NIR Calibrations for Soybean Seeds and Soy Food Composition Analysis: Total Carbohydrates, Oil, Proteins and Water Contents

Conventional chemical analysis techniques are expensive, time consuming, and often destructive. The non-invasive Near Infrared (NIR) technology was introduced over the last decades for wide-scale, inexpensive chemical analysis of food and crop seed composition (see Williams and Norris, 1987; Wilcox and Cavins, 1995; Buning and Diller, 2000 for reviews of the NIR technique development stage prior to 1998, when Diode Arrays were introduced to NIR). NIR spectroscopic measurements obey Lambert and Beer’s law, and quantitative measurements can be successfully made with high speed and ease of operation. NIR has been used in a great variety of food applications. General applications of products analyzed come from all sectors of the food industry including meats, grains, and dairy products (Shadow, 1998)

NIR Calibrations for Soybean Seeds and Soy Food Composition Analysis: Total Carbohydrates, Oil, Proteins and Water Contents [v.2]

Conventional chemical analysis techniques are expensive, time consuming, and often destructive. The non-invasive Near Infrared (NIR) technology was introduced over the last decades for wide-scale, inexpensive chemical analysis of food and crop seed composition (see Williams and Norris, 1987; Wilcox and Cavins, 1995; Buning and Diller, 2000 for reviews of the NIR technique development stage prior to 1998, when Diode Arrays were introduced to NIR). NIR spectroscopic measurements obey Lambert and Beer’s law, and quantitative measurements can be successfully made with high speed and ease of operation. NIR has been used in a great variety of food applications. General applications of products analyzed come from all sectors of the food industry including meats, grains, and dairy products (Shadow, 1998).
Novel NIR calibrations for rapid, reliable and accurate composition analysis of a variety of several soy based foods and bulk soybean seeds were developed and validated in a six-year collaborative project with a large number of different samples (N >~12, 000). The availability of such calibrations is important for establishing NIR as a secondary method for composition analysis of foods and soybeans both in applications and fundamental research

Automated protein structure calculation from NMR data

Current software is almost at the stage to permit completely automatic structure determination of small proteins of < 15 kDa, from NMR spectra to structure validation with minimal user interaction. This goal is welcome, as it makes structure calculation more objective and therefore more easily validated, without any loss in the quality of the structures generated. Moreover, it releases expert spectroscopists to carry out research that cannot be automated. It should not take much further effort to extend automation to ca 20 kDa. However, there are technological barriers to further automation, of which the biggest are identified as: routines for peak picking; adoption and sharing of a common framework for structure calculation, including the assembly of an automated and trusted package for structure validation; and sample preparation, particularly for larger proteins. These barriers should be the main target for development of methodology for protein structure determination, particularly by structural genomics consortia

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Assessment of research needs for sustainability of unconventional machining processes

AbstractThe use of unconventional machining (UCM) practices is increasing for product manufacture particularly when machining difficult to cut materials and when high precision is required. There is plenty of research conducted on improving sustainability of traditional machining. However, sustainability studies on unconventional/non-traditional machining practices are few. This review aims to determine the current state of the art in sustainability assessment of unconventional machining practices and identify gaps in research. An extensive review was carried out and analysed using a qualitative data analysing software. The analysis shows that only 25 publications directly and indirectly discuss the matter of sustainability of UCM. Out of this almost 70% of publications were recorded after year 2006 showing a clear evidence of uncovered research gap in the field with a growing interest. Despite this trend, evidence on studies which are explicitly dedicated to analyse the sustainability of UCM are rare

QCD Thermodynamics from the Lattice

We review the current methods and results of lattice simulations of quantum chromodynamics at nonzero temperatures and densities. The review is intended to introduce the subject to interested nonspecialists and beginners. It includes a brief overview of lattice gauge theory, a discussion of the determination of the crossover temperature, the QCD phase diagram at zero and nonzero densities, the equation of state, some in-medium properties of hadrons including charmonium, and some plasma transport coefficients.Comment: 74 pp. 31 figs. To appear in the European Physical Journal A and Advances in Physics of Particles and Nuclei. Added references, corrected typos, and updated the discussion of the thermal heavy quark/antiquark potential. Added and updated references. Final versio