Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties

2.2. New Approaches 707 2.2.1. Optical Sensor Systems 707 2.2.2. Mass Spectrometry 708 2.2.3. Ion Mobility Spectrometry 708 2.2.4. Gas Chromatography 709 2.2.5. Infrared Spectroscopy 709 2.2.6. Use of Substance-Class-Specific Sensors 709 2.3. Combined Technologies 710 3. Companies 710 4. Application Areas 710 4.1. Food and Beverage 712 4.2. Environmental Monitoring 715 4.3. Disease Diagnosis 716 5. Research and Development Trends 718 5.1. Sample Handling 719 5.2. Filters and Analyte Gas Separation 719 5.3. Data Evaluation 720 6. Conclusion 721 7. References 722

/pdf/electronic-nose-current-status-and-future-trends-475b22mugv.pdf

Electronic nose: current status and future trends.

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy Our intention is for the review to serve as a source of up-to-date information on the past and current role of vis–NIR spectroscopy in soil science It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pretratments, covariations in data sets, and mathematical data preprocessing Field analyses and strategies for the practical use of vis–NIR are considered We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function To do this, research in soil spectroscopy needs to be more collaborative and strategic The development of the Global Soil Spectral Library might be a step in the right direction

/pdf/visible-and-near-infrared-spectroscopy-in-soil-science-l2f7v53g20.pdf

Visible and Near Infrared Spectroscopy in Soil Science

Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

The testing and rapid detection of pathogenic organisms is a crucial protocol in the prevention and identification of crises related to health, safety and wellbeing. Pathogen detection has become one of the most challenging aspects in the food and water industries, because of the rapid spread of waterborne and foodborne diseases in the community and at significant costs. With the prospect of inevitable population growth, and an influx of tourism to certain water bodies testing will become a requirement to control and prevent possible outbreaks of potentially fatal illnesses. The legislation is already particularly rigorous in the food industry, where failure to detect pathogenic materials represents a catastrophic event, particularly for the elderly, very young or immune-compromised population types. In spite of the need and requirement for rapid analytical testing, conventional and standard bacterial detection assays may take up to seven days to yield a result. Given the advent of new technologies, biosensors, chemical knowledge and miniaturisation of instrumentation this timescale is not acceptable. This review presents an opportunity to fill a knowledge gap for an extremely important research area; discussing the main techniques, biology, chemistry, miniaturisation, sensing and the emerging state-of-the-art research and developments for detection of pathogens in food, water, blood and faecal samples.

A review of methods for the detection of pathogenic microorganisms

Visible (VIS) and near infrared reflectance spectroscopy (NIRS) was used to identify and authenticate different meat muscle species. Samples from beef (n: 100), lamb (n: 140), pork (n: 44) and chicken (n: 48) muscles were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (400-2500 nm) in a monochromator instrument in reflectance. Both Principal Component Analysis (PCA) and dummy partial least-squares regression (PLS) models were developed to identify different meat species. The models correctly classified more than 80% of the meat sample muscles according with the muscle specie. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of meat muscle species.

Identification of animal meat muscles by visible and near infrared reflectance spectroscopy

The use of visible (vis) and near-infrared spectroscopy (NIR) was explored as a tool to discriminate between samples of Australian commercial white wines of different varietal origins (Chardonnay and Riesling). Discriminant models were developed using principal component analysis (PCA), principal component regression (PCR), and discriminant partial least-squares (DPLS) regression. The samples were randomly split into two sets, one used as a calibration set (n = 136) and the remaining samples as a validation set (n = 133). When used to predict the variety of the validation set samples, the DPLS models correctly classified 100% of Riesling and up to 96% of Chardonnay wines. These results showed that vis-NIR might be a suitable and alternative technology that can be easily implemented by the wine industry to discriminate Riesling and Chardonnay commercial wine varieties. However, the relatively limited number of samples and varieties involved in the present work suggests caution in extending the potential of such a technique to other wine varieties.

Feasibility study on the use of visible and near infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins

Near-infrared reflectance spectroscopy (NIRS) was used for the analysis of soil samples for silt, sand, clay, calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), copper (Cu) and iron (Fe) A total of 332 samples of different soils from Uruguay (South America) were used The samples were scanned in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance Cross validation was applied to avoid overfitting of the models The coefficient of determination in calibration (R^2_{\rm cal}) and the standard errors in cross validation (SECV) were 0·80 (SECV: 6·8), 0·84 (SECV: 6·0), 0·90 (SECV: 3·6) in per cent for sand, silt and clay respectively For both macro and microelements the R^2_{\rm cal} and SECV were 0·80 (SECV: 0·1), 0·95 (SECV: 2·9), 0·90 (SECV 0·8), for K, Ca, Mg in g/kg respectively, and 0·86 (SECV: 0·82) and 0·92 (SECV: 25·5) for Cu and Fe in mg/kg It was concluded that NIRS has a great potential as an analytical method for soil routine analysis due to the speed and low cost of analysis

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0021859602002836

Daniel Cozzolino

Papers

Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

A review of methods for the detection of pathogenic microorganisms

Identification of animal meat muscles by visible and near infrared reflectance spectroscopy

Feasibility study on the use of visible and near infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins

The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics