Combining absolute and relative information in studies on food quality

A common problem in food science concerns the assessment of the quality of food samples. Typically, a group of panellists is trained exhaustively on how to identify different quality indicators in order to provide absolute information, in the form of scores, for each given food sample. Unfortunately, this training is expensive and time-consuming. For this very reason, it is quite common to search for additional information provided by untrained panellists. However, untrained panellists usually provide relative information, in the form of rankings, for the food samples. In this paper, we discuss how both scores and rankings can be combined in order to improve the quality of the assessment

The constrained median : a way to incorporate side information in the assessment of food samples

A classical problem in the field of food science concerns the consensus evaluation of food samples. Typically, several panelists are asked to provide scores describing the perceived quality of the samples, and subsequently, the overall (consensus) scores are determined. Unfortunately, gathering a large number of panelists is a challenging and very expensive way of collecting information. Interestingly, side information about the samples is often available. This paper describes a method that exploits such information with the aim of improving the assessment of the quality of multiple samples. The proposed method is illustrated by discussing an experiment on raw Atlantic salmon (Salmo salar), where the evolution of the overall score of each salmon sample is studied. The influence of incorporating knowledge of storage days, results of a clustering analysis, and information from additionally performed sensory evaluation tests is discussed. We provide guidelines for incorporating different types of information and discuss their benefits and potential risks

In vitro degradation behavior and cytocompatibility of Mg–Zn–Zr alloys

Zinc and zirconium were selected as the alloying elements in biodegradable magnesium alloys, considering their strengthening effect and good biocompatibility. The degradation rate, hydrogen evolution, ion release, surface layer and in vitro cytotoxicity of two Mg–Zn–Zr alloys, i.e. ZK30 and ZK60, and a WE-type alloy (Mg–Y–RE–Zr) were investigated by means of long-term static immersion testing in Hank’s solution, non-static immersion testing in Hank’s solution and cell-material interaction analysis. It was found that, among these three magnesium alloys, ZK30 had the lowest degradation rate and the least hydrogen evolution. A magnesium calcium phosphate layer was formed on the surface of ZK30 sample during non-static immersion and its degradation caused minute changes in the ion concentrations and pH value of Hank’s solution. In addition, the ZK30 alloy showed insignificant cytotoxicity against bone marrow stromal cells as compared with biocompatible hydroxyapatite (HA) and the WE-type alloy. After prolonged incubation for 7 days, a stimulatory effect on cell proliferation was observed. The results of the present study suggested that ZK30 could be a promising material for biodegradable orthopedic implants and worth further investigation to evaluate its in vitro and in vivo degradation behavior

Integrating expert and novice evaluations for augmenting ordinal regression models

We consider a predictive modelling problem, where the goal is to predict the absolute evaluation of an object on an ordinal scale, traditionally known as an ordinal regression problem. We present a framework that is capable of learning such a model while combining different types of information: absolute evaluations by experts and relative evaluations by novices. We propose and solve a linearly constrained convex optimization problem that takes both types of information into account, and is capable of attributing an ordinal label to a new object based on its features. We do this by relying on principles from machine learning and optimization theory, combined with ideas from information fusion. Experimental results demonstrate the enhanced performance of ordinal regression models when incorporating relative evaluations in the form of rankings

A combined scoring and ranking approach for determining overall food quality

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Predicting consumer acceptance of packaged meat using L1-regularized ordinal regression

Modified atmosphere packaging (MAP) is a technique that is commonly used to extend the shelf-life of fresh meat. Unfortunately, MAP packages prevent consumers from judging the freshness of the packaged meat based on its smell. Recent research efforts have led to the development of (prototypes of) optical sensors that allow to measure the concentration of multiple volatile organic compounds (VOCs) in the headspace of a package in a non-destructive manner. These measurements can be used to assess the perceived freshness of the meat in that package. To make such an assessment, a model (or relationship) is needed to predict the consumer appreciation of the meat in a package with given VOC concentrations. In this work, we present a machine learning strategy that can be used to learn such a relationship from data. Interestingly, the available data to learn this relationship have several properties that complicate the learning process. For example: consumer appreciation (the response variable) is measured on an ordinal scale, appreciation is personal and not consistent over consumers, the input is high-dimensional (a large number of VOCs), yet only a limited number of instances is available. To be able to deal with these complicating elements, we propose an L1-regularized ordinal regression approach that is capable of exploiting multiple types of data simultaneously. Moreover, this approach allows for an automated selection of VOCs that are important to model consumer appreciation

Osteonecrosis of the jaw during biyearly treatment with zoledronic acid for aromatase inhibitor associated bone loss in early breast cancer: A literature review

Osteonecrosis of the jaw (ONJ) is one of the most relevant and specific complication of biphosphonates. ONJ in patients receiving zoledronic acid every 3 to 4 weeks is frequently described, but the ONJ biyearly regimen used to reduce aromatase inhibitor associated bone loss (AIBL), is rarely reported. A literature review, focusing on the important trials using zoledronic acid to reduce AIBL, found that the mean risk of developing ONJ when zoledronic acid is used biyearly varies between 0.12% and 0.7%