Evaluation of Designed Calibration Samples for Casein Calibration in Fourier Transform Infrared

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Research Note

Evaluation of Designed Calibration Samples for Casein Calibration in Fourier Transform Infrared

Analysis of Milk

Werner Luginbu¨hl*

Swiss Federal Dairy Research Station, Chemistry and Physics Department, Liebefeld, CH-3003 Bern (Switzerland) (Received February 28, 2002; accepted April 16, 2002)

The determination of casein in milk by infrared spectrometry still suffers from deficiencies specific to the spectrometers used.

Measurements with filtometers are based on the difference in the absorbance before and after casein precipitation. Computerised Fourier Transform Infrared (FT-IR) spectrometry allows the use of more spectral information but robust casein calibrations were achieved only with high numbers of natural calibration samples. This is expensive due to the costly reference analyses needed for calibration. To reduce effort and costs of casein calibrations we studied the usefulness of commercial calibration milks (designed for fat, protein, and lactose calibration) for casein calibrations. We tested calibration models with and without natural milk samples and assessed the validation results according to International Organization for Standardization and International Dairy Federation standards. We found the combined use of commercial designed samples and fresh natural raw milk samples a very promising approach to the partial least squares casein calibration of FT-IR spectrometers. Measurement uncertainties for casein as low as 0.020 g/100 g for the bias and 0.047 g/100 g for the standard error of prediction (on 25 validation samples of high variability) were achieved with a calibration of 38 samples. This accuracy complies with the requirements of the International Dairy Federation standard 141C:2000 for the infrared analysis of milk.

Keywords: FT-IR spectrometry; milk; casein determination; calibration; reference material

Introduction

The determination of casein contents in raw milk is of particular interest for the cheesemaker because casein is the fraction of milk proteins which precipitates during the cheese-making process. The economic beneﬁt for the cheesemaker strongly depends on the cheese yield and therefore, on the casein content of the milk. Classical methods such as Kjeldahl nitrogen analysis are too time consuming and expensive for the purpose of rapid mass analysis. The success of mid-infrared spectrometry as a rapid indirect method for the determination of fat, total protein and lactose in milk encouraged the development of several procedures for infrared spectrometric casein measurement. Early attempts were based on the measurement of protein contents before and after precipitation of casein by various precipitation chemi- cals (Sjaunja and Schaar, 1984; Karman et al., 1987;

Barbano and Dellavalle, 1987; Taha and Puhan, 1992).

All these methods were developed for infrared ﬁltometers (interference ﬁlter instruments) and could, therefore, not be optimized with respect to the spectral

ranges used for calibration and prediction. This approachwas still laborious and only the improved possibilities of computerised Fourier transform infrared (FT-IR) spectrometry opened the way to measure casein contents in milk without pretreatment of the samples.

Hewavitharana and van Brakel (1997) studied the performance of FT-IR spectrometry combined with principal component regression (PCR) and partial least squares (PLS) calibration models. Important factors suchas spectral resolution, spectral ranges, background spectra, spectral preprocessing, and calibration algorithms were varied and their influence assessed on the basis of about 270 calibration samples and between five and 26 validation samples not included in the calibration. Their findings were very promising (bias and standard error of prediction (SEP) of casein in the order of 0.04 and 0.06 g/100 g, respectively, for their best PLS calibration) and they concluded ‘. . .that FT-IR spectrometry is capable of the direct measurement of the casein in raw milk’. Also, in 1997 Foss Electric published an Application Note for the FT-IR spectrometer ‘MilkoScan FT 120’ (Foss Electric, 1997) withabout the same performance characteristics as reported by Hewavitharana and van Brakel. The casein

*E-mail: werner.luginbuehl@fam.admin.ch

0023-6438/02/$35.00 doi:10.1006/fstl.902

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calibration was based on 250 milk samples from three diﬀerent countries.

Near-infrared (NIR) spectrometry was ﬁrst tested by Diaz-Carrillo on goat’s milk (Diaz-Carrilloet al., 1993).

They not only established calibrations (with about 50 samples) for fat, total protein, lactose, and casein but also for the casein fractionsas-casein,b-casein and k-casein with some success. However, their method required a sample pretreatment (adsorption of milk on glass ﬁbre ﬁlters and drying for 4 h) before measurement. The application of NIR transmission spectrometry to measure the composition of cow milk was successfully tested by Laporte and Paquin (1999). SEP as low as 0.05 g/100 g were reported for casein in milk.

Though very good validation results were obtained it was recommended to use large calibration sets (150 calibration samples) to achieve accurate and robust calibrations.

A study on the simultaneous determination of casein, whey protein, and total protein by FT-IR and PLS in aqueous model solutions of sodium-caseinate and whey protein has recently been published by Matyssek (Matyssek et al., 2000). Very promising results were reported with respect to the accuracy in terms of the root mean square error (RMSE) which were lower than 0.06 g/100 g for eachcomponent. However, this study was rather limited in the number of measured samples (13 for calibration, five for validation, none of them natural milk samples) and the effect of lactose variability was not experimentally investigated but only supposed (on theoretical considerations) to raise the number of PLS-factors in the calibration model without negative effect on the accuracy of the IR prediction.

The purpose of the present work was the evaluation of the performance characteristics of various casein calibration models for FT-IR measurements using sets of commercial calibration samples from the Swiss Federal Dairy ResearchStation (designed for the calibration of fat, protein, and lactose determination of mid-infrared spectrometers) and natural raw cow’s milk samples both for calibration and validation. Withthis approach, we intend to contribute to the economical set-up of robust casein calibrations withonly moderate sets of calibration samples, thus reducing time and costs spent on rather expensive reference analyses.

Materials and Methods

Milk samples

Four commercially obtainable calibration sets consisting of 13 calibration samples eachwere obtained from the National Reference Laboratory for Milk and Dairy Products (part of the Swiss Federal Dairy Research Station) in intervals of 3 mo. This reference material was developed to support the calibration of infrared spectrometers for the determination of fat, protein, and lactose in raw milk according to the IDF International Standard 141C:2000 (International Dairy Federation (IDF), 2000) and was not initially designed for casein calibration. Freshraw milk samples for validation and

calibration purposes were obtained in two batches of 12 samples each(tank milk from 12 farms) within 3 wk time. All samples were preserved withBronopol/

Natamycin (Broad Spectrum Mictrotabs II, DAF Control Systems, San Ramon, U.S.A.).

Reference analyses

The casein reference values of all samples were determined by Kjeldahl methods (IDF, 1964). In order to know the variability of the samples the contents of fat, crude protein, and lactose (calibration sets only) were also measured (Boehringer, 1986; IDF, 1993, 1996). The composition of the samples and the abbreviations used to denote the various subsets of samples are summarized inTable 1.

Infrared spectrometry

All samples were warmed up to 401C in a water bath and mixed gently prior to spectra recording at this temperature. Single beam IR spectra (3000–900 cm¹, resolution 4 cm¹, triangular apodization, 16 scans per spectrum, one-sided interferograms) were collected on a FT-IR spectrometer (FTS-7, tungsten IR source, DTGS-detector; Bio-Rad, Cambridge, U.S.A.), equipped withan autosampler/homogenizer (Delta Instruments, NL) and a thermostated (401C) transmission cell (CaF2, d=37mm). Reference (background) spectra of demineralised water were recorded at the beginning and at the end of each sample series with identical conditions.

Spectra processing and data analysis

Single beam spectra were converted into absorbance withGRAMS/32 (Galactic Industries, 1999) using the respective single beam water spectra as background.

PLS cross-validation models were set up and analysed withPLSplus/IQ (Galactic Industries, 1997). The spectral ranges included in the calibration are the windows W1, W2, and W3 indicated in Fig. 1 (3000–2600, 1800–1700 and 1600–1000 cm¹). These spectral windows were selected on the basis of the coeﬃcient of determination R² between the casein contents and the absorbances of the 52 calibration spectra in calibration/validation model A (Table 3). The diagnostic statistics of cross validation (standard error of cross-validation (SECV), coeﬃcient of determination R², and bias) were computed by the same programme.

Validation statistics as well as the evaluation of the overall accuracy of the calibration models according to the procedures given in the ISO standards, ISO 8196-1:2000 and ISO 8196-2:2000 (ISO, 2000a,b) were carried out withExcel (Microsoft, 1997). Results will be given and discussed for the four calibration/validation models shown in Table 3. The repeatability of the spectrometric casein determination was estimated by predicting the casein contents (using calibration model C) of 20 raw milk spectra from one milk sample, recorded consecutively under the same conditions as

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applied in the present study (spectra from earlier work;

unpublished results). The principal diﬀerence between the calibration/validation models A–D is the absence (models A and B) or presence (models C and D) of natural raw milk samples in the calibration step.

Results and Discussion

The concentration ranges of the calibration sets R1–R4 listed in Table 1 comply with the requirements of the IDF Standard 141C:2000 (IDF, 2000) for the calibration ranges for individual cow milk (fat 2.5–7.0 g/100 g, protein 2.5–5 g/100 g, no range is speciﬁed for lactose).

The high variability of the composition of the calibration samples is clearly reflected in the variability of the spectral data. Figure 1 shows the 13 spectra of calibration set R3, along withan indication of the spectral windows W1, W2, and W3 used in the casein calibrations. The assignment of the main IR absorption bands to the constituents of milk has been given elsewhere (Luginbu¨hl and Eyer, 1992). Though no absorption band is unique for casein we can confirm the findings of Hewavitharana (Hewavitharana and van Brakel, 1997) that the absorbance at the amide II band (1550 cm¹) is highly correlated with the casein content (r=0.94, model A). Highcorrelations are also found at 1451 cm¹(r=0.95) and 1256 cm¹(amide III,r=0.85).

The design of the commercial calibration sets is orthogonal (linearly independent) with respect to the

concentrations of fat, protein, and lactose in order to prevent mathematical problems in matrix inversion applied in the algorithms of multiple linear regression.

The linear independence was conﬁrmed by the calcula- tion of the Pearson correlation coeﬃcients r between casein, fat and lactose contents of R1–R4 as indicated in Table 2.

The compositional variation among the 24 raw milk samples of the validation sets V1 and V2 was rather small, but this is only of importance for calibration/

validation model A in Table 3 where no designed calibration set is included in the validation measurements. The models B–D each includes set R4 in the validation step and therefore, span the pertinent concentration ranges in prediction as well as in calibration.

The results from cross-validation are not very useful to the assessment of the performance of the casein determination by the calibration models tested. None of the statistics (SECV, bias, R²) in Table 4 allows a clear decision on the ‘best’ calibration model. The number of PLS-factors (proposed by the software on the basis of the predicted error sum of squares (PRESS) statistics) is reasonably low, indicating that the spectra contain suﬃcient pertinent information related to the casein content of the calibration samples.

The quality of the casein calibration models A–D may better be assessed by the statistics obtained in the evaluation of the validation results according to the standards ISO 8196-1:2000 and ISO 8196-2:2000 (ISO, 2000a,b) as given in Table 5. The order of magnitude Table 1 Composition of the calibration sets R1–R4 and the natural raw milk sample sets V1 and V2 (all contents in g/100 g)

Casein Fat Protein Lactose-monohydrate

Subset N Mean Min Max Mean Min Max Mean Min Max Mean Min Max

R1 13 3.27 1.96 4.66 4.76 2.50 7.03 3.86 2.56 5.18 5.03 4.22 5.88

R2 13 3.27 1.97 4.65 4.71 2.46 6.97 3.81 2.51 5.11 4.82 4.03 5.62

R3 13 3.25 1.95 4.68 4.66 2.47 6.85 3.82 2.52 5.19 4.69 3.85 5.59

R4 13 3.17 1.92 4.54 4.61 2.40 6.88 3.77 2.50 5.06 4.84 4.06 5.65

V1 12 2.56 2.35 2.74 4.04 3.65 4.60 3.23 2.95 3.46 F F F

V2 12 2.54 2.38 2.81 3.97 3.63 4.16 3.22 3.02 3.59 F F F

- 0.2 0 0.2 0.4 0.6

3000 2500 2000 1500 1000 Wavenumber [cm^-1]

Absorbance [-]

W1 W2 W3

Fig. 1 FT-IR spectra of one complete commercial calibration set (13 samples, set R3). W1, W2, and W3 indicate the spectral ranges included in the PLS calibrations

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of the intercept of the linear regression (Fig. 2 shows the validation data for model C) between the casein reference values and the infrared analysis of the validation samples corresponds to the presence or absence of natural raw milk samples in the calibration (the same applies for the bias). Models A and B with rather high intercept values were set up only with designed calibration samples (commercial sets R1–R4), whereas models C and D (with very small intercepts) bothcontain 12 natural raw milk samples in the casein calibration. It may be concluded that the quality of the IR prediction is improved if a noticeable proportion of the calibration samples are natural milk samples which

model the natural milk matrix. This means that unbiased results may be expected only if the concepts of ‘natural calibration’ and ‘controlled calibration’

(Martens and Naes, 1996a) are combined to form a mixed calibration model which meets the fundamental requirement: ‘all phenomena that vary (in X) in th e target population, must be spanned in the calibration set and described in the calibration model’ (Martens and Naes, 1996b). The positive effect of including natural milk samples into the calibration step is also reflected in the increase of the coefficient of determinationR²when moving from model B (no natural samples) to model C (12 natural milk samples). The residual standard deviation of regression, syx, may be directly compared to the requirement of the IDF standard 141C:2000 (IDF, 2000) which requires this quantity not to exceed 0.06 g/100 g for fat, protein, and lactose, respectively.

