analytical chemistry

Lithium in American Eggs

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1. Introduction

In our previous analysis, we tested the lithium levels of ten American foods. 

All ten foods were found to contain levels of lithium above the limit of detection, but some foods contained a lot more than others — ground beef contained up to 5.8 mg/kg lithium, corn syrup up to 8.1 mg/kg lithium, and goji berries up to 14.8 mg/kg lithium. 

But of the ten foods we looked at, eggs appeared to contain the most, up to 15.8 mg/kg lithium when analyzed with ICP-OES: 

The Results of the Previous Study 

So for our next study, we decided to look at more eggs. 

The first reason to look at more eggs was to confirm the results of our first study, and confirm that these numbers could be replicated.

The second reason to look at more eggs was to start getting a better sense of the diversity of results. Where the first study gave us a small amount of breadth by comparing several foods, the second study would give us a small amount of depth by comparing several eggs. 

The third reason to look at more eggs was that we might be able to find an outlier, a sample of food that contains far more than 15 mg/kg lithium. Eggs containing 15 mg/kg lithium are somewhat of a public health concern; how much more concerning would it be to find eggs that contain 50 mg/kg, or 100 mg/kg. 

(There are reports of such outliers in other foods, in particular from work by Sievers & Cannon in the early 1970s, who reported an “extraordinary” lithium content of 1,120 mg/kg in wolfberries from the Gila River Valley.)

As in the previous study, this project was run with the support of the research nonprofit Whylome, and funded by a generous donation to Whylome from an individual who has asked to remain anonymous. General support for Whylome in this period was provided by the Centre For Effective Altruism and the Survival and Flourishing Fund. 

Special thanks to all the funders, Sarah C. Jantzi at the Plasma Chemistry Laboratory at the Center for Applied Isotope Studies UGA for analytical support, and to Whylome for providing general support. 

The technical report is here, the raw data are here, and the analysis script is here. Those documents give all the technical details. For a more narrative look, read on. 

2. General Methods

2.1 Eggs

First, we collected a sample of eggs from grocery stores around America.

We started by purchasing several cartons of eggs from grocery stores near Boulder, Colorado. We bought several different brands, and tried to get a fair mix of eggs, both white and brown, conventional and organic. 

However, this was still not enough diversity for our purposes. So in the meantime, we asked friends from around the country to mail us cartons of eggs. 

Fun fact: Eggs don’t actually require refrigeration, Americans are basically the only weirdos who even keep them in the fridge. Especially when it’s mild outside, they keep for many weeks at room temperature. So shipping these eggs was relatively easy — really it’s just about packaging them with lots of padding so they don’t break. Most of the eggs arrived intact and we’re very grateful for the great care in packaging and shipping taken by our egg donors (ha). 

The list of eggs is summarized in greater detail in the technical report.

From most cartons, we took two samples of 4 eggs. This gave us two measurements per carton, which should give us some sense of how much variation there is within an individual carton.

Each sample was homogenized/blended with a stick blender for 1 minute to obtain a smooth, merengue-like texture. The blended mixture was then transferred to drying dishes and dried in a consumer-grade food dehydrating oven.

We also pulled out one brand for more testing, to assess individual egg-to-egg variability. From the carton of Kroger Grade AA, we took two samples of 4 eggs as normal. Then we took three more samples of individual eggs. The single eggs were blended and dried just as the larger 4-egg samples were. 

When all samples were dried, they were crumbled into a powder, weighed, put into polypropylene tubes, and shipped off to the lab for further processing.

2.2 Digestion

Food samples need to be digested before they can be analyzed by ICP-OES. Based on our results from the previous study, we used a “dry ashing” digestion approach, where samples are burned at high temperatures, and the ash is dissolved in nitric acid. 

Incineration causes organic compounds to exit the sample as CO2 gas, but elements like sodium, potassium, magnesium, and lithium are non-volatile and remain behind in the ash.

2.3 Analysis

ICP-OES generates a tiny cloud of high-energy plasma, the “inductively-coupled plasma” of the acronym, and injects a cloud of liquid droplets into that plasma (hence the need for digestion). ICP-OES then examines the light that is emitted by the plasma as the liquid sample hits it.

In addition to lithium, we also analyzed all samples for sodium. Sodium is chemically similar to lithium, and most foods contain quite a lot, which nearly guarantees a good signal in every sample.

This makes sodium a useful point of comparison. At every step, we can compare the lithium results to the sodium results, to see if general patterns of findings match between the two elements.

3. Results

All samples were analyzed as one project, but for clarity of understanding, we’re going to report this project in two parts, as two studies.

In Study One, we look at the main body of results — eggs analyzed as four-egg batches from a single carton.  

In Study Two, we look only at the Kroger Grade AA eggs — analyzed as two four-egg batches and three one-egg batches, to assess individual egg-to-egg variability.

