FT-NIR spectroscopy for the quality characterization of apricots (Prunus armeniaca L.)

Abstract: The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm⁻¹. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R² values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended. Practical Application : The results of the study could be used as a tool for the assessment of the ripening stage during the harvest and the quality during the postharvest storage of apricot fruits.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

A waveguide technique for non-destructive determination of egg quality parameters

The input of a waveguide probe for shell eggs was connected to a sinewave sweeper oscillator and the signal at the output was captured by a spectrum analyser. A first analysis was carried out in the range from 3 to 20 GHz with a span of 1 GHz to investigate which 1 GHz frequency range contains most information for predicting the main quality indices of eggs during 15 days of storage. Simple linear regression models were therefore set up and the coefficient of determination was calculated. The absorbance spectra in the range thus identified (from 10.5 to 11.5 GHz) were used to predict the quality indices by means of an artificial neural network (ANN). The R² values of the obtained ANN in validation mode were 0.918, 0.854 and 0.912 for the air cell, the thick albumen height and the yolk index, respectively. The correlations between the quality parameters and tests carried out on albumen, yolk and plastic eggs for simulating the air cell showed how one index can be indirectly predicted through another one.     

Impact device for measuring the flesh firmness of kiwifruits

The device used in the present study consists of a conveyer belt that throws the fruit onto a flat horizontal plate connected to a load cell. The vertical distance between plate and conveyer belt (drop height) as well as the speed of the belt can be continuously adjusted. Tests were carried out by selecting three different values of drop height and speed. The Magness-Taylor (MTf) index was used as reference, destructive parameter, to describe the flesh firmness and to set-up predictive models. The digitalized time history of the force was analysed to extract some mechanical indices (peak force, impact duration and impulse) used to predict MTf by simple or multiple regression analyses. Moreover, each point of the entire time history was processed by artificial neural network (ANN) software to predict MTf. The goodness of fit, expressed as R², was up to 0.823 with the regression models. On the whole, the peak force was the best predictor. The ANNs did not involve a substantial increase in goodness of fit with respect to the best regression models: +8.3%, as mean, 37% as maximum. The speed or position at which the fruit impacts the plate can represent an important parameter influencing the MTf prediction. Free dropping of the fruit instead of throwing onto the plate by the conveyer did not provide a better prediction. The impact device did not cause mechanical damage to the kiwifruits.     

Effects of the Combined Treatment with a G-Quadruplex-Stabilizing Ligand and Photon Beams on Glioblastoma Stem-like Cells: A Magnetic Resonance Study

Glioblastoma multiforme is a malignant primary brain tumor with a poor prognosis and high rates of chemo-radiotherapy failure, mainly due to a small cell fraction with stem-like properties (GSCs). The mechanisms underlying GSC response to radiation need to be elucidated to enhance sensitivity to treatments and to develop new therapeutic strategies. In a previous study, two GSC lines, named line #1 and line #83, responded differently to carbon ions and photon beams, with the differences likely attributable to their own different metabolic fingerprint rather than to radiation type. Data from the literature showed the capability of RHPS4, a G-quadruplex stabilizing ligand, to sensitize the glioblastoma radioresistant U251MG cells to X-rays. The combined metabolic effect of ligand #190, a new RHPS4-derivative showing reduced cardiotoxicity, and a photon beam has been monitored by magnetic resonance (MR) spectroscopy for the two GSC lines, #1 and #83, to reveal whether a synergistic response occurs. MR spectra from both lines were affected by single and combined treatments, but the variations of the analysed metabolites were statistically significant mainly in line #1, without synergistic effects due to combination. The multivariate analysis of ten metabolites shows a separation between control and treated samples in line #1 regardless of treatment type, while separation was not detected in line #83.     

When does lean hurt? – an exploration of lean practices and worker health and safety outcomes

This research takes a first step toward a more complete understanding of the effects of lean production on both operational and worker health and safety performance. Previous operations management literature considered only the operational performance implications of lean while previous safety literature considered only the worker health and safety implications of lean. This research considers both perspectives by providing empirical evidence on the impact of lean on operational and health and safety performance. Results from 10 case studies show that the adoption of lean practices and or an overall lean philosophy has a positive impact on operational and health and safety performance. However, there are some nuances in the role of individual practices associated with lean. The plants with the worst operational and health and safety performance in the sample were those that adopted just-in-time practices without human resource and prevention practices. The results show how both the social and technical components of lean are required for lean to have positive operational and health and safety impacts.     

Greenhouse gas emissions and non-renewable energy use profiles of bio-based succinic acid from <Emphasis Type="Italic">Arundo donax</Emphasis> L. lignocellulosic feedstock

The European Union recognizes the priority of new bio-based industrial pathways, such as bio-based succinic acid (bio-SA). This study has investigated, through a life cycle method, the cradle-to-factory gate greenhouse gas (GHG) emissions and non-renewable energy use (NREU) of bio-SA from lignocellulosic giant reed (GR) feedstock grown on marginal lands in Southern Italy (GR bio-SA). The aims were to: (1) evaluate the environmental performance of the GR bio-SA and (2) discuss the GR bio-SA profile with respect to its fossil counterparts and alternative bio-SA routes. For 1 kg of GR bio-SA, the gross GHG emissions amounted to 3.9 kg CO₂ eq, while through the inclusion of the biogenic C potentially stored in SA molecule (1.47 kg CO₂ eq) and soil organic matter (0.44 kg CO₂ eq), the final net global warming potential would be nearly halved. Similarly to current starch-based SA supply chains, the GR bio-SA showed: (1) better gross GHG profile compared to the fossil adipic acid (GHG emissions reduced by 55%) and (2) comparable net GHG emissions in comparison with petrochemicals SA and maleic acid. The total NREU for 1 kg of GR bio-SA amounted to 26.6 MJ, with reduced energy consumption by about 55–79% relative to fossil counterparts, thanks to the on-site energetic valorization of lignin and holocellulose residues with relatively high heating values. The soy protein concentrate and the inorganic chemicals used in the co-fermentation showed up the prevailing contributions to the GHG and NREU profiles of the GR bio-SA, suggesting the need to optimize nitrogen and carbon sources of the growth medium.     

Technologies for the removal of phenol from fluid streams: a short review of recent developments

The available technologies for the abatement of phenol from water and gaseous streams are briefly reviewed, and the recent advancements summarized. Separation technologies such as distillation, liquid–liquid extraction with different solvents, adsorption over activated carbons and polymeric and inorganic adsorbents, membrane pervaporation and membrane–solvent extraction, have been discussed. Destruction technologies such as non-catalytic, supercritical and catalytic wet air oxidation, ozonation, non-catalytic, catalytic and enzymatic peroxide wet oxidation, electrochemical and photocatalytic oxidation, supercritical wet gasification, destruction with electron discharges as well as biochemical treatments have been considered. As for the abatement of phenol from gases, condensation, absorption in liquids, adsorption on solids, membrane separation, thermal, catalytic, photocatalytic and biological oxidation have also been considered. The experimental conditions and the performances of the different techniques have been compared.