Estimation of Minced Pork Microbiological Spoilage through Fourier Transform Infrared and Visible Spectroscopy and Multispectral Vision Technology

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Recent Studies and Applications of Hydrogel-Based Biosensors in Food Safety

Food safety has increasingly become a human health issue that concerns all countries in the world. Some substances in food that can pose a significant threat to human health include, but are not limited to, pesticides, biotoxins, antibiotics, pathogenic bacteria, food quality indicators, heavy metals, and illegal additives. The traditional methods of food contaminant detection have practical limitations or analytical defects, restricting their on-site application. Hydrogels with the merits of a large surface area, highly porous structure, good shape-adaptability, excellent biocompatibility, and mechanical stability have been widely studied in the field of food safety sensing. The classification, response mechanism, and recent application of hydrogel-based biosensors in food safety are reviewed in this paper. Furthermore, the challenges and future trends of hydrogel biosensors are also discussed.

Effect of heat treatment on microbiological safety of supermarket food waste as substrate for black soldier fly larvae (Hermetia illucens)

Supermarket food waste, constituting 13% of global food waste, can be upcycled as substrate for black soldier fly larvae (BSFL) and converted into larval biomass. Since presence of food pathogens in supermarket food waste is likely, microbiological safety should be ensured when using waste as insect substrate. Heat treatment provides a suitable pre-treatment to reduce microbiological contaminations. This study investigated the effect of different temperature-time combinations on the microbiological safety of supermarket food waste as BSFL substrate. Artificial supermarket food waste without meat and fish (SFW) was inoculated with both Salmonella and Staphylococcus aureus (7.0log cfu/g) and treated at 50 and 60 °C for 10, 20 and 30 min. While 50 °C was insufficient for adequate pathogen reduction, 60 °C only required 10 min to reduce the Enterobacteriaceae and S.aureus counts to < 1.0logcfu/g and for absence of Salmonella in 25 g. Heat-treated SFW could be stored for two days at ambient temperature or refrigerated without pathogen growth. Treatment of supermarket food waste containing meat and fish at 60 °C for 10 min caused similar results as for SFW, but S.aureus persisted (2.4logcfu/g), possibly by protective effects of fat and/or proteins. Finally, BSFL rearing experiments on SFW revealed significantly higher larval mass, bioconversion efficiency and waste reduction than on Gainesville diet, with no notable differences between untreated and heat-treated SFW. Rearing BSFL on supermarket food waste is possible, and unsafe food waste can be heated to obtain safety without eliminating nutrients necessary for rearing.

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

The objective of this study was to assess the potential to predict the microbial beef spoilage indicators by Raman and Fourier transform infrared (FT-IR) spectroscopies. Vacuum skin packaged (VSP) beef steaks were stored at 0 °C, 4 °C, 8 °C and under a dynamic temperature condition (0 °C ∼ 4 °C ∼ 8 °C, for 36 d). Total viable count (TVC) and total volatile basic nitrogen (TVB-N) were obtained during the storage period along with spectroscopic data. The Raman and FTIR spectra were baseline corrected, pre-processed using Savitzky-Golay smoothing and normalized. Subsequently partial least squares regression (PLSR) models of TVC and TVB-N were developed and evaluated. The root mean squared error (RMSE) ranged from 0.81 to1.59 (log CFU/g or mg/100 g) and the determination coefficient (R²) from 0.54 to 0.75. The performance of PLSR model based on data fusion (combination of Raman and FT-IR data) is better than that based on Raman spectra and similar to that of FT-IR. Overall, Raman spectroscopy, FT-IR spectroscopy, and a combination of both exhibited a potential for the prediction of the beef spoilage.

