A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Fluorescence-guided laser removal of chemically damaged cornea.

PURPOSE: To correlate the observed fluorescence spectrum with the depth of ablation during 193 nm argon-fluoride excimer laser ablation of chemically damaged corneas. SETTING: Laser facility, Cedars-Sinai Medical Center, Los Angeles, California, USA. METHODS: Three cadaver New Zealand white rabbit corneas were exposed to 1 N hydrogen chloride for 10 seconds. The resultant opaque corneas were ablated to perforation using the excimer laser. Laser-induced fluorescence was collected at 45 degrees from incidence and channeled into an ultraviolet-visible spectrometer coupled to an optical multichannel analyzer reading a diode array detector. The detector recorded single-shot fluorescence spectra. The data were examined by principal component analysis, and the evolution of eigenvectors and their weighting coefficients were used to compare data among corneas. The results were correlated with histopathological sections. RESULTS: The eigenvalues of 3 principal components corresponded to 88.9%, 10.0%, and 0.4% of the data in acid-burned corneas. Compared to that in undamaged corneas, more information was stored in the first principal component and the third eigenvector was distinctly altered. Acid-scarred tissue blue shifted the dominant fluorescence peak compared to that in normal corneal tissue. CONCLUSIONS: After severe hydrogen chloride burn to the rabbit corneal surface, monitoring the dominant peak wavelength shift of excimer-laser-induced fluorescence can detect the transition between severely acid-damaged and underlying tissue.

Multispectral imaging of tissue absorption and scattering using spatial frequency domain imaging and a computed-tomography imaging spectrometer.

We present an approach for rapidly and quantitatively mapping tissue absorption and scattering spectra in a wide-field, noncontact imaging geometry by combining multifrequency spatial frequency domain imaging (SFDI) with a computed-tomography imaging spectrometer (CTIS). SFDI overcomes the need to spatially scan a source, and is based on the projection and analysis of periodic structured illumination patterns. CTIS provides a throughput advantage by simultaneously diffracting multiple spectral images onto a single CCD chip to gather spectra at every pixel of the image, thus providing spatial and spectral information in a single snapshot. The spatial-spectral data set was acquired 30 times faster than with our wavelength-scanning liquid crystal tunable filter camera, even though it is not yet optimized for speed. Here we demonstrate that the combined SFDI-CTIS is capable of rapid, multispectral imaging of tissue absorption and scattering in a noncontact, nonscanning platform. The combined system was validated for 36 wavelengths between 650-1000 nm in tissue simulating phantoms over a range of tissue-like absorption and scattering properties. The average percent error for the range of absorption coefficients (µa) was less than 10% from 650-800 nm, and less than 20% from 800-1000 nm. The average percent error in reduced scattering coefficients (µs') was less than 5% from 650-700 nm and less than 3% from 700-1000 nm. The SFDI-CTIS platform was applied to a mouse model of brain injury in order to demonstrate the utility of this approach in characterizing spatially and spectrally varying tissue optical properties.

Fluorescencia inducida por el láser éxcimer (193 nm) en córneas cadavéricas de conejos / 193 nm excimer laser-induced fluoscence in rabbit cadaveric corneas

Observar la fluorescencia durante la ablación pulsada con láser éxcimer en córneas cadavéricas de conejos. Se expusieron 4 córneas cadavéricas de conejo a la energía del láser éxcimer de ArF a 1= 193nm. La fluorescencia resultante se registró a 45º de la incidencia del rayo y fue canalizada por un espectrómetro conectado a un analizador óptico multicanal capaz de leer un detector de diodo. La ablación continuó hasta la perforación. Los espectros obtenidos fueron sometidos a análisis por componentes principales (ACP). Se registraron los espectros del epitelio al endotelio. Se observaron cambios de espectros a medida que la ablación progresó. se evidenció una disminución obvia del pico de longitud de onda más corta a 367 nm. El ACP reveló que el espectro era consistente con datos obtenidos en córneas de humanos. Los resultados obtenidos pueden contribuir al conocimiento de los procesos que ocurren durante la ablación clínica con el láser éxcimer