3.1 Study One

 For starters, here is a histogram of the distribution of lithium measurements in our egg samples: 

We’ve previously speculated that the distribution of lithium in food would be lognormal, as it is in drinking water, and indeed this looks very lognormal. 

For comparison, here’s the distribution of sodium:

Note that the x-axis is extremely different between the two plots! This is not surprising; eggs contain a lot more sodium than lithium.

For a sanity check, the USDA says that “Egg, whole, raw, fresh” contains 142 mg sodium per 100 g egg. Converted, that’s 1,420 mg/kg, which approximately matches these results, though the mean in this sample is much lower at only 987.3 mg/kg. The median is 963.0 mg/kg, and the standard deviation is 288.8 all told.

Slightly surprising are those three samples that (according to the analysis) contain almost no sodium — their values in the data are 7.6 mg/kg, 1.5 mg/kg, and one measurement below the limit of quantification. 

3.1.1 By Batch

More interesting is the breakdown by batch.

As a reminder: each carton of eggs (aside from the Trader Joe’s eggs, due to an oversight) was used to create two batches of four eggs each. Then, each batch was tested in triplicate, so each carton was tested six times. Here, each bar indicates a batch. Each batch has three dots, representing each of the three results from the tests done in triplicate: 

The main finding is that lithium was detectable in nearly all eggs. This suggests that ICP-OES is more than sensitive enough for this type of work, and that in general, eggs contain appreciable levels of lithium. 

Most egg samples contained between 0.5 and 5 mg/kg. The few readings of “zero” in the plot actually mean “less than about 0.04 mg/kg moist weight”.

Hypothetically speaking, the batches were all well-mixed. Eggs were blended with a stick blender for a full minute (to a very creamy consistency, think meringue), then dried and crumbled, and the dried bits mixed up. So it’s quite surprising that after all that, there’s so much variance within the batches.

Some of the batches show close agreement between different samples from the same batch. Both Simple Truth AA batches have only a very small amount of variation. Whole Foods Batch 2 is bang on every time. 

But other batches show a lot of variation. Batch 1 of Organic Valley and Batch 1 of Eggland’s best both contain one sample that is a huge outlier. You might dismiss these as some kind of one-off analysis error. But some of these cases, like both CostCo batches or the first Land-O-Lakes batch, show disagreement between all three samples. 

We wondered if this might mean that these batches were imperfectly blended. This would be quite surprising, given the lengths we went to to ensure that the batches were well-mixed. 

If the batches were perfectly blended, then all three samples should contain identical levels of lithium. The only differences between the results would then be errors in the analysis, not real differences in the samples. But if errors were the only source of noise, you would expect to see similar levels of variation in every batch. 

Two explanations seem likely.

First, lithium is very strange. In our last study, we saw that sometimes you get very different numbers for the exact same piece of food. Maybe the differences between different samples from the same batch comes from the fact that it’s hard to get accurate measurements for lithium levels in food.

Second, perhaps eggs are just goopy. It’s possible that despite our best efforts to completely blend the samples, they are still less than perfectly mixed, so some samples from the same batch contain more or less lithium than others. 

We can test these explanations by comparing the lithium results to the sodium results for the same set of batches and samples. If the variance is the result of a problem with lithium detection, then the sodium results should be much more consistent within batches. But if the variation comes from the eggs being imperfectly blended, then we should see similar variation in the sodium results as in the lithium results. 

3.1.2 Sodium

Here are the sodium results: 

Sure enough, there is a lot of variation between sodium levels, even within single batches. This suggests that the variation we saw in the lithium results is not the result of something weird about lithium. It’s probably something general about the samples or the analysis. 

Some of the variation in sodium lines up with the lithium results. The Whole Foods batches show great precision for both lithium and sodium, suggesting that they are especially well-blended or homogenous or something. But there is also some disagreement. For lithium, Organic Valley Batch 2 was much more precise than Organic Valley Batch 1. For sodium, it is the opposite. 

Sodium does show something unique — three very clear outliers with readings of almost exactly zero sodium (specifically 7.6 mg/kg, 1.5 mg/kg, and one reading below the limit of quantification). 

These look like errors of the analysis rather than real measurements. All three are outliers from the sodium data in general, more than three standard deviations below the mean. All three are from different batches and starkly disagree with the other samples from that batch. And we have strong external reasons to expect that any bit of egg will contain more than zero sodium.

In addition, we notice that these three cases with exceptionally low sodium levels are the exact same three cases that registered as below the limit of quantification for lithium. This suggests that none of these readings are real, that there were three samples where something went wrong, and the analysis for some reason registered hugely low levels of sodium and no lithium. If true, that means that all real measurements detected lithium above the limit of quantification.

The other variables we considered, like location, egg color, and whether or not the eggs were organic, didn’t seem to matter. Maybe differences would become apparent with a larger sample size, but they’re not apparent in these data.

3.2 Study Two

You might expect that hens from the same farm, eating the same feed, would all have roughly similar amounts of lithium in their eggs. For the same reason, it seems likely that any two eggs in the same carton wouldn’t be all that different, and would contain similar amounts of lithium.

All the above seems likely, but we actually have no evidence. It’s an assumption, and exactly the kind of assumption that could really confuse us if we assume wrong. It’s worthwhile to check.

Certainly the results from Study One call the assumption into question. A thoroughly blended mix of four eggs seems like it should have homogenous levels of lithium throughout. But empirically, that isn’t what we saw. We saw a lot of variation. Maybe the variation within those 4-egg batches comes from differences between the four eggs.

To test this, we did another round of analysis, focusing on a single carton of Kroger eggs. As before, of the 12 eggs in the dozen we took two groups of four to create two four-egg batches.

In addition, we took three of the remaining four eggs, and used them to create three one-egg batches, mixing and sampling just that single egg. The one-egg batches each consisted of a single egg from this carton, blended well. The one-egg batches were also tested in triplicate, i.e. three samples from the same egg. 

Here are the results: 

These four-egg batches look much like the four-egg batches tested in Study One. They show a lot of variation between the samples tested in triplicate.

The single-egg batches, on the other hand, did indeed have lower variance than the 4-egg batches. There was much closer agreement between different samples from the same eggs, than samples from different eggs. Certainly we see a difference between the egg used for Batch 3, which all samples indicate contains about 1 mg/kg lithium, and the egg used for Batch 4, which all samples indicate contains about 5 mg/kg lithium

This suggests that there really may be appreciable egg-to-egg variation. This could be the result of other factors, including simple randomness, but the tightness of the single-egg analyses is suggestive. And the fact that the variance seems much lower in single-egg batches implies that the mixed four-egg batches are imperfectly blended.

The sodium results for these batches seem to confirm this, with greater variation in sodium in the four-egg batches than in the one-egg batches: 

Again, this suggests that the patterns we observe in the lithium data are the result of actual results in the world, or the analysis in general, rather than some artifact of the lithium analysis in particular.

4. Discussion

Nearly all egg samples contained detectable levels of lithium, and around 60% of samples contained more than 1 mg/kg lithium (fresh weight). These results appear to confirm that eggs generally contain lithium.

If you accept the argument that the three samples with conspicuously low sodium readings are the result of a failure of analysis, then all egg samples contained detectable levels of lithium. 

In terms of diversity of results, samples varied from as much as 15 mg/kg Li+ to as little as less than 1 mg/kg Li+. Variation did not seem to be related to the geographic purchase origin of the eggs. Nor were there any obvious differences between organic and non-organic, or white and brown eggs. This suggests that these are not major sources of variation. 

However, we did see evidence of a lot of variation in lithium levels between individual eggs, even between individual eggs from the same carton. 

While there was a lot of variation between samples, some samples showed a great deal of consistency, especially samples from single eggs. This suggests that dry ashing followed by ICP-OES has high precision when analyzing food samples for lithium. Though these results do not speak to whether or not this analytical method is accurate for such samples, they do suggest that these are real measurements and not merely the result of noise or analytical errors.  

One of our hopes for this study was to find an egg that contained more than 15 mg/kg lithium, that we could subject to other, less sensitive analytical methods. This would let us get a sense of accuracy by triangulation, comparing the results of different methods when analyzing samples of the same egg.

We did in fact find eggs that contain such high concentrations. Above we reported the lithium concentrations in fresh weight, because those are the numbers that are relevant if you are eating eggs. But in terms of analysis thresholds, the numbers that matter are the dry weight. For dry weight, some of these egg samples contain as much as 60 mg/kg lithium. That’s more than enough to be above the sensitivity of a technique like AAS. 

As we are quite interested in trying to confirm the accuracy of lithium analyses in food, one next step will be to replicate these analyses using other analytical techniques like AAS.

How Much Lithium is in Your Twinkie?

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1. Introduction

How much lithium is in your food? Turns out this is harder to answer than you might think.

You might be interested in this question because clinical doses of lithium (50-300 mg/day) are a powerful sedative with lots of nasty side effects. Many of these side effects also show up in people taking subclinical doses (1-50 mg/day). Even trace doses (< 1 mg/day) seem to have some effects. And the EPA is concerned about exposure to levels as low as 0.01 mg/L and 0.06 mg/L. 

There are lots of different methods you can use to estimate the lithium in a sample of food. This usually involves some kind of chemical liquefication (“digestion” in the parlance) paired with a tool for elemental analysis. You need digestion to analyze food samples, because some analysis techniques can only be performed on liquids, and as you may know, many foods are solids or gels. Mmmmm, gels. *HOMER SIMPSON NOISES*

Most modern studies use ICP-MS for analysis of metals like lithium, combined with digestion by nitric acid (HNO3). ICP-MS is preferred because it can analyze many elements at once and it is considered to be especially sensitive. HNO3 is preferred because it is fast and cheap compared to alternatives. 

Studies that use HNO3 digestion with ICP-MS tend to find no more than trace levels of lithium in their food samples — only about 0.1 mg/kg lithium in most foods, and no foods above 0.5 mg/kg. Examples of these studies include Ysart et al. (1999), which surveyed 30 elements in a wide variety of UK foods and found no more than 0.06 mg/kg lithium in any food; Saribal (2019), which measured the levels of 19 elements in cow’s milk samples from supermarkets in Istanbul, and found less than 0.04 mg/L lithium in all samples; and Noël et al (2006) which surveyed the levels of 9 elements in “1319 samples of foods typically consumed by the French population”, finding 0.154 mg/kg or less lithium in all foods (though they reported slightly higher amounts in water).  

But as we’ve reviewed in previous posts, the literature as a whole is split. Studies that use other analysis techniques like ICP-OES or AAS, and/or use different acids like H2SO4 or HCl for their digestion, often find more than 1 mg/kg in various foods, with some foods breaking 10 mg/kg. Examples include studies like Ammari et al. (2011), which found 4.6 mg/kg lithium in spinach grown in the Jordan Valley; Anke, Arnhold, Schäfer, and Müller (1995) which found more than 1 mg/kg lithium in many German foods, including 7.3 mg/kg lithium in eggs; and in particular we want to mention again Sievers & Cannon (1973), which found up to 1,120 mg/kg lithium in wolfberries (a type of goji berry) growing in the Gila River Valley.

1.1 State of the Art Isn’t Great

From the existing literature alone, it’s hard to say what concentrations are present in today’s food. Different papers give very different answers, and often seem to contradict each other. It’s hard to get oriented.

We don’t want to give the impression that there’s a consensus to be boldly defied, or that there are two opposing camps. It’s more like this: hardly anyone has even tried to do a decent job of even looking for lithium in food or taking it seriously, and we are here to smack them and tell them to pay attention to something that has been ignored. This is not a well-studied question. It is a subject that has been the topic of few papers and even fewer authors. It is a small literature and very confused.

Hardly anyone can even be bothered to look for lithium. When it does appear in a study, half the time it feels just tacked on to a list of things that the authors actually care about (like in the France study above). Many of these studies are really looking for toxic metals like lead and cadmium, which are obviously important things to check for in our food. But this makes lithium an afterthought. And when authors don’t care, fundamental issues of analysis can easily be overlooked. The assumption seems to be that you can just throw everything into the same machine and get a good measurement for every element without any extra effort. But as we’ll see in a moment, that may not be the case. 

As we hinted at above, the analytical methods may be the root of the problem. Studies that use HNO3 digestion with ICP-MS report minor trace levels of lithium in food. Studies that use other forms of digestion or other analytical techniques report much higher levels, often above 1 mg/kg. This makes us think that the different analyses are the reason why these papers get such different estimates. However, we couldn’t find any head-to-head comparisons in the literature, and it isn’t clear if the problem lies with ICP-MS, HNO3 digestion, or both.  

1.2 Effects of Lithium

This is more than a purely academic question: lithium is psychoactive, and exposure through our food could have real health effects. 

Clinical doses, which usually range between 56 mg and 336 mg elemental lithium per day, act as a mood stabilizer and sedative. These doses also cause all kinds of nonspecific adverse effects, including confusion, constipation, headache, nausea, weakness, and dry mouth. 

Some people take subclinical doses of lithium (usually 1-20 mg or so), and when we went on r/Nootropics and asked people what effects and side-effects they experienced taking doses in this range, people reported a whole bundle, the 10 most common being: increased calm, improved mood, improved sleep, increased clarity / focus, brain fog, “confusion, poor memory, or lack of awareness”, increased thirst, frequent urination, decreased libido, and fatigue. 

Even the trace amounts of lithium in our drinking water (< 1 mg/L) may have some effects. A epidemiological literature with roots dating back to the 1970s (meta-analysis, meta-analysis, meta-analysis) suggests that long-term exposure to trace levels of lithium in drinking water decreases crime, reduces suicide rates, reduces rates of dementia, and decreases mental hospital admissions, and this is supported by at least one RCT. The EPA is even concerned about exposure to levels as low as 0.01 mg/L and 0.06 mg/L, describing them as “concentrations of lithium that could present a potential human-health risk”, though they don’t say why.

1.3 Measurement

Trusting your methods is the basis of all empirical work. The disagreement in the existing literature is important because we don’t have a good sense of how much lithium is in our food. It’s concerning because it suggests we might not know how to measure lithium in our food even when we try! This looks like a crisis of methods either way. 

High enough levels of lithium in our foods would be dangerous, so we should know how to take a piece of food and figure out how much lithium is inside it. But there isn’t much research on this topic, and it looks like different methods may give very different answers — if this is true, then we don’t know how to accurately test foods for lithium. And it’s likely that lithium levels in the environment are increasing due to both lithium production and fossil fuel prospecting — see Appendix B for more. 

As an analogy, we should know how to measure mercury levels in fish in case it’s ever a problem — our chemists should be able to check fish samples periodically and get a good estimate of the mercury levels, an estimate we feel we can rely on. Because if we can’t measure it, then we don’t know if it’s a problem. High levels could slip by undetected if our methods aren’t right for the job.

1.4 Head-to-Head

Before we can really figure out how much lithium there is in food, we need to find analytical methods that have our full confidence. And the simplest way to test our methods is a head-to-head comparison. 

This seemed easy enough, so we set up a project with research nonprofit Whylome to put a set of foods through different digestions and put the resulting slurries in different machines, and see if they give different answers. By comparing different digestions and analytical methods on a standard set of food samples, we should be able to see if different techniques lead to systematically different results.

Based on the patterns we saw in the literature, we decided to compare two analysis techniques (ICP-MS and ICP-OES) and three methods of digestion (nitric acid, hydrochloric acid, and dry ashing). Details about these techniques are in the technical report, and in the methods section below.

We originally wanted to compare more analysis techniques (AAS, flame photometry, and flame emission methods) but weren’t able to find a lab that offered these techniques – they are somewhat oldschool and not in common use today. More on this below.

It turned out that the type of analysis didn’t make much difference, but the way in which samples were digested for analysis was surprisingly impactful. And the technique that’s most commonly used today seems to underestimate lithium, at least compared to alternatives.

This project was funded by a generous donation to Whylome from an individual who has asked to remain anonymous. General support for Whylome in this period was provided by the Centre For Effective Altruism and the Survival and Flourishing Fund. 

Special thanks to all the funders, Sarah C. Jantzi at the Plasma Chemistry Laboratory at the Center for Applied Isotope Studies UGA for analytical support, and to Whylome for providing general support. 

The technical report is here, the raw data are here, and the analysis script is here. Those documents give all the technical details. For a more narrative look at the project, read on. 

2. Methods

The basic idea is to test a couple different analytic approaches on a short list of diverse foods. 

Most modern analyses use either ICP-MS or ICP-OES. Some of these papers find low concentrations of lithium in food; some of them find high concentrations. We wanted to compare these two techniques to see if they might be the cause of the differences in measurements.

Based on what we had seen in the literature, we decided to compare two analysis techniques (ICP-MS and ICP-OES) and three methods of digestion (nitric acid, hydrochloric acid, and dry ashing), fully crossed, for a total of six conditions. 

2.1 Food

As this is our first round of testing, we wanted a diverse set of foods that could give us some sense of the American food environment in general. Therefore we were looking for a mix of foods that were animal-based and plant-based, highly-processed and unprocessed, a mix of fruits, vegetables, dairy, carbs, and meats. We also made sure to include some foods that previous literature had suggested could be extremely high in lithium (like eggs and goji berries), to see if we could confirm those results. Twinkies made the cut because they’re highly processed and highly funny.

In the end, we settled on the following list:

  • Milk 
  • Carrots 
  • Eggs 
  • Ketchup 
  • Spinach 
  • Corn syrup 
  • Goji berries 
  • Twinkies 
  • Ground beef 
  • Whey powder  

All foods were purchased in August of 2022 at grocery stores around Golden, Colorado. Foods were immediately dried, blended, and divided into tubes for further processing, with weight measurements taken at each step of the process. 

For example, this is how we prepared the eggs. A carton of twelve eggs were cracked into a stick blender, and blended until well-mixed. A subset of the resulting egg blend was then dehydrated, enough to produce all of the needed material with some to spare. The dried egg (more like flakes at this point) was crushed and mixed well. All samples were taken from this egg powder. Three samples each were submitted to every method of analysis, so every result is an estimate of the concentration of the target element averaged across the whole carton. Put another way, our sample size was one (1) carton of eggs, not 12 eggs separately. As the egg blend was well-mixed, all samples should in principle have the same concentration of elements, suggesting that any variation between samples is the result of analytic noise rather than variation between different eggs or different cartons.

the aforementioned eggs post-dehydration (but before crushing/powderizing)

The member of the team who prepared the samples had this to say:

Making a “Twinkie puree” out of a bowl of twinkies, and then precisely weighing it out into drying trays and placing it in a dehydrator, is probably the strangest thing I have ever done in the name of science. My trusty stick blender really struggled with twinkies, and I had to take a pause because the overworked motor started to make a burning smell. “Twinkiepuree” has unusual visco-elastic properties which make it worth the effort.

Samples were analyzed in triplicate, and each replicate was done entirely separate (its own digestion and its own analysis of the resulting post-digestion solution). Order was randomized, to minimize the risk of “carry-over” from one analysis to the next.

2.2 Digestion

In the literature, most analyses that found low levels of lithium used digestion by nitric acid. To see if this might be the cause of the differences in results, we decided to compare nitric acid digestion to some other digestion approaches. In the end we settled on two other kinds of digestion: 1) digestion with hydrochloric acid, and 2) “dry ashing”, where samples are burned at high temperatures, then the ash is dissolved in nitric acid.

Dry ashing is a good complement to these acid digestion techniques because while oily foods are very chemically resistant to oxidizers, they are also very flammable. Greasy foods full of hydrocarbon chains that may not perfectly come apart in an acid are likely to be fully broken down by incineration. Incineration causes organic compounds to exit the sample as CO2 gas, but elements like sodium, potassium, magnesium, and lithium are non-volatile and remain behind in the ash.

2.3 Analysis

Both ICP-MS or ICP-OES generate a tiny cloud of high-energy plasma, the “inductively-coupled plasma” of the acronym. And both methods inject a cloud of liquid droplets into that plasma. The difference is that ICP-OES examines the light that is emitted by the plasma as the liquid sample hits it, while ICP-MS examines the actual particles of matter (ions) that are emitted by the plasma as the sample hits it, by directing those ions towards a sensor.

3. Results

The first surprise was that hydrochloric acid digestion visibly failed to digest 6 of the 10 foods. Digestions were clearly incomplete and significant solid matter was still visible after the procedure. The 6 foods were carrots, ketchup, spinach, corn syrup, goji berries, and twinkies. This is an interesting mix since it includes fibrous, sugary, and oily foods, so there’s no obvious trend as to what worked and what didn’t.

Without complete digestion, the measurements we got from ICP-OES couldn’t be expected to be at all accurate. So while we have these results, they probably aren’t meaningful, and we discontinued hydrochloric acid digestion for all other samples.

The main results are all ten foods in four conditions: ICP-MS after HNO3 digestion, ICP-OES after HNO3 digestion, ICP-MS after dry ashing, and ICP-OES after dry ashing.

Little difference was found between the results given by ICP-MS and ICP-OES, other than the fact that (as expected) ICP-MS is more sensitive to detecting low levels of lithium. However, a large difference was found between the results given by HNO3 digestion and dry ashing.

In samples digested in HNO3, both ICP-MS and ICP-OES analysis mostly reported that concentrations of lithium were below the limit of detection. The highest numbers given by this technique were in spinach, which was found to contain about 0.2-0.3 mg/kg lithium, and goji berries, which ICP-MS found to contain up to 1.2 mg/kg lithium.

In comparison, all dry ashed samples when analyzed by both ICP-MS and ICP-OES were found to contain levels of lithium above the limit of detection. Some of these levels were quite low — for example, carrots were found to contain only about 0.1-0.5 mg/kg lithium. But other levels were found to be relatively high. The four foods with the highest concentrations of lithium, at least per these analysis methods, were ground beef (up to 5.8 mg/kg lithium), corn syrup (up to 8.1 mg/kg lithium), goji berries (up to 14.8 mg/kg lithium), and eggs (up to 15.8 mg/kg lithium). 

These results are summarized in greater detail in the technical report, and in this figure: 

4. Which technique is more accurate? 

We think that dry ashing (which gives the higher estimates for lithium) is probably more accurate, and here are some reasons why. 

Reason #1: Many water samples contain some lithium, and some water samples contain a lot of lithium — sometimes more than 1 mg/L, and occasionally a lot more than 1 mg/L. Unlike food samples, water samples require no digestion, so measurements of water samples are probably quite accurate. 

Most food is grown using water and contains some water [CITATION NEEDED]. It would be strange if food, which is made out of water (plus some other things) always contained less lithium than the water it is made out of. More likely, there’s something else that can interfere with the analysis when foods aren’t completely digested. 

Reason #2: The analysis lab we used has a “buy one element, get one free” deal, so for all of the foods we submitted, we requested sodium analysis (Na+) on top of the lithium (Li+). We figured, why not, it doesn’t cost any extra.

If there were something unusual about the lithium analysis, you’d expect sodium to behave differently. Specifically, you’d expect each analytical method to find similar levels of sodium in every food. So we compiled the sodium data and ran the same analysis as lithium. And sure enough, it does. Here’s a comparison of the results for lithium and sodium:

(Note that the y axes are different scales. There is way less lithium than sodium in these foods, so when analyzing lithium we are much closer to the limits of quantitation.)

If you were validating the equivalence of sample prep procedures based on Na+, you’d say “looks good, great agreement between ashing and HNO3 digestion.” This isn’t at all true for Li+. Why? We have no idea. But it further supports the suspicion that Li+ is more slippery for some reason, an excellent comparison that highlights just how strange the lithium results are. 

This also seems to rule out various “operator error” explanations. If someone were dropping vials or putting them in the machine backwards or something, you would see weird patterns for both lithium and sodium results. The fact that the sodium results look totally normal suggests that something weird is happening for lithium in particular.

Reason #3: Imagine taking pictures with a camera. If you point the camera at something dark, the resulting picture comes out dark. If you point it at something bright, the resulting picture comes out bright. This is a good sign that the camera is working as intended, and that you’re operating it correctly. If your pictures always come out dark, something is probably wrong. Maybe you forgot to take off the lens cap.

We see something similar in these data. Dry ashing sometimes gives low measurements, like in milk and carrots, which it always found to contain less than 0.6 mg/kg lithium. Dry ashing sometimes gives high measurements, like in eggs and goji berries. There’s a lot of noise, but we know that it can produce numbers both large and small. 

In comparison, HNO3 digestion always gives tiny numbers. Most of the time it finds that lithium levels are below the limit of detection. When it does seem to detect an actual amount of lithium, the levels are always low, never above 1.2 mg/kg. These numbers look less like actual estimates and more like a problem with the instrument. A cheap digital camera can’t take a good picture at night, even when it’s working perfectly well.

Reason #4: Several parts of the literature hint that spectroscopy techniques are a bad way to measure lithium in food. These comments are often vague, but it seems like people already have reason to think that these methods underestimate the amount of lithium.

For example, Drinkall et al. (1969) mention that they chose to use AAS (“the Unicam SP90 Atomic Absorption Spectrophotometer, [with] a propane-air flame”) because of their concern about “spectral interference occasioned by elements other than lithium” in spectroscopy techniques.

Manifred Anke, who did more work on lithium levels in food than maybe anyone else, makes this somewhat cryptic comment in his 2003 paper:

Lithium may be determined in foods and biological samples with the same techniques employed for sodium and potassium. However, the much lower levels of lithium compared with these other alkali metals, mean that techniques such as flame photometry often do not show adequate sensitivity. Flame (standard addition procedure) or electrothermal atomic absorption spectrophotometry are the most widely used techniques after wet or dry ashing of the sample. Corrections may have to be made for background/matrix interferences. Inductively coupled plasma atomic emission spectrometry [another name for ICP-OES] is not very sensitive for this very low-atomic-weight element.

We can also point to this article by environmental testing firm WETLAB which describes several potential problems in lithium analysis. “When Li is in a matrix with a large number of heavier elements,” they say, “it tends to be pushed around and selectively excluded due to its low mass. This provides challenges when using Mass Spectrometry.” They also indicate that “ICP-MS can be an excellent option for some clients, but some of the limitations for lithium analysis are that lithium is very light and can be excluded by heavier atoms, and analysis is typically limited to <0.2% dissolved solids, which means that it is not great for brines.” We’re not looking at brines, but digested food samples will also include many heavier atoms and some dissolved solids, and might face similar problems. 

The upshot is that various sources say something like, “when testing foods, you have to do everything right or you’ll underestimate the amount of lithium”. We can’t tell exactly what these sources think is the right way to do this kind of analysis, but everyone talks about interference and underestimation, and no one mentions overestimation. This makes us suspect that the lower HNO3 digestion numbers are an underestimation and the higher dry ashing numbers are more accurate.

ICP techniques can detect all the elements from lithium to uranium, which means that lithium is just on the threshold of what can be detected. It wouldn’t be terribly surprising if lithium were an edge case, since it is on the edge of detection for ICP analysis. Interference might push it over the edge of the threshold. And interference would only lead to mistakenly lower measurements, not mistakenly higher measurements. This suggests the higher measurements are more accurate.

Reason #5: There are a few cases where teams have used HNO3 digestion and still report high concentrations of lithium in food, in particular Voica, Roba, and Iordache (2020). 

This suggests that maybe there’s some trick to HNO3 digestion that can make it give higher, more accurate results, numbers that are consistent with dry ashing. Maybe these teams know something we don’t.

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All of these are reasons to suspect that the higher dry ashing numbers are more accurate. However, the truth is that at this point, nobody knows.

Given this uncertainty, it could be that neither technique is accurate. The true levels of lithium in these foods might be in between, or could be even higher than what was detected by dry ashing. 

Using other analysis techniques like AAS or AES or FAES would be a good way to triangulate between these two conflicting methods. Unfortunately we have not been able to find a lab that offers AAS or other alternative methods of chemical analysis. Can anyone help us?

Accuracy aside, one thing that stands out is that none of these techniques are very precise. For three samples of the same well-blended corn syrup, dry ashing with ICP-OES gives estimates of 0.7155 mg/kg, 1.5892 mg/kg, and 8.1207 mg/kg lithium. HNO3 digestion with ICP-OES generally doesn’t report any lithium at all, but for spinach, it gives estimates of 0.3914 mg/kg, 0.2910 mg/kg, and 0.3595 mg/kg. These are for three identical samples of well-blended spinach. In theory they should be the same! But all four techniques appear to have relatively low precision across the board. 

5. What does this mean for analytical chemistry? 

Two different analytical techniques gave two very different answers when looking at the exact same samples. This seems like an anomaly worth investigating.

These unusual findings may result from the fact that lithium is the third-lightest element and by far the lightest metal. It’s a real weird ion, so this may just be lithium being lithium. But even so, if the nitric acid completely digests a sample and gives a clear, homogeneous solution, it would seem like there is nowhere for Li+ to hide. From first principles, you’d expect this to work.

It’s also possible that this points to a more consistent limitation of common analytical techniques. Certainly it would be a problem if the techniques we used to estimate mercury in fish, or arsenic in rice, consistently underestimated the concentrations of these metals. 

It may be smart to run similar studies to compare analytical techniques for estimating other metals in foods, to make sure there aren’t any other hidden surprises like this one. If work along these lines turns up many similar surprises, well, maybe that means we don’t understand analytical chemistry as well as we think. 

6. Next Steps

We would like to test a lot more samples, and get a better sense of how much lithium is in all kinds of different foods. 

But before we can do that, we have to figure out this mystery around different analytical techniques. It doesn’t make sense to go out and use one method to test a thousand different foods when we don’t know if that method is at all reliable or accurate.

So first off, we will be trying to figure out which technique is most accurate, and if we can, we’ll also try to figure out why these different analytical techniques give such strikingly different results. 

There are a few ways we can do this:

  • We can add known amounts of lithium to food samples in a spike-in study. 
  • We can also spike-in elements that might be interfering with lithium detection. 
  • We can try other kinds of digestion or other analytical techniques (like AAS) as a tiebreaker, and see if they agree more with the HNO3 numbers or the dry ashing numbers. 
  • Or we can study more samples — it’s possible that a food containing 1000 mg/kg would register above the limit of detection for both techniques. 
  • If you have any other clever ideas, please let us know! 

In the meantime, here are some ways you can help:

If you have access to the necessary equipment, please replicate our work. We’ve included all the checks we could think of, but it’s still possible that there was some mistake in our procedure, something backwards about the results. Independent labs should confirm that they get similar results when comparing HNO3 digestion to dry ashing in ICP-MS and ICP-OES analysis. 

An even bigger favor would be to extend our work. If you are able to replicate the basic finding, it would be jolly good to tack on some new foods or try some new analytical techniques. Do you have access to AAS for some reason? Wonderful, please throw an egg into the flame for us. 

If you’re not an analytical chemist but you are a person of means who is both curious and skeptical, you could conceivably hire a lab to replicate or replicate and extend our work. If you’re interested in doing this, we would be happy to advise.

And if you want to help fund more of this research, please contact us. You can also donate to Whylome directly.

Thanks again to our anonymous donor, to Sarah Jantzi, and to Whylome for supporting this research. 

Finally, thank you for reading!

APPENDIX A: Wait what is the background for this study?

Hello, we are SLIME MOLD TIME MOLD, your friendly neighborhood mad scientists. 

We started getting into this question because in our opinion, the evidence suggests that exposure to subclinical doses of lithium is responsible for the obesity epidemic — you can read all about it in Part VII and Interludes C, G, H, and I of our series, A Chemical Hunger. 

We also understand that not everyone finds this evidence convincing. That’s ok. Even if you don’t think lithium causes obesity, this project is still important for other reasons: 1) lithium might have other health effects, so 2) we should be able to test food for lithium concentrations so we can know how much we’re consuming and act accordingly. And in general, this looks like it might be a gap in analytical chemistry. We should know how to analyze things; so let’s close that gap.

APPENDIX B: Where is all this lithium coming from?

We’ve already written quite a bit about this, so if you want the full story, you should read those posts: in particular Part VII, Interlude G, Interlude H, and Interlude I of A Chemical Hunger. 

But the short version is this. Starting around 1950, people started mining more and more lithium and never looked back, and some of what we mine eventually ends up as contamination. Lithium goes in batteries, which end up in landfills. It also goes in the lithium grease used in cars and other heavy machinery, which ends up in runoff. Deeper aquifers often contain more lithium, so drilling deeper wells may have also increased our exposure. 

Graph showing world lithium production from 1900 to 2007, by deposit type and year. The layers of the graph are placed one above the other, forming a cumulative total. Reproduced from USGS.

But the biggest contributor is probably fossil fuels. Coal often contains lithium, which can contaminate groundwater through coal ash ponds. Oil and natural gas extraction often creates oilfield brines or “produced water” that can contain incredible concentrations of lithium. In theory these brines are safely disposed of, but in practice they often contaminate groundwater, are spilled in quantities of hundreds of thousands of gallons, or are spread on roads in their millions of gallons as a winter de-icer. 

Oh and sometimes people use oilfield brines to irrigate crops. Yes, really.