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The 1986 Food Research Institute-Tanaka et al. model predicts the safety of shelf-stable process cheese spread formulations using the parameters of moisture, pH, NaCl, and disodium phosphate (DSP) to inhibit toxin production by Clostridium botulinum. Although this model is very reliable for predicting safety for standard-of-identity spreads, the effects of additional factors have not been considered. The objective of this study was to create a predictive model to include the interactive effect of moisture, pH, fat, sorbic acid, and potassium-based replacements for NaCl and DSP to reflect modern reduced-sodium recipes. Eighty formulations were identified using a central composite design targeting seven factors: 50 to 60% moisture, pH 5.4 to 6.2, 0 to 0.2% sorbic acid, 10 to 30% fat, 1.7 to 2.4% NaCl, 0.8 to 1.6% DSP, and 0 to 50% potassium replacement for sodium salts. Samples were inoculated with proteolytic C. botulinum spores at 3 log spores per g, hot filled into sterile vials, and stored anaerobically at 27°C. Samples were assayed at 0, 1, 2, 3, 4, 8.5, 17.5, 26, and 40 weeks for the presence of botulinum toxin using the mouse bioassay. A parametric survival model was fit to the censored time-to-toxin data. All linear, quadratic, and pairwise effects were considered for model fit. As hypothesized, the effects of pH, sorbate, moisture, DSP, and NaCl were highly significant (P < 0.001). Fat concentration and potassium replacement effects were significant at P < 0.021 and P < 0.057, respectively. The model consistently predicted the safety failure of the toxic samples, but it also predicted failure for some samples that were not toxic. This model is an adjunct to existing models by adding the factors of potassium salts, fat, and sorbic acid to predict the botulinal safety of prepared process cheese products but is not intended to be a substitute for formulation evaluation by a competent process authority.

Caterpillars of Cactoblastis cactorum secrete onto the surface of host cactuses droplets of an oily fluid that issues from the orifices of their paired mandibular glands. The fluid contains a series of 2-acyl-1,3-cyclohexanediones that, collectively, have been shown to elicit trail-following behavior from the caterpillars. This study reports the results of bioassays to determine the ability of two specific compounds, previously shown to be prominent components of the mandibular glands of pyralid caterpillars, 4-hydroxy-2-oleoyl-1,3-cyclohexanedione and 2-oleoyl-1,3-cyclohexanedione, to elicit trail-following behavior from the larvae of C. cactorum. Additionally bioassayed were structural fragments of these molecules. The relative effectiveness of the chemicals in eliciting trail following, the effect of varying concentration on the trail-following response, the importance of specific functional groups to the trail-following response, and the threshold sensitivity of the caterpillar to the pheromone were determined. The study showed that while all the tested compounds elicited some degree of trail following, they differed significantly in their effectiveness. The most effective of the compounds was 4-hydroxy-2-oleoyl-1,3-cyclohexanedione, which, on a per unit volume basis, was as effective as whole gland extract. Caterpillars secreted large quantities of fluid from the glands, and the threshold response to 4-hydroxy-2-oleoyl-1,3-cyclohexanedione occurred at a relative high application rate compared to trail pheromones of other social caterpillars and eusocial insects. This and the observation that the trail marker is secreted from the mandibular glands suggests that the use of 2-acyl-1,3-cyclohexanediones as trail markers is secondary, and that these compounds function primarily in some other, as yet undetermined, context.

The cactus moth, Cactoblastis cactorum (Berg) (Lepidoptera: Pyralidae), spends most of its larval life feeding within the cladodes of Opuntia cactuses, but the gregarious caterpillars begin their life outside the plant, and in the later instars make intermittent excursions over plant surfac- es to access new cladodes and to thermoregulate. The study reported here showed that when the caterpillars move en masse , they mark and follow trails that serve to keep the cohort together. Artificial trails prepared from hexane extracts of the caterpillar’s paired mandibular glands were readily followed by the caterpillars. The glands are remarkably large, and their fluid contents, which constitute approximately 1% of the total wet mass of a caterpillar, are secreted onto the substrate as they move. Although the caterpillars also lay down copious quantities of silk, the ma- terial in itself neithxer elicits trail following nor is it a requisite component of pathways that elicit trail following. Previous analyses of the mandibular glands of other species of pyralid caterpillars showed that they contain a series of structurally distinct 2-acyl-1,3 cyclohexane diones. Chemical analysis indicates that the glands of C. cactorum contain structurally similar compounds, and bio- assays indicate that trail following occurs in response to these chemicals. While the mandibular glands’ fluids have been shown to act as semiochemicals, effecting both interspecific and intra- specific behavior in other species of pyralids, the present study is the first to report their use as a trail pheromone.

Now that the team has determined a way to adjust the process to change viscosity by adjusting the steam pressure, Maria has been asked to determine a strategy for monitoring and controlling viscosity during production. She has seen that there is not much consistency in what is currently being done and that each line operator has their own practice for how often they monitor viscosity and how they adjust process settings based on what they see.

