Abstract Samples distributed in proficiency testing sche-mes (PTS) need to be homogeneous in order to be surethat if a laboratory has a result different from the otherlaboratories, its error can be attributed to its analysismethod and not to its sample. This control must be doneaccording to the ISO 13528 draft standard before sendingthe samples to the laboratories. It can be done by deter-mining homogeneity targets by sub-contracting to accred-ited laboratories using reference methods, but this engen-ders logistic and financial problems. That is why a homo-geneity check using Near Infrared Spectroscopy (NIR)has been developed for agricultural and food industriessamples prepared for PTS at Bipea (Bureau Interprofes-sionnel d’Etudes Analytiques). To evaluate the homogene-ity among samples, this procedure involves a comparisonof NIR spectra, the determination of global homogeneitycriteria and the use of control charts. The method of con-trol developed and carried out at Bipea allows the rapidand easy monitoring of the performance of the samplepreparation.

Keywords Homogeneity · Near infrared spectroscopy ·Spectra comparison · Distances · Control charts · Samples for PTS

Introduction

Laboratories participate in proficiency testing schemes(PTS) to control the accuracy of their results. PTS can bedefined as “regularly organised interlaboratory compar-isons to determine the testing performance of laboratories,which test the same or similar items”. The procedure for aPTS is as follows: each participant in a given PT schemereceives a sample prepared by the organiser and has to de-termine a list of analytes, the results are sent back to theorganiser, data are analysed and conventional true valuesand tolerance values are calculated according to a refer-ence procedure. Finally, the participating laboratories cancheck their accuracy and detect eventual bias.

The Bureau Interprofessionnel d’Etudes Analytiques(Bipea) is a PTS organiser for agricultural, agro-food andenvironmental fields, and counts more than 900 laborato-ries participating in more than 40 schemes. The require-ments for PTS organisers are described in ISO guide 43[1] and in a new standard under preparation, ISO 13528draft norm [2].

One of the most important requirements of a PT organ-iser is to provide homogenous samples. In that way, onecan be sure that if a laboratory has a result different fromthat of the other laboratories, its error can be attributed toits analysis method and not to the particular sample. Com-plete homogeneity is the state of a batch of a material ofwhich all the elements are rigorously identical whereasheterogeneity is the state of a batch of material of whichall the elements are not rigorously identical. Two degreesof heterogeneity can be distinguished: heterogeneity ofconstitution (it is inherent and depends on the nature ofthe material) and heterogeneity of distribution (it can bereduced by homogenisation or increased by segregation)[3]. From a batch of raw material, various steps of ho-mogenisation and division with the correct equipment al-low the preparation of N homogeneous samples.

The aim of the homogeneity control is to check if thesamples are identical before sending them to the laborato-ries. Therefore, in our context, the homogeneity aimed for

Marie E. Lafargue · Max H. Feinberg ·Jean-Jacques Daudin · Douglas N. Rutledge

Homogeneity check of agricultural and food industries samples using near infrared spectroscopy

Anal Bioanal Chem (2003) 375 : 496–504DOI 10.1007/s00216-002-1731-y

Received: 15 July 2002 / Revised: 8 November 2002 / Accepted: 27 November 2002 / Published online: 12 February 2003

ORIGINAL PAPER

M. E. Lafargue (✉)Bipea (Bureau Interprofessionnel d’Études Analytiques), 6/14 avenue Louis Roche, 92230 Gennevilliers, Francee-mail: bipea@wanadoo.fr

M. H. Feinberg · D. N. RutledgeInstitut National Agronomique Paris-Grignon, UMR INAPG-INRA Ingéniérie Analytique pour la Qualité des Aliments, 16 rue Claude Bernard, 75005 Paris, France

J.-J. DaudinInstitut National Agronomique Paris-Grignon, UMR INAPG-INRA Biométrie, 16 rue Claude Bernard, 75005 Paris, France

© Springer-Verlag 2003

is the identity between samples (i.e. the between-samplesimilarity). For example, if there is 10% of barley in theinitial batch of wheat, in the end we want to have N sam-ples all with 10% barley and 90% wheat. It is not thewithin-sample homogeneity that interests us, but the be-tween-sample homogeneity.

Bipea has been producing samples for more than 30years and has validated preparation devices which are pe-riodically checked to perform acceptable grindings, ho-mogenisations and divisions. At the moment, however,the homogeneity of the samples is controlled “a posteri-ori” by examining the results of the laboratories. To im-prove the quality of its services to the member laborato-ries and to fulfil quality-control requirements, Bipea hasset up a procedure using various methodologies in orderto monitor the homogeneity of the samples before they aresent to the laboratories.

The procedure is the following for each fabrication: Nsamples are prepared and ten samples are taken accordingto a sequence depending on the number N. If the ten sam-ples are considered homogeneous after the control thenthe complete prepared lot is considered homogeneous andcan be sent to the laboratories. In addition to the homo-geneity check before the sending of samples (a priori con-trol), a check of the homogeneity is still done, as before,by examining the laboratories’ results (a posteriori con-trol).

The control procedure, based on ten samples takenover the whole preparation, follows the requirements de-scribed in ISO 13528, i.e. analysis in replicate of the tencontrol samples taken from the population of samples anddetermination of “homogeneity targets”. According to thisstandard, the between-samples standard deviation must beless than 0.3×the standard deviation of the results of agiven proficiency test. In other words, the between-sam-ple variability must represent less than 30% of the totalvariability of the proficiency test results. This limit of30% is of course critical as the nature of the homogeneitytarget, its concentration and matrix effects will all influ-ence the variability. However, as it is this value which isrequired by the standard, it must be used. Later, thanks tothe results of homogeneity controls, it should becomepossible to propose and justify the use of other limitsadapted to specific analytes and products.

Recently, a number of papers dealing with homogene-ity checking performed for the certification of referencematerials have been published [4, 5, 6]. They all involvethe determination of the certified analytes. In Bipea PTschemes, the number of such analytes can be as high as40. For both practical and financial reasons, it is not feasi-ble to monitor each analyte. Furthermore, to choose a sin-gle parameter can be dangerous as samples may be homo-geneous with respect to one constituent but heterogeneouswith respect to another.

That is why an alternative approach based on Near In-frared Spectroscopy (NIRS) was investigated. NIRS is awell established technique for the analysis of cereals andagro-food products. NIRS permits non-destructive, rapid,cheap and direct analysis (without sample preparation)

and gives global information about the chemical composi-tion of the product (not limited to just one analyte). NIRSis widely applied for quantitative analysis of chemicalconstituents such as protein content, moisture and fats incereals, animal feeds, fats, meat and milk, as well as car-bohydrates in fruit juices and alcohol in beverages [7, 8].

The aim of this paper is to present the methodology forthe NIRS control of homogeneity of agricultural and foodproducts.

Materials and methods

Samples

The samples that can be checked by NIRS are presented in Table 1.They are divided into two groups: solids analysed by reflectionsuch as cereals, oilseeds, animal feeds and human foodstuffs on theone hand, or liquid and pasty products analysed by transmissionsuch as beverages and molasses on the other hand. In the presentpaper, flour and alfalfa will be treated. All the samples monitoredare taken during the preparation of the N samples for PTS as de-scribed in the introduction.

Analytical method

Depending on the nature of the products, the preparation of thesamples and the NIR measurements are different and have beenadapted after testing various presentations of the products to theNIR beam in order to have good quality NIR measurements (i.e.reproducibility of the measurements, acquisition of spectra withoutsignal saturation).

The sampling of the control samples is done very carefully inorder to ensure the representativeness of the analysed product. Hallnoted the influence of the sample dividing method on forage qual-ity measured by NIRS [9].

Solid and pulverulents products

If necessary before division, in case of very heterogeneous prod-ucts such as pellets of alfalfa, grinding is done with a laboratorymill (Model 3600, Perten Instruments). Each controlled samplewhich may be as big as 1.5 kg is sub-divided and two sub-sampleswith masses between 100 and 250 g, according to the product, areconserved. A rifles divisor is used for seeds (wheat, rapeseed ...)and a rotary separator for ground products and pulverulents (sem-olina, ground animal feeds...). The two sub-samples are analysedby NIRS in a quartz cell (3 or 9 cm diameter).

Liquid and pasty products

As water is the main constituent of the liquids and pasty products,it is necessary to have a very thin optical pathlength as water ab-sorbs a lot in the near infrared zone. To obtain such conditions, theliquid and pasty products are sub-sampled in vials after agitationand drops, taken with Pasteur pipettes, are put onto 0.1 µm Thomacells (used normally for microscope cell countings) and covered bya slide. In the case of carbonated liquids like ciders, sparkling wineor cola, decarbonation by agitation is necessary.

A Fourier Transform near infrared spectrometer (Bruker Vec-tor 22N/C) equipped with an integration sphere is used for the NIRmeasurements. Solid and pulverulent samples are analysed by dif-fuse reflection whereas liquid and pasty products by transmission.Sub-samples are randomly analysed and the spectra are recordedwith the OPUS acquisition software (Bruker). Due to instrumentallimitations, the studied spectral range is 9000 to 3800 cm–1 (or

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1111 to 2631 nm) for the reflection mode and 10,500 to 5200 cm–1

(or 952 to 1923 nm) for the transmission mode. The resolution isfixed at 8 cm–1 and each spectrum is the average of 32 scans of thesub-sample. The sample holder is continuously rotated in the re-flection mode, whereas in transmission mode the sample is static.

To take into account possible differences between the superfi-cial layer which interacts with the infrared beam, and the rest ofthe product, three spectra are acquired for each sub-sample afterremixing. These three repetitions are not done successively, nei-ther are the two sub-samples.

Spectral data processing and pretreatment

The spectra obtained in NIRS can be considered as multivariatefingerprints of the chemical and the physical properties of theanalysed product. In diffuse reflection spectroscopy, the spectrumis affected by the principal factors:

1. non-specific light scattering from the surface of the sample,2. the variable spectral pathlength through the products, and3. the chemical composition of the products.

The first two sources are related to the physical properties of theproducts (particles size and distribution) and account for the ma-jority of the variance between spectra, while variance due to chem-ical composition is usually small [10]. In order to reduce the effectof scatter and enhance the contribution of chemical composition, it

is necessary to perform mathematical pretreatment such as Stan-dard Normal Deviate (SNV) already presented by many authors[11, 12]. In our application, the aim is to verify that the samples arehomogenous both with respect to their chemical composition andto their granulometry. That is why both pretreated and raw spectraare analysed in the study. To illustrate this, Figs. 1 and 2 presentspectra of wheat flour and of alfalfa. For spectra acquired by trans-mission, scattering is not a problem because of the nature of theproducts (liquids and pasty). Spectral processing is done using theOpus (Bruker) and JMP (SAS Institute) software.

Methods applied for homogeneity checking

Comparison of spectra

For classical near infrared applications, chemical parameters suchas the protein or moisture content of wheat or flour are determined.In our application, instead of determining these parameters, whichinvolves lengthy calibration and validation steps, the idea is tocompare the entire spectrum of each sample—a spectrum beingconsidered as the physical and chemical fingerprint of the product,and for this reason, much richer in information than a single calcu-lated parameter. The assessment of the homogeneity among sam-ples is therefore done by comparing their spectra. However, thiscomparison of entire spectra requires the definition of a similaritycriterion. Two metrics were selected for testing:

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Table 1 Products controlled by the near infrared technique

Analysis by: Family of products Products

Reflection Cereals Common and durum wheatBarleyMaizeSemolina, flour

Oilseeds RapeseedSunflowerLinseedSoybean

Animal feeds Corn gluten feedSunflower, soybean, peanut and rapeseed cakesPig, chicken, rabbit and turkey feedPremixPets foodsMilk substituteFish flourDesiccated green foragesHayDehydrated alfalfa

