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.

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems.

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.

Multispectral imaging of hand eczema.

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.