Color measurement of animal source foods

Rapid and objective assessment of food color is necessary in quality control. The color evaluation of animal source foods using a computer vision system (CVS) and a traditional colorimeter is examined. With the same measurement conditions, color results deviated between these two approaches. The color returned by the CVS had a close resemblance to the perceived color of the animal source foods, whereas the colorimeter returned not typical colors. The effectiveness of the CVS is confirmed by the study results. Considering these data, it could be concluded that the colorimeter is not representative method for color analysis of animal source foods, therefore, the color read by the CVS seemed to be more similar to the real ones.

Advances in Forensic Sedimentology

Forensic sedimentology is a relatively recently realized field. Sedimentological methods used to solve cases have evolved as the field has developed, beginning with simple identification of minerals and progressing to the examination of individual grains using highly advanced scanning electron microscopes. More simple methods, such as color analysis, are still used today, but in addition, forensic sedimentologists look at surface textures and grain size distribution. For instance, quartz grains were used in a forensic technique as sediment fingerprint. The particle size distribution is one of the important tests when analysing sediments and soils in geological studies. For forensic work, the particle size distribution of sometimes very small samples requires precise determination using a rapid and reliable method with a high resolution. FRITSCH laser granulometer offers rapid and accurate sizing of particles in the range 0.04–2000 μm for a variety of sample types, including soils, unconsolidated sediments, dusts, powders, and other particulate materials.

Enhanced Image Captioning with Color Recognition Using Deep Learning Methods

Automatically describing the content of an image is an interesting and challenging task in artificial intelligence. In this paper, an enhanced image captioning model—including object detection, color analysis, and image captioning—is proposed to automatically generate the textual descriptions of images. In an encoder–decoder model for image captioning, VGG16 is used as an encoder and an LSTM (long short-term memory) network with attention is used as a decoder. In addition, Mask R-CNN with OpenCV is used for object detection and color analysis. The integration of the image caption and color recognition is then performed to provide better descriptive details of images. Moreover, the generated textual sentence is converted into speech. The validation results illustrate that the proposed method can provide more accurate description of images.

Incorporation of Sukkari Date in Probiotic-Enriched Fermented Camel Milk Improves the Nutritional, Physicochemical, and Organoleptical Characteristics

Camel milk and dates are well-known for their great nutritional and therapeutical benefits. Therefore, the study aimed to combine the benefits of fermented camel milk (FCM) and Sukkari date (SKD) in a naturally sweetened FCM. Six treatments of FCM using ABT-5 cultures with 0, 5, 7.5, 10, 12.5, and 15% SKD were carried out. Chemical, physicochemical, rheological properties were studied, while organoleptical attributes and probiotic strains viability were monitored during cold storage (4 °C) up to 15 days. Results showed that fortification with SKD increased total solids (TS), ash, dietary fiber, and carbohydrate content compared to plain FCM. Water holding capacity (WHC) values increased with low and medium SKD levels then decreased with high SKD levels. Minerals such as K, P, Mg, Zn, Fe, and Cu were significantly increased, while Na was significantly decreased. Increased SKD levels in FCM resulted in significant increases in total phenolic content (TPC), total flavonoids (TF), total flavonols (TFL), and antioxidant activity (AOA). Instrumental color analysis exhibited a significant change in L*, b*, BI, and ∆E due to adding SKD in a dose-dependent manner. The viability of Streptococcus thermophiles, Lactobacillus acidophilus, and Bifidobacterium bifidum was increased by adding low and medium SKD levels, resulting in a higher number than the accepted threshold for a probiotic effect. Adding 10 and 12.5% SKD recorded the best-balanced flavor score at the beginning and after up to 15 days of storage, respectively. Conclusively, the current study revealed that fortification with SKD at 7.5–12.5% improved the nutritional quality without adverse effects on the technological, organoleptic characteristics, and probiotics viability and provided acceptable, nutritious, and healthy benefits to FCM.

A Four Stage Image Processing Algorithm for Detecting and Counting of Bagworm, Metisa plana Walker (Lepidoptera: Psychidae)

The bagworm is a vicious leaf eating insect pest that threatens the oil palm plantations in Malaysia. The economic impact from defoliation of approximately 10% to 13% due to bagworm attack might cause about 33% to 40% yield loss over 2 years. Due to this, monitoring and detecting of bagworm populations in oil palm plantations is required as the preliminary steps to ensure proper planning of control actions in these areas. Hence, the development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm, using image segmentation has been researched and completed. The color and shape features from the segmented images for real time object detection showed an average detection accuracy of 40% and 34%, at 30 cm and 50 cm camera distance, respectively. After some improvements on training dataset and marking detected bagworm with bounding box, a deep learning algorithm with Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm was applied leading to the percentage of the detection accuracy increased up to 100% at a camera distance of 30 cm in close conditions. The proposed solution is also designed to distinguish between the living and dead larvae of the bagworms using motion detection which resulted in approximately 73–100% accuracy at a camera distance of 30 cm in the close conditions. Through false color analysis, distinct differences in the pixel count based on the slope was observed for dead and live pupae at 630 nm and 940 nm, with the slopes recorded at 0.38 and 0.28, respectively. The higher pixel count and slope correlated with the dead pupae while the lower pixel count and slope, represented the living pupae.

