Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

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.

Seabream quality monitoring throughout the supply chain using a portable multispectral imaging device

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision making, reducing food waste and increasing sustainability. In that framework, a portable multispectral imaging sensor was used, while the acquired data in combination with neural networks were evaluated for the prediction of fish fillets quality. Images of fish fillets were acquired using samples from both aquaculture and retail stores of different packaging and fish parts. The obtained products (air or vacuum packaged) were further stored at different temperature conditions. In parallel to image acquisition, microbial quality was estimated as well. The data were used for the training of predictive neural models that aimed to estimate total aerobic counts (TAC). The models were developed and validated using data from aquaculture and were externally validated with samples purchased from the retail stores. The set up allowed the evaluation of models for the different parts of the fish and conditions. The performance for the validation set was similar for flesh (RMSE: 0.402-0.547) and skin side (RMSE: 0.500-0.533) of the fish fillets. The performance for the different packaging conditions was also similar, however, in the external validation, the vacuum-packaged samples showed better performance in terms of RMSE compared to the air-packaged ones. Models irrespective of packaging condition are very important for cases where the products' history is unknown although the prediction capability was not as high as in the models per packaging condition individually. The models tested with unknown samples (i.e., from retail stores) showed poorer performance (RMSE: 1.061-1.414) compared to the models validated with data partitioning (RMSE: 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated.

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging

In the food industry, quality and safety issues are associated with consumers' health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels' intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.

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.

Application of Fourier Transform Infrared (FT-IR) Spectroscopy, Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO₂, 19% O₂, 48% N₂) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R²) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSE_C) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R² values of 0.79 and 0.77, and RMSE_C values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R² (0.34 and 0.17) and the highest RMSE_C (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.

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.

Development of Multifunctional Pullulan/Chitosan-Based Composite Films Reinforced with ZnO Nanoparticles and Propolis for Meat Packaging Applications

Pullulan/chitosan-based multifunctional edible composite films were fabricated by reinforcing mushroom-mediated zinc oxide nanoparticles (ZnONPs) and propolis. The ZnONPs were synthesized using enoki mushroom extract and characterized using physicochemical methods. The mushroom-mediated ZnONPs showed an irregular shape with an average size of 26.7 ± 8.9 nm. The combined incorporation of ZnONPs and propolis pointedly improved the composite film's UV-blocking property without losing transparency. The reinforcement with ZnONPs and propolis improved the mechanical strength of the pullulan/chitosan-based film by ~25%. Additionally, the water vapor barrier property and hydrophobicity of the film were slightly increased. In addition, the pullulan/chitosan-based biocomposite film exhibited good antioxidant activity due to the propolis and excellent antibacterial activity against foodborne pathogens due to the ZnONPs. The developed edible pullulan/chitosan-based film was used for pork belly packaging, and the peroxide value and total number of aerobic microorganisms were significantly reduced in meat wrapped with the pullulan/chitosan/ZnONPs/propolis film.

Development of Multifunctional Pullulan/Chitosan-Based Composite Films Reinforced with ZnO Nanoparticles and Propolis for Meat Packaging Applications

Pullulan/chitosan-based multifunctional edible composite films were fabricated by reinforcing mushroom-mediated zinc oxide nanoparticles (ZnONPs) and propolis. The ZnONPs were synthesized using enoki mushroom extract and characterized using physicochemical methods. The mushroom-mediated ZnONPs showed an irregular shape with an average size of 26.7 ± 8.9 nm. The combined incorporation of ZnONPs and propolis pointedly improved the composite film’s UV-blocking property without losing transparency. The reinforcement with ZnONPs and propolis improved the mechanical strength of the pullulan/chitosan-based film by ~25%. Additionally, the water vapor barrier property and hydrophobicity of the film were slightly increased. In addition, the pullulan/chitosan-based biocomposite film exhibited good antioxidant activity due to the propolis and excellent antibacterial activity against foodborne pathogens due to the ZnONPs. The developed edible pullulan/chitosan-based film was used for pork belly packaging, and the peroxide value and total number of aerobic microorganisms were significantly reduced in meat wrapped with the pullulan/chitosan/ZnONPs/propolis film.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm². Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm², respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

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.

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.

Application of a path-modelling approach for deciphering causality relationships between microbiota, volatile organic compounds and off-odour profiles during meat spoilage

Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.

Detection of Meat Adulteration Using Spectroscopy-Based Sensors

Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.

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.

Quest of Intelligent Research Tools for Rapid Evaluation of Fish Quality: FTIR Spectroscopy and Multispectral Imaging Versus Microbiological Analysis

The aim of the present study was to assess the microbiological quality of farmed sea bass (Dicentrarchus labrax) fillets stored under aerobic conditions and modified atmosphere packaging (MAP) (31% CO₂, 23% O₂, 46% Ν₂,) at 0, 4, 8, and 12 °C using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with data analytics, taking into account the results of conventional microbiological analysis. Fish samples were subjected to microbiological analysis (total viable counts (TVC), Pseudomonas spp., H₂S producing bacteria, Brochothrix thermosphacta, lactic acid bacteria (LAB), Enterobacteriaceae, and yeasts) and sensory evaluation, together with FTIR and MSI spectral data acquisition. Pseudomonas spp. and H₂S-producing bacteria were enumerated at higher population levels compared to other microorganisms, regardless of storage temperature and packaging condition. The developed partial least squares regression (PLS-R) models based on the FTIR spectra of fish stored aerobically and under MAP exhibited satisfactory performance in the estimation of TVC, with coefficients of determination (R²) at 0.78 and 0.99, respectively. In contrast, the performances of PLS-R models based on MSI spectral data were less accurate, with R² values of 0.44 and 0.62 for fish samples stored aerobically and under MAP, respectively. FTIR spectroscopy is a promising tool to assess the microbiological quality of sea bass fillets stored in air and under MAP that could be effectively employed in the future as an alternative method to conventional microbiological analysis.

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

MSdB-NMF: MultiSpectral Document Image Binarization Framework via Non-negative Matrix Factorization Approach

In this paper, we propose a novel method for Multispectral document image binarization (MSdB) through the Non-negative Matrix Factorization (NMF) approach. We propose a three-step MSdB-NMF framework: i) NMF-based feature extraction algorithm by introducing a new optimization problem; ii) post-processing method iii); apply any existing gray/RGB binarization scheme. In the first step, we extract N features out of B spectral bands (N < B) and their corresponding coefficient matrix. We introduce a novel objective formulation that considers the robustness (related to the noise and various types of degradations) and sparseness (related to the ratio of text pixels versus the background). We employ the multiplicative updating rules to solve the proposed minimization problem and prove the convergence of the proposed feature extraction algorithm. In the next step, we select an appropriate feature vector, equivalently the corresponding coefficient vector. We propose to select it either visually or automatically via a post-processing method, which uses the benchmark binarization methods as baseline. In the last step, we apply some existing binarization methods such as Sauvola and Howe over the selected coefficient vector. Our proposed binarization framework is applicable for any kind of MS or hyperspectral (HS) document image without considering any prior knowledge such as the side information about the spectral bands of MS/HS document image. We evaluate our proposed binarization framework over two MS document image datasets. The experimental results confirm that our proposed framework outperforms several state-of-theart binarization schemes including the winner of the contest in MS-TEx-2015.

Microbial detection and identification methods: Bench top assays to omics approaches

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.

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.

Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

BACKGROUND

Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.

RESULTS

Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (R_c ² ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g^-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (R_p ² ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g^-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.

CONCLUSION

Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

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

Spectroscopic and imaging methods coupled with multivariate data analysis have been increasingly studied for the assessment of food quality. The objective of this work was the estimation of microbiological quality of minced pork using non-invasive spectroscopy-based sensors. For this purpose, minced pork patties were stored aerobically at different isothermal (4, 8, and 12 °C) and dynamic temperature conditions, and at regular time intervals duplicate samples were subjected to (i) microbiological analyses, (ii) Fourier transform infrared (FTIR) and visible (VIS) spectroscopy measurements, and (iii) multispectral image (MSI) acquisition. Partial-least squares regression models were trained and externally validated using the microbiological/spectral data collected at the isothermal and dynamic temperature storage conditions, respectively. The root mean squared error (RMSE, log CFU/g) for the prediction of the test (external validation) dataset for the FTIR, MSI, and VIS models was 0.915, 1.173, and 1.034, respectively, while the corresponding values of the coefficient of determination (R²) were 0.834, 0.727, and 0.788. Overall, all three tested sensors exhibited a considerable potential for the prediction of the microbiological quality of minced pork.

Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality

Determining the quality of meat has always been essential for the food industry because consumers prefer superior quality meat. Therefore, the food industry requires the development of a rapid and non-destructive method for meat-quality determination. Over the past few years, a number of techniques have been presented for monitoring meat–chemical attributes. However, most previous techniques are quite expensive, destructive, and require complex hardware to operate. Thus, in this work, we demonstrate a low-cost sensing technique (eliminating the expensive equipment and complicated design) for meat–chemical quality detection. The newly developed system was integrated with a low-cost monochrome camera and ordinary light-emitting diode (LED) light sources, with fifteen different wavebands ranging from 458 to 950 nm. The monochrome camera captures images of the meat sample across a spectral range from 458 to 950 nm using a single snapshot method. The chemical values (e.g., moisture, fat, and protein) were also determined using conventional methods. The collected images were combined to produce a multispectral data cube and to extract spectral data. Partial least squares (PLS) and support vector regression (SVR) modeling were used on the extracted spectra and chemical values. The developed models for meat samples displayed accurate chemical-component prediction ( R 2 > 0.80). Our model, based on a monochrome sensor using only fifteen wavebands, provided reasonable results compared with the previously developed expensive spectroscopic techniques. Therefore, this complementary metal-oxide semiconductor (CMOS) based multispectral sensing technique may have the potential to detect meat quality, thereby facilitating a simple, fast, and cost-effective method applicable to small-scale meat-processing industries.

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview

As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

Evaluation of Fourier transform infrared spectroscopy and multispectral imaging as means of estimating the microbiological spoilage of farmed sea bream

The objective of the present study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI), in tandem with multivariate data analysis, as means of estimating the microbiological quality of sea bream. Farmed whole ungutted fish were stored aerobically at 0, 4 and 8 °C. At regular time intervals, fish samples (i.e. cut portions) were analysed microbiologically, while FTIR and MSI measurements also were acquired at both the skin and flesh sides of the samples. Partial least squares regression (PLSR) models were calibrated to provide quantitative estimations of the microbiological status of fish based on spectral data, in a temperature-independent manner. The PLSR model based on the FTIR data of fish skin exhibited good performance when externally validated, with the coefficient of determination (R²) and the root mean square error (RMSE) being 0.727 and 0.717, respectively. Hence, FTIR spectroscopy appears to be promising for the rapid and non-invasive monitoring of the microbiological spoilage of whole sea bream. Contrarily, the MSI models' performance was unsatisfactory, delimitating their potential exploitation in whole fish quality assessment. Model optimization results concerning fish flesh indicated that MSI may be propitious in skinned fish products, with its definite competence warranting further investigation.

Potential use of multispectral imaging technology to identify moisture content and water-holding capacity in cooked pork sausages

BACKGROUND

The traditional detection methods for moisture content (MC) and water-holding capacity (WHC) in cooked pork sausages (CPS) are destructive, time consuming, require skilled personnel and are not suitable for online industry applications. The goal of this work was to explore the potential of multispectral imaging (MSI) in combination with multivariate analysis for the identification of MC and WHC in CPS.

RESULTS

Spectra and textures of 156 CPS treated by six salt concentrations (0-2.5%) were analyzed using different calibration models to find the most optimal results of predicting MC and WHC in CPS. By using the fused data of spectra and textures, partial least squares regression models performed well for determining the MC and WHC, with a correlation coefficient (r) of 0.949 and 0.832, respectively. Additionally, their spatial distribution in CPS could be visualized via applying prediction equations to transfer each pixel in the image.

CONCLUSION

Results of satisfactory detection and visualization of the MC and WHC showed that MSI has the potential to serve as a rapid and non-destructive method for use in sausage industry. © 2017 Society of Chemical Industry.

Organ Segmentation in Poultry Viscera Using RGB-D

We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11 % is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28 % using only basic 2D image features.

Classification of white maize defects with multispectral imaging

Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q²) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study.

Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy

In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively.