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International Dairy Journal 11 (2001) 9398

Moisture, solids-non-fat and fat analysis in butter

by near infrared spectroscopy

Mar!a Hermidaa, Jose M. Gonzaleza, Mar Sanchezb, Jose L. Rodriguez-Oterob,*aLaboratorio de Mouriscade, Diputaci!on Provincial de Pontevedra, 36500 Lal n (Galicia), Spain

bFacultad de Veterinaria, Instituto de Investigaci!on y An !alisis de Alimentos, Universidad de Santiago, 27002 Lugo (Galicia), Spain

Received 10 July 2000; accepted 25 February 2001

Abstract

Moisture, solids-non-fat and fat were analysed in butter by near infrared (NIR) spectroscopy without any previous sample

treatment. A set of 102 samples was used to calibrate the instrument by modified partial least-squares regression. The following

statistical results were achieved: standard error of calibration (SEC)=0.192 and square correlation coefficient R2 0:90 formoisture, SEC=0.086 and R2 0:93 for solids-non-fat, and SEC=0.168 and R2 0:94 for fat. To validate the calibrationperformed, a set of 24 butter samples was used. Standard errors of validation were 0.255, 0.071 and 0.377 for moisture, solids-non-

fat and fat, respectively, and R2 for the regression of measurements by reference method versus NIR analysis were 0.83, 0.94 and

0.72 for moisture, solids-non-fat and fat, respectively. To compare, for all three components of the validation set, the results

obtained by NIR spectroscopy with those obtained by the reference methods, linear regression and paired t-test were applied

and the methods did not give significantly different results for p 0:05. When mean square prediction error analysis was applied, itcould be concluded that a strong calibration model was obtained and that for all three components the main error was unexplained.

# 2001 Elsevier Science Ltd. All rights reserved.

Keywords: NIR spectroscopy; Butter analysis

1. Introduction

At present, with the great increase in food manufac-

ture and trade, a large number of analyses of both raw

materials and finished products are in demand. The

classical analytical methods are slow, expensive and

need highly qualified staff; therefore these methods are

not sufficiently effective in satisfying the current needs of

the food industry.

The main advantages of near infrared (NIR) spectro-

scopy for food analysis lies in its speed, no or little

sample pre-treatment and the avoidance of the use of

chemicals (Osborne, Fearn, & Hindle, 1993). NIR

spectroscopic analysis of milk (Albanell, C !aceres, Caja,

Molina, & Gargouri, 1999; Laporte & Paquin, 1999),

cheese (Rodr!guez-Otero, Hermida, & Cepeda, 1995;

Sorensen & Jepsen, 1998a, b; Wittrup & Norgaard,

1998) and other dairy products (Rodr!guez-Otero &

Hermida, 1996; Rodr!guez-Otero, Hermida, & Centeno,

1997; Laporte & Paquin, 1998) is a very useful technique

for the determination of major components in milk and

dairy products. However, little work has been done in

butter analyses; in the literature reviewed only one

article was found (Weaver, 1984). Measurements at 20

different wavelengths with equipment fitted with optical

filters were described in this article, and calibration was

performed by multiple linear regression (MLR) for

moisture. Evaluation of data on fat and solids-non-fat

was not, however, included.

Moisture determination is the most important control

in butter production, especially in modern factories

where hundreds of tons may be produced daily. The

economy of manufacture demands that the final

products be as near as possible to the target value. But

in addition to moisture, solids-non-fat and fat limits are

specified in butter regulations; therefore these two

parameters must be analysed routinely.

Since studies on butter analysis with modern full

scanning instruments have not been found in the

literature, the aim of this work is the application of

NIR spectroscopy to the analysis of all three major*Corresponding author. Tel.: +34-982-252231.

E-mail address: jlrotero@lugo.usc.es (J.L. Rodriguez-Otero).

0958-6946/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.

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components of butter: moisture, solids-non-fat and fat,

without any previous sample treatment. We also study

the effectiveness of different multivariate analysis

techniques (Martens & Naes, 1993).

2. Materials and methods

2.1. Samples

A total of 126 butter samples were divided into two

groups: 102 samples for calibration and 24 samples for

validation of the calibration performed. Samples of

salted as well as unsalted butter were included in both

groups. Samples were supplied by the principal factories

in, and purchased from the markets of Galicia (NW

Spain) and Portugal.

2.2. Reference analysis

Water, solids-non-fat and fat contents were deter-

mined in accordance with International Dairy Federa-

tion (IDF) Standard 80 (1977). The water content was

determined by drying a known mass of butter at

102 28C and weighing it afterwards to determine the

loss of mass. The solids-non-fat content was determined

after extracting the fat from the dried butter with light

petroleum (boiling range between 308C and 608C) and

weighing the residue. The fat content was calculated as a

percentage by subtracting the percentages of water

content and solids-non-fat content from 100.