Food pulses Peas, horse and broad beans

Human Food Baby foodMeal substituteChocolate powderMilk powderTomato paste

Transmission Alcoholic beverages Wines (red, white, rosé, dry white, sparkling, sweet, aromatized)Spirits (cognac, whiskey, rum, calvados, aniseed spirit, tequila, crude alcohol)Liqueur wines (Porto)Ciders

Non alcoholic beverages Fruit juices and nectars (orange, tomato, grape)Syrups (mint, grenadine)Colas, sodas

Molasses Sugar cane, sugar beet

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– Dmax calculated from the Euclidian distance between each spec-trum and the average spectrum (d2

(i,m)=Σ[Sij–Smj]2), computedfor each series of ten control samples. Dmax is the maximal dis-tance (Dmax=maxd2 [i,m]) and is considered as a measure of thedispersion of the samples;

– the Standard deviation spectrum (S spectrum) calculated fromthe ten spectra obtained for each series of ten control samples.The S spectrum shows the variability between the control sam-ples at each absorbency and is also considered as a measurementof the homogeneity between samples.

In order to decide if the difference between samples is or is not sig-nificant, it is necessary to have threshold limits: if the Dmax and theS spectrum are within the defined limits then it can be concludedthat the differences among samples is not significant. Using set of“homogeneous” samples from Bipea’s PTS, these thresholds wereestimated. To detect outlier samples, control charts like those usedfor quality control were applied as described below.

Application of the control chart method to homogeneity checking

The theory of control charts has been extensively presented by sev-eral authors [13, 14, 15, 16, 17] and will not be addressed here.Two types of control charts were used in this study, usually calledthe “mean” chart (X bar chart) and the “standard deviation” chart(S chart). Basically a control chart consists of a Central Line (CL)for the target value, close to which the measurements are expectedto remain, and the upper and intermediate limits for control andwarning, respectively.

For the homogeneity control, the target value is set to zero asthe parameter measured is a degree of homogeneity but the CentralLine is plotted nevertheless because it represents the average de-gree of homogeneity. Only the upper control limit is plotted be-cause the lower limit is the zero (which cannot be reached becauseperfect homogeneity does not exist in nature...).

The standard formulae used to calculate the limits are:

– For the X bar chart:Upper Control Limit UCL = CL + 3s/√n Upper Warning Limit UWL = CL + 2s/√n

where s is equal to the standard deviation of the process under con-trol and n the number of replicates.

– For the S chart:Upper control limit: F×s (F depending on the number of repli-cates, values of F found in Ref. [17]) with s as the standard de-viation of the process under control.

For these calculations, data distribution is assumed to be Normal,which needs to be checked. If the data are not Normally distrib-uted, the risk of wrongly rejecting a point outside of the limits willchange. Normality was tested with Kolmogorov–Smirnov test andNormal probability plot [17].

Two parameters are required to set up a mean chart and definethe limits—a centre line defined by the average level of homo-geneity and a standard deviation that measures the accepted vari-ability between homogeneous samples. These parameters must bedetermined when the process, here the preparation of the samples,is under control, that is, the process is producing homogeneoussamples. The parameters are estimated for each kind of product us-ing sets of control samples from preparations judged homogeneousbased on the results of PT Schemes.

To illustrate the setting up of control charts, the example of the“Flour/Alveograph” PTS is taken:

– eighteen sets of ten wheat flour control samples from eighteendifferent preparations were available,

– analysis of each set under conditions of repeatability (each setwas analysed three times),

– calculation of the three values of Dmax for each set, correspond-ing to the three repetitions,

– calculation of the average Dmax (or central line of the chart) withthe 54 (3×18) values of Dmax,

Fig. 1 Wheat flour NIR spectra. (A) Raw spectra. (B) SNV pre-treated spectra

Fig. 2 Alfalfa NIR spectra. (A) Raw spectra. (B) SNV pretreatedspectra

– calculation of the variance within each set (with the three Dmaxvalues calculated) and then the average of the eighteen variancesto obtain the final variance. Its square root gives an estimate ofthe final standard deviation,

– calculation of the warning and control limits.

The results are presented in Table 2 for raw data and can be used tobuild Dmax charts for raw data. These steps are repeated for the datapretreated by normalization and SNV. Such charts are establishedfor each kind of product.

It was determined from the Normality tests that the Dmax plot-ted on the control charts are not distributed Normally but follow aGamma probability distribution function. As the limits are basedon the assumption of Normality, it is necessary to evaluate the riskof wrongly rejecting a data (Type I error).

With Normally distributed data and a classical control chart:

– The risk of having a data point wrongly between the warninglimit and the control limit is equal to 2.3% and not 4.6% (prob-ability=95.4 when k=2) because only the upper limit is used(one-sided risk),

– The risk of having a data point wrongly below the control limitis equal to 0.135% and not 0.27% (probability=99.73 when k=3)because only the upper limit is used (one- sided risk).

With data distributed according to a Gamma law, the computationsgave:

– The risk of having a data point wrongly between the warninglimit and the control limit is between 3 and 5% depending on thevalues of the Gamma law parameters,

– The risk of having a data point wrongly below the control limitis between 0.3 and 2% depending on the values of the Gammalaw parameters.

Therefore, the risk to say wrongly that samples are not homoge-neous, because Dmax is wrongly below the control limit, is higherthan if the Dmax were Normally distributed (2% instead of0.135%). The value of 2% is a risk for Bipea to reject a preparation(Type I error) and it is acceptable and has then non-incidence forthe customers (laboratories).

For the setting up of the S charts, the same principle is fol-lowed. For each set of control samples, an S spectrum is calculatedand the average S spectrum is determined and the upper controllimit calculated. The data plotted on the charts are the standard de-viation spectra. As for the case of the Dmax chart, an S chart is cre-ated for the wheat flour. S charts were also created for the otherproducts and for each pretreatment.

Samples acceptance rules and validation

The control limits determined for the Dmax charts and the S chartscan be used to judge whether control samples are homogeneous. Ifthe Dmax and the S spectrum found by near infrared analysis for agiven control of homogeneity are within the fixed limits, then theset of samples will be considered homogeneous and the totalpreparation will be also. If they are outside the limits, there is arisk of heterogeneity. In this case, there are several possibilities:destruction of the samples and start of a new preparation, analysisby reference methods, integration of the heterogeneity into the PTresults (i.e. extension of the tolerance values).

The sets of control samples taken from preparations are con-sidered to be identical following the processing of the laboratories’

results. In order to validate this homogeneity check by NIRS, de-termination of the homogeneity targets by the reference methodshave been done for each PTS. These analyses were subcontractedto accredited laboratories, except of course when an accreditationprogram for a given analysis did not exist. Analyses of variancewere performed on the results to estimate whether the differencesamong samples were statistically significant and to calculate thebetween-samples standard deviation (Se). The Se value must repre-sent less than 30% of the total variability of the given PTS results.

Results

Examples of control charts for wheat flour and alfalfa

The setting up of the control charts with definition of thelimits was done with the control samples of the 1999/2000and 2000/2001 campaigns. A campaign begins in July andends in June and generally there is a proficiency test eachmonth for each scheme, except in July and August. Dur-ing the 2001/2002 campaign, the control charts were used.Each month, the control samples were analysed by NIRS,and Dmax and S spectra were calculated from the spectra.Figures 3 and 4 show D max charts for 2 different schemesand 2 products in particular: Flour from the Flour/Alveo-graph PTS and alfalfa from the Forages PTS. Each monthbetween September 2001 and May 2002, a D max is plot-ted. For both products, the Dmax are within the control lim-

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Table 2 Flour. Results of the setting up of the Dmax chart (on rawspectra)

Number of sets of control samples 18Number of replicates 3Average Dmax (central line) 0.16Standard deviation of Dmax 0.03Upper warning limit (UWL) 0.19Upper control limit (UCL) 0.21

Fig. 3 D max charts for the Flour/Alveograph PTS on wheat flour,campaign 2001/2002. (A) Raw spectra. (B) SNV pretreated spectra

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its, which allows us to conclude that the samples are notsignificantly different. It is noticeable that the informationgiven by parts A and B of each figure are different: part A(on raw data) underlines homogeneity due to granulome-try whereas part B concerns homogeneity due to chemicalcomposition. Therefore, for the wheat, Fig. 3, it appearsthat differences between samples are caused more by theirgranulometry characteristic than by their chemical com-position.

Those conclusions are confirmed by the S charts inFigs. 5 and 6. With the new data acquired during the cam-paign 2001/2002, the limits of the charts will be calcu-lated again to improve them and this will be repeated atthe end of each campaign. The results without pretreat-ment (part A of the figure) and the results with SNV pre-treatment (part B of the figure) can be compared, the for-mer reflecting more the particle sizes and distributionswhile the latter reflects the chemical composition. The Sspectra in Figs. 6 and 7, part A, show that the spectralvariations follow the raw spectra presented in Figs 1 and2, part A: the intensity variation is constant along thespectral range. On the contrary, the S spectra in Figs 6 and7, part B, are not constant along the spectral range andthere are some zones more variable than others. Thismeans that the differences between spectra due to granu-

Fig. 4 Dmax charts for the Forages PTS on alfalfa, campaign 2001/2002. (A) Raw spectra. (B) SNV pretreated spectra

Fig. 5 Standard deviation charts for the Flour/Alveograph PTS onwheat flour, December 2001 and April 2002. (A) Raw spectra. (B) SNV pretreated spectra

Fig. 6 Standard deviation charts for the Forages PTS on alfalfa,December 2001 and April 2002. (A) Raw spectra. (B) SNV pre-treated spectra

larity are almost constant along the spectral range,whereas the differences due to the chemical compositionchange along the spectral range.

Furthermore, it is noticeable that the Dmax values andthe S spectra are higher for alfalfa than for flour: the Dmaxchart centre lines are equal to 0.16 (raw data) and 0.14(SNV data) for flour and 0.55 (raw data) and 0.48 (SNVdata) for alfalfa. This can be explained by the greater de-gree of inherent heterogeneity of alfalfa compared toflour, for both chemical composition and granularity.

Performance of the control of the checking procedure

For the “Baking ability test” PTS, samples of commonbarley are prepared and sent to the participating laborato-ries for analysis of the wheat and of the flour obtained.During the campaign 2001/2002, a control was able to de-tect the presence of a few percentage of barley in the sam-ples of common wheat. Dmax and S spectrum are plotted inthe charts in Figs 7 and 8 for the control performed in Jan-uary 2002. They are all outside of the control limits and itcould be concluded that there was a problem of hetero-geneity among the samples. Actually, it turned out that thewheat contains a small percentage of barley and that thequantity of barley present was not sufficient for it to be

uniformly distributed among the samples to give repre-sentative samples of the product. This is summarised inFig. 9 where two wheat samples containing six barleyseeds are sampled to be analysed. The surface of the prod-

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Fig. 7 Dmax charts for the “Baking test” PTS on wheat, January2002. (A) Raw spectra. (B) SNV pretreated spectra

Fig. 8 Standard deviation charts for the “Baking test” PTS onwheat, January 2002. (A) Raw spectra. (B) SNV pretreated spectra

Fig. 9 Schematic representation of sampling for infrared analysisof two samples of wheat containing the same small amount of bar-ley. The analysed surface of the two samples obtained after divi-sion is not the same

uct scanned by the infrared light is different for the twosamples because there is not enough barley to have repre-sentative sub-samples.

To solve the problem, the barley was removed fromeach sample manually and the sub-samples were analysedagain. The new Dmax and S spectra are reported in thecharts in Figs. 8 and 9 (control 2). The result is clear: thesamples are not significantly different after removing thesmall quantity of barley. Another solution would be togrind the samples in order to have homogeneous and rep-resentative sampled product. It is noticeable that the dif-ference between the wheat samples containing barley isdue not only to the chemical composition but also to thegranularity, as shown in Figs. 8 and 9.

Validation with reference methods

A series of control samples were sent to accredited labo-ratories for the determination of homogeneity targets. Theresults of the analyses are shown in Table 3 for the flour ofthe PT of January 2001 and for the alfalfa of February2001. The homogeneity target for both flour and alfalfa isthe protein content (expressed as %).

According to the ISO 13528 standard, the ratio (ex-pressed as %) of the between-samples standard deviationto the total standard deviation of the PTS must be below30%. The calculated ratios are 9.4% for the flour and16.9% for the alfalfa which are below the threshold value.

Furthermore, the observed F values obtained by analy-sis of variance are less than the critical F value (3.02) forboth products. Thus, the results of the control show thatthe samples are not significantly different for that homo-geneity target, i.e. protein content. It can be concluded

that the samples may be considered homogeneous; thismeans that all the manufactured samples may be consid-ered homogeneous for a given proficiency test. It can benoted that the degree of heterogeneity is higher for the al-falfa than for the flour, as was found in the previous con-trol charts. Since homogeneity among the samples de-pends largely on the nature of the product, it may be ad-visable to adapt the value of 30% prescribed in the normas a function of the product: for some products the re-quirement should be lower, for others it may need to behigher.

Discussion and conclusion

The method of control by NIRS presented in the paper issuitable to check the homogeneity of the samples manu-factured for Bipea’s PTS. Actually, it is characterised by:

– polyvalent technique adapted to numerous products,– rapid analysis,– non-expensive analyses, and– simple decision rules (use of control charts).With this control by NIRS, it is possible to have a quickresponse in case of heterogeneity problems without beingdependent on external conditions such as the response de-lay of subcontractors.

The application of pretreatments permits to distinguishheterogeneity due to the granularity from that due to thechemical composition. The limit of the technique is theimpossibility to detect compounds present in very smallquantities, such as vitamins or mycotoxins. It is generallyadmitted that the limit of detection of the NIR technique isusually about 0.5 to 1% of a compound in the product;

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Table 3 Flour and alfalfa. Re-sults of the control by refer-ence method with protein con-tent (expressed in g/100 g) ashomogeneity target

Sample January 2001 February 2001

Wheat Flour Alfalfa Pellets

Rep. 1 Rep. 2 Rep. 1 Rep. 2

1 9.52 9.49 24.4 24.92 9.51 9.52 24.0 24.53 9.48 9.57 24.7 24.74 9.52 9.5 24.4 24.35 9.51 9.53 24.5 24.46 9.51 9.54 24.1 24.47 9.48 9.48 24.8 24.28 9.48 9.48 24.4 24.29 9.47 9.46 24.8 24.6

10 9.51 9.51 24.0 24.0Mean 9.50 24.4Repeatability standard deviation, Sr 0.02 0.2Between samples standard deviation, Ss 0.01 0.1Assigned value of the PTS 9.52 23.6Robust standard deviation of the PTS, σ 0.14 0.6(Ss/σ)×100 9.4 16.9F value 0.23 1.99Critical value of F 3.02 3.02

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this means that only heterogeneity due to chemical con-stituents such as moisture or protein content can be easilydetected. The heterogeneity due to compounds present astraces (vitamins, minerals...) and contaminants cannot bedetected by the technique. If the steps of preparation ofthe samples give homogeneous samples for all the majorchemical constituents, then it is reasonable to think theother constituents should be homogeneous too. This hy-pothesis could be verified for some trace compounds for alimited number of products, but it is difficult to do so forall the products and all traces. The choice of homogeneitytargets is sometimes difficult and the comparison of thecomplete spectra could be a solution to avoid this, even ifit is not perfect (i.e. small quantity of barley in wheat).

To demonstrate the performance of NIRS to detectsmall differences between samples, studies of heteroge-neous samples, artificially manufactured, are currently be-ing carried out at Bipea.

In the future, the method of homogeneity checking byNIRS will be extended to other products such as oils andfats. It will be interesting to apply the procedure to sam-ples prepared for besatz (impurities) determination PTS,in order to see the capacity of the technique to detect im-purities, such as damaged grains, foreign seeds...

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