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within 60 days of storage at 4 C. Similar antimicrobial effects may be seen with lactic acid spoilage bacteria, since the behavior of L. monocytogenes is similar to this group; however, con rmation studies are needed. Siragusa et al. (1999) incorporated nisin in a polyethylene-based plastic lm and observed its activity against inoculated B. thermosphacta on vacuum-packaged beef surface tissue sections. An initial reduction of 2.0 log10 CFU/cm2 of B. thermosphacta was observed within 2 days of refrigerated storage at 4 C. After 20 days of storage, samples with nisin-containing plastic showed signi cantly fewer bacterial numbers compared with control, 5.8 vs. 7.2 log10 CFU/cm2, respectively. Edible antimicrobial coatings, besides their main function, also hold meat juices, reduce rancidity and myoglobin oxidation (oxygen barrier), restrict volatile compound loss, and reduce off-odor absorption during the storage of refrigerated meats (Kerry et al. 2006). A calcium alginate edible lm containing nisin, citric acid, EDTA, and Tween-80 was effective against Salmonella Typhimurium on chicken skin, with count reductions ranging from 2 to 3 log10 CFU/ml after 72-hour exposure at 4 C (Natrajan and Sheldon 2000b). Dipping of uninoculated chicken drumsticks in the same solution followed by overwrap packaging in foam tray packs containing nisin-treated lm/absorbent tray pads and storage at 4 C caused projected shelf life extension up to 2.2 days based on initial psychrotrophic plate count reduction of up to 2.3 log10 CFU/g (Natrajan and Sheldon 2000b). Marcos et al. (2007) demonstrated the effectiveness of enterocin-containing biodegradable lms (alginate, zein, and polyvinyl alcohol) to control L. monocytogenes on air-packed and vacuum-packed sliced cooked ham. Millette et al. (2007) incorporated nisin in alginate lm and beads through covalent links and were able to reduce S. aureus numbers on beef muscle slices ( lm) or ground beef (beads) at 4 C. A

To assist in the interpretation of the factors, factor rotation may be performed. Factor rotation corresponds to a transformation (usually, orthogonal) of the coordinate axes, leading to a different set of factor loadings. We may look upon factor rotation as analogous to a scientist attempting to elicit greater contrast and detail by adjusting the focus of a microscope. The sharpest focus occurs when each variable has high factor loadings on a single factor, with low to moderate loadings on the other factors. For the houses example, this sharp focus already occurred on the unrotated factor loadings (e.g.,

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problem However, for a particular classi cation problem, these measures may not select the optimal model The reason is that each classi cation problem carries with it a unique set of costs and bene ts, which stem from the particular set of circumstances unique to that business or research problem The cost of a false positive (wrongly predicting positive response) may be low in certain environments but higher in other environments For example, in direct marketing, a false positive may cost no more than a postcard, while in HIV testing, a false positive on the ELISA test will be more expensive, leading to second-level HIV testing (On the other hand, of course, false negatives in HIV testing are very serious indeed, which is why the ELISA test allows a higher rate of false positives, to maintain the false negative rate as low as possible.

10 5

2:18

) In business problems, such as our direct mail marketing problem, company managers may require that model comparisons be made in terms of cost/bene t analysis Recall from Discovering Knowledge in Data: An Introduction to Data Mining [2] that it is useful to construct a cost/bene t table when performing classi cation This is done to provide model comparison in terms of anticipated pro t or loss by associating a cost or bene t with each of the four possible combinations of correct and incorrect classi cations Let us consider each of the four possible decision outcomes (true negative, true positive, false negative, and false positive) and assign reasonable costs to each decision outcome 1 True negative (TN) The model predicted that this customer would not respond to the direct mail marketing promotion, so no postcard was mailed to him or her.

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Leds data byte 2: Power Color (0 255) 0 = green, 255 = red. Intermediate values are intermediate colors. Leds data byte 3: Power Intensity (0 255) 0 = off, 255 = full intensity. Intermediate values are intermediate intensities. Example: To turn on the dirt detect and spot LEDs, make the status LED red, and to light the power LED green at half intensity, send the serial byte sequence [139] [25] [0] [128]

To follow along with this project, copy to your hard drive the scrollTick.fla file from this chapter s folder in the CD-ROM that accompanies this book. Use your operating system to disable the file s read-only attribute.

In reality, this customer would not have responded to the promotion Therefore, the correct decision was made No costs were incurred, since no postcard was sent; no sales were made, and no prospective sales were lost 2 True positive (TP) The model predicted that this customer would respond to the direct mail marketing promotion, so a promotion was mailed to him or her In reality, this customer would indeed have responded to the promotion Therefore, again the correct decision was made The direct mailing cost, with materials, postage, and handling, is $2 per promotion unit mailed However, this particular TP customer, upon receiving the postcard, would have come into the store to make purchases The question then becomes: How much money would we reasonably expect the customer to spend, and how much of that amount spent could be considered pro t Table 7.

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