Maria use a response surface design to determine factor level combinations that will deliver the desired viscosity.

Maria discusses her first year on the job learnings with her professor and receives a valuable resource list.

Maria and Alex meet the next morning to plan the screening design to identify the important factors affecting viscosity. Maria had already decided on a 16-run resolution 4 design for her six factors. She and Alex will now need to decide on the ranges for each factor.

Maria uses regression and correlation analyses to determine ingredient and production factors affecting viscosity.

The issue of dispensation of product was put on hold due to potential out of specification viscosity levels passed, and the team focus has resumed delivering the best product on a consistent basis. Lisa's recent feedback had been that there was a positive impact on the BBQ lines since the SPC system was implemented. She asks Maria to present the dynamic SPC monitoring system implemented earlier on the BBQ lines to the operations management team.

Steve opens up the research and development leadership team meeting bragging about the power of the work that Maria has just finished. Design of experiments has presented a powerful approach that had not been used before. Maria's work has helped the plant not only to deliver more consistent BBQ viscosity, but also the systematic approach has led to a better understanding of how factors such as steam pressure and line speed affect viscosity. Steve has in mind several improvement opportunities, and he hopes that by applying the design of experiments methodology to them, the results will be similarly positive.

Lisa is right! As a plant manager, she ought to be able to demonstrate that the investment made in a new line is a good investment. Alternatively, if the new asset brings no additional, or only marginal, benefit why should she upgrade the rest of the lines? Lisa needs more than a summary of line performances. She should be able to defend the case with confidence, that the asset investment has merits. She ought to be able to clearly answer the question: Is there a significant difference between line 4 and line 6 viscosities?

Maria uses analysis of variance (ANOVA) and multiple paired comparison (MPC) procedures to compare the production line viscosities.

Maria learns about statistical approaches to sampling and develops a sampling plan to determine product dispensation.

Maria is asked to compare the viscosities from two production lines and uses descriptive statistics and graphs to make a determination.

Maria discovered large differences in performance of all production lines across the two production shifts, with generally consistent production within a shift. First shift production is consistently higher in viscosity. But, as Lisa pointed out, it is not clear whether the differences in viscosities between the shifts are real differences or they are due to the measurements being taken by different lab technicians.

Trained panels have been used to evaluate the sensory properties of food products for a number of years. Time–intensity sensory methodologies have been developed to identify and quantify the temporal sensory properties of foods and beverages. The data collected is represented in a time dependent intensity curve. Over the years, several multivariate data analysis techniques have been proposed to characterize time–intensity curves. One specific technique, fitting a parametric model to individual respondent curves, has been recently proposed. The model parameters quantify meaningful characteristics of the time–intensity curves: up and down slopes, times at which the curves reach and begin descent from the peak height, and the peak height itself. The use of statistical experimental designs to direct the creation of product prototypes so that the effects of ingredient levels and/or processing condition changes can be statistically modeled has become prevalent in the food industry in the last decade. Use of these designs in projects for cost reduction, quality improvement and variation reduction has helped to make the product development process more scientific and efficient. A common application of these designs in the product development process has been with consumer acceptance measures as responses to determine optimal product formulations. This paper discusses how the combination of the two methodologies has been used to identify ingredient levels and/or processing conditions that most affect product texture variability (product texture in this case being a temporal phenomenon). The parametric model fitting process, assessment of respondent repeatability and reproducibility, and the statistical modeling of the time-intensity response curve parameters with respect to the statistical experimental design are described in detail. A discussion of how the resultant modeling directed future product development efforts demonstrates the utility of pairing these methodologies.

In an attempt to determine what influences liking of the 28 beverages in the study, a combination of data analysis techniques was employed. While statistical rigor is an essential component of all sensory and consumer data analyses, these analyses would be remiss without the partnership of the expertise of both a sensory and statistical professional. This paper will discuss how specific statistical methodologies have been guided by a combination of solutions that provide the most acceptable statistical approach and the most interpretable sensory solution. Cluster analysis was used to determine the existence of consumer subgroups or clusters based on the liking patterns across the product set. Two consumer studies were conducted and for each, two distinct subgroups were identified. Cross tabulation of consumer subgroups with that of respondent demographics was employed to identify differences relevant to the subgroups. Rotated principal component analysis was used to create the sensory dimensions that describe/differentiate the product set. As a result six sensory dimensions were identified. Using the principal component scores, the products were grouped into nine flavor profile groups. LOESS curve fitting in a multi-panel display was used to relate the mean product liking scores of each consumer subgroup to the sensory component scores. This format allows for: (1) the determination of the nature of each sensory dimension's effect on liking for the subgroups in each of the two studies, and (2) an understanding of why the liking pattern for the subgroups differ. Through this it will be shown that the sensory dimensions effect on product liking differs for each of the subgroups in both of the studies. Finally, consumer attribute ratings were correlated with the sensory component scores to gain an understanding of the consumers' description of the product set.

