Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5 million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.

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Combined processes effects of osmotic dehydration in sucrose solutions and freezing on apple cubes preservation were analysed. Two multifactorial experimental designs, in two levels, were conducted consecutively to quantify the effects of the following factors: temperature, osmotic dehydration time, concentration of the osmotic medium and freezing rate. The response variables considered were: sensory evaluation, colour, texture, water activity ( aw) and reducing and total sugars. The first experimental design selected fast freezing as the best process to preserve texture and colour of the fruit. From the second experimental design, under fast freezing, were obtained the following optimal levels: 55 ºBx for the concentration of the osmotic medium, 35 ºC for the syrup temperature and 60 min for the osmotic dehydration time. A test of acceptability was performed under these conditions with 80 potential consumers on a 7-point hedonic scale, which gave 93% acceptance. Glass transition temperature (Tg') of the maximally cryoconcentrated liquid was –41.89 ºC for the product processed under optimum conditions. Significant correlations ( P= 0.05) were found between sensory and instrumental responses.

In this research the automatic classification of commercial potato chips by computer vision was studied. The general objective was to design a tool that would be able to classify objectively potato chips according to their color in different categories. For this purpose, sensory measurements of color in 100 potato chips were correlated with the corresponding objective measurements obtained by computer vision system. Potato chips with and without ruffles of different brands were used for training and validation experiments. Sensory evaluations were done with a special chart that classifies potato chips in seven color categories. Simultaneously, the color of the same potato chips classified by the sensory panel, was determined objectively by a computer vision system in L*, a*, b* units. A linear regression model was good enough to predict potato chip sensory color values from the corresponding instrumental measurements by computer vision. The linear model after following the process of crossed validation crossed presented an error of ∼4% for smooth chips (without ruffles) and ∼7% for chips with ruffles. PRACTICAL APPLICATIONS The automatic classification methodology presented for potato chips is general and has a wide range of potential uses. It could be applied not only to other potato cultivars and frying conditions but also to other less heterogeneous raw materials and unit operations different than potato and frying, respectively. The computer vision system used in this research could as well be very useful in the food industry as a large amount of information can now be obtained from measurements at the pixel level that allows a better characterization of foods and thus improves quality control.

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