Analisis Karoten

8/10/2019 Analisis Karoten

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Carotene Analysis

Bill Aalbersberg

Introduction

Vitamin A deficiency is one of the major public health nutritionproblems in many developing countries (including atoll nationsin the South Pacific) (WHO, 1982). Its main manifestation isxerophthalmia, which may lead to irreversible blindness. Themajor source of vitamin A in most developing countries iscarotenoids, which occur in very high levels in a number ofgreen leaves (Tee, 1988).

Recent epidemiological evidence and experimental studiesindicate that carotenoids may also be associated with reducedrisk of certain types of cancer. This is likely to be related to theantioxidant properties of the extended polyene system in thesecompounds (Tee, 1988).

There are several carotenes that have pro-vitamin A activity(Table 1). Their common feature is the -ionene ring. Beta-carotene, having two such rings, has roughly twice the activityas the other carotenoids. These compounds are transformed intoretinol (vitamin A) by enzyme action in the liver and intestine.Of the above carotenoids the four that normally occur in fruitsand vegetables, in order of relative amounts, are beta-carotene,cryptoxanthin, alpha-carotene, and gamma-carotene. Theefficiency of absorption and conversion of beta-carotene is suchthat ingestion of 6 mg results in formation of 1 mg of retinol.The other three have half this activity. Thus:

vitamin A activity (mg) = beta-carotene(mg) +6

(alpha-carotene + gamma-carotene + cryptoxanthin) (mg)

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Retinol itself does not occur in fruits and vegetables.

Proceedings of the 3rd OCEANIAFOODS Conference


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Table 1. Chemical structures of some provitamin A carotenolds that occurIn foods.

Other carotenoids that occur in green leaves, although lacking inpro-vitamin A activity, may still be important in relation tocancer prevention. Various studies have shown that leaves,besides beta-carotene, contain substantial amounts of lutein,violaxanthin and neoxanthin. Pigments that occur in someleaves and generally in smaller amounts are alpha-carotene,beta-carotene-5,8-epoxide, cryptoxanthin, lutein-5,6-epoxide,eloxanthin, taraxanthin, zeaxanthin and antheraxanthin. In termsof polarity, which is important for their chromatographicseparation, carotenoids can be classed as the hydrocarboncarotenes and xanthophylls, which may contain one oxygen(e.g. cryptoxanthin) or two oxygens (e.g. lutein).

Carotenoids are destroyed/altered to mixtures of cis-transisomers by the action of acids and free halogens especially inthe presence of light and high temperature. They are also easily

oxidised in the presence of oxygen and other oxidising agents.For these reasons, care must be taken in carotenoid analysis toremove light and limit the exposure to oxygen.


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Method

Methods will vary depending on whether all carotenoids need

to be extracted and quantified or merely those with Vitamin Aactivity. Extraction procedure must guarantee that allcompounds of interest are removed from the food.Acetone/hexane or tetrahydrofuran are commonly used. Solventis then removed under a stream of nitrogen. Samples should beanalysed as soon as possible after extraction.

For determining the vitamin A activity of carotenoids, theAOAC method (Williams, 1984) has been a standard method.This involves isolation of carotenes by column chromatographyand quantification by visible spectroscopy assuming the entiresample is beta-carotene. Alpha-, beta-, and presumably gamma-

carotene will be eluted but not cryptoxanthin. Thus, sampleshigh in alpha-carotene (e.g. carrots) will give erroneously highvitamin A activity (since the alpha-carotene is counted as (3-carotene even though it is only half as active). Fruits containingsubstantial amounts of cryptoxanthin will be erroneously lowby this method.

The method originally used by the Pacific Food CompositionProgramme and perhaps for some Australian results involvedcolumn chromatography to separate alpha- and -carotene andcryptoxanthin. The fate of gamma-carotene is unclear. In ourhands, especially for samples such as green leaves with large

amounts of carotenoid compounds, complete separation ofalpha-carotene from beta-carotene and cryptoxanthin from otherxanthophylls was not always possible. This resulted in thedetection of alpha-carotene and cryptoxanthin in foods that didnot contain them. Another problem using columnchromatography is the lengthy analysis time. We have found,however, that slightly reduced or increased pressure can speedthe column chromatography without seriously affectingseparation.

High-pressure liquid chromatography is a powerful analyticaltool with the ability to separate compounds inseparable by

column chromatography. By the use of internal standards thismethod can also be used to quantify these compounds.



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In recent years, several HPLC systems have been developed toanalyse carotenoids (e.g. Khachik and Beecher, 1985, Speek et al,1988, Heinonen et al, 1989). In our work, the method developedby E-Siong Tee of the Institute of Medical Research in Malaysia

was used as a starting point since it could be performed usingequipment that was already present in our laboratories (Tee andLim, 1991a). A C18 reverse phase column was used. A solventmixture of acetonitrile, methanol and ethyl acetate in the ratio80:18:2 was found to give the best separation of alpha- and -carotenes. Even then, as has been noted for other HPLCmethods, these isomers were not completely separated butsufficiently so for quantification. Typical retention times for thecarotenoid standards used were lutein and zeaxanthin (3.3minutes), cryptoxanthin (6.0 minutes), gamma-carotene (9.0minutes), alpha-carotene (10.2 minutes) and beta-carotene (10.8minutes). Lycopene has a retention time of about 7.5 minutes

according to Tee (1988). The use of gradient solvent systemsand lower flow rates have been shown to separate individualxanthophylls (Khachik et al, 1986) but much longer retentiontimes are involved.

One remaining problem was the presence of chlorophylls in theleaf extracts. These are eluted in the HPLC experiment atretention times that interfere with some of the carotenoids.Some chlorophyll decomposes on the columns, reducing theirlifetime. Treatment of the extract with sodium hydroxideremoves chlorophyll but also destroys a percentage of thexanthophylls and perhaps some carotenes. Column

chromatography does not elute chlorophylls, which suggeststhat this could be used instead of saponification.

The method used in this work to quantify carotenoids wastherefore a combination of column chromatography and HPLC.The extract was separated into carotenes and xanthophylls byelution on an open column with 10% acetone and 20% acetone,respectively, and quantified by visible absorption. The presenceand relative amounts of the three carotenes and cryptoxanthinwere then determined by HPLC. If cryptoxanthin and alpha-andgamma-carotenes are absent, the quantification can beperformed by spectrophotometry as in the AOAC method.

Otherwise, their HPLC are quantified using internal standards.



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The analysis of standard mixtures by this method gaverecoveries of greater than 95% for the four compounds ofinterest.

Basic Hydrolysis

Samples are dissolved in 40 cm3 of ethanol and 2.5 g ofpotassium hydroxide dissolved in 2.5 cm3of water added. Themixture was heated to reflux for 30 minutes, cooled andextracted with hexane until the extract was colourless. Thehexane extracts were washed with water and then dried oversodium sulphate. The hexane was then removed on a rotaryevaporator and the sample stored under nitrogen. All operationswere performed in a dark room.

Open-Column Chromatography

A suitable volume (e.g. 10 cm3) of the test solution was pipettedinto a glass column prepacked with a mixture of activatedmagnesia (Sea Sorb 43) (Fisher Scientific Co or Sigma ChemicalCo) and diatomaceous earth (Hyflo Super Cel) (Fisher ScientificCo), in the ratio of 1:3. Carotenes were eluted from the columnwith approximately 80 cm3of 10% (v/v) acetone in hexane. Thesolvent was changed to 20% acetone in hexane and a furthercoloured fraction eluted, which contained xanthophylls. Theeluate was evaporated on a water-bath with the aid of a streamof nitrogen and made up to a suitable volume (e.g. 10 cm 3) withhexane. The absorbance of the solution was read in a

spectrophotometer at 450 ran and the concentration of carotenescalculated by comparison against a calibration curve preparedwith the -carotene standard.

High-pressure Liquid Chromatography (HPLC Method)HPLC Conditions

A Waters high-pressure liquid chromatograph with detector setat 450 nm wavelength was used. A stainless steel 30 cm x 3.9mm ID 10 um uBondapak Clg column was used for thechromatographic separation. This was preceded by a WatersGuard-PAK pre-column module housing a disposable Guard-

PAK pre-column insert packed with the same material as that inthe analytical column. Sample injection volumes, dispensedusing a Rheodyne 7125 injector, were usually 40 to 50 l. A



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Waters 6000A solvent delivery system was used to deliver themobile phase (acetonitrile-methanol-ethyl acetate, 80:18:2, v/v) .at the rate of 2.0 cm3/min. Peak areas were quantitated bycalculation of areas and comparison with a standard -apo-

carotenal, which had been added to the extract.

HPLC Chromatography of Carotenoids

Hexane in the test solution was first evaporated on a water-bathwith the aid of nitrogen gas. The residue was immediatelyredissolved in a suitable volume of the mobile phase. Afterpassing through a 0.45 m regenerated cellulose membranefilter, suitable volumes were injected into the chromatograph.Identification of the carotenoids for HPLC profiles was carriedout by comparing with a reference carotenoid mixture similarlychromatographed. Column fractions were injected under the

same conditions to ascertain the presence of cryptoxanthin, andalpha- and gamma-carotene. Carotenes were quantified bycomparison of the area with -carotene and cryptoxanthin bycomparison with a known amount of added internal standard.

References

Ball, G.F.M. (Ed.) 1988: Fat soluble vitamin assays in foodanalysis. A comprehensive review. Elsevier Science Publishers,Ltd, London.

Heinonen, M.I; Ollilainen, V.; Linkola, E.K.; Varo, P.T.;

Koivistoinen, P.E. 1989: Carotenoids in Finnish foods:Vegetables, fruits and berries. j.Agric. Food Chem. 37: 655, 9.

Khachik, F.; Beecher, G.R. 1985: Decapreno--carotene as aninternal standard for the quantification of the hydrocarboncarotenoids by high-performance liquid chromatography. j.Chromat. 346: 237-246.

Kachik, F.; Beecher, G.R.; Whittaker, N. 1986: Separation,identification and quantification of the major carotenoid andchlorophyll constituents in extracts of several green vegetablesby liquid chromatography. j.Agric. Food Chem.: 630.



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South Pacific Commission 1991: Nutrient composition ofcommonly eaten Pacific fruits and green leafy vegetables,Pacific foods: Pacific Island Food Composition ProgrammeNews 3, SPC, Noumea.

Speek, A.J.; Speek-Saichua, S.; Schreurs, W.H.P. 1988: Totalcarotenoid and -carotene contents of Thai vegetables and theeffect of processing.Food Chem. 27: 245-257.

Tee, E.S. 1988: Carotenoids and Retinoids in human nutrition.Institute of Medical Research, Kuala Lumpur.

Tee, E.S.; Lim, C.L. 1991a: The analysis of carotenoids andretinoids; a review.Food Chemistry 41:147-193.

Tee, E.S.; Lim, C.L. 1991b: Reanalysis of Vitamin A of Malaysian

vegetables and fruits by the AOAC and HPLC methods.FoodChemistry 41: (In Press).

WHO 1982: Control of Vitamin A Deficiency andXerophthalmia. Tech. Rep. Ser. No. 672. WHO, Geneva.

Williams, S. (Ed.) 1984: Official Methods of Analysis of theAOAC. (14th ed.), Association of Official Analytical Chemists,Virginia, pp. 830-836.



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Interlaboratory Studies

Pieter Scheelings

Australian Government Analytical Laboratories, Adelaide

Before commencing any detailed discussion on interlaboratorytrials, it may be useful to define interlaboratory studies and toreview the purpose of interlaboratory trials.

Interlaboratory trials broadly refer to analytical studies onselected samples by a group of analysts or laboratories underspecified conditions.

Interlaboratory trials are normally undertaken for the followingreasons:

*validation of a new or revised method of analysis,

*composition certification of a reference material,

*establishing the analytical proficiency of collaboratinganalysts and/or laboratories.

Validation of New Analytical Methods

New or revised analytical methods need to undergo in-housevalidation trials to establish:

*accuracy of recovery of analyte,

*an acceptable level of precision (repeatability),

*ruggedness (user friendly),

*analyte concentration range,

*matrix sensitivity,

*interferences.



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When the preliminary validation trials have been completed, themethod is normally submitted to an interlaboratory trial toestablish between-laboratory precision (reproducibility) andconfirm the previously determined characteristics of the

method.

Having established an acceptable level of accuracy andprecision, the method can then be employed by laboratoriesparticipating in collaborative studies or common purposeprograms.

Certification of Reference Materials

Materials that are deemed to have suitable properties and levelsof analytes can have the levels of analytes "certified" by a groupof analysts using a diverse range of analytical techniques. For

example the level of trace elements in a sample can bedetermined using atomic absorption spectroscopy (AA), furnaceAA (FAA), inductively coupled plasma spectroscopy (ICP), ICPcoupled to mass spectrometry (ICP/MS), isotope ratio massspectrometry (IRMS), X-Ray Fluorescence (XRF) and neutronactivation analysis (NAA). Organic analytes are quantitatedusing gas-liquid chromatography (GLC), high performanceliquid chromatography (HPLC), mass spectrometry (MS),nuclear magnetic resonance spectroscopy (NMR) or fouriertransform infra-red spectroscopy (FTIR).

As most analytical techniques have some inbuilt bias, the

employment of a range of methods/techniques is expected toprovide a consensus value that is likely to be closer to the "true"value of the analyte than if only one technique had been used.

Proficiency of Collaborating Analysts

When a group of analysts or laboratories participate in aspecific project, it is important to establish a common oracceptable level of proficiency or expertise.

This is to ensure that all data points generated by thecollaborators have equal value or reliability.



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The organisation and management of interlaboratory studies,whatever the purpose, is crucial and particular attention must begiven to:

*stability and homogeneity of the test material,

*number of participating laboratories,

*technical reputation of laboratories,

*method familiarity of analysts,

*instructions to participants (e.g. sample handling/storage;analysis and data reporting),

*statistical treatment of data.

AGAL Proficiency Studies

AGAL's quality assurance protocol as well as NATA'saccreditation guidelines require AGAL Laboratories toundertake a number of in-house and external proficiencystudies. In many cases, samples are prepared and submitted tolaboratories as Youden Pairs. These are "duplicate" sampleswith slightly different levels of analytes. The data from Youdenstudies provides both accuracy estimates (from the consensusvalue) and precision data. Furthermore, Youden diagramsprovide a visual presentation of the importance of random and

systematic (bias) errors in the study. A brief summary ofYouden studies is given in Appendix 1.

AGAL Check Sample Program

As the organisation, management and reporting on Youden Pairproficiency studies is very demanding, AGAL has introduced a"check sample" program that can provide for rapid "feedback"on the analytical performance of participating laboratories.

The check samples may be "incurred" or spiked samples thatare distributed to laboratories for inclusion in their routine

analytical programs.



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Whilst the check samples have to meet basic requirements ofstability and homogeneity, these samples do not have to gothrough the rigorous preparation of proficiency study samples.

Results are reported to the co-ordinator within a specifieddeadline (normally 10 working days). The collated results arethen returned to the participants within the next two weeks.

The emphasis on fast reporting enables participants to identifyoutlying results quickly and difficulties with the methodology.

In AGAL where there maybe only two to five participatinglaboratories, the program is used to identify the level ofagreement between the laboratories and the agreement ofindividual results with the 'spiked' or incurred level of theanalyte(s).

Control Samples

A key analytical quality control technique that should beroutinely adopted by analytical laboratories is the use of controlsamples.

The development and routine use of appropriate controlsamples by collaborating laboratories provides a relativelyconvenient and cost effective safeguard for the analyticalperformance of the participating analysts.

Control samples like reference materials need to have thefollowing characteristics:

*stability,

*homogeneity,

*a matrix similar to that of samples under test,

*analytes at the level of the test sample,

*cheap and relatively easy to obtain.



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The employment of appropriate control samples during routineanalysis provides evidence that the analytical process is in"statistical control".

This is conveniently demonstrated by the use of a control chart(Figure 1) on which the analytical values of control samples arerecorded. By maintaining the control chart in realtime, theanalyst is able to monitor changes in the performance of themethod at an early stage. Warning limits and control limitsbased on two and three standard deviations from the mean valueare used to initiate corrective actions. Results for which controlvalues exceed the three standard deviation limit are discarded.

Whilst the employment of control charts is primarily an in-house quality control activity, use of common control samples(and charts) can provide a valuable assessment of the analytical

proficiency of each collaborating laboratory. A priority task ofthis workshop may be the identification of suitable controlsamples that can be distributed to collaborating laboratories forroutine use.

