Pomegranate molasses authentication using hyperspectral imaging system coupled with automatic clustering algorithm.

Pomegranate molasses is made from concentrated pomegranate juice with nothing added. Due to its nutritional value, limitation in production, and high production cost, this product may be adulterated by date syrup. This study was done to differentiate various types of pomegranate molasses and investigate the possibility of nonauthenticity detection in pomegranate molasses samples using the hyperspectral imaging (HSI) technique compared with physicochemical measurement analysis. The physicochemical properties (brix index, sucrose, acidity, ash content, pH, and formalin index) of 24 samples were measured as the reference analysis method, and it was found that the formalin index was a good factor for pomegranate molasses authenticity evaluation. Additionally, an HSI system (400-1000 nm) was used as a nondestructive and rapid screening method to capture spectral data of the samples. The evolutionary wavelength selection algorithm was applied to select effective wavelengths in sample clustering based on the obtained Davies-Bouldin index. Next, principal component analysis was used to visually interpret the spectral data of the sample when using the selected wavelengths and the whole spectra of the samples. Finally, an automatic clustering algorithm by the artificial bee colony as an unsupervised method was developed for the clustering of the authentic and nonauthentic samples. The method did not need descriptively labeled samples and obtained agreed satisfactorily with the degree of nonauthenticity in the samples. This study showed that the developed HSI technique coupled with an automatic clustering algorithm could detect date syrup nonauthenticity in pomegranate samples from the level of 5% adulteration.

Modeling of ionic liquids viscosity via advanced white-box machine learning.

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9 MPa) and temperatures (253.15-573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Predictive factors of delayed bleeding after percutaneous nephrolithotomy requiring angioembolization.

Objectives: To investigate the predictive factors of delayed post-percutaneous nephrolithotomy (PCNL) haemorrhage because of arteriovenous fistula (AVF) or pseudoaneurysm (PA) and compare the factors between AVF and PA. Patients and methods: This is a case-control study with a case-to-control ratio of 1:3. Out of 5077 patients who underwent PCNL from April 2015 to April 2018 in three different teaching hospitals, 113 had post-PCNL haemorrhages because of AVF and/or PA. Seventy-two patients met the inclusion criteria and entered the study as cases, while 216 patients without any postoperative complications were selected as controls. Results: Of all 72 studied patients with complications after PCNL, 35 (48.6%) had AVF, and the rest had PA. The regression model revealed that a history of diabetes (odds ratio [OR]: 2.799, 95% confidence interval [CI]: 1.392-5.630, p-value = 0.004) and renal anomalies (OR: 2.929, 95% CI: 1.108-7.744, p-value = 0.03) were associated with developing delayed post-PCNL haemorrhage. However, no differences were seen between AVF and PA regarding selected variables (p-value > 0.05). Conclusion: History of diabetes and renal anomalies were predictive factors for delayed post-PCNL haemorrhage, but no predictive factors were found to differentiate PA and AVF from one another.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics.

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.

Distal ureteral calculi in a patient with ileal conduit and urinary diversion treated via antegrade ureteroscopic lithotripsy: A case report.

Ureteral calculi management in patients with urinary diversion is challenging for most urologists. The surgeon should consider the patient's diversion type, BMI, stone size and location, and his/her experience with the procedure. We report an 85-year-old ileal conduit diversion man presented with ureteral calculi and treated via antegrade ureteroscopic lithotripsy.

Retroperitoneal mass, a rare manifestation of mucinous adenocarcinoma of appendix: A case report.

The retroperitoneum (RP) might be affected by a variety of infections, inflammations, and tumors, including benign and malignant ones. Although primary malignant tumors are the most prevalent ones in this anatomic area, metastatic and invasive tumors rarely involve the retroperitoneum. Gastrointestinal stromal tumors (GISTs) are considered as the most common tumors that invade the retroperitoneum, but, to the best of our knowledge, it is the first time a surgery team has encountered the appendiceal tumor as a huge retroperitoneal mass. A 68-year-old man was referred to the emergency department with abdominal distension and weight loss. In his course of hospitalization, a huge right retroperitoneal mass was detected by a computed tomography (CT) scan; after that, the patient underwent laparotomy, evacuation of massive mucinous tissue located in the right retroperitoneum, and right hemicolectomy due to appendiceal tumor. The histopathological examination showed "mucinous appendiceal neoplasm." This is the first case study showing the invasion of an appendiceal tumor through the visceral peritoneum into the retroperitoneum, so an invasion of the peritoneal tumor to the retroperitoneum should be considered when a urologist approaches retroperitoneal masses.

