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Published in final edited form as: J Food Compost Anal. 2011 Dec;24(8):1147–1152. doi: 10.1016/j.jfca.2011.04.006

Evaluation of the comprehensiveness and reliability of the chromium composition of foods in the literature ☆

Mayly Y Thor 1,*, Lisa Harnack 1, Denise King 1, Bhaskarani Jasthi 1, Janet Pettit 1
PMCID: PMC3467697  NIHMSID: NIHMS328162  PMID: 23066174

Abstract

In the early 1960s, trivalent chromium Cr3+ became recognized as an essential trace element due to its potential metabolic and cardiovascular benefits. No comprehensive chromium database currently exists; thus a thorough review of the literature was conducted to examine the availability and reliability of chromium data for foods. A number of key issues were identified that challenge the feasibility of adding chromium to a food and nutrient database. Foremost, dietary chromium data reported in the literature prior to 1980 cannot be relied on because of problematic analytical issues before that time. Next, paucity of data emerged as an issue that could impede database completeness. Finally, large variation in reported chromium content of foods may render disputable representative chromium values. This variation has been speculated to originate from differences in growing and particularly processing foods. Furthermore, contamination of chromium from laboratory equipment and/or materials is possible and also believed to contribute to the variation observed in reported values. As a result, database developers must carefully consider the availability and reliability of information on the chromium composition of foods when deciding whether to incorporate chromium into or exclude it from a nutrient database.

Keywords: Chromium; Chromium variability; Food composition; Commonly consumed foods; Food and nutrient database; Literature review; Analytical methods, Contamination issues; Data availability

1. Introduction

Chromium was identified as a component of biological tissues in the late 1940s but it was not until the mid-1950s that scientific evidence suggested that this trace mineral might have biologic activity. The work of Mertz and Schwarz in 1955 and 1959 led to coining the term “glucose tolerance factor” or GTF and to identifying Cr3+ as the active ingredient of GTF. Since then, a multitude of studies have emerged, suggesting that chromium may play an important role in metabolic and lipid disorders. One of the foremost significant studies supporting its essentiality emerged in 1977 when Jeejeebhoy et al. reported that chromium supplementation through Total Parenteral Nutrition (TPN) dramatically improved glucose regulation in a patient who had developed glucose intolerance characteristics. This study provided valuable information for establishing the first U.S. dietary recommendations for chromium. The Estimated Safe and Adequate Daily Dietary Intake (ESADDI) was set at 50–200 μg/day for adults by the Food and Nutrition Board in 1980 (Food and Nutrition Board, 1980). The most recent dietary recommendations set for chromium in the U.S. were issued in 2001 as Adequate Intake (AI) of 25–35 μg/day for adults (Food and Nutrition Board and Institute of Medicine, 2001).

Despite various key studies that helped acknowledge the biochemistry of chromium and its role in nutrition, controversies have surrounded this trace mineral, including its essentiality, its exact role in glucose regulation, and the potential benefits of chromium supplementation, all of which have been refuted by many scientists (Nielsen, 1996; Vincent and Stallings, 2007). Inadequacies in quantifying chromium concentrations in foods have undermined the methodological integrity of some studies on the topic, thereby contributing to the uncertainty.

There are several analytical methods used to detect chromium in foods, beverages, and other biological samples: Neutron Activation Analysis (NAA), Mass Spectrometry (MS), Atomic Emission Spectrometry (AES), and Atomic Absorption Spectrometry (AAS). Each of the spectral methods is coupled with a different atomization process before mass or atomic emission/absorption measurements can be performed. Graphite Furnace Atomic Absorption Spectrometry (GFAAS) has been the preferred technique for determining chromium concentrations in biological samples since it became commercially available in 1968 (Veillon and Patterson, 1999; Willis and Stevens, 1988; Wolf, 1982). However, the validity of values derived from this method prior to1978 is believed to be poor due to analytical issues. First, standard reference material certified for chromium analysis was not available to use for calibration and refinement of analytical and contamination control methods (Kumpulainen, 1988, 1992). Second, inaccurate background correction resulted in unreliable measurement of chromium content in biological samples (Veillon and Patterson, 1999). Since the elucidation and correction of these analytical problems, additional studies have evaluated the chromium composition of various foods.

Chromium is present in foods in all food groups. This poses a challenge for database developers who wish to incorporate chromium into their databases because analytical values are needed for an array of foods if a high level of database completeness for the nutrient is to be achieved. It is speculated that external factors, specifically growing and processing, affect the chromium content of foods. This may also be a hurdle for database developers because multiple analytical values for individual foods may be needed to evaluate the appropriateness of inclusion of representative chromium composition values for foods. The objective of this study was to evaluate the availability and reliability of analytical data on the chromium composition of foods reported in the literature between 1980 and 2007. The evaluation was framed around the needs of food and nutrient databases in which representative values for an array of foods are needed.

2. Material and methods

In 2007, a comprehensive literature search was performed to identify articles published between 1980 and 2007 that provided analytical data on the chromium composition of foods. Only literature sources with analytical chromium values published after 1980 were selected due to the methodology and apparatus problems that arose prior to that time. The search engines Google, AGRICOLA, OVID, and PubMed were utilized to identify articles. Search terms utilized included the following key word and phrases: chromium; chromium a trace mineral; chromium in food; chromium content of foods; chromium composition of foods; chromium analysis in foods; chromium database; chromium food and nutrient database. Journal articles identified through the search engines were reviewed to confirm that original chromium composition values for foods were provided within these sources. Also; the reference lists in the articles were reviewed to identify additional sources of chromium composition values for foods.

Journal articles identified through the search process just described were used to record the following information in Excel spreadsheets for each food for which an analytical value was provided: food name, food description (if available), chromium value(s), and analytical method. After data abstraction was completed the food items in the spreadsheets were classified into major food categories (e.g. fruit, vegetable, meat). Using data in the spreadsheets, frequencies were calculated with respect to variables such as analytical method and number of values available for various food groups and specific food items.

Unpublished sources of chromium values such as U.S. and non-U.S. databases that contain data on the chromium composition of foods were not included in this review because several of these databases were not readily available due to subscription requirement. Also, there were several barriers to reviewing values included in databases that NCC could readily access. First, documentation on the source of chromium values was sometimes lacking or given in a language other than English. Second, these databases were not complete for chromium composition data. Third, the analytical method used was generally not specified. Finally, some of these databases included values from the published papers selected for this review. Since unpublished values were not included in this study, the number of analytical values available for various food categories may be somewhat underestimated.

In order to determine the extent to which chromium data for foods are available, various analyses were conducted. First, information on the most commonly consumed foods in the U.S. within common food categories was tabulated based on data from the United States Department of Agriculture Food Supply Series and the National Health and Nutrition Examination survey (Bachman et al., 2008; Economic Research Service (ERS), 2010). Next, the number of analytical chromium values was tallied for each food in the rankings.

To evaluate the extent to which chromium values are available for foods typically included in food and nutrient databases, an attempt was made to identify a chromium value for each of the approximately 2600 core foods in the University of Minnesota Nutrition Coordinating Center (NCC) Food and Nutrient Database. Core foods in this database are single ingredient foods (e.g. beef, apple, honey) and basic multi-ingredient foods (e.g. bread, sausage, cereal). The core foods in the database are used to create nutrient composition values for multi-ingredient foods. Because the core foods serve as building blocks, it is important to assign valid and reliable values to these foods, and keep missing values to a minimum.

The process of identifying chromium values in the literature for core foods involved first aiming for identifying directly matching food items from the list of those with analytical values. If the process of direct matching was possible, unit conversion of the chromium value(s) of the analyzed foods was performed as needed so that all values assigned to core foods were in μg of chromium per 100 g of food. After the direct matching and unit conversion processes were completed, an attempt was made to impute values for core foods in which a chromium value for one form of the food was assigned via direct matching. With this imputation process, the chromium value for a food in one form is applied to other forms of the same food. For example, the same chromium value could be assigned to both raw and cooked broccoli if only one chromium value was available for either form of the food. The chromium values for canned and other processed foods were excluded from this imputation process because scientific evidence indicates that these particular foods have higher chromium content than fresh foods as a result of leaching from stainless steel equipment used in processing (Anderson and Kozlovsky, 1985; Kumpulainen, 1992).

To examine the reliability of available chromium values, the mean, standard deviation, median, and range were calculated for foods for which multiple chromium values were available.

3. Results and discussion

A total of 51 articles published between 1980 and 2007 were identified. These articles included dietary chromium analyses performed in 20 different countries. The frequencies at which the various analytical methods were used to analyze foods reported in the articles were as follows: 2% NAA, 24% ICP-AES (Inductively Coupled Plasma Atomic Emission Spectroscopy), 11% ICP-MS (Inductively Coupled Plasma Mass Spectroscopy), 38% GFAAS (Graphite Furnace Atomic Absorption Spectroscopy), 15% FAAS (Flame Atomic Absorption Spectroscopy), 2% other, and 8% of studies used AAS but the source of energy for atomization was not clearly stated to provide a specific classification. Table 1 specifies the analytical method(s) used in each of the 51 articles reviewed.

Table 1.

Summation of the research studies and their corresponding analytical methods identified for this investigation.

NAA ICP-AES ICP-MS GFAAS
Singh and Garg (2006) Baucells et al. (1989) c Ashton et al. (2005) Anderson and Bryden (1983)
Bhattacharjee et al. (1998) c Ayaz et al. (2006) Anderson et al. (1992)
Eitenmiller et al. (1985) Bibak et al. (1998a) Anderson et al. (1988)
Ikem and Egiebor (2005) Bibak et al. (1998b) Bratakos et al. (2002)
King et al. (1990) Bibak et al. (1999) Cabrera et al. (1996)
Lisiewska et al. (2006) Brunborg et al. (2006) Cabrera-Vique et al. (1997)
Ozcan (2004) Garcia et al. (1999)
Pedro et al. (2006) Garcia et al. (2000)
Pegg et al. (2006) Gyori and Prokisch (1999)
Porter and Jones (2003) Kovacs et al. (2007)
Ragaee et al. (2006) Krzysik et al. (2008)
Sika et al. (1995) Mateos et al. (2003)
Vlieg et al. (1991) Miller-Ihli (1996)
Raczajska et al. (2005)
Stoddard-Gilbert and Blincoe (1989)
Tinggi et al. (1997)
Van Schoor et al. (1986)
Vos and Hovens (1986)
Wilplinger et al. (1995)
Wrobel et al. (2000)
FAAS Other Method not clearly stated b
Amaro-Lopez et al. (1997a) Schmitt and Weaver (1982) a Anderson et al. (1990)
Amaro-Lopez et al. (1997b) Chandrashekar and Deosthale (1993)
Baucells et al. (1989) c Leterme et al. (2006)
Bhattacharjee et al. (1998) c Roussel et al. (2007)
Farre and Lagarda (1986)
Giri et al. (1990)
Nabhan et al. (1985)
Puchyr and Shapiro (1986)
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a

Analytical method involved radionuclides detection.

b

AAS used but source of energy for atomization not clearly stated to provide a specific classification.

c

Reference listed twice as study used more than one analytical method.

3.1. Comprehensiveness

Across the articles reviewed, a total of 1382 chromium values were reported for 856 foods. The discrepancy in these two numbers is due to the fact that certain food items were analyzed more frequently than others, resulting in publication of more than one chromium value. For example, there were 48 chromium values available for asparagus out of a total of 243 total values for the vegetable group. The food group with the most values was the vegetable group whereas the fat group had the least data (see Fig. 1 for distribution of analytical chromium values by food group).

Fig. 1.

Fig. 1

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Number of chromium values identified in the literature for various food groups.

Table 2 shows the number of analytical chromium values available in the literature for the most commonly consumed protein sources, fruits and fruit juices, vegetables, milk and dairy products, grains, and fats in the U.S. Multiple analytical values were available for most of the commonly consumed foods.

Table 2.

Commonly consumed foods and the number of analytical chromium values available from the literature.

Commonly consumed foods (descending order)a Number of
analytical
values
Protein sources, include meat, poultry, fish, eggs, nuts
 Beef 8
 Chicken 7
 Pork 9
 Eggs 13
 Peanuts 1
 Turkey 2
 Fish and shellfish 82
Fruits and fruit juices
 Orange juice 5
 Apple 13
 Banana 7
 Apple juice 2
 Strawberries 3
 Watermelon 1
 Orange 7
 Raisins 1
 Peach 4
Vegetables
 Potatoes 20
 Canned tomatoes 2
 Head lettuce 3
 Dry edible beans 34
 Romaine and leaf lettuce 4
 Onion 10
 Tomato 4
 Cabbage 5
 Carrot 13
 Celery 7
Milk and dairy products
 Mozzarella cheese 0
 Cheddar cheese 0
 2% milk (fluid) 0
 Whole milk (fluid) 4
 Nonfat dry milk 0
 Skim milk (fluid) 5
 1% milk (fluid) 0
 American natural cheese 0
 Ice cream 3
 Yogurt 11
Grainsb
 Ready-to-eat cereals 156
 Nonwhole grain yeast bread 6
 Whole grain yeast bread 4
 Hot cereals 13
 Popcorn 0
 Nonwhole grain based desserts 10
 Pizza 3
 Pasta and pasta dishes 3
 Mexican mixed dishes 0
 Crackers 1
Fats
 Salad and cooking oils 7
 Shortening 0
 Butter 7
 Margarine 5
 Edible beef tallow 0
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a

Based on ERS USDA 2008 Average daily per capita MyPyramid equivalents from the U.S. food availability, adjusted for spoilage and other waste.

b

Ranking from Bachman et al. (2008).

Fig. 2 shows the proportion of core foods in NCC Food and Nutrient Database that could be assigned a chromium value by direct match and the proportion of core foods for which a chromium value could be imputed via the procedure described earlier. In total, 39% of the core foods could be assigned a chromium value via direct matching or imputation. With 61% of core foods lacking chromium values, substantial additional information on the chromium composition of foods and imputation would be required if missing values were to be kept to a minimum in the database.

Fig. 2.

Fig. 2

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Percentage of foods in NCC Food and Nutrient Database for which a chromium value can be assigned using direct matching or imputing.

3.2. Reliability

Table 3 lists means, standard deviations, medians, and range chromium values for the most commonly consumed foods in the U.S. for which two or more analytical values were identified in the literature. For many of the foods listed, the range in chromium values was very wide which was reflected in large standard deviations for many foods and sizeable differences between the means and medians. This variability appeared across the food groups examined. The large variation in analytical chromium values observed for many foods raises concerns regarding the potential representativeness of chromium values assigned to foods in a food and nutrient database. If nutrient values assigned to foods in a database are not close to representing the typical nutrient content of foods, the usefulness of the database for many applications may be seriously undermined. As an example, an individual’s dietary chromium intake estimated using chromium values included in a food and nutrient database may be over or under-estimated if the actual chromium content of foods consumed by the individual deviate greatly from the representative chromium values assigned to foods in the database.

Table 3.

Commonly consumed foods and their corresponding analytical chromium values (μg/100 g food) available from the literature, only foods with two or more values are reported.

Commonly consumed foods (descending order)A n Mean Median Range SD
Protein sources, include meat, poultry, fish, eggs, nuts
 Beef: meat, beef, ground beef 3o,f,r 168.390 9.000 1.280-494.890 282.784
 Chicken: chicken breast 2b,f 8.300 - 0.600-16.000 10.889
 Pork: ham 3u,r,b 2.144 2.230 0.003-4.200 2.100
 Eggs: egg, whole, cooked 6u,c,b,r,h,f 2.252 0.455 0.001-10.000 3.883
 Peanuts: peanut butter 2c,b 2.800 - 1.800-3.800 1.414
 Fish and shellfish: shrimp 3l,f,v 15.800 21.000 0.400-26.000 13.569
Fruits & fruit juices
 Orange juice 5r,c,o,b,i 0.523 0.403 0.100-0.910 0.366
 Apple 7u,b,q,d,r,f,j 8.212 3.250 0.002-39.733 14.163
 Banana 7u,c,r,b,h,m,j 4.856 0.800 0.001-16.420 6.766
 Apple juice 2i,c 0.155 - 0.010-0.300 0.205
 Strawberries 3r,h,q 1.652 0.970 0.787-3.200 1.343
 Orange 7u,c,b,h,f,r,j 4.900 1.700 0.001-25.500 9.200
 Peach 2f,h 6.200 - 5.000-7.400 1.697
 Cantaloupe 3u,gD 4.334 5.000 0.001-8.000 4.041
Vegetables
 Potato: potato, peeled, raw 4q,t,e,h 1.098 0.570 0.300-2.950 1.252
 Head lettuce 3c,o,b 0.511 0.132 0.100-1.300 0.684
 Dry edible beans: pinto beans 2pD 58.000 - 28.000-88.000 42.426
 Romaine and leaf lettuce 4u,f,h,r 11.045 5.745 0.001-32.690 14.694
 Onion, fresh 4h,f,j,s 51.018 34.200 1.670-134.000 59.300
 Tomato, fresh 7u,c,r,b,h,f,j 8.178 0.700 0.003-46.100 17.027
 Cabbage 4q,h,f,s 16.595 7.890 0.600-50.000 22.917
 Carrot 6b,q,r,h,f,n 3.246 1.737 0.400-9.000 3.532
 Celery 3b,f,n 5.100 7.000 0.300-8.000 4.187
Milk & dairy products
 Whole milk (fluid) 3o,c,h 1.069 0.200 0.097-2.910 1.595
 Skim milk (fluid) 4u,o,h,f 0.946 0.892 0.001-2.000 1.062
 Yogurt 4u,r,f,h 1.540 1.570 0.001-3.020 1.698
 American cheeseC 3c,o,b 2.137 2.000 1.400-3.010 0.814
GrainsB
 Ready-to-eat cereals: Kellogg’s Raisin Bran 3c,a,o 11.567 13.200 8.000-13.500 3.092
 Nonwhole grain yeast bread: white bread 6u,c,o,f,h,k 9.066 4.720 0.003-30.450 11.596
 Whole grain yeast bread: whole wheat bread 6u,o,b,w,f,k 14.883 10.500 0.008-38.200 15.660
 Hot cereals: Nabisco quick prepared cream of wheat 3aD 7.167 8.600 3.900-9.000 2.836
Fats
 Butter 6c,f,r,o,b,f 2.726 0.679 0.300-13.000 5.042
 Margarine 4r,b,o,f 1.914 0.309 0.040-7.000 3.393
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A

Based on ERS USDA 2008 Average daily per capita MyPyramid equivalents from the U.S. food availability, adjusted for spoilage and other waste.

B

Ranking from Bachman et al. (2008).

C

Ranking data not available for this cheese in ERS USDA files but food included in the table as chromium values were available.

D

Different values from same study.

Variation in nutrient content of foods is not a novel concept. In fact, food composition studies have documented that all the following factors may affect the nutrient composition of foods: geographic variation in soil mineral content, seasonal factors, plant cultivar, and manufacturing processes (Greenfield and Southgate, 2003; Pedro et al., 2006). In the case of chromium, external contamination from processing sources are believed to contribute to the amount of chromium present in foods to such an extent that different types and lots of foods have resulted in chromium concentrations with wide variation (Anderson et al., 1992; Offenbacher and Pi-Sunyer, 1983). Furthermore, analytical conditions that do not optimize for chromium analysis have been documented to amplify analytical values, as this trace mineral is ubiquitous, even in the laboratory setting (Anderson and Kozlovsky, 1985; Kumpulainen, 1992). In the past, it was suggested that laboratory methods used in published studies may not have addressed these contamination issues (Veillon and Patterson, 1999). The variation in chromium values seen in Table 3 for commonly consumed foods is consistent with the notion that contamination issues may be persistent in some laboratory analyses. However, the variation could also be attributable in whole or part to other factors such as manufacturers’ processing of foods.

4. Conclusion

Our evaluation of the comprehensiveness and reliability of chromium composition values for foods in the literature revealed two main impediments to assigning representative chromium values to foods in a comprehensive food and nutrient database. First, analytical chromium data is available for many commonly consumed foods, but overall, a relatively limited number of foods have been analyzed. Second, the large variation in the chromium content of individual foods originally identified by Anderson et al. (1992) remains an issue. Although the first challenge (limited availability of analytical values) may potentially be overcome if analyses are conducted for foods lacking analytical data, the second issue (large variation in analytical values) may be insurmountable. While laboratory contamination could potentially be minimized by refining analytical control methods, the variation due to other factors such as differences in soil chromium content and processing equipment reflect true variation in the chromium composition of foods in the food supply. Assigning representative chromium values to foods in a food and nutrient database may not be appropriate in this context. If representative values are assigned it may be important to acknowledge the limited usefulness of these values for estimating dietary chromium intake of individuals.

Acknowledgements

The authors wish to gratefully acknowledge the generous advice and valuable information provided by K. Patterson, N. Bryden, and J. Harnly of the Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture.

Footnotes

☆

This paper was an oral presentation at the 34th National Nutrient Databank Conference, Grand Forks, North Dakota (USA), July 12–14, 2010.

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