Review of pork and pork products as a source for transmission of methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic bacterium that can cause infection in animals and humans. Recently, MRSA from food-producing or farm animals has been identified as livestock-associated MRSA (LA-MRSA). The spread of LA-MRSA is particularly found in pork and pork products because LA-MRSA has been widely known to infect pigs. The most common type of LA-MRSA identified in pork and pork products is the clonal complex LA-MRSA 398 (LA-MRSA CC398). The MRSA strains on the surface of pork carcasses can be spread during the handling and processing of pork and pork products through human hands, cutting tools, and any surface that comes into direct contact with pork. Food infection is the main risk of MRSA in pork and pork products consumed by humans. Antibiotics to treat food infection cases due to MRSA infection include vancomycin and tigecycline. The spread of MRSA in pork and pork products is preventable by appropriately cooking and cooling the pork and pork products at temperatures above 60°C and below 5°C, respectively. It is also necessary to take other preventive measures, such as having a clean meat processing area and disinfecting the equipment used for processing pork and pork products. This review aimed to explain epidemiology, transmission, risk factors, diagnosis, public health consequences, treatment of food poisoning, and preventing the spread of MRSA in pork and pork products.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Use of Fourier Transform Infrared Spectroscopy for Monitoring the Shelf Life and Safety of Yogurts Supplemented With a Lactobacillus plantarum Strain With Probiotic Potential

The current study aimed to explore the performance of a probiotic Lactobacillus strain as an adjunct culture in yogurt production and to assess Fourier transform infrared spectroscopy as a rapid, noninvasive analytical technique to evaluate the quality and the shelf life of yogurt during storage. In this respect, bovine milk (full-fat) was inoculated with the typical yogurt starter culture without (control case) or with the further addition of Lactobacillus plantarum T571 as an adjunct (probiotic case). The milk was also inoculated with a cocktail mixture of three strains of Listeria monocytogenes in two different initial levels of inoculum, and the fermentation process was followed. Accordingly, yogurt samples were stored at 4 and 12°C, and microbiological, physicochemical, molecular, and sensory analyses were performed during storage. Results showed that the lactic acid bacteria exceeded 7 log CFU/g during storage in all samples, where the probiotic samples displayed higher acidity, lower pH, and reduced counts of Lb. monocytogenes in a shorter period than the control ones at both temperatures. Pulsed-field gel electrophoresis verified the presence of the probiotic strain until the end of storage at both temperatures and in adequate amounts, whereas the survival and the distribution of Listeria strains depended on the case. The sensory evaluation showed that the probiotic samples had desirable organoleptic characteristics, similar to the control. Finally, the spectral data collected from the yogurt samples during storage were correlated with microbiological counts and sensory data. Partial least squares and support vector machine regression and classification models were developed to provide quantitative estimations of yogurt microbiological counts and qualitative estimations of their sensory status, respectively, based on Fourier transform infrared fingerprints. The developed models exhibited satisfactory performance, and the acquired results were promising for the rapid estimation of the microbiological counts and sensory status of yogurt.

Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 10³ colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R²), and the bias (B_f) and accuracy (A_f) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited A_f and B_f between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R² and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.

Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis

The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n ​= ​215) were conducted at 0, 5, 10, and 15 ​°C for up to 480 ​h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm² for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm² for MSI data and 1.078 log CFU/cm² for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm²) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm². Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm² in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

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Poultry meat’s vulnerability to spoilage demands rapid quality assessment LWT-Food Sci. Technol.methods.

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FT-IR and MSI are non-invasive methods applied in a variety of meat products.

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SorfML is a web platform providing diverse machine learning algorithms.

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FT-IR analysis via lars predicted efficiently microbial loads of TVC.

Food Quality Assessed by Chemometrics

Food market globalization, food security as well as increasing consumer demand for safe, minimally processed and healthy food impose the need to establish new approaches for identifying and assessing food quality markers. Nowadays, food industry stakeholders are challenged to assure food quality and safety without compromising several prerequisites such as sustainable and ecologically resilient food production, prolonged shelf life, satisfactory sensory quality, enhanced nutritional value and health-promoting properties. In addition, food fraud related to deliberate product mislabeling or economically intended adulteration is of major concern for both industry and regulatory authorities due to cost and public health implications. Notwithstanding the great number of state-of-the-art analytical tools available for quantifying food quality markers, their implementation results in highly complex and big datasets, which are not easily interpretable. In this context, chemometrics e.g., supervised and unsupervised multivariate exploratory analyses, design-of-experiment methodology, univariate or multivariate regression modelling etc., are commonly implemented as part of food process optimization and food quality assessment. In this Special Issue, we aimed to publish innovative research and perspective papers on chemometric-assisted case studies relating to food quality assessment, food authenticity, mathematical modelling and optimization of processes involved in food manufacturing.

A machine learning workflow for raw food spectroscopic classification in a future industry

Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry.