We investigated the use of Fourier transform near infrared spectroscopy (FT-NIRS), which is a method of measuring light absorbance signatures, to derive ages from eastern Bering Sea walleye pollock (Gadus chalcogrammus) otoliths. This approach is based on a predictive model between near infrared spectra in the otolith and fish age, which is calibrated and validated. The advantage of FT-NIRS over traditional methods is the speed and repeatability with which age estimates are generated. The application of FT-NIRS to walleye pollock otoliths yielded r2 values between 0.91 and 0.95 for the calibration models and good validation performance (between 0.82 and 0.93). This approach can be expected to predict fish age within ±1.0 year of age 67% of the time. When comparing approaches, the FT-NIRS had as good or slightly better precision (75% agreement) than the traditional ageing (66% agreement) and showed little or no bias at age before 12 years of age. Once the predictive FT-NIR model is calibrated and validated, age estimates using FT-NIRS can be done at 10 times the rate compared to traditional methods.

Recent application of Fourier transform near infra-red spectroscopy (FT-NIRS) to predict age in fish otoliths has gained attention among fisheries managers as a potential alternative to costly production ageing of managed species. We assessed the age prediction capability of FT-NIRS scans in whole otoliths from red snapper, Lutjanus campechanus, collected from the US Gulf of Mexico and US Atlantic Ocean (South Atlantic). Otoliths were scanned with an FT-NIR spectrometer and resulting spectral signatures were regressed with traditionally estimated ages via partial least squares regression to produce calibration models, which were validated for predictive capability against test sets of otoliths. Calibration models successfully predicted age with R2 ranging 0.94–0.95, mean squared error ≤1.8 years, and bias <0.02 years. Percent agreement between FT-NIRS and traditional ages was lower than within-reader agreement for traditional estimates, but average percent error was similar and Kolmogorov–Smirnov tests were not significantly different (p ≥ 0.06) between traditional and FT-NIRS predicted ages for optimal calibration models. Ages >31 years were not well predicted, possibly due to light attenuation in the thickest otoliths. Our results suggest that FT-NIRS can improve efficiency in production ageing for fisheries management while maintaining data quality standards.

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.