the destructuration of chicken muscle that compromises sensory, nutritional and image quality attributes, and the fate of males in the egg and dairy sectors, which have heavily specialised their animals. Quality can be gained but can also be lost throughout the farm-to-fork continuum. Our review highlights critical factors and periods throughout animal production and food processing routes, such as on-farm practices, notably animal feeding, preslaughter and slaughter phases, food processing techniques, and food formulation. It also reveals on-farm and processing factors that create antagonisms among quality attributes, such as the castration of male pigs, the substitution of marine-source feed by plant-based feed in fish, and the use of sodium nitrite in meat processing. These antagonisms require scientific data to identify trade-offs among quality attributes and/or solutions to help overcome these tensions. However, there are also food products that value synergies between quality attributes and between production and processing phases, particularly Geographical Indications, such as for cheese and dry-cured ham. Human epidemiological studies have found associations between consumption of animal-source foods and increased or decreased risk for chronic non-communicable diseases. These associations have informed public health recommendations. However, they have not yet considered animal production and food processing conditions. A concerted and collaborative effort is needed from scientists working in animal science, food process engineering, consumer science, human nutrition and epidemiology in order to address this research gap. Avenues for research and main options for policy action are discussed.

An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms

Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

Review: Factors affecting sheep carcass and meat quality attributes

Sheep meat comes from a wide variety of farming systems utilising outdoor extensive to indoor intensive with animals of various ages at slaughter. In Europe, slaughter may occur from 4 weeks of age in suckling light lambs to adult ages. More than any other animal species used for meat production, there are strong country-specific preferences for sheep meat quality linked to production system characteristics such as dairy or grassland-based systems. This article critically reviews the current state of knowledge on factors affecting sheep carcass and meat quality. Quality has been broken down into six core attributes: commercial, organoleptic, nutritional, technological, safety and image, the latter covering aspects of ethics, culture and environment associated with the way the meat is produced and its origin, which are particularly valued in the many quality labels in Europe. The quality of meat is built but can also deteriorate along the continuum from the conception of the animal to the consumer. Our review pinpoints critical periods, such as the gestation and the preslaughter and slaughter periods, and key factors, such as the animal diet, via its direct effect on the fatty acid profile, the antioxidant and volatile content, and indirect effects mediated via the age of the animal. It also pinpoints methodological difficulties in predicting organoleptic attributes, particularly odour and flavour. Potential antagonisms between different dimensions of quality are highlighted. For example, pasture-feeding has positive effects on the image and nutritional attributes (through its effect on the fatty acid profile of meat lipids), but it increases the risk of off-odours and off-flavours for sensitive consumersand the variability in meat quality linked to variability of animal age at slaughter. The orientation towards more agro-ecological, low-input farming systems may therefore present benefits for the image and nutritional properties of the meat, but also risks for the commercial (insufficient carcass fatness, feed deficiencies at key periods of the production cycle, irregularity in supply), organoleptic (stronger flavour and darker colour of the meat) and variability of sheep carcass and meat quality. Furthermore, the genetic selection for lean meat yield has been effective in producing carcasses that yield more meat, but at a penalty to the intramuscular fat content and eating quality of the meat, and making it more difficult to finish lambs on grass. Various tools to assess and predict quality are in development to better consider the various dimensions of quality in consumer information, payment to farmers and genetic selection.

A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers’ urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

Prediction of the intramuscular connective tissue components of fresh and freeze-dried samples by near infrared spectroscopy

This study compared the performance of near-infrared spectroscopy (NIRS) models on fresh and freeze-dried beef muscle samples to predict intramuscular connective tissue (IMCT) components and to determine whether the accuracy of the models differed among different muscles from beef cattle. The hypothesis was that the water content of muscle samples would negatively influence the accuracy of the models, which would differ among muscles. Fresh and freeze-dried samples (n = 171) of four muscles were used to develop NIRS models to predict the contents IMCT. For the total collagen content, the standard error of cross validation (SECV) for model using freeze-dried samples (0.75 mg OH-prol/g DM) was lower than that for model using fresh samples (0.84 mg OH-prol/g DM). For cross-links and proteoglycans, the SECV for models using fresh sample spectra was lower than that for models using freeze-dried sample spectra. The accuracy of the prediction of the models also differed among predicted muscle types.

The Effect of Freeze-Drying Pretreatment on the Accuracy of Near Infrared Spectroscopic Food Analysis to Predict the Nutritive Values of Japanese Cooked Foods

The official testing methods for establishing nutritive values are accurate but relatively costly and time-consuming. Near infrared spectroscopy (NIRS) is potentially an alternative method that can analyze several components in a few minutes using an exclusively electronic instrument with no need for a laboratory expert. However, the accuracy of commercial NIRS spectroscopic food analyzers is not sufficient for Japanese food labeling, because of interference from moisture contained in the foods. This study aims to assess the effect of a freeze-drying pretreatment on the accuracy of NIRS food analysis. Thirty-four samples, consisting of six food items habitually consumed in Japan and cooked by different cooking methods were treated by milling then freeze-drying. They were analyzed by a commercial NIRS instrument (Calorie Answer^TM) with calibration curves developed based on other freeze-dried samples. The obtained nutritive values (energy, protein, lipid, carbohydrate and moisture) were corrected to the values before freeze-drying using the vaporized moisture content. The same samples before freeze-drying were also analyzed using the official testing methods to assess the analytical accuracy using NIRS after freeze-drying, and further analyzed using the same NIRS with the commercial calibration curves to assess the effect of freeze-drying. The accuracies were better for the freeze-dried samples than for the wet samples. The magnitude of the error in energy and carbohydrate was significantly associated with the retained moisture content in the freeze-dried sample. In conclusion, freeze-drying was an effective pretreatment for improving the accuracy of NIRS analyses of Japanese cooked foods, although it is still time-consuming and needs additional investment.

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

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

Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat

The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.

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

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

Contribution of connective tissue components, muscle fibres and marbling to beef tenderness variability in longissimus thoracis, rectus abdominis, semimembranosus and semitendinosus muscles

BACKGROUND

The present study aimed to identify relationships between components of intramuscular connective tissue, proportions of the different fiber types, intramuscular fat and sensory tenderness of beef cooked at 55 °C. Accordingly, four muscles differing in their metabolic and contractile properties, as well as in their collagen content and butcher value, were obtained from dairy and beef cattle of several ages and sexes and were then used to create variability.

RESULTS

Correlation analyses and/or stepwise regressions were applied on Z-scores to identify the existing and robust associations. Tenderness scores were further categorized into tender, medium and tough classes using unsupervised learning methods. The findings revealed a muscle-dependant role with respect to tenderness of total and insoluble collagen, cross-links, and type IIB + X and IIA muscle fibers. The longissimus thoracis and semitendinosus muscles that, in the present study, were found to be extreme in their tenderness potential were also very different from each other and from the rectus abdominis (RA) and semimembranosus (SM). RA and SM muscles were very similar regarding their relationship for muscle components and tenderness. A relationship between marbling and tenderness was only present when the results were analysed irrespective of all factors of variation of the experimental model relating to muscle and animal type.

CONCLUSION

The statistical approaches applied in the present study using Z-scores allowed identification of the robust associations between muscle components and sensory beef tenderness and also identified discriminatory variables of beef tenderness classes. © 2020 Society of Chemical Industry.

Determination of the food dye indigotine in cream by near-infrared spectroscopy technology combined with random forest model

Artificial pigment is a common food additive in cream products. If added in excess, it will do harm to human body. At present, there is no research on the detection of cream pigment by Near Infrared (NIR) spectroscopy. In this paper, a method based on random forest was applied to determine the indigotine in cream. Weighting in the experiments was accomplished using analytical balances with precision as low as 0.0001 g. The NIR spectra data of cream with different concentration of indigotine were recorded. The original spectra was pretreated by SG smoothing, mean centering and second derivative. Random forest was applied to establish a quantitative analysis model for cream pigment content, and multiple evaluation criteria were selected to comprehensively evaluate the model. The R² was 0.9402, RMSEP was 0.2509 and RPD was 4.0893. Consequently, NIR spectroscopy, combined with data pretreatments and random forest model, was confirmed to be an interesting tool for non-destructive evaluation of pigment content in cream.

Microwave spectroscopy based on the waveguide technique for the nondestructive freshness evaluation of egg

A rectangular waveguide equipped with a network analyzer was used to assess the quality indices of shell egg. The scattering parameters of the eggs were acquired in the range of 0.9-1.7 GHz and they were then used to calculate microwave spectra of the samples. PLS and ANN regression methods were implemented to predict the egg quality indices and SIMCA and ANN classification methods were applied to classify the eggs based on their storage time. The best predictive models, however, obtained from ANN analysis where the yolk coefficient, air cell height, thick albumen height, Haugh unit, and albumen pH could be predicted with the residual predictive deviation (RPD) values of 3.500, 3.000, 2.411, 2.033, and 1.829, respectively. To classify the eggs according to their storage time, both SIMCA and ANN analyses resulted in the total accuracy of 100% when return loss spectra were used as the input.

Advances in Sheep and Goat Meat Products Research

The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.

Feasibility of near infrared transmittance spectroscopy to predict fatty acid composition of commercial processed meat

BACKGROUND

The new European Regulation 1169/2011 concerning nutrition declaration of food products compels the addition of saturated fatty acids, whereas the declaration of monounsaturated and polyunsaturated fatty acids remains voluntary. Therefore, the industry is interested in a more rapid, easy and less cost-effective analysis method for accomplishing this labelling regulation. The present study aimed to evaluate the ability of near infrared transmittance spectroscopy (wavelengths between 850 and 1050 nm) to predict the fatty acid (FA) composition of commercial processed meat samples (n = 310).