AGAL now routinely uses a number of control samples that arereadily available from supermarket shelves. For example orangefruit juice available in 250 ml tetrapaks have been employed forcontrols in vitamin C analysis whilst tomato juice packed in 170ml cans are suitable for -carotene. The application of fruitjuice controls for sugar profiles and wheat and soy bean floursfor B-vitamins is being investigated. Finally, the question that

needs to be addressed at this workshop is what does theOCEANIAFOODS Group require from an interlaboratory studyprogram?

The answer will depend very much on the level or extent ofcollaboration between the OCEANIAFOODS laboratoriesanticipated in the future.

The level of interlaboratory co-operation that OCEANIAFOODSmembers might consider for adoption is as follows:

1. agreement on the use of standard (validated) methods of

analysis. This should lead to the compilation of ananalytical methods manual,



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2. a consensus on the use of a basic set of Quality Controland Quality Assurance parameters,

3. routine employment of (common) control samples,

4. development of a check sample program,

5. development of an OCEANIAFOODS interlaboratoryproficiency program,

6. participation in external proficiency studies,

7. laboratory accreditation by an external agency.

Figure 1: Control chart of laboratory control samples analysedover a period of time



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Appendix 1 - Youden Pairs

In proficiency studies using Youden Pairs, each participating laboratory receives andanalyses two samples varying slightly in composition of the analytes under test. After

collation of all the data, the results of the study are plotted on cartesian axes with thex-axis representing the analytical results of Sample 1 and the y-axis the analyticalresults of Sample 2 (Figure 2).

A vertical line drawn through the mean value of Sample 1 and a horizontal line drawnthrough the mean value of Sample 2 divides the Youden diagram into quadrants.

In an analytical study where random errors predominate, the results are scatteredequally in all quadrants of the diagram. Systematic errors on the other hand tend tocongregate points in the lower left and upper right quadrants. Individual laboratory

bias is therefore indicated by points stringing out along a line drawn through theorigin and dual median point. Significant within-laboratory variability is indicated bydisplacement away from this line.

Mandel and Lashof describe a sphericity test based on the square root of the ratio ofthe major and minor axes of the ellipse. This indicates whether the elongation of the

ellipse along its major axis can be considered statistically significant, indicatingsignificant between-laboratory variability.

A more complete treatment of Youden Diagrams and of the modifications may beobtained from the following references:

W J Youden, Graphical Diagnosis of Interlaboratory Test Results. Industrial QualityControl pp 24-28 May 1958.

Mandel and Lashof, Interpretation and Generalisation of Youden's Two-SampleDiagram. Journal of Quality Technology, 6 (1) Jan 1974.

Figure 2: Youden plot of results of Youden pairs provided by collaboratinglaboratories


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APPENDIX 2: RECOMMENDED QUALITY CONTROL ANDQUALITY ASSURANCE PROCEDURES

Quality Control refers to aplanned system of activities whosepurpose is to control the quality of a product or service.

Quality Assurance refers to a planned system of activities whosepurpose is to ensure that the quality control program is effective.

Standard Quality Control Activities

*employment of qualified and experienced analysts

*use of standard or official methods validated for the test sample

*use of reagent blanks, spikes, laboratory controls and certifiedreference samples

*use of control charts

*use of replicate analyses

*adherence to principles of Good Laboratory Practice in regard to:.staff training.equipment maintenance and calibration.staff management and supervision.sample management including identification, integrity and

control.data reporting and audit

*accreditation by external organisations

Standard Quality Assurance Activities

*in-house test sample program

*external check sample program

*participation in interlaboratory proficiency studies

* regular audits of adherence to principles of Good LaboratoryPractice particularly in regard to:.instrument calibration and maintenance.acceptable recovery rates of spiked and reference samples.sample integrity (particularly when testing for labile

analytes).data recording and interpretation

* audits by external organisation


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Data Integrity

David Apimerika

A Food Composition Data Integrity programme has beendeveloped that takes account of the relationships betweennutrients and other food constituents and identifies anomalies inthe data. Each relationship is coded into Integrity as a "test" tobe performed on selected food records. One or more of these"tests" is selected and run over any set of foods or food groups.

