Hyperspectral imaging for assessment of total fat in salmon fillets: A comparison between benchtop and snapshot systems

Portable hyperspectral Imaging (HSI) based on snapshot sensors is ideal for on-line applications, with very fast data acquisition, small camera and flexibility for implementation. The objective of the study was to assess a snapshot HSI system for on-line assessment of salmon by bench marking it against a conventional hyperspectral system. Two different HSI systems: (a) linescan (550–1700 nm) and (b) snapshot (Vis: 470–630 nm and Vis-NIR: 670–950 nm) were investigated. For each sample, one image was collected using linescan while two images per camera were collected using each snapshot followed by chemical analysis of samples to measure total fat content. Partial Least Squares Regression (PLSR) was successfully utilized to develop fat prediction models. Two strategies were followed: (1) Models for each dataset including stitched data from two snapshots and (2) HSI technology vs spectral range: new datasets were generated in such a way that both linescan and the device being compared (e.g., linescan vs Vis snapshot) had the same number of wavelengths in the similar wavelength range. For strategy (1) all models showed good prediction accuracy, yielding Rp2 in the range of 0.78–0.94 and SEP in the range of 1.58–3.10 except for Vis snapshot. For strategy (2) all models performed well, yielding Rp2 in the range of 0.77–0.94 and SEP in the range of 1.74–3.19 except for linescan vs Vis snapshot. Additionally, the stitched data model was used to generate fat prediction map of a salmon fillet and compared to its reference fat map. Results showed that snapshot HSI can be successfully used to predict fat in salmon fillets and have great potential for on-line applications.