Models A and C comply withthis requirement but model A has poor values for slope and intercept due to the lack of natural milk samples in the calibration. The SEP (i.e. the standard deviation of the differences between the casein reference values and the IR prediction values of the validation samples) is an estimate of the expected precision of the differences found between the two methods because it depends on the measurement uncertainties of the reference method as well as the infrared method. The IDF standard 141C:2000 requires the SEP not to be higher than 0.1 g/100 g for individual milk samples (for fat, protein, lactose); and the bias (mean difference) shall not exceed 0.2/ ffiffiffi

pn

g/100 g (i.e.

0.04 g/100 g for n=25) (IDF, 2000). These quality criteria are fulfilled by the calibration models C and D which both contain 12 natural milk samples in the calibration, but not by the models A and B without natural milk samples included in calibration. The confidence interval CI finally allows the statistically correct estimate of the overall accuracy error or measurement uncertainty: bias 7tn2,1a/2, according to the definition of the accuracy of indirect methods by the international standards, ISO 8196-1:2000 and ISO 8196-2:2000 (ISO 2000a,b).

Conclusions

The results discussed above show that it is possible to establishcalibrations for the infrared determination of casein in milk on the basis of the commercial calibration set (consisting of 13 designed samples withorthogonal fat, protein, and lactose concentrations) provided by the Table 2 Pearson’s correlation coeﬃcients r between

the contents of fat, casein and lactose-monohydrate of calibration sets R1–R4 forn = 52

Fat Lactose-monohydrate Casein

Fat 1

Lactose monohydrate 0.014 1

Casein 0.011 0.005 1

Table 3 Casein calibration/validation models tested Model

Samples in PLS calibration

Samples in validation (prediction)

A 52 samples

(sets R1–R4)

24 samples (sets V1 and V2)

B 26 samples

(sets R2 and R3)

37 samples

(sets R4, V1 and V2)

C 38 samples

(sets R2, R3, half V1, half V2)

25 samples

(R4, half V1, half V2)

D 25 samples

(set R3, half V1, half V2)

25 samples (sets R4, half V1, half V2)

Table 4 Cross-validation statistics of casein calibration models A–D

Model PLS factors

SECV (g/100 g)

Bias

(g/100 g) R²

A 9 0.028 0.002 0.9993

B 6 0.026 0.000 0.9994

C 7 0.037 0.001 0.9984

D 5 0.050 0.002 0.9964

Table 5 Validation statistics of the casein determination with independent samples, according to the standards ISO 8196-1:2000 and ISO 8196-2:2000 (ISO 2000a,b)

Model n Slope

Intercept (g/100 g)

s_yx (g/100 g)

SEP (g/100 g)

Bias (g/100 g)

CI*

(g/100 g) R²

A 24 0.7936 0.594 0.036 0.046 0.085 0.076 0.9082

B 37 0.9503 0.191 0.072 0.080 0.056 0.147 0.9894

C 25 0.9821 0.031 0.045 0.047 0.020 0.094 0.9971

D 25 1.0171 0.008 0.068 0.068 0.040 0.140 0.9936

*Conﬁdence interval CI=7t_{n2, 0.975}(ISO, 2000b)

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National Reference Laboratory for Milk and Dairy Products of the Swiss Federal Dairy Research Station if about the same number of natural milk samples are included in the calibration to model the matrix of natural milk. The advantage of this calibration concept is the relatively low eﬀort and the very moderate costs of the casein calibration. The accuracy of the casein measurements on the basis of models C or D with 38 or 25 calibration samples in terms of bias and SEP (or the conﬁdence interval, or the residual standard deviation, respectively) is of the same order of magnitude as reported in the pioneering FT-IR work of Hewavithar- ana (Hewavitharana and van Brakel, 1997) who used about 270 calibration samples in their best calibration model. Model C even complies withall quality criteria stated in the IDF standard 141C:2000 (IDF, 2000) for the calibration of the major milk constituents. The performance of the calibration approach presented here has not yet been investigated in the practice of a dairy or animal recording laboratory but there is no doubt that the use of designed calibration samples reduces the number of calibration samples needed to set up a robust calibration and enhances the reliability of the casein determination (at least) in the concentration ranges not usually accessible withnatural samples.

Acknowledgements

The author thanks Helga Batt for the extra eﬀort in determining the casein reference values of the samples used in this study.

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cref [g/100 g]

c_IR [g/100 g]

2.0 3.0 4.0 5.0

Fig. 2 Reference vs. predicted casein contents of 25 validation samples in calibration model C