The need for this program arose as a massive update of thenutrient data was undertaken in 1991 in response to a demandfor approximate data where analytical data were incomplete andmissing. These approximate mean values required anindependent check for consistency and validity. The reasons forthis independent assessment were:

*the Database Management System, Advanced Revelations,cannot dynamically check and maintain these relationships.This is true for any DBMS,

*the Data Editing system required a fast data entry cycle andbuilding in the nutrient relationship check into the systemproved too slow and cumbersome,

*Integrity does not require examination of food records in editmode before pointing out anomalies in nutrient relationships. Areport on the whole database is then feasibly possible within a

short period of time,

*the Integrity system becomes more modular in designphilosophy. Any changes in nutrient relationship definitions canbe made independently of the editing or viewing programs.

The Food Composition Database holds over 1200 food recordscontaining up to 249 nutrients. Each nutrient is described by amean value, a set of source codes and often other relevantinformation. These mean values exist on a wet weight and drymatter basis, and both are stored in the database. All analysis byIntegrity is performed on the wet weight basis mean values.



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An anomaly can arise from several directions. Each associatedwith the different phases of data accumulation and processing.The phases and sources of possible problems can be brokendown into three basic categories:

1. the limitations of measuring instruments and methods usedby the scientists analysing and presenting foodcomposition data,

2. the presence of unusual constituents in a food, e.g. highlyvolatile oils,

3. errors introduced by the handling of the data.

Executions:

The menu prompts for the selections needed to run Integrity.

These selections are:

Choice of tests: The user is able to choose one or more tests toperform over the database. Each test keeps track of the numberof anomalies that occurs in the relationship that it encapsulates.

Choice of foods: The user is able to select from one food recordright up to the entire food database. Gradations occur in theability of the user being able to select major food groups orselect foods based on the food name (e.g. all foods with theletters "apple" present in the food name to select all the foodsthat are apples or contain apples as an ingredient).

Output: Three choices of output are selectable: screen, printerand file. This gives the ability to view quickly the result if onlya few foods are chosen, or print out a report as soon as the runhas finished. This may take several hours if many foods andtests are selected. The choice of sending the report to file isgiven if the report is to be dispatched electronically to someother destination.

DESCRIPTION OF INTEGRITY PROGRAM - Each testincludes an acceptable range, not listed here, to allow forvariance related to rounding, and expected analytical andbiological variability.

1. Water (g) + Dry matter (g) = 100 g.

2. Total metabolisable energy calculated from the energyproducing components.



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3. Available Carbohydrate = Available Sugars + Starch (plusminor constituents such as glycogen, inulin,oligosaccharides).

4. Total Niacin Equivalents = Niacin + Potential Niacin from

Tryptophan.

5. Total Non Starch Polysaccharides (NSP) = Soluble NSP +Insoluble NSP.

6. Potential Niacin from Tryptophan (mg/100 g EP) =Tryptophan (g/100 g EP) / 0.06.

7. Individual amino acids (g/100 g EP) = individual aminoacids (mg/g total nitrogen) x Total Nitrogen (g) x 0.001.

8. Total Available Sugars = sum of individual availablesugars.

9.

10.

Total Proximates = Water + Protein + Total Fat +Available Carbohydrate + Dietary Fibre + Ash + Alcohol.

Ash (g) > sum of the individual elements.

11. Total Protein = Total Nitrogen x nitrogen conversionfactor.

12. Validity of the nutrient mean values (numeric or trace (T)).

13. Validity of the nutrient source codes.

14. Conversion of nutrient mean values on a dry matter basisto their appropriate wet weight basis.

15. An approximate food density value = CSM weight (g)/CSM volume (ml).

16. Total Vitamin A Equivalents (ug) = Retinol (ug) + [betaCarotene equivalents (ug)/6].

19. Sum of individual amino acids (g/100 g EP) < TotalProtein, 100 g EP.

20. Sum of individual fatty acids (g/100 g TFA) is in the rangeof 97 to 100.



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21.Sum of individual Saturated, Monounsaturated andPolyunsaturated fatty acids (g/100 g TFA) = Totals ofSaturated, Monounsaturated and Polyunsaturated fatty acids(g/100 g TFA) respectively.

22.Sum of individual Saturated, Monounsaturated andPolyunsaturated fatty acids (g/100 g EP) = Totals of Saturated,Monounsaturated and Polyunsaturated fatty acids (g/100 g EP)respectively.

23.Totals of Saturated, Monounsaturated and Polyunsaturatedfatty acids (g/100 g EP) = Totals of Saturated, Monounsaturatedand Polyunsaturated fatty acids (g/100 g TFA) x fatty acidconversion factor x total fat x 0.01.

24.Individual fatty acids (g/100 g EP) = individual fatty acids(g/100 g TFA) x fatty acid conversion factor x total fat x 0.01.

CSM is common standard measureEP is edible portionTFA is total fatty acids



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Software DevelopmentFurther Developments of the Australian

Nutrient Data Bank

Janine Lewis, Senior NutritionistNational Food Authority, Canberra

The Australian Nutrient Data Bank (ANDB) was originallydeveloped in 1986-87 to support the revision of Australia's foodcomposition tables. The objectives of the system were to:

*store,*process,*report nutrient analysis data.

Details of the ANDB development process and a generaldescription of the system were presented to the firstOCEANIAFOODS Conference in 1987 (1,2). At the conclusionof the original development period, it was considered that thesystem was sufficiently functional to produce the nutrientcomposition data and associated information for the nationalreference Composition of Foods, Australia (COFA) (3) but thatfurther enhancements would be necessary at a future time.There was an opportunity to enhance the system in 1990, afterthree years of operation. During the first three years, the cycleof data storage, processing and reporting was undertaken twiceand resulted in publication of the first two volumes of COFA.By the end of that time, sufficient experience of the system was

gained to assess thoroughly its flexibility, responsiveness,processing efficiency and overall design. The review concludedthat the system had particular deficiencies and limitations,which required attention in order to meet the work objectives ofthe compilers, and that a series of enhancements was required toimprove existing functions and to increase the capability of thesystem.

Before details of some of the enhancements are explained, abrief description of ANDB as it was originally developed isprovided below.

The operation of ANDB can be divided into two major

processes. The first element is data aggregation and calculation;the second one is data reporting. Separate diagrams of the datacalculation process, and the reporting process are given inFigures 1 and 2 respectively. The solid-line depictions represent



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those functions of ANDB that were part of the originaldevelopment.

In summary, each box represents a functional unit comprising

one or two databases. The original process commenced withmanual entry of laboratory results and coded sample descriptioninformation onto the Food Analysis database. Accurateinformation was maintained by the validation of the descriptioncodes at the time of entry. The set of nutrient values for eachanalysed food was identified by a unique Food AnalysisIdentifier (FAI). The foods for publication were compiled asaggregates or weighted aggregates of data from one or moreFAIs. The weighting data specifications and FAIs were added tothe Food database. In addition, serving size descriptions andquantities were added for each food. Also, the allocation ofindividual foods to separate report formats was specified on thisdatabase. The final nutrient data were calculated according tothe compilation specifications. Part of the calculation process

involved the production of specific values such as energycontent, retinol equivalents through the application ofcustomised equations. Numerical data that were finalised forrelease were stored in the Print database. Reports were producedaccording to user-defined specifications held in the Structuredatabase.

In August 1990, a consultant programmer was engaged for eightmonths to enhance ANDB and thus improve its functionality,flexibility and efficiency.

The types of enhancements required were:

*the creation of new functions,

*the incorporation of related external systems within ANDB,

*improvements and refinements to existing functions to reduceoperational inefficiencies.

Each of these points is discussed below.

New FunctionsAdditional Report Formats

Prior to the enhancements, the ANDB could produce camera-

ready output only for Composition of Foods, Australia.However, because of an increasing demand for foodcomposition publications in other formats such as a condensed



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tabulated version of the previously published data, and asimplified version for consumers, additional formats werecreated. Apart from the pre-set formats required for thesepublications, the system now provides the additional flexibility

to create landscape and portrait tables as required.Recipe Data Calculation

In the former system, the Print database was the final repositoryfor calculated data. With the development of a new recipecalculation system, ingredient nutrient data in the Print databasecan be further combined and calculated to produce nutrient datafor recipe foods. On completion of the calculation process, thenutrient data for recipes are also stored in the Print database.Although the recipe calculation facility has not been used asyet, it will greatly expand the range of foods in the food tablesand as such, provides a more comprehensive set of nutrientdata.

