Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy

Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods

Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing the safety of Animal-Source Foods (ASFs). Studies that link ML with HACCP monitoring in ASFs are limited. The present review provides an overview of ML, feature extraction, and selection algorithms employed for food safety. Several non-destructive techniques are presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, and hyperspectral imaging combined with ML algorithms. Prospects include enhancing predictive models for food safety with the development of hybrid Artificial Intelligence (AI) models and the automation of quality control processes using AI-driven computer vision, which could revolutionize food safety inspections. However, handling conceivable inclinations in AI models is vital to guaranteeing reasonable and exact hazard assessments in an assortment of nourishment generation settings. Moreover, moving forward, the interpretability of ML models will make them more straightforward and dependable. Conclusively, applying ML algorithms allows real-time monitoring and predictive analytics and can significantly reduce the risks associated with ASF consumption.

ConvColor DL: Concatenated convolutional and handcrafted color features fusion for beef quality identification

Beef is an important food product in human nutrition. The evaluation of the quality and safety of this food product is a matter that needs attention. Non-destructive determination of beef quality by image processing methods shows great potential for food safety, as it helps prevent wastage. Traditionally, beef quality determination by image processing methods has been based on handcrafted color features. It is, however, difficult to determine meat quality based on the color space model alone. This study introduces an effective beef quality classification approach by concatenating learning-based global and handcrafted color features. According to experimental results, the convVGG16 + HLS + HSV + RGB + Bi-LSTM model achieved high performance values. This model's accuracy, precision, recall, F1-score, AUC, Jaccard index, and MCC values were 0.989, 0.990, 0.989, 0.990, 0.992, 0.979, and 0.983, respectively.

Effect of High-Pressure Processing Treatment on the Physicochemical Properties and Volatile Flavor of Mercenaria mercenaria Meat

High-pressure processing (HPP) technology can significantly improve the texture and flavor of Mercenaria mercenaria. This study aimed to investigate the effect of HPP treatment with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and a holding time of 8 min at 20 °C on the physicochemical properties and volatile flavors of M. mercenaria. The significant changes in hardness, resilience, and water holding capacity occurred with increasing pressure (p < 0.05), resulting in improved meat quality. Scanning electron microscopy (SEM) was utilized to observe the decomposition of muscle fibers in M. mercenaria due to varying pressures, which explains the differences in texture of M. mercenaria. Different pressure treatments also had an influence on the volatile flavor of M. mercenaria, and the quantities of low-molecular-weight aldehydes (hexanal, heptanal, and nonanal) with a fishy taste decreased dramatically following 400 and 500 MPa HPP treatments. Furthermore, the level of 2-Methylbutyraldehyde, which is related to sweetness, increased significantly following 400 MPa HPP treatment. The study found that 400 MPa HPP treatment resulted in minor nutrient losses and enhanced sensory quality. The results of this study provide a theoretical basis for the application of HPP treatment to M. mercenaria.

Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy

The potential of four dimension reduction methods for near-infrared spectroscopy was investigated, in terms of predicting the protein, fat, and moisture contents in lamb meat. With visible/near-infrared spectroscopy at 400-1050 nm and 900-1700 nm, respectively, calibration models using partial least squares regression (PLSR) or multiple linear regression (MLR) between spectra and quality parameters were established and compared. The MLR prediction models for all three quality parameters based on the wavelengths selected by stepwise regression achieved the best results in the spectral region of 400-1050 nm. As for the spectral region of 900-1700 nm, the PLSR prediction model based on the raw spectra or high-correlation spectra achieved better results. The results of this study indicate that sampling interval shortening and of peak-to-trough jump features are worthy of further study, due to their great potential in explaining the quality parameters.

Nondestructive detection and visualization of protein oxidation degree of frozen-thawed pork using fluorescence hyperspectral imaging

This study evaluated the feasibility of non-destructive detection of carbonyl and total sulfhydryl contents by fluorescence hyperspectral imaging (F-HSI) to visualize the protein oxidation degree of pork during freezing-thawing process. Fluorescence hyperspectral image acquisition and chemical analysis were carried out on pork samples treated with different freeze-thaw cycles. Variational Mode Decomposition (VMD) was used to preprocess the raw spectrum, and Mutual Information-Variance Inflation Factor (MI-VIF) was applied to select the feature wavelengths. The Partial least squares regression (PLSR) models based on selected 19 wavelengths obtained good performance in predicting carbonyl content with R2p of 0.9275 and RMSEP of 0.0812 nmol/mg, and sulfhydryl content with R2p of 0.9512 and RMSEP of 1.2979 nmol/mg. The distribution maps of carbonyl and total sulfhydryl content were established based on the optimal prediction models. The results confirmed that the contents of carbonyl and total sulfhydryl in pork could be successfully predicted by F-HSI, so as to monitor the protein oxidation degree of pork during freezing-thawing.

