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Grapefruit (GF) Citrus Χ paradisi Macfad (F. Rutaceae) is one of the major citrus fruits that encompass a myriad of bioactive chemicals and most unique among citrus fruits. Nevertheless, no study has yet to assess comprehensively its multitudinous constituents, health benefits, and valuable waste products. Hereto, the present review provides an updated comprehensive review on the different aspects of GF, its juice production, waste valorization, enhancement of its byproducts quality, and compared to other citrus fruits. Grapefruit uniqueness among other citrus fruits stands from its unique taste, flavor, and underlying complex chemical composition. Despite limonene abundance in peel oil and grapefruit juice (GFJ) aroma, nootkatone and sulfur compounds are the key determinants of its flavor, whereas flavanones contribute to its bitter taste and in conjunction with limonoids. Different postharvest treatments and juice processing are reviewed and in context to its influence on final product quality and or biological effects. Flavanones, furanocoumarins, and limonoids appear as the most prominent in GF drug interactions affecting its metabolism and or excretion. Valorization of GF peel is overviewed for its utilization as biosrobent, its oil in aromatherapy, limonene as antimicrobial or in cosmetics, fruit pectin for bioethanol production, or as biosorbent, and peel phenolics biotransformation. The present review capitalizes on all of the aforementioned aspects in GF and further explore novel aspects of its juice quality presenting the full potential of this valued multi-faceted citrus fruit.

In this work, we develop chitosan/xylan-coated magnetite (CsXM) nanoparticles as eco-friendly efficient adsorbents for the facile removal of contaminants from water. Characterization of CsXM using Fourier Transform Infra-Red (FTIR) Spectroscopy, Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Transmission Electron Microscopy (TEM), Zeta potential measurements, and Brunauer-Emmet-Teller (BET) analysis, confirmed the successful preparation of a chitosan/xylan complex coated over magnetite, which is characterized by being mesoporous, thermally stable and of neutral charge. Three contaminants, Pb(II), salicylic acid (SA), and congo red (CR), were chosen as representative pollutants from three major classes of contaminants of emerging concern: heavy metals, pharmaceuticals, and azo dyes. Pb(II), SA, and CR at initial concentrations of 50 ppm were removed by 64.49, 62.90, and 70.35%, respectively, on applying 6 g/L of CsXM. The contaminants were successfully removed in ternary systems, with Pb (II) and SA being more competitive in their adsorption than CR. Adsorption followed the Freundlich isotherm model and the pseudo-second order kinetic model, while the binding was suggested to occur mainly via chemical chelation for Pb(II) and physical interaction for SA and CR, which demonstrates the multifunctional potential of the nanoparticles to capture different contaminants regardless of their charge.

Green analysis is defined as the analysis of chemicals in a manner where sample extraction and analysis are performed with least amounts of steps, low hazardous materials, while maintaining efficiency in terms of analytes detection. Coffee and cocoa represent two of the most popular and valued beverages worldwide in addition to their several products i.e., cocoa butter, chocolates. This study presents a comprehensive overview of green methods used to evaluate cocoa and coffee seeds quality compared to other conventional techniques highlighting advantages and or limitations of each. Green techniques discussed in this review include solid phase microextraction, spectroscopic techniques i.e., infra-red (IR) spectroscopy and nuclear magnetic resonance (NMR) besides, e-tongue and e-nose for detection of flavor. The employment of multivariate data analysis in data interpretation is also highlighted in the context of identifying key components pertinent to specific variety, processing method, and or geographical origin.