2.3. NIR analysis

A wavelength scanning instrument, NIRSystems 6500

(NIRSystems, Silver Spring, MD), with a scanning

range from 400 to 2500 nm and wavelength increments

of 2 nm was used. Instrument checks recommended by

the manufacturer were performed daily prior to use.

Samples were analysed at room temperature (approxi-

mately 208C) in a closed 30 mm thick cuvette, using the

transport module. Reflectance measurements of mono-

chromatic light were made from 1108 to 2492 nm. The

means of 25 spectra scans were taken for each sample;data was recorded as log 1=R, where R is the reflectanceenergy.

2.4. NIR calibration

ISI software was used (ISI software, 1992). Scatter

correction was performed by standard normal variate

transformation (SNV) and detrend method (Barnes,

Dhanoa, & Lister, 1989) and by multiplicative scatter

correction (MSC) (Geladi, McDougall, & Martens,

1985).

A general Mahalanobis distance (H statistic) was

calculated from principal components analysis (PCA)

scores. The H values were standardized by dividing

them by the average H value for the calibration file. If

a new spectrum sample differed by more than 3.0

standardized units from the mean spectrum of the

calibration file, the sample was defined as a global Houtlier, liable to give inaccurate predictions.

The calibrations were performed by MLR, principal

components regression (PCR) and modified partial

leasts-square (MPLS) regression (Martens & Naes,

1993) using first and second derivatives of the spectra

(Meuret, Dardenne, Biston, & Poty, 1993).

* The first derivative of the spectra was calculated

using a subtraction gap and smoothing segment of 4

data points (1, 4, 4).* The second derivative of the spectra was calculated

using a subtraction gap and smoothing segment of 6

data points (2, 6, 6).

The optimal number of terms for the calibration

minimising overfitting was based on the standard error

of cross-validation (SECV). The approach used was as

follows: 80% of the samples from the calibration set

were used for calibration, and for the remaining 20%

the standard error of prediction (SEP) was calculated.

This operation was carried out a total of 5 times, each

time using a different group for calibration and

prediction. The SECV was calculated as the square root

of the average of the squares of the 5 SEP. The final

calibration equation was developed with the totalsamples of the calibration set using the number of

factors with the lowest SECV.

The standard error of calibration (SEC) was calcu-

lated, and the critical T value for eliminating outliers

was fixed at 2.5 (T=residual/SEC).

To check the calibration performed the validation set

was used. The standard error of validation (SEV) and

square correlation coefficient (R2) of reference analyses

versus NIR values were calculated. Statistical errors

were calculated in accordance with Workman (1992).

3. Results and discussion

3.1. Spectral information and butter samples composition

Fig. 1 shows the mean spectra of butter in the

calibration set. The following bands can be observed:

at 1210 nm the second overtone of the CH stretch of

CH2 groups; at 1450 nm the first overtone of the OH

stretch; at 1728 and 1764 nm the first overtone of the

CH stretch of CH2 groups; at 1940 nm a combination

of the second overtone of the OH bend and stretching

band of water; at 2310 nm the second overtone of the

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CH bend of CH2 groups; at 2350 nm a stretch and bend

combination band of CO (Osborne et al., 1993).

Table 1 shows the range of chemical composition and

standard deviation (Sd) of butter samples in the

calibration and validation sets.

3.2. Repeatability

Repeatability standard deviation (Sr) was calculatedfor reference methods and for NIR spectroscopy in

accordance with IDF Standard 128 (1985) by analysing

in duplicate 12 butter samples. Table 2 shows that for

the repeatability standard deviation better values were

achieved in the NIR spectroscopy analysis than by using

the reference methods. This is certainly due to the

simplicity of the instrumental method in comparison

with the reference methods.

3.3. Calibration

The calibration set was selected with the aim of

achieving a strong calibration for butter with and

without added salt by maximising variability among

samples composition and obtaining a wide range of

spectra to avoid H outliers in the validation set.

Therefore, the mean value for H statistic for the

validation set of samples was 1.15 with respect to the

mean value of the calibration set and no H outliers

were found. Consequently, no statistical differences were

found between the spectra of the validation and

calibration sets, indicating that the calibration set was

wide enough.

In order to evaluate the different calibrations, the

SEC, SECV and R2 of the calibration set, and the bias,

SEV, R2 and mean square prediction error (MSPE) of

the validation set were evaluated. Although 102 samples

were used for calibration, T outliers were eliminated

and the final number of samples used to calibrate each

parameter is shown in Table 3.

MPLS regression achieved the best calibration equa-

tions in all cases. Relatively small differences were found

for the statistical results of calibrations, when compar-

ing the use of SNV and detrend method with MSC for

scatter correction of radiation and when using first or

second derivative of the spectra. Therefore SNV and

detrend and first derivative were chosen for all three

calibrations, with the aim of simplifying the discussion.

