Anisotropic light propagation in bovine muscle tissue depends on the initial fiber orientation, muscle type and wavelength

Optical characterization of the liver tissue affected by fibrolamellar hepatocellular carcinoma based on internal filters of laser-induced fluorescence

Laser-induced fluorescence (LIF) spectroscopy has recently gained regards for diagnosis of the cancer in various tissues of the human body. This method in its conventional form, when used for assay of highly scattering media, encounters a lot of noise due to multiple scattering and inner filter effects which overshadows the sensitivity and specificity of the method. Here, angular dependence of the LIF spectral shift due to the reabsorption events have been investigated for characterization of the bio-tissues. The aim was to determine the tissue morphological changeovers due to the cancer progression. The assessment of a rare type of the liver cancer i. e. fibrolamellar hepatocellular carcinoma revealed the significant difference in optical anisotropy of the parenchyma and liver tumor. As a result, utilizing LIF spectroscopy as a fast, highly sensitive and easy-to-use method one can evaluate the optical anisotropy for diagnosing tissues during the cancer progression.

Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics

The fibrous structure of meat muscle makes it an anisotropic optical material. As such, spectral information varies with the orientation of the muscle. In this study, spectral data from pork cuts were obtained by a transverse scan (TRANSCAN), radial scan (RADISCAN), and longitudinal scan (LONGSCAN) by using hyperspectral imaging. The information was used to develop and compare the prediction models for intramuscular (IMF) content prediction by partial least square regression (PLSR), support vector machines regression (SVMR), and backpropagation artificial neural network (BPANN). The three modeling algorithms showed equal capability for modeling IMF in pork. The accuracy of the prediction models from the three scans was in the order of TRANSCAN ≥ RADISCAN ≥ LONGSCAN. Successive projection algorithm reduced the wavelengths to 93%. The reduced wavelengths were used to build new models that showed similar accuracy to the models of the original wavelengths. This study shows that muscle orientation influences the accuracy of the prediction models.

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat

Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat structure. The use of HSI in the meat and food industry has observed a significant growth in the last decade, yet its use for assessment of meat it is not optimal yet. The application of HSI for assessment of meat is reviewed with focus on its ability to capture meat unique chemical and structural characteristics. While HSI is widely used for assessment of chemical composition, a limited number of evidences on its ability to handle the effect of different sources of variation on the assessment is found. The use of spatially resolved spectroscopy has been able to detect structural information related to animal background, muscle type, rigor process and ageing. Similarly the use of texture features seem to capture unique characteristics of meat.