RESULTS

Good predictions were achieved for the absolute content of saturated, unsaturated, monounsaturated and polyunsaturated FA, as well as ω-6 groups, and also for a few individual FA (C16:0, C18:0, C18:1n9, C18:2n6 and 18:1n7), with the coefficient of determination in cross-validation being > 0.90 and the residual prediction deviation being > 3.15. Unsatisfactory models were obtained for the relative content of FA.

CONCLUSION

Near infrared transmittance spectroscopy can be considered as a reliable method for predicting the main groups of FA in processed meat products, whereas predictions of individual FA are less reliable. © 2017 Society of Chemical Industry.

Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods

Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.

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

Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

Modern Safety Control for Meat Products: Near Infrared Spectroscopy Utilised for Detection of Contaminations and Adulterations of Premium Veal Products

This article gives insights into the development of a modern “green analytical technique” based on near infrared (NIR) spectroscopy to detect harmful frauds or accidental contaminations with pork and pork fat in processed veal products. A project carried out at the Institute of Analytical Chemistry and Radiochemistry of the University of Innsbruck led to promising results in the field of food safety and control. The aim of the project was to develop quick and reliable methods for different fields of application based on non-destructive NIR spectroscopy. In this connection, three specialised techniques were successfully developed, each using an individual instrumental setup and data evaluation process. Methods suited for accurate laboratory measurements (benchtop instrument), ready for in- and at-line applications or high-throughput-analyses (utilising a fibre optic probe) and a method characterised by flexibility and user friendliness with the ability to perform on-site analyses (handheld spectrometer) were developed within the scope of this project. It was possible to detect contamination with pork as well as contamination with pork fat from 50% w/w down to 10% w/w in handmade veal sausages. Satisfying results were achieved even for additional measurements in which spectra were recorded directly through a polymer packaging. A limit of detection of 3% w/w was found for analyses of pure minced veal samples contaminated with pork.

Sustainable and consumer-friendly emerging technologies for application within the meat industry: An overview

New and emerging robust technologies can play an important role in ensuring a more resilient meat value chain and satisfying consumer demands and needs. This paper outlines various novel thermal and non-thermal technologies which have shown potential for meat processing applications. A number of process analytical techniques which have shown potential for rapid, real-time assessment of meat quality are also discussed. The commercial uptake and consumer acceptance of novel technologies in meat processing have been subjects of great interest over the past decade. Consumer focus group studies have shown that consumer expectations and liking for novel technologies, applicable to meat processing applications, vary significantly. This overview also highlights the necessity for meat processors to address consumer risk-benefit perceptions, knowledge and trust in order to be commercially successful in the application of novel technologies within the meat sector.

Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis)

A visible/near-infrared hyperspectral imaging (HSI) system (400-1000 nm) coupled with wavelet analysis was used to determine the total volatile basic nitrogen (TVB-N) contents of prawns during cold storage. Spectral information was denoised by conducting wavelet analysis and uninformative variable elimination (UVE) algorithm, and then three wavelet features (energy, entropy and modulus maxima) were extracted. Quantitative models were established between the wavelet features and the reference TVB-N contents by using three regression algorithms. As a result, the LS-SVM model with modulus maxima features was considered as the best model for determining the TVB-N contents of prawns, with an excellent RP(2) of 0.9547, RMSEP=0.7213 mg N/100g and RPD=4.799. Finally, an image processing algorithm was developed for generating a TVB-N distribution map. This study demonstrated the possibility of applying the HSI imaging system in combination with wavelet analysis to the monitoring of TVB-N values in prawns.

Rapid Characterization of Fatty Acids in Oleaginous Microalgae by Near-Infrared Spectroscopy

The key properties of microalgal biodiesel are largely determined by the composition of its fatty acid methyl esters (FAMEs). The gas chromatography (GC) based techniques for fatty acid analysis involve energy-intensive and time-consuming procedures and thus are less suitable for high-throughput screening applications. In the present study, a novel quantification method for microalgal fatty acids was established based on the near-infrared spectroscopy (NIRS) technique. The lyophilized cells of oleaginous Chlorella containing different contents of lipids were scanned by NIRS and their fatty acid profiles were determined by GC-MS. NIRS models were developed based on the chemometric correlation of the near-infrared spectra with fatty acid profiles in algal biomass. The optimized NIRS models showed excellent performances for predicting the contents of total fatty acids, C16:0, C18:0, C18:1 and C18:3, with the coefficient of determination (R²) being 0.998, 0.997, 0.989, 0.991 and 0.997, respectively. Taken together, the NIRS method established here bypasses the procedures of cell disruption, oil extraction and transesterification, is rapid, reliable, and of great potential for high-throughput applications, and will facilitate the screening of microalgal mutants and optimization of their growth conditions for biodiesel production.