Fig. 1. Mean near infrared spectrum of butter samples.

Table 1

Range of chemical composition (% w/w) and Sd of butter in the

calibration and validation sets

Set Moisture Solids-non-fat Fat

Range Sd Range Sd Range Sd

Calibration 13.0117.05 0.675 1.143.12 0.321 81.0985.52 0.761

Validation 13.8616.44 0.604 1.292.60 0.282 81.3184.34 0.617

Table 2

Repeatability standard deviation (% w/w) of determination of

moisture, solids-non-fat and fat by reference and NIR methods

Method Moisture Solids-non-fat Fat

Reference 0.052 0.060 0.085

NIR 0.028 0.012 0.023

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Table 3 shows the number of PLS terms, SEC, SECV

and R2 values for moisture, solids-non-fat and fat for

the calibration set. The number of PLS terms is not too

high, less than 1 per 10 samples of the calibration set.

Therefore, no overfitting should be expected, the SEC

values are only about 20% lower than those of the

SECV for moisture and solids-non-fat and 30% for fatR2 values were between 0.90 and 0.94. These figures can

be considered acceptable, bearing in mind that butter is

a product with a very narrow range of composition

variation, owing to the high level of standardisation in

butter factories.

The best values for the SEV and R2 of the validation

set (Table 4) were those obtained for solids-non-fat,

perhaps because of its higher variation range due to the

inclusion of samples of salted butter, which helps to

broaden the range of this parameter. On the other hand,

the worst SEV and R2 values were those of fat, in our

opinion, due to the fact that in the fat determination by

the reference method two errors are accumulated, as a

consequence of subtracting the percentages of water and

solids-non-fat from 100. The bias value is low for

moisture and zero for solids-non-fat and fat.

To compare the results obtained by NIR spectroscopyfor all three components of the validation set with those

obtained by the reference methods, linear regression and

paired t-test were applied (IDF, Standard 128, 1985):

(i) When calculating the slope and intercept of NIR

values versus reference values, no statistical differ-

ences (p 0:05) were found from the theoreticalvalues 1.00 and 0.00, respectively.

(ii) The calculated t values were lower than the

theoretical t values (p 0:05).

Therefore the null hypothesis was retained: the twomethods did not give significantly different results.

Graphic comparisons between reference values and

NIR predicted values of the global validation set are

shown in Figs. 24.

The MSPE is the sum of three types of errors

(Dhanoa, Lister, France, & Barnes, 1999): errors in

central tendency, errors due to regression and errors

due to uncontrolled disturbance or unexplained errors

(Table 5). Errors in central tendency are also known

as mean bias; this error is very small in percentage

terms for all three components analysed; the highest

value was for moisture, where the bias value was also the

highest.

The errors linked to regression will be equal to zero

when the slope of regression is unity. This error also

accounts for a small proportion of the MSPE and in the

Table 3

Statistical data for the calibration set (% w/w)

Component N PLS terms SEC R2 SECV

Moisture 96 6 0.192 0.90 0.242

Solids-non-fat 99 8 0.086 0.93 0.103

Fat 93 8 0.168 0.94 0.240

Table 4

Statistical data for the validation set (% w/w)

Component N Bias SEV R2

Moisture 24 0.04 0.255 0.83

Solids-non-fat 24 0.00 0.071 0.94

Fat 24 0.00 0.377 0.72

Fig. 2. Validation set: moisture content. Reference method versus NIR.

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case of fat, where the slope is not far from unity, for

only 0.40%. Therefore it can be stated that a strong

calibration model was obtained.

The unexplained error accounts for a high percentage

of the MSPE, more than 87% for all three components

and for fat even nearly 100%.

Fig. 3. Validation set: solids-non-fat content. Reference method versus NIR.

Fig. 4. Validation set: fat content. Reference method versus NIR.

Table 5

Mean square prediction error and its components (percentage in parenthesis) calculated in accordance with Dhanoa et al. (1999)

Component MSPE Errors in central tendency Errors due to regression Unexplained error

Moisture 0.0680 1.610-4 0.0070 0.0600

(2.35%) (10.31%) (87.34%)

Solids-non-fat 0.0053 6.2510-6 0.0007 0.0046

(0.12%) (12.71%) (87.17%)

Fat 0.1300 2.110-5 0.0005 0.1300

(0.02%) (0.40%) (99.58%)

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4. Conclusions

NIR spectroscopy is an adequate technique for the

analysis of moisture, solids-non-fat and fat in butter

without any previous sample treatment. Owing to the

simplicity of the instrumental method, better values of

repeatability were achieved by NIR spectroscopy thanby the reference methods. The best calibