Robust colour calibration of an imaging system using a colour space transform and advanced regression modelling

White and honey Chlorella vulgaris: Sustainable ingredients with the potential to improve nutritional value of pork frankfurters without compromising quality

This study aimed to evaluate the effect of the chlorophyll-deficient microalgae mutants, honey (yellow) and white Chlorella vulgaris, (3%) on the nutritional, physicochemical, microbiological, and sensory characteristics of frankfurters. The presence of microalgae resulted in increased PUFA content and higher PUFA/SFA ratio, but lower n-6/n-3 ratio and lipid indices (P < 0.05). C. vulgaris inclusion in frankfurters increased (P < 0.05) Na, K, Ca, P, and Zn and improved the Na/K ratio, but lowered Mn, and in the case of white C. vulgaris, Cu content, compared to the control. The higher protein content decreased water release from emulsions elaborated with microalgae. White C. vulgaris inclusion decreased cohesiveness and springiness of the frankfurters. Due to the presence of pigment, microalgae inclusion led to a decrease in redness and an increase in yellowness of frankfurters. The presence of microalgae resulted in lower (P < 0.05) bacterial counts and did not affect TBARs during storage. The addition of microalgae in frankfurters produced acceptable sensory characteristics but resulted in lower scores compared to reference products.

Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image

The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.

Camera Color Correction for Cultural Heritage Preservation Based on Clustered Data

Cultural heritage preservation is a crucial topic for our society. When dealing with fine art, color is a primary feature that encompasses much information related to the artwork's conservation status and to the pigments' composition. As an alternative to more sophisticated devices, the analysis and identification of color pigments may be addressed via a digital camera, i.e., a non-invasive, inexpensive, and portable tool for studying large surfaces. In the present study, we propose a new supervised approach to camera characterization based on clustered data in order to address the homoscedasticity of the acquired data. The experimental phase is conducted on a real pictorial dataset, where pigments are grouped according to their chromatic or chemical properties. The results show that such a procedure leads to better characterization with respect to state-of-the-art methods. In addition, the present study introduces a method to deal with organic pigments in a quantitative visual approach.

Application of computer tongue image analysis technology in the diagnosis of NAFLD

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Extraction of Carotenoids from Pumpkin Peel and Pulp: Comparison between Innovative Green Extraction Technologies (Ultrasonic and Microwave-Assisted Extractions Using Corn Oil)

Natural pigments improve aesthetic value as well as antioxidant potential of a food product. This study was designed to determine the effects of green extraction techniques on carotenoids, polyphenols and antioxidant activities of pulp and peel of two varieties of pumpkin (Cucurbita maxima). Innovative green extractions (IGE; Ultrasound and Microwave-Assisted Extractions) synergised with corn oil (used as green solvent) were compared with conventional extraction (CE; hexane/isopropyl alcohol; 60:40, v/v). Results showed total carotenoids to be almost double on employing IGE (PM2-UAE-peel = 38.03 ± 4.21; PM4-UAE-peel = 33.78 ± 1.76 µg/g) when compared to conventional extraction (PM2-CE-peel = 19.21 ± 4.39; PM4-CE-peel = 16.21 ± 2.52 µg/g). Polyphenolic contents ranged between 510.69 ± 5.50 and 588.68 ± 7.26 mg GAE/100 g of extract in IGE, compared with conventional extracts (269.50 ± 2.17 to 318.46 ± 6.60 mg GAE/100 g) and percent inhibition of 2,2-Diphenyl-1-picrylhydrazyl (DPPH) ranging between 88.32 ± 1.51 and 93.53 ± 0.30% in IGE when compared with conventional extraction (50.61 ± 1.44 to 57.79 ± 2.09%). Further, oxidative stability of carotenoids extracts from IGE (protection factor = 1.59 ± 0.01 to 1.81 ± 0.05) were found to be significantly higher (p < 0.05) than conventional extracts. Based on results, this study supports the use of innovative green extraction techniques to obtain bioactive pigments like carotenoids. It is anticipated that results generated will find potential applications in food, pharmaceutical and cosmetic industries.

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.

Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction

Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed “digital color fusion microscopy” (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available.

A Potential Use of 3-D Scanning to Evaluate the Chemical Composition of Pork Meat

The aim of this study was to determine the possibility of 3-D scanning method in chemical composition evaluation of pork meat. The sampling material comprised neck muscles (1000 g each) obtained from 20 pork carcasses. The volumetric estimation process of the elements was conducted on the basis of point cloud collected using 3-D scanner. Knowing the weight of neck muscles, their density was calculated which was subsequently correlated with the content of basic chemical components of the pork meat (water, protein and fat content, determined by standard methods). The significant correlations (P ≤ 0.05) between meat density and water (r = 0.5213), protein (r = 0.5887), and fat (r = -0.6601) content were obtained. Based on the obtained results it seems likely to employ the 3-D scanning method to compute the meat chemical composition.

PRACTICAL APPLICATION

The use of the 3-D scanning method in industrial practice will allow to evaluate the chemical composition of meat in online mode on a dressing and fabrication line and in a rapid, noninvasive manner. The control of the raw material using the 3-D scanning will allow to make visual assessment more objective and will enable optimal standardization of meat batches prior to processing stage. It will ensure not only the repeatability of product quality characteristics, but also optimal use of raw material-lean and fat meat. The knowledge of chemical composition of meat is essential due to legal requirements associated with mandatory nutrition facts labels on food products.

Color calibration of an RGB camera mounted in front of a microscope with strong color distortion

This paper aims at showing that performing color calibration of an RGB camera can be achieved even in the case where the optical system before the camera introduces strong color distortion. In the present case, the optical system is a microscope containing a halogen lamp, with a nonuniform irradiance on the viewed surface. The calibration method proposed in this work is based on an existing method, but it is preceded by a three-step preprocessing of the RGB images aiming at extracting relevant color information from the strongly distorted images, taking especially into account the nonuniform irradiance map and the perturbing texture due to the surface topology of the standard color calibration charts when observed at micrometric scale. The proposed color calibration process consists first in computing the average color of the color-chart patches viewed under the microscope; then computing white balance, gamma correction, and saturation enhancement; and finally applying a third-order polynomial regression color calibration transform. Despite the nonusual conditions for color calibration, fairly good performance is achieved from a 48 patch Lambertian color chart, since an average CIE-94 color difference on the color-chart colors lower than 2.5 units is obtained.