A Review of Hyperspectral Imaging for Chicken Meat Safety and Quality Evaluation: Application, Hardware, and Software

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems

Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-and-error experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.

Application of seed mucilage as functional biopolymer in meat product processing and preservation

Meat products consumption is rising globally, but concerns about sustainability, fat content, and shelf life. Synthetic additives and preservatives used for extending the shelf life of meat often carry health and environmental drawbacks. Seed mucilage, natural polysaccharides, possesses unique functional properties like water holding, emulsifying, and film forming, offering potential alternatives in meat processing and preservation. This study explores the application of seed mucilage from diverse sources (e.g., flaxseed, psyllium, basil) in various meat and meat products processing and preservation. Mucilage's water-holding and emulsifying properties can potentially bind fat and decrease the overall lipid content in meat and meat-based products. Moreover, antimicrobial and film-forming properties of mucilage can potentially inhibit microbial growth and reduce oxidation, extending the shelf life. This review emphasizes the advantages of incorporating mucilage into processing and coating strategies for meat and seafood products.

A LIBSVM quality assessment model for apple spoilage during storage based on hyperspectral data

To assess the quality of apple samples during storage, this study proposes a spoilage benchmark based on hyperspectral data feature indicators and the Mahalanobis Distance (MD). Additionally, a quality assessment model was developed utilizing LIB Support Vector Machine (LIBSVM). Initially, a spoilage benchmark for apple samples was preliminarily established using hyperspectral data feature indicators, including the color feature, texture feature of sample hyperspectral images, and wavelet packet energy (WPE) of sample spectral information. Secondly, this study utilized the successive projection algorithm (SPA) to extract three wavelength sets sensitive to changes in the three indicators. This process resulted in the identification of 20 feature wavelengths based on the three sets. Subsequently, the spoilage benchmark for apple samples was verified using MD based on the spectral information of feature wavelengths. Ultimately, utilizing pre-processed spectral information enhanced by the sliding window algorithm and spoilage benchmark, the LIBSVM quality assessment model was developed, achieving a training set accuracy of 99.94% and a test set accuracy of 99.66%. Moreover, to assess the strength and applicability of the model, a verification experiment was conducted using a different set of apple samples. The training set accuracy was 100% and the test set accuracy was 99.83%. These findings indicate that the model can effectively indicate the level of spoilage in each sample during long-term storage. This also serves to demonstrate the robustness of the model and the effectiveness of the spoilage benchmark determination method during apple storage.

Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review

Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.

Comparison of the impact of UV-light emitting diode and UV lamp at pilot-plant scale level on quality parameters and consumer perception of fresh chicken meat

The present study compared the impact of two UV light devices: conventional UV lamp and UV-LED on the colour, pH, lipid and protein oxidation of fresh chicken breast meat aerobically stored at 4 °C for 10 days. Lipid oxidation was the most impacted quality attribute in UV lamp treated meat, unlike UV-LED that showed no effect compared to non-treated meat. Slight changes were observed in colour, pH and protein oxidation of chicken samples subjected to UV lamp and UV-LED. To evaluate these changes from a consumer perspective, the different treatment samples were stored at 4 °C for 3 days and colour likeness, odour likeness and overall appearance were assessed by consumer sensory analysis. However, alterations in quality parameters of chicken meat caused by UV light did not decrease overall acceptance in the sensory analysis. UV-LED was the preferred chicken meat by the participants, even compared to non-treated meat.

Impact of Gamma Irradiation and Kale Leaf Powder on Amino Acid and Fatty Acid Profiles of Chicken Meat under Different Storage Intervals

The present study was planned to determine the effect of kale leaf powder and gamma rays on variations in the pH, amino acid and fatty acid profiles of chicken meat at different storage intervals. Significant changes (p ≤ 0.05) in the pH, amino acid and fatty acid profiles of chicken meat following different treatments (KLP (1% and 2%) and gamma irradiation (3k Gy)) were reported at 0, 7 and 14 days of storage. The pH value of the chicken meat sample decreased with the addition of kale leaf powder, whereas the value increased following a gamma irradiation dose of 3 kGy and with the passage of time. During different storage intervals, the minimum reduction in the amino acid and fatty acid quantities in the chicken meat samples was reported after gamma irradiation treatment. However, with the addition of KLP, the amount of amino acids and fatty acids in the chicken meat samples increased. Conclusively, the pH was observed to be reduced in the meat following combined treatment (irradiation + KLP), whereas the 2% KLP treatment improved the amino acid and fatty acid profiles of the chicken samples.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