Characteristics of Pork Muscles Cooked to Varying End-Point Temperatures

M. biceps femoris (BF), m. semimembranosus (SM) and m. semitendinosus (ST) from fresh pork ham were evaluated for characteristics of quality after cooking to an internal endpoint temperature of 62 °C or 73 °C. Fresh ham muscles from the left side (N = 68) were cut into 2.54 cm thick chops and allocated to cooking loss, Warner–Bratzler shear force (WBSF), pH and instrumental cooked color analysis. Cooking losses were greater (p < 0.0001) for SM and chops cooked to an internal temperature of 73 °C (p < 0.0001), whereas WBSF did not differ (p = 0.2509) among the three muscles, but was greater (p < 0.0001) in chops cooked to 73 °C. Fresh muscle’s pH was greater (p < 0.05) in ST than BF or SM. Lastly, the interactive effect (p < 0.05) of muscle × endpoint temperature for ST chops cooked to 73 °C was lighter (L*), but, when cooked to 62 °C, they were more red (a*), more yellow (b*) and incurred less color change from red to brown than BF or SM. The current results suggest it is plausible for BF, SM and ST to be considered for alternative uses instead of traditional value-added manufacturing.

LabVIEW Based Real-time Color Measurement System

Colorimetry is of paramount importance to the agricultural industry. Colorimetry refers to the processing of agricultural products for consumer needs from a marketing point of view, and therefore the agricultural industry spends a lot of money and time classifying each product. In the past, agricultural professionals had to use program codes that are difficult to learn, and even the most basic image analysis for agricultural product classification required mastering different program libraries. Today, the LabVIEW platform offers a flexible, fast, easy-to-learn, and complete image analysis infrastructure with various useful modules. For this reason, in this study, a method analysis for color perception with a simple USB webcam and software developed for real-time color analysis on the LabVIEW platform is presented and its success in the basic color analysis is tried to be revealed. The basic application developed for this purpose in LabVIEW v2019 using NI Vision Development Module v19 and NI IMAQ v19 modules. The basic fact that is the LabVIEW application is the idea that LabVIEW can only be analyzed with expensive IEEE 1394, but it should be known that these analyzes can be done with USB webcams. For this purpose, the application includes a USB webcam driver that can be stacked seamlessly. USB Webcam and colorimeter measurement-based results of ƔR factors for each of RGB color channels are 1.161232, 0.506287, 0.432229; ƔG factors for each of RGB color channels are 0.519619, 1.025383, 1.201444; at last ƔB factors for each of RGB color channels are 0.600362, 0.714016, 1.413406, respectively.

The Development of Digital Image Colorimetric Quantitative Analysis of Multi-Explosives Using Polymer Gel Sensors

Polymer gel sensors on 96-well plates were successfully used to detect four different multi-explosives, including 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), nitrite, and perchlorate. The products of reactions between the explosives and the polymer gel sensors were digitally captured, and the images were analyzed by a developed Red–Green–Blue (RGB) analyzer program on a notebook computer. RGB color analysis provided the basic color data of the reaction products for the quantification of the explosives. The results provided good linear range, sensitivity, limit of detection, limit of quantitation, specificity, interference tolerance, and recovery. The method demonstrated great potential to detect explosives by colorimetric analysis of digital images of samples on 96-well plates. It is possible to apply the proposed method for quantitative on-site field screening of multi-explosives.

Multilayered Complexity Analysis in Architectural Design: Two Measurement Methods Evaluating Self-Similarity and Complexity

This article contributes to clarifying the questions of whether and how fractal geometry, i.e., some of its main properties, are suitable to characterize architectural designs. This is done in reference to complexity-related aesthetic qualities in architecture, taking advantage of the measurability of one of them; the fractal dimension. Research in this area so far, has focused on 2-dimensional elevation plans. The authors present several methods to be used on a variety of source formats, among them a recent method to analyze pictures taken from buildings, i.e., 2.5-dimensional representations, to discuss the potential that lies within their combination. Color analysis methods will provide further information on the significance of a multilayered production and observation of results in this realm. In this publication results from the box-counting method are combined with a coordinate-based method for analyzing redundancy of proportions and their interrelations as well as the potential to include further layers of comparison are discussed. It presents a new area of box-counting implementation, a methodologically redesigned gradient analysis and its new algorithm as well as the combination of both. This research shows that in future systems it will be crucial to integrate several strategies to measure balanced aesthetic complexity in architecture.