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.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Effects of selenium yeast and jujube powder dietary supplements on conformational and functional properties of post-mortem chicken myofibrillar protein

The aim of the study was to evaluate the effects of selenium yeast and jujube powder on the structure and functional properties of post-mortem myofibrillar protein (MP) in white feather broilers. Changes in the structure (surface hydrophobicity, secondary structure, and tertiary structure), functional properties (solubility, turbidity, emulsifying, and foaming characteristics), and gel properties (gel strength, springiness, and water-holding capacity) of the MPs of white feather broiler, which were fed with different concentrations of selenium yeast or/and jujube powder (selenium yeast: 0,0.3, and 0.6 mg/kg; jujube powder: 8% to replace corn) for 42 days, were determined at 0, 24, and 72 h post-mortem. The results showed that with increasing concentrations of selenium yeast and jujube powder in the diet, the α-helix content, solubility, emulsification, and foaming of post-mortem chicken MP increased significantly (P < 0.05). The gel strength, springiness, and water-holding capacity of MP also increased, but the differences between the treatment groups were not significant (P > 0.05). In addition, the β-folding content and turbidity of MP decreased significantly (P < 0.05). Both the increase in selenium yeast levels and the addition of jujube powder improved the structural integrity and functional properties of MP. The best improvement effect was found in the combination group of high-dose selenium yeast and jujube powder, and there were significant interactions between them in the indices of α-helix, β-folding, turbidity, emulsification, and foam stability of MP. In conclusion, supplementing diets with seleniumyeast and jujube powder could maintain the structural stability of MPs in post-mortem chicken breast, leading to good functional properties. The results of this study may provide new insights into the effects of pre-slaughter feeding on post-mortem muscle MP conformation control and quality improvement.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

The Mathematical Models of the Operation Process for Critical Production Facilities Using Advanced Technologies

The paper presents data on the problems of monitoring and diagnosing the technical conditions of critical production facilities, such as torpedo ladle cars, steel ladles. The accidents with critical production facilities, such as torpedo ladle cars, lead to losses and different types of damages in the metallurgical industry. The paper substantiates the need for a mathematical study of the operation process of the noted critical production facilities. A Markovian graph has been built that describes the states of torpedo ladle cars during their operation. A mathematical model is presented that allows determining the optimal frequency of diagnostics of torpedo ladle cars, which, in contrast to the existing approaches, take into account the procedures for preventive diagnostics of torpedo ladle cars, without taking them out of service. Dependence of the utilization coefficient on the period of diagnostics of PM350t torpedo ladle cars was developed. The results (of determining the optimal period of diagnostics for PM350t torpedo ladle cars) are demonstrated. The system for automated monitoring and diagnosing the technical conditions of torpedo ladle cars, without taking them out of service, has been developed and described.

Prediction of Trained Panel Sensory Scores for Beef with Non-Invasive Raman Spectroscopy

The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. Another steak was removed from the muscle and aged for 14 days before being assessed for 12 sensory traits by a trained panel. The most accurate coefficients of determination of cross validation (R2CV) calibrated within the current study were for the trained sensory panel textural scores; particularly tenderness (0.46), chewiness (0.43), stringiness (0.35) and difficulty to swallow (0.33), with practical predictions also achieved for metallic flavour (0.52), fatty after-effect (0.44) and juiciness (0.36). In general, the application of mathematical spectral pre-treatments to Raman spectra improved the predictive accuracy of chemometric models developed. This study provides calibrations for valuable quality traits derived from a trained sensory panel in a non-destructive manner, using Raman spectra collected at a time-point compatible with meat management systems.

Water holding properties of Atlantic salmon

With global seafood production increasing to feed the rising population, there is a need to produce fish and fishery products of high quality and freshness. Water holding properties, including drip loss (DL) and water holding capacity (WHC), are important parameters in determining fish quality as they affect functional properties of muscles such as juiciness and texture. This review focuses on the water holding properties of Atlantic salmon and evaluates the methods used to measure them. The pre- and postmortem factors and how processing and preservation methods influence water holding properties and their correlations to other quality parameters are reviewed. In addition, the possibility of using modelling is explained. Several methods are available to measure WHC. The most prevalent method is the centrifugation method, but other non-invasive and cost-effective approaches are increasingly preferred. The advantages and disadvantages of these methods and future trends are evaluated. Due to the diversity of methods, results from previous research are relative and cannot be directly compared unless the same method is used with the same conditions.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R²_CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R²_CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing, &nbsp;thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

Technological innovations or advancement in detecting frozen and thawed meat quality: A review

Frozen storage is one of the main storage methods for meat products. Freezing and thawing processes are important factors affecting the quality of stored foods. Deterioration of texture, denaturation of protein, decline of water holding capacity etc. are among the major quality issues during freezing that must be addressed. A number of advanced technologies are now available to detect the quality changes that can occur during freezing and/or thawing. This paper presents an overview of the techniques commonly used for the detection of meat product quality; these include: advanced microscopy, molecular sensory science and technology, nuclear magnetic resonance, hyperspectral technology, near infrared spectroscopy, Raman spectroscopy etc. These direct and indirect measurement techniques can characterize the quality of meat product from many different angles. The objective of this review is to provide an in-depth understanding of possible quality changes in meat products during freezing and thawing cycle so as to improve the quality of frozen and thawed meat products in industrial practice.