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

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

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Single-shot compressive hyperspectral imaging with dispersed and undispersed light using a generally available grating

Commercially available hyperspectral cameras are useful for remote sensing, but in most cases snapshot imaging is difficult due to the need for scanning. The coded aperture snapshot spectral imager (CASSI) has been proposed to simultaneously acquire a target scene's spatial and spectral dimensional data, employing a refractive prism as a disperser. This paper proposes a CASSI-based technique using a generally available diffraction grating of a Ronchi ruling and blazed grating and its improvement using the undispersed zeroth-order light. The feasibility and performance of the proposed technique are experimentally validated, and the grating parameters are identified.

Overview of Rapid Detection Methods for Salmonella in Foods: Progress and Challenges

Salmonella contamination in food production and processing is a serious threat to consumer health. More and more rapid detection methods have been proposed to compensate for the inefficiency of traditional bacterial cultures to suppress the high prevalence of Salmonella more efficiently. The contamination of Salmonella in foods can be identified by recognition elements and screened using rapid detection methods with different measurable signals (optical, electrical, etc.). Therefore, the different signal transduction mechanisms and Salmonella recognition elements are the key of the sensitivity, accuracy and specificity for the rapid detection methods. In this review, the bioreceptors for Salmonella were firstly summarized and described, then the current promising Salmonella rapid detection methods in foodstuffs with different signal transduction were objectively summarized and evaluated. Moreover, the challenges faced by these methods in practical monitoring and the development prospect were also emphasized to shed light on a new perspective for the Salmonella rapid detection methods applications.

An Introduction to Current Trends in Meat Microbiology and Hygiene

Purpose of Review

This editorial review aims to provide readers with an introduction to the Current Clinical Microbiology Report Special Issue “Meat Microbiology and Hygiene.” It will provide an overview of overarching trends and developments in this field, introduce the articles presented in this Special Issue, and attempt to offer a glimpse into the future of meat microbiology and hygiene.

Recent Findings

Meat production has been subjected to transformative changes within the last decade, and the focus of assuring meat safety has shifted to account for changing consumer demands as well as new microbial risks such as strains carrying antimicrobial resistance determinants.

Summary

Assuring that meat products meet high safety standards remains crucial to consumers worldwide. New risk-based meat safety assurance systems leveraging latest technological advances are needed to protect consumers and promote public health.

From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy

Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.

Software test quality evaluation based on fuzzy mathematics

The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.

Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%.

Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods: A Comprehensive Review of Recent Technological Advances

Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested.

The effect of chitosan coatings enriched with the extracts and essential oils of Elettaria Cardamomum on the shelf-life of chicken drumsticks vacuum-packaged at 4 °C

This study was conducted to investigate the microbial, chemical, and sensory quality of chicken drumsticks vacuum-packaged at 4 °C, using chitosan (CH) coating containing ethanol extracts and the essential oils (EO) of Elettaria Cardamomum. The treatments were stored for 16 days in cold conditions and investigated in three-day intervals. Total volatile base nitrogen analysis showed that, on the 6th day, the uncoated treatment showed unacceptable values, while treatments containing the EO and extracts stayed below the specified level even on the 16th day. In addition, during storage, the Peroxide values for the uncoated sample were higher than the documented for the coated groups. Results of the Thiobarbituric acid reactive substances index revealed that the sample containing the EO of E. Cardamomum is the best treatment. Regarding to pH on the 16th day, the microbial growth in the mixed sample was 0.46 units lower than the control group. Microbial analysis showed that coating significantly reduce the growth of all five groups of bacteria at 4 ± 1 °C; thus, on the 6th day, the differences between mesophiles, Enterobacteriaceae, psychrotrophic, and H₂S-producing bacteria with the control group were 4.5, 4.5, 2.5, and 2 logs Cfu/g, respectively (p < 0.05). Furthermore, the lactic acid bacteria growth was completely stopped. Finally, it was found that adding EO and extracts could significantly preserve the sensory quality of the samples. Thus, it was concluded that vacuum-packaged CH coatings enriched with the extract and EO of E. Cardamomum can preserve the quality of chicken drumsticks during storage in refrigerators.

The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars-the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)-that support the development and implementation of vibrational spectroscopy applications.