Rheological, physicochemical, and microstructural properties of asphalt binder modified by fumed silica nanoparticles.

Warm mix asphalt (WMA) is gaining increased attention in the asphalt paving industry as an eco-friendly and sustainable technology. WMA technologies are favorable in producing asphalt mixtures at temperatures 20-60 °C lower in comparison to conventional hot mix asphalt. This saves non-renewable fossil fuels, reduces energy consumption, and minimizes vapors and greenhouse gas emissions in the production, placement and conservation processes of asphalt mixtures. At the same time, this temperature reduction must not reduce the performance of asphalt pavements in-field. Low aging resistance, high moisture susceptibility, and low durability are generally seen as substantial drawbacks of WMA, which can lead to inferior pavement performance, and increased maintenance costs. This is partly due to the fact that low production temperature may increase the amount of water molecules trapped in the asphalt mixture. As a potential remedy, here we use fumed silica nanoparticles (FSN) have shown excellent potential in enhancing moisture and aging susceptibility of asphalt binders. In this study, asphalt binder modification by means of FSN was investigated, considering the effects of short-term and long-term aging on the rheological, thermal, and microstructural binder properties. This research paves the way for optimizing WMA by nanoparticles to present enhanced green asphalt technology.

New insights into the interactions between asphaltene and a low surface energy anionic surfactant under low and high brine salinity.

HYPOTHESIS: The hyperbranched chains on the tail of low surface energy surfactants (LSES) causes lowering of surface free energy and rock wettability alteration, offering significant improvement in oil recovery in asphaltene oil reservoirs. EXPERIMENTS: Oil sweep efficiency was determined by fluid displacement in pure brine and LSES-brine solutions in a microfluidic pattern that was representative of a sandstone cross-section. Interfacial tension (IFT), wettability alteration, Raman and X-ray photoelectron spectroscopy (XPS) were used to measure the changes of asphaltene interactions with oil-aged substrate after surface treating with brine and surfactant-brine solutions. FINDINGS: The hyperbranched LSES yielded a significant increase in the original-oil-in-place (OOIP) recovery (58%) relative to brine flooding (25%), even in the presence of asphaltene. Raman spectra showed the LSES-brine solutions to be capable of causing change to the asphaltene aggregate size after centrifugation treatment.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins.

The wide range of geographical cocoa production areas and the increasing consumption trend towards single origin products induced the necessity to verify and certify cocoa beans origin for quality assurance purposes. In this study cocoa beans of various origins were examined by machine olfaction and machine vision techniques. Fifty-nine fermented and dried Forastero cocoa beans from 23 different geographical origins (Africa, Americas, Southeast Asia) were investigated using Proton Transfer Reaction-Quadrupole interface-Time of Flight-Mass Spectrometry and Hyperspectral Imaging to elucidate the geographical information in the beans. The volatile and spectral fingerprints showed the same tendency in clustering samples from Africa separate from those from the Americas. High variability was observed for the Southeast Asian samples, which is most likely related to differences in fermentation. Machine olfaction and machine vision characterization provided a similar degree of separation but are complementary rapid techniques, which may be further developed for use in practical settings.

Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization.

Saffron quality characterization is an important issue in the food industry and of interest to the consumers. This paper proposes an expert system based on the application of machine vision technology for characterization of saffron and shows how it can be employed in practical usage. There is a correlation between saffron color and its geographic location of production and some chemical attributes which could be properly used for characterization of saffron quality and freshness. This may be accomplished by employing image processing techniques coupled with multivariate data analysis for quantification of saffron properties. Expert algorithms can be made available for prediction of saffron characteristics such as color as well as for product classification.