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.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed

This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups-0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R² and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model's polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R² and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.

Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy

The main purpose of this study was to investigate if Raman spectra recorded at the exact same position as drip loss measurements could improve prediction of drip loss in pork. One ventral and one dorsal cylindrical plug, cut from a standardized slice from Longissimus lumborum, were used to determine drip loss by EZ-DripLoss method and to collect Raman spectra, while ultimate pH was measured at another location. Partial least squares regression models were developed using spectra from each plug individually or averaged spectra from both plugs. The best models used spectra from the ventral plug, resulting in r_cv²=0.75, root mean square error of cross-validation (RMSECV) = 1.27% and ratio of prediction to deviation (RPD) =2.0 for EZ-DripLoss and r_cv²=0.72, RMSECV = 0.05 and RPD = 2.0 for ultimate pH. Results indicate that Raman spectroscopy can be used for rough screening of drip loss and pH in pork, and that the location chosen for collection of spectra can be very important for successful predictions.

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.

Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics

Rapid prediction of beef quality remains a challenge for meat processors. This study evaluated the potential of Raman spectroscopy followed by chemometrics for prediction of Warner-Bratzler shear force (WBSF), intramuscular fat (IMF), ultimate pH, drip-loss and cook-loss. PLS regression models were developed based on spectra recorded on frozen-thawed day 2 longissimus thoracis et lumborum muscle and validated using test sets randomly selected 3 times. With the exception of ultimate pH, models presented notable performance in calibration (R² ranging from 0.5 to 0.9; low RMSEC) and, despite variability in the results, promising predictive ability: WBSF (RMSEP ranging from 4.6 to 9 N), IMF (RMSEP ranging from 0.9 to 1.1%), drip-loss (RMSEP ranging from 1 to 1.3%) and cook-loss (RMSEP ranging from 1.5 to 2.9%). Furthermore, the loading values indicated that the physicochemical variation of the meat influenced the models. Overall, results indicated that Raman spectroscopy is a promising technique for routine quality assessments of IMF and drip-loss, which, with further development and improvement of its accuracy could become a reliable tool for the beef industry.

Preliminary investigation of the use of Raman spectroscopy to predict beef spoilage in different types of packaging

In this study, pH, meat color analysis, microbial counts and Raman spectroscopic data were obtained from beef steaks stored at 4 °C for up to 21 days using two different packaging methods: vacuum (VP) and modified atmosphere packaging (MAP). Models using partial least square regression (PLSR), indicated that Raman spectroscopy was able to predict total viable counts (TVC) and lactic acid bacteria (LAB) measured at 21d post mortem (TVC in VP: R²_cv = 0.99, RMSEP = 0.61; TVC in MAP: R²_cv = 0.90, RMSEP = 0.38; LAB in VP: R²_cv = 0.99, RMSEP = 0.54; LAB in MAP: R²_cv = 0.75, RMSEP = 0.60). The results of this study demonstrate that Raman spectroscopy may have potential for the rapid determination of meat spoilage.

Evaluating the performance of a miniaturized NIR spectrophotometer for predicting intramuscular fat in lamb: A comparison with benchtop and hand-held Vis-NIR spectrophotometers

This study compares a miniaturized spectrophotometer to benchtop and hand-held Vis-NIR instruments in the spectral range of 900-1700 nm for prediction of intramuscular fat (IMF) content of freeze-dried ground lamb meat; and their ability to differentiate fresh lamb meat based on animal age (4 vs 12 months). The performance of the miniaturized spectrophotometer was not affected by sample temperature equilibration time. Partial Least Square regression models for IMF showed R_cv² = 0.86-0.89 and RMSECV = 0.36-0.40 values for all instruments. Day-to-day instrumental variation adversely affected performance of the miniaturized spectrophotometer (R²_p = 0.27, RMSEP = 1.28). This negative effect was overcome by representing day-to-day variation in the model. The benchtop spectrophotometer and miniaturized spectrophotometer differentiated lamb meat by animal age. The miniaturized spectrophotometer has potential to be a fast, ultra-compact and cost-effective device for predicting IMF in freeze-dried ground lamb meat and for age classification of fresh lamb meat.

Is production system a relevant attribute in consumers' food preferences? The case of Iberian dry-cured ham in Spain

Spanish consumers have a strong preference for Iberian meat products, as they perceive them to be of extra sensorial and nutritional quality. The production of these meat products depends on multiple variables, such as genetics, livestock production systems and, above all, the feed provided. The aim of this paper is to study the preferences of Spanish consumers for the various types of Iberian dry-cured ham, analysing whether they are willing to pay the premium required by the highest-quality products. The methodological approach combined a sensory analysis and a choice-based conjoint experiment with obtained through tasting sessions in Extremadura (SW of Spain). Findings of the sensory test have shown that there are significant differences in odour, texture and taste, explained mainly by the type of feed pigs were fed. The main results of the choice experiment have also shown that the type of feed is the most preferred attribute by consumers, in line with the sensory analysis.