Label-free autofluorescence and hyperspectral imaging of cerebral amyloid-ß lesions in aged squirrel monkeys.

The observation of amyloid-ß (Aß) lesions using autofluorescence in transgenic mice and human Alzheimer disease patients has been reported frequently. However, no reports verify the autofluorescence of spontaneous Aß amyloidosis in animals, to our knowledge. We validated the autofluorescence of Aß lesions in spontaneous squirrel monkey cases under label-free conditions; lesions had intense blue-white autofluorescence in fluorescence microscopy using excitation light at 400-440 nm. Thioflavin S staining and immunohistochemistry of the same specimens revealed that this blue-white autofluorescence was derived from Aß lesions. Hyperspectral analysis of these lesions revealed a characteristic spectrum with bimodal peaks at 440 and 460 nm, as reported for Aß lesions in mice. Principal component analysis using hyperspectral data specifically separated the Aß lesions from other autofluorescent substances, such as lipofuscin. A non-labeled and mechanistic detection of Aß lesions by hyperspectral imaging could provide valuable insights for developing early diagnostic techniques.

Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique.

Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.

Quantification and visualization of meat quality traits in pork using hyperspectral imaging.

Accurate and rapid determination of meat quality traits plays key roles in food industry and pig breeding. Currently, most of the spectroscopic instruments developed for meat quality determination can only obtain the spectral average value of the sample, so it is difficult to evaluate the spatial variation of meat quality traits. In this study, we evaluated the predictive potential of 14 meat quality traits based on large-scale VIS/NIR hyperspectral images collected by SpecimIQ. When predictions were based solely on hyperspectral data, the prediction accuracy (R2cv) for the majority of meat qualities ranged from 0.60 to 0.70. After adding texture information, the prediction accuracy of all traits is improved by different magnitudes (R2cv increases from 1.5% to 16.4%). Finally, the best model was utilized to visualize the spatial distribution of Fat (%) and Moisture (%) to assess their homogeneity. These results suggest that hyperspectral imaging has great potential for predicting and visualizing various meat qualities, as well as industrial applications for automated measurements.

Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years.

The percentage of intramuscular fat content of lamb meat is a key index of consumer acceptability. Hyperspectral imaging is a potential technique for in-line measurements of intramuscular fat in fresh meat. However, little work has been conducted to investigate the robustness of hyperspectral imaging data and associated multivariate models over time. Fifteen trials consisting of eight independent flocks across five years were used to quantify robustness of partial least squares regression (PLSR) models developed using data collected with the same imaging system. Two models were developed; one using data from the first year of the trials, and a progressive model that cumulatively includes data in chronological order. The two models performed similarly, in terms of the coefficient of determination (R2), standard error of prediction (SEP) and bias, when experimental conditions were consistent. However, under varying imaging conditions, the progressive model was able to account for this variability resulting in higher R2 and lower SEP.

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.

A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging.

This study demonstrates a novel approach to develop global calibration models for predicting intramuscular fat (IMF) and pH across various red meat species and muscle types. A total of 8 hyperspectral imaging (HSI) datasets were used from different experiments, comprising data from three species: beef, lamb and venison across various muscle type, slaughter season and measurement conditions. Prediction models were developed using Partial Least Squares Regression (PLSR) and Deep Convolutional Neural Networks (DCNN) using a total of 1080 and 1116 samples for IMF and pH, respectively. Models for pH and IMF via both techniques yielded high Rc2 (0.86-0.93) and low SEC values. Also, reasonably accurate prediction performance was observed with high Rp2 (0.86-0.89) and low SEP values. Overall results illustrated the comprehensiveness of these global calibration models with the ability to predict IMF and pH of red meat samples irrespective of species and muscle type.

Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant.

This study evaluated a range of diffuse reflectance spectroscopic (Vis-NIR spectrophotometers) and imaging (Hyperspectral imaging cameras) instruments for predicting pH, IMF and shear force values of beef in a meat processing pilot plant. A total of 364 beef striploin samples were evaluated and prediction models were developed using PLSR. Models for pH and IMF (except Vis snapshot camera) showed good fit with high Rcv2 (0.29-0.92) and low SECV values. Good prediction accuracy with high Rp2 (0.44-0.90), RPD and low SEP values was also observed. While low values of Rp2 for shear force was observed, the expected curvilinear relationship between predicted values of shear force and predicted values of pH was observed suggesting that spectroscopic measurements were able detect biophysical factors associated to these two attributes. Overall, it can be concluded that diffuse reflectance spectroscopy combined with chemometrics has a great potential to be used as an on/in-line quality monitoring system for the meat processing industry.

Prediction of quality traits and grades of intact chicken breast fillets by hyperspectral imaging.

1. In this study, hyperspectral imaging was evaluated for its usefulness to predict quality traits and grading of intact chicken breast fillets. 2. Lightness of colour (L*) and pH of the fillets were measured as quality traits, and samples were then selected and graded to three different quality categories, i.e., dark, firm and dry (DFD), normal (NORM), and pale, soft and exudative (PSE) based on these two quality traits. Based on the prediction performance of full wavelength partial least square regression (PLSR) models, the spectral range of visible and near-infrared (Vis-NIR) was more suitable for the evaluation of quality traits and grading than the range of near-infrared (NIR). Key wavelengths of each quality trait and grade value were selected by the regression coefficient (RC) method. 3. The new key wavelength PLSR models showed good predictive performances (Rp = 0.85 and RMSEp = 2.18 for L*, Rp = 0.84, and RMSEp = 0.13 for pH, and Rp = 0.80 and RMSEp = 0.44 for quality grading). The classification accuracy for grades was 85.71% (calibration set) and 81.82% (prediction set), respectively. Finally, distribution maps showed that quality traits and grades of samples were able to be visualised. 4. These results suggested that hyperspectral imaging has the potential for quality prediction of fresh chicken meat.

Literature review: spectral imaging applied to poultry products.

Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging (HSI), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: "poultry" and "hyperspectral imaging". A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.

Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat.

The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.

Non-destructive assessment of the myoglobin content of Tan sheep using hyperspectral imaging.

This study aimed to develop simplified models for rapid and nondestructive monitoring myoglobin contents (DeoMb, MbO2 and MetMb) during refrigerated storage of Tan sheep based on a hyperspectral imaging (HSI) system in the spectral range of 400-1000 nm. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were applied to correlate the spectral data with the reference values of myoglobin contents measured by a traditional method. In order to simplify the LSSVM models, competitive adaptive reweighted sampling (CARS) and Interval variable iterative space shrinkage approach (iVISSA) were used to select key wavelengths. The new CARS-LSSVM models of DeoMb and MbO2 yielded good results, with R2p of 0.810 and 0.914, RMSEP of 1.127 and 2.598, respectively. The best model of MetMb was new iVISSA-CARS-LSSVM, with an R2p of 0.915 and RMSEP of 2.777. The overall results from this study indicated that it was feasible to predict myoglobin contents in Tan sheep using HSI.