The antibiotic Ciprofloxacin HCl (CPH) is a representative example of pharmaceutical contaminants of emerging concern that are frequently released in wastewater effluents and can cause hazardous health effects. In this work, we investigated the potential of utilizing porous polymeric membranes incorporating metal nanoparticles for removing CPH from water. In this regard, polylactic acid/polyurethane membranes were impregnated in situ with single, binary, and tertiary systems of nanoparticles of cobalt, copper, and nickel, among others. Membranes loaded with cobalt exhibited the best performance among all the examined membranes. They yielded removal efficiencies above 80% at an initial CPH concentration range of 10–50 ppm and pH 6.5, thus exceeding those of the bare membranes by about 1.3 times under the same conditions. As confirmed by Brunauer, Emmet, and Teller (BET) analysis, the incorporation of cobalt nanoparticles into the mesoporous membranes increased their surface area and pore volume by 5 and 10 times, respectively. Thermogravimetric analysis (TGA) showed that cobalt nanoparticles had no catalytic influence on the dissociation of the membrane polymeric chains. Fourier transform infrared (FTIR) and zeta potential measurements suggested that binding could possibly occur via physical interactions along with catalytic degradation.

Grapefruit (GF)

Commercial PET films were surface treated and subsequently coated with either rosemary (RME) or clove (CE) extracts. Surface treatments involved (1) corona treatment, (2) chemical modification, and (3) plasma treatment. Radical scavenging activity (RSA) of both pure plant extracts and coated film extracts were determined using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) method. RME-coated films showed a % RSA of 25.6%, 22.4%, and 24.1% for plasma, chemical modification, and corona treatment, respectively, at an extract concentration of 1402 ppm, respectively, while pure RME showed a %RSA of 16.0%. Respective %RSA values for CE were 25.0% for plasma, 25.2% for chemically modified, and 25.2% for corona-treated films at 1402 ppm, while pure CE showed a %RSA of 47.6%. Thiobarbituric acid (TBA) test, performed on ground fish muscle wrapped in all types of employed films, showed a remarkable decrease in the degree of fish oxidation ranging between 50.0 and 80.0% after 6 days of storage. Contact angle measurements confirmed that surface chemically modified films had the highest adhesion strength followed by corona and plasma-treated films. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) data also supported contact angle measurements. Finally, the oxygen permeability of surface-treated films did not differ from untreated films indicating that surface treatment did not affect film barrier properties.

Many repetitive construction field operations exhibit a phenomenon known as the learning or experience effect. A learning curve is generated when the time or cost required to complete one cycle of an activity is plotted as a function of the cycle number. For practicing construction engineers and managers, the greatest potential value of learning curves lies in their ability to predict future performance, instead of fitting historical data. This paper presents a new method for using learning curves to predict the time or cost to complete the remaining cycles of an activity in progress, to assess the accuracy of this method, and to compare the accuracy of this method with the standard forecasting technique used in construction cost reporting. Using the proposed method, the accuracy of predicting the time or cost required to complete an ongoing activity improves dramatically for about the first 25-30% of the activity and then levels off to within 15-20% of the actual value. Compared to the standard method using the cumulative average, the new learning curve method is shown to be more accurate. The analysis quantifies the trade-off between accuracy of predicting future performance and the timeliness and potential value of such a prediction.

Mathematical learning curve models can be used to predict the time or cost required to perform future cycles in a repetitive construction activity. The analyst has a choice of several methods of representing the data, usually trading off between response and stability of forecasting information. Traditionally, learning curve data has been evaluated using either unit data or cumulative-average data. This paper evaluates those two methods and two other techniques: the moving average and the exponentially weighted average. For the 54 construction activities evaluated, unit data gives the most accurate prediction of the time or cost to complete the remaining cycles of the activity. Cumulative-average data gives the least accurate prediction. Compared to unit data, the exponentially weighted average can predict future performance with only a slight loss of accuracy early in the activity, but equal accuracy later in the activity. The exponentially weighted average may offer an improved combination of stability and response, depending on the smoothing parameter chosen.

Many repetitive construction field operations exhibit a learning curve, over which the time or cost per cycle decreases as the cycle number increases. This paper evaluates several mathematical models to determine which best describes the relationship between the activity time or cost and the cycle number. For completed activities, cubic learning curve models are found to provide the most reliable statistical fit, and linear models provide the least reliable fit. The real potential value of learning curves is their ability to predict the time or cost needed to perform future activities. This paper presents a methodology for predicting future activity time or cost based on completed activity data. The best predictors of future performance are found to be linear models. The cubic models that best describe completed activities are poor predictors of future performance.