Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy

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.

Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples

Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R2P of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R2P values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.

Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems.

Recent advances in spoilage mechanisms and preservation technologies in beef quality: A review

Beef is a popular meat product that can spoil and lose quality during postharvest handling and storage. This review examines different preservation methods for beef, from conventional techniques like low-temperature preservation, irradiation, vacuum packing, and chemical preservatives, to novel approaches like bacteriocin, essential oil, and non-thermal technologies. It also discusses how these methods work and affect beef quality. The review shows that beef spoilage is mainly due to enzymatic and microbial activities that impact beef freshness, texture, and quality. Although traditional preservation methods can extend beef shelf life, they have some drawbacks and limitations. Therefore, innovative preservation methods have been created and tested to improve beef quality and safety. These methods have promising results and potential applications in the beef industry. However, more research is needed to overcome the challenges and barriers for their commercialization. This review gives a comprehensive and critical overview of the current and emerging preservation methods for beef and their implications for the beef supply chain.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.