Erik Tønning

Appropriateness of culinary preparations of potato (Solanum tuberosum L.) varieties and relation to sensory and physicochemical properties

BACKGROUND Appropriateness of potatoes for culinary preparations is defined as the optimal use of a given variety for a specific culinary preparation. The aim of this study was to examine the sensory attributes and physicochemical characteristics (dry matter content, starch properties, instrumental texture) of 11 potato varieties in relation to their appropriateness for boiling, oven-frying or mashing. The reproducibility of appropriateness was studied by including the effect of two growing conditions and yearly variation combined with internal references. RESULTS The potato varieties, tested by 31 evaluators in two years, showed three groupings differing in appropriateness, which could be explained by a few sensory attributes: yellowness, mealiness, creaminess and butter taste. The physicochemical data showed a large variation of 23-25% in dry matter content, amylose content and starch granule size and an extreme variation in texture hardness. High reproducibility was found in the ranking of potatoes across years and culinary preparations. Hence, if a variety was judged well appropriate for boiled potatoes, it was also judged well appropriate as mashed or oven-fried potatoes. CONCLUSION This study showed the relevance of using sensory descriptors appropriateness in an objective manner. A few sensory descriptors can be used in consumer-labelling appropriateness of potatoes.

Multivariate data analysis of dynamic amperometric biosensor responses from binary analyte mixtures—application of sensitivity correction algorithms

In this paper, it is demonstrated that a single-receptor biosensor can be used to quantitatively determine each analyte in binary mixtures using multivariate data analysis tools based on the dynamic responses received from flow injection peaks. Mixtures with different concentrations of two phenolic compounds, catechol and 4-chlorophenol, were measured with a graphite electrode modified with tyrosinase enzyme at an applied potential of -50mV versus Ag/AgCl. A correction algorithm based on measurements of references in-between samples was applied to compensate for biosensor ageing as well as differences caused by deviations between biosensor preparations. After correction, the relative prediction errors with partial least squares regression (PLS-R) for catechol and 4-chlorophenol were 7.4 and 5.5%, respectively, using an analysis sequence measured on one biosensor. Additional validation mixtures of the two phenols were measured with a new biosensor, prepared with the same procedure but with a different batch of tyrosinase enzyme. Using the mixture responses for the first sensor as a calibration set in PLS-R, the relative prediction errors of the validation mixtures, after applying correction procedures, were 7.0% for catechol and 16.0% for 4-chlorophenol. These preliminary results indicate that by applying correction algorithms it could be possible to use less stable biosensors in continuous on-line measurements together with multivariate data analysis without time-consuming calibration procedures.

Prediction of wastewater quality using amperometric bioelectronic tongues.

Wastewater samples from a Swedish chemi-thermo-mechanical pulp (CTMP) mill collected at different purification stages in a wastewater treatment plant (WWTP) were analyzed with an amperometric enzyme-based biosensor array in a flow-injection system. In order to resolve the complex composition of the wastewater, the array consists of several sensing elements which yield a multidimensional response. We used principal component analysis (PCA) to decompose the arrays responses, and found that wastewater with different degrees of pollution can be differentiated. With the help of partial least squares regression (PLS-R), we could link the sensor responses to the Microtox® toxicity parameter, as well as to global organic pollution parameters (COD, BOD, and TOC). From investigating the influences of individual sensors in the array, it was found that the best models were in most cases obtained when all sensors in the array were included in the PLS-R model. We find that fast simultaneous determination of several global environmental parameters characterizing wastewaters is possible with this kind of biosensor array, in particular because of the link between the sensor responses and the biological effect onto the ecosystem into which the wastewater would be released. In conjunction with multivariate data analysis tools, there is strong potential to reduce the total time until a result is yielded from days to a few minutes.

Bulk Functionality Diversification by Unsupervised Single-Kernel Near-Infrared (SKNIR) Sorting of Wheat

ABSTRACT We explored the effects of fractioning heterogeneous bulk wheat by fast unsupervised single-kernel near-infrared (SKNIR) sorting according to an internal complex NIR functionality trait using a fast prototype kernel sorter designed for postharvest bulk sorting. Sorting into three functionality fractions was performed on low quality lots from an organic field experiment from two growth years and two locations. Sorted lots were mixtures originally diversified by three different preceding catch crops. The resulting 12 fractions, as well as the 12 original wheat lots were characterized by 20 standard quality variables of grains and flours. The data was analyzed by principal component analysis (PCA) and analysis of variance (ANOVA). Within each year and location/cultivar, the SKNIR fractionation had significant positive effect on bulk grain density, protein, wet gluten content, Zeleny sedimentation volume, farinograph water absorption, farinograph softening, falling number, gelatinization temperature,...

Single-kernel near infrared analysis of bulk wheat heterogeneity a theory of sampling reference study

Most often an analytical result, be it a destructive standard analysis or a non-destructive NIR spectrum, is a property estimate measured on an analytical volume considerably smaller than the volume of the lot (L) being characterised. The inevitable estimation errors generated during the mass reduction process, together with analysis, are substantiated by Pierre Gy’s Theory of Sampling (TOS),