John Mandel of the National Bureau of Standards defined repeatability and reproducibility measures to assess within laboratory and between laboratory measurement variability in inter-laboratory (round robin) analytical studies. This paper will demonstrate that these same measures can be applied to the problem of assessing sensory panelist performance. Repeatability and reproducibility will be defined in the context of both analytical inter-laboratory studies and sensory panels. Formulas for the measures will be given. Examples using these measures with sensory panel data and their interpretation will be discussed. Since a typical sensory panel evaluates a much larger number of attributes compared with the number of analytical measurements in an analytical round robin test, some practical considerations as to how to use these measures efficiently will be proposed. Finally, a graphical method of displaying sensory panel data will be demonstrated as an alternative to using these measures, while keeping the repeatability and reproducibility concepts intact.

Highly trained sensory panels have long been used to evaluate food products on perhaps dozens of attributes. Principal components analysis is one of a number of multivariate data analysis techniques commonly used in analyzing sensory panel data. More recently, response surface designs have been used to direct the creation of product prototypes so that the effects of ingredient levels and/or processing conditions can be modeled. This paper will discuss how the two methodologies have been used together in projects where the goal is to identify ingredient levels and/or processing conditions that best match a target product's sensory profile. Some unique problems arise when analyzing and interpreting the results of response surface models when the number of responses is quite large. This paper will explain how some of these problems have been addressed through the detailed discussion of the development of a cost reduced product. Six ingredients were systematically varied in a response surface design to create 48 prototypes. The prototypes and the target product were then measured on 33 sensory attributes. Design selection, data collection, response surface modeling, rotated principal components analysis and the use of both desirability and distance functions to identify ingredient level combinations that meet the product development objectives will be discussed in detail using the data analyses from this project. Recommendations for next steps in the product development process will also be given.

We examine the causes of spacing dependence of the nulling bias voltage in the vibrating capacitor contact potential measurement technique. In addition to effects already recognized in the literature, namely, nonuniform work functions, nonparallel surfaces, fringe fields, and capacitive coupling to distant surfaces, we investigate the effects of finite gain and spurious signals in feedback loop systems. We argue that much of the spacing dependence reported in the literature may be due to microphonic signals, which are very difficult to eliminate. We also discuss the means by which existing spacing dependence can be minimized.

The factors governing the choice of preamplifier type for the vibrating capacitive probe used in contact potential measurements are examined. Two types are compared: a high input impedance voltage amplifier and a current amplifier. The latter has been increasingly used in recent years due to its great advantages in dealing with parasitic input capacitance. We extend previous analyses, elucidating other advantages of the current amplifier. Particularly important are (i) the reduction of spurious microphonic signals, implying lower systematic error, and (ii) the white noise spectrum of its equivalent contact potential noise, which allows random error to be effectively reduced by increased averaging periods.

Phosphorylation and O-GlcNAcylation of keratin 18 (K18) are highly dynamic and involve primarily independent K18 populations. We used in vitro phosphorylation and O-GlcNAcylation of wild-type, phospho-Ser52, glyco-Ser48, and Ser-to-Ala mutant 17mer peptides (K18 amino acids 40-56), which include the major K18 glycosylation (Ser48) and phosphorylation (Ser52) sites, to address whether each modification blocks the other. The glyco-K18 peptide blocks Ser52 phosphorylation by protein kinase C, an in vivo K18 kinase, while the phospho-K18 peptide blocks its O-GlcNAcylation. Our findings support the reciprocity of these two post-translational modifications. Therefore, regulation of protein Ser/Thr phosphorylation and glycosylation at proximal sites can be interdependent and provides a potential mechanism of counter regulation.

The importance of dietary sulforaphane in helping maintain good health continues to gain support within the health-care community and awareness among U.S. consumers. In addition to the traditional avenue for obtaining sulforaphane, namely, the consumption of appropriate cruciferous vegetables, other consumer products containing added glucoraphanin, the natural precursor to sulforaphane, are now appearing in the United States. Crucifer seeds are a likely source for obtaining glucoraphanin, owing to a higher concentration of glucoraphanin and the relative ease of processing seeds as compared to vegetative parts. Seeds of several commonly consumed crucifers were analyzed not only for glucoraphanin but also for components that might have negative health implications, such as certain indole-containing glucosinolates and erucic acid-containing lipids. Glucoraphanin, 4-hydroxyglucobrassicin, other glucosinolates, and lipid erucic acid were quantified in seeds of 33 commercially available cultivars of broccoli, 4 cultivars each of kohlrabi, radish, cauliflower, Brussels sprouts, kale, and cabbage, and 2 cultivars of raab.