This study explores the potential of using multispectral imaging (MSI) techniques to predict the freshness of whole gutted Atlantic cod (Gadus morhua) throughout its shelf life during storage on ice. Spectral data were acquired from key anatomical regions - the gills, skin, and eyes - and analyzed using chemometrics methods, including partial least squares regression (PLSR) and artificial neural networks (ANNs). These models were trained to predict sensory evaluations performed by trained panelists using the Quality Index Method (QIM) as well as chemical- and microbiological analyses, total viable counts (TVC) and total volatile base nitrogen (TVB-N). Among the regions analyzed, the gills provided the most accurate predictions of the QIM score, with the ANN model achieving an R2CV = 0.87 and an RMSECV of 2.0. Spectral analysis highlights the role of near-infrared (NIR) wavelengths in capturing spoilage-related biochemical and structural changes, complementing the visible spectrum, which primarily captures color changes. Our findings suggest that MSI combined with chemometric techniques could serve as an efficient, non-destructive alternative to traditional sensory freshness evaluations.

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

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.

Multispectral Imaging is an increasingly applied technique for the estimation of several quality parameters across the food chain. The microbiological quality and safety as well as the detection of food fraud are among the most significant aspects in food quality and safety assessment. MSI analysis was performed using a VideometerLab instrument (Videometer A/S, Videometer, Herlev, Denmark), while more than 9000 food samples were examined in total, for the assessment of microbiological quality and the detection of food fraud. For estimating microbial populations, total aerobic counts (TAC) were determined. Several regression and classification algorithms were employed, including partial least squares regression (PLS-R), support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), tree-based algorithms etc. The slope of the regression line, root mean squared error (RMSE), coefficient of determination (R-squared) and accuracy score were used as metrics for the evaluation of models’ performance. In adulteration case, the prediction of different levels of pork in chicken meat and vice versa yielded high accuracy scores i.e., over 90% , while, using the SVM algorithm, the presence of bovine offal in beef was successfully detected. Additionally, Random Forest algorithm was efficient (accuracy>93% ) in discriminating seabass and seabream fish fillets. Concerning microbiological quality, as indicated by the performance indices, the developed models exhibited satisfactory performance in predicting microbial load in different foods (RMSE<1.00, R-squared>0.80). Indicatively, MSI spectral data combined with PLS-R could satisfactorily predict TAC and Pseudomonas spp. counts on the surface of chicken fillets regardless of storage temperature and batch variation based on the performance metrics (R-squared: 0.89, RMSE: 0.88) while, this algorithm presented also satisfactory performance in estimation microbial populations in brown edible seaweed (R-squared: 0.80, RMSE: 0.90). However, in this case, selecting the appropriate analytical approaches and machine learning algorithms is still challenging.

Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.

BACKGROUND:Hand eczema is a disease with large variation in clinical presentation and severity. Scoring systems for quantitative severity assessment exist. However, they are observer-dependent. An objective quantitative tool for scoring of hand eczema would improve categorization of hand eczema.OBJECTIVE:To investigate the usefulness of multispectral imaging in assessing severity of hand eczema with respect to extent and the different morphological features.METHODS:Patients with hand eczema (n = 60) and healthy controls (n = 28) were included. The severity of hand eczema was assessed by a dermatologist using the Hand Eczema Severity Index (HECSI) and a global assessment (Physician Global Assessment [PGA]). Multispectral imaging of the hand was performed on all patients and controls using the VideometerLab Instrument.RESULTS:Areas of the morphological elements identified by multispectral imaging were statistically significantly correlated with the PGA scores. Analyzed by Cohen's kappa, a moderate agreement between imaging-based severity assessment and PGA was found. The imaging-based severity assessment was also correlated with HECSI (Spearman rho 0.683, P < .001). Still, the imaging-based algorithm was not capable of differentiating hand eczema patients from controls.CONCLUSIONS:Multispectral imaging allows quantitative measurements of different skin parameters to be performed. In its present form, multispectral imaging cannot replace the clinical assessment of a dermatologist. However, after refinement, this or similar technologies could prove useful.

—Mobile Probing Kit is a low tech and low cost methodology for obtaining inspiration and insights into user needs, requirements and ideas in the early phases of a system’s development process. The methodology is developed to identify user needs, requirements and ideas among knowledge workers characterized as being highly nomadic and thus potential users of mobile and ubiquitous technologies. The methodology has been applied in the IST MAGNET Beyond project in order to obtain user needs and requirements in the process of developing pilot services. We report on the initial findings from applying this methodology in the early phases of this large scale research and development process.