Incorporation of Related External Systems into ANDBThe Data Referencing System

This report facility generated a data reference key that relatedthe nutrient data per food to codes for published references orimputed data. The reference key and the list of references wereproduced as camera-ready copy for Composition of Foods,Australia. This system was originally developed as part of apilot to the data bank. Because of insufficient time during theinitial development phase of ANDB, this facility was convertedto access the ANDB databases, but reports continued to berequested from outside the ANDB system. Also, the report

outputs were sent to a dataset for editing into the final form forpublication. The enhancements incorporated all of the referencesystem into ANDB and refined the reports to produce final copyand thus eliminated the need for manual editing.

The Nuttab Database

NUTTAB is an abbreviation for Nutrient Data Table for Use inAustralia. The NUTTAB database (4) was first released in 1987and was derived from a simple mainframe database created forthe analysis of national dietary survey data. The first version ofNUTTAB contained a mixture of Australian data and overseasvalues. With the publication of successive volumes of COFA,

annual revisions of NUTTAB were made, incorporating newly-published foods and revising existing overseas data to the newAustralian values. For the 1989 and 1990 versions of NUTTAB,revision of the external database was done by



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manual data entry and updating. Not only were additionalresources required for this task, but the potential existed fordata handling errors to be introduced. The facility to produceNUTTAB from ANDB was one of the major enhancements.Because NUTTAB contains a mixture of analysed and imputedvalues, and includes foods from overseas sources, this meantthat, for the first time, the data bank needed to store data otherthan well-documented Australian analyses. Special efforts weremade to identify and separately maintain those data without theneed to create new, separate databases for them.

Improvements and Refinements to Existing Functions

There are many examples of improved efficiencies that could bementioned. One of the major improvements was the creation ofthe facility for electronic transfer of laboratory data. In theformer system, nutrient analysis data were manually enteredone nutrient at a time. With up to 60 nutrients or components

per food analysis and a processing time of around 15 secondsper nutrient, manual entry of data was very labour-intensive andtime-consuming. Two solutions to this problem wereimplemented.

First, the entry of single nutrients was modified to allow entryat one time of a full screen of 12 nutrients and relatedinformation onto the Food Analysis database. Thisenhancement has reduced the number of days required to entera commissioned laboratory program fully onto the data bank.The second solution was the transfer of data by diskette fromthe main laboratory conducting the food analysis program (theAustralian Government Analytical Laboratory, South Australia).

The process occurs in two steps. The program of foods andnutrients for analysis are created on ANDB. A diskettecontaining that program is sent to the laboratory. The laboratoryuploads the information onto its computer and, on completionof the analyses, downloads the total analytical results attachedto the original program of foods and nutrients. The diskette isreturned and the data are uploaded to the Food Analysisdatabase whereupon a report detailing variations to the originalfood and nutrient specifications is automatically generated.Another facility associated with this enhancement is the abilityto compute the financial cost of each commissioned programbased on previously-entered quotations from the laboratory.



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Concluding Remarks

Opportunities for enhancing a computer system do not comealong very often. When there is an opportunity to extend and

modify a system, it is wise for user team leaders to maximisethat opportunity to its fullest extent by being well prepared.Therefore, prior to commencement, it is important for users tohave a very clear idea of the capabilities and current limitationsof their system and to have in mind potential solutions, not onlytheir final endpoints but also options for their development.Throughout the development process, design andimplementation decisions should all be made in the context offuture user requirements. Accordingly, user team leaders needto give careful consideration to both their immediate and longerterm requirements from the system so that when solutions areimplemented, the framework for future developments can alsobe incorporated.

References

1.Cashel, K. 1987: Development of the Australian Nutrient DataBank - nutrition aims.Proc. First Oceaniafoods Conference: 45-50.

2.Carmody, J. 1987: Development of the Australian NutrientData Bank - computer aspects. Proc. First OceaniafoodsConference: 51-61.

3.Composition of foods 1989-90: Australia Volumes 1-5.Department of Community Services and Health, Canberra.

4.NUTTAB series 1987-91/92. Nutrient data table for use inAustralia. Department of Community Services andHealth/National Food Authority, Canberra.



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Figure 1. ANDB CALCULATION PROCESS


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Figure 2. ANDB REPORT PROCESS

Analisis Karoten

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