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NEW MULTIPLIERS FOR ESTIMATING THE PHENYLALANINE CONTENT OF FOODS FROM THE PROTEIN CONTENT

Great news! Our paper proposing new multipliers for estimating the PHE content of a food from it protein content has been accepted for publication in the Journal of Food Composition and Analysis. It will take a bit before the paper appears, but I will post a link to the article as soon as it is available. Meanwhile, Here is abstract:

 Phenylalanine (Phe) is a key nutrient in the dietary management of phenylketonuria (PKU). Since the protein content of many foods is readily available, estimating the Phe content of a food is facilitated by an understanding of the statistical distribution of the Phe:protein ratio in common foods. In particular, from the minimum and maximum Phe:protein ratio, one can obtain an upper bound and a lower bound on the Phe content of any given food from its protein content. Currently, the multipliers commonly used are 30 and 50. In this document, we present and compare the statistical distribution of the Phe:protein ratio in two databases, namely the USDA National Nutrient Database and the Danish Food Composition Databank. Based on this data, we suggest replacing the 30 –  50 multipliers by 20 –  65. When used to estimate the Phe content from the protein content, these multipliers yield estimates that are correct for more than 97% of the data analyzed (as opposed to less than 76. 3% for the multipliers 30 – 50). Furthermore, we confirm that the commonly used average of Phe:protein ratio for the foods in the categories of fruits (30) and vegetables (40) are more or less accurate.

Perhaps you have heard that multiplying the protein content of a food by 50 gives a maximum for the Phe content of the food. I personally heard this many times in the past. For example, if the food label states that one serving contains 1g or protein, then I was explained that the maximum Phe content is 1.5×50=75mg. Well, it turns out that many foods have more Phe than that, and so one should multiply by 65 to get an accurate maximum. The data to support that is in the paper.

The new multipliers have been incorporated into our PHE estimation app. The web browser version of the app is freely available at

https://engineering.purdue.edu/brl/PKU/method_0.html

My student Jieun also made an Android version, which you can download (for free!) from this webpage: https://engineering.purdue.edu/brl/PKU/PheEstimation.html

Enjoy!

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Estimating the Phenylalanine (Phe) content of “Sixlets” (using Phe:protein ratios)

I was recently asked to estimate how much Phe is in one serving (10 pieces) of Sixlets. Here is what I did.

1) I found the Nutrition Fact Label and ingredients lists (http://www.shopwell.com/sixlets…/candy/p/8966903792)

2) I noted that the Nutrition Fact Label states that there is zero gram of protein. Due to rounding, this actually means that there is no more than 0.5g of protein per serving.

3) I looked at the ingredient list and noted all ingredients that contain protein. I then looked up the mg Phe per g protein ratio of each of these ingredients in this (free) food list I mentioned in my previous post.

I found that
1 g protein from whey contains 32mg Phe
1 g protein from cocoa contains 34mg Phe
1 g protein from carob contains 33mg Pge
1 g protein from cornstarch contains 50mg Phe

4) Since cornstarch has the highest phe:protein ratio at 50mg, then one serving of Sixlets contains no more than 0.5 x 50= 25 mg Phe. However, corn starch is ingredient #8, so there is probably a very small amount of cornstarch. Furthermore, cornstarch contains a very small amount of protein. So we can neglect the cornstarch and assume that all Phe comes from the next highest Phe:protein ratio (cocoa at 34mg).  Thus we get that one serving of Sixlets contains no more than 0.5 x 34= 17 mg Phe.

Final Answer: no more than 17mg Phe per serving.

Proposing a new concept in online learning: “slectures”

Last Spring, I proposed the concept of “slecture” as a new way for students to learn by teaching.

What’s a slecture? Simply put, a slecture is an online lecture made by students.

More specifically, the idea is to have one or more students give a second hand account of a lecture or a course they took, using text, videos, pictures, or whatever other online medium they see fit. They do so with the approval of the instructor, sometimes even with full access to the instructor’s teaching material, including lecture videos. However, the instructor does not have to review the accuracy of the material after it is produced and the students bare the blame for any inaccuracy it might contain.  Students even have the freedom to enhance the course content with their own explanations and comments, using other references if needed.

The plan was to make all slectures freely available on the  Project Rhea website. After working on this for close to a year, we now have a few nice examples to showcase on the website. Take a peek and let us know what you think!

Link to slecture page on Project Rhea.

A (free) Food List in PDF Format

Dear PKUers,

I want to share with you this food list, which my graduate student Jieun and I just published. It combines all the foods in the USDA Database, along with another (Danish) database. The focus of our food list is the Phe:protein ratio of the food, but it also lists the mg Phe per gram food (first column). Feel free to print it if you like. However, I find it easier to just search through the pdf on my computer.

Link to (free) Food List in PDF Format by Kim and Boutin

Enjoy!

How to estimate the Phe content of a food from its protein content: Part 4

This blog entry is the last in a series of four posts that introduce this simple web app which estimates the phenylalanine (Phe) content of a food from its protein content.

https://engineering.purdue.edu/brl/PKU/method_0.html

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In Part 1, Part 2, and Part 3, I explained how the rounded protein content listed on the package of a food can be used to estimate the Phe content of the food. More specifically, I showed how to obtain a lower bound and an upper bound on the Phe content using the rounded protein content, the rounding error, and the multipliers 20-64.5. As you may have noticed, the upper bound (the maximum) can sometimes be quite far from the lower bound (the minimum), and so it may be hard to guess what the true Phe content of the food really is from this estimate.

But there are many ways to obtain a more accurate estimate.

The first thing one can do is check which of the ingredients contains Phe (if any).

If the protein content is listed as 0g (meaning that the true protein content is between 0g and 0.5g), then it is possible that no ingredient contains any Phe. If that’s the case, then you’re lucky because you will know for sure that the food is free of Phe. For quick reference, we  put a list of Phe-free foods and ingredients in pdf format towards the bottom of our web app (right below Question 2). Feel free to share!

Another interesting (lucky!) case is when the only ingredients containing Phe are fruits. An example of this would be fruit juice, or roll up fruit snacks without gelatin.

One remarkable thing about fruits is that their Phe:protein ratio tends to be lower than for other foods. In our study, my graduate student Jieun Kim and I found that the vast majority of fruits have between 20mg Phe per gram protein, and 39mg Phe per gram protein. Therefore, for a fruit-based food, the multiplier 64.5 can be replaced by 39.

This is why we added “Question 2” to our app. If you state that the only Phe containing ingredient in the food are fruits (by clicking “yes” for Question 2), then the maximum Phe content is obtained by multiplying the maximum protein content by 39 (instead of 64.5). This gives a smaller range of possible Phe, and thus a more accurate estimate.

I guess that’s all I have to say about this first app for now. I hope my explanation was clear.

Look out for another, more sophisticated (and more accurate!) app coming soon.

How to estimate the Phe content of a food from its protein content: Part 3

This blog entry is the third in a series of four posts that introduce this simple web app which estimates the phenylalanine (Phe) content of a food from its protein content.

https://engineering.purdue.edu/brl/PKU/method_0.html

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A little word about precision and round-up.

In the  previous post, I explained how to use the protein content listed on the Nutrition Fact Label of a food to obtain an upper bound and a lower bound on the Phe content of the food. The explanation assumed that the protein content was rounded up to the nearest gram, and so the true protein content could have been up to 0.5g more/less than the stated protein content.

However, the protein content is not always rounded up to the nearest gram. Sometimes, in particular on the label of some food produced in Asia, the protein content is rounded to the nearest 0.1g. For example, a package of ramen noodles might state that it contains 1.4g of protein per serving. In that case, you can simply enter 1.4 into our app, and it will automatically take into account the higher precision of the protein content. More specifically, it will multiply 1.35 by 20 to find the minimum Phe, and it will multiply 1.45 by 64.5 to find the maximum Phe.

As you might have learned in high school chemistry, the last digit of a rounded up number indicates the precision of the measurement.  So while a rounded up protein content of  1g would indicate a true protein content between 0.5 and 1.5, a rounded up protein content of 1.0 would indicate a true protein content between 0.95 and 1.05.

Try it! Enter “1.0” for the protein content in our app and check the result. You will get a different maximum and minimum Phe content then if you had entered “1”.

More in Part 4.

How to estimate the Phe content of a food from its protein content: Part 2

This blog entry is the second in a series of four posts that introduce this simple web app which estimates the phenylalanine (Phe) content of a food from its protein content.

https://engineering.purdue.edu/brl/PKU/method_0.html

—-

In the previous post, I explained how the 20-64.5 multipliers can be used to obtain an upper bound (i.e., a maximum) and a lower bound (i.e., a minimum) for the Phe content of a food from its protein content. However, in the previous computation, we assumed that the protein content was known exactly. Unfortunately, on the Nutrition Fact Label, the protein content is rounded up to the nearest gram. This means that, if the protein content listed is 1g, then the true protein content can be anywhere between 0.5g and 1.5g.

In that case, one can find out a lower bound on the Phe content by multiplying the minimum possible protein content, namely 0.5g in our example, by the multiplier 20. So we have 0.5g x 20=10, and thus the Phe content cannot be less than 10mg.

Now to find an upper bound on the Phe content, you have to multiply the maximum possible protein content, namely 1.5g in our example, by the multiplier 64.5. So we have 1.5 x 64.5= 96.75m and thus the Phe content cannot be more than 96.75mg.

Now image that the protein content listed in the nutrition fact label is 0g. Because the number is rounded up to the nearest gram, then the true protein content can be anywhere between 0g and 0.5g. In that case, we find the minimum Phe by multiplying zero by 20, which gives zero, and thus it is possible that the food contains zero Phe. To find the maximum Phe, we multiply 0.5 by 64.5, which gives 32.25, and thus the maximum Phe content is 32.25mg.

If you want to to this automatically, you can use our app at https://engineering.purdue.edu/brl/PKU/method_0.html. Just enter the protein content in grams in the first box, and click “no” and “no”  for Question 1 and Question 2. (I will explain why we ask Question 2 in a future post.) Then click “show results” and a box will pop up with the minimum and maximum Phe value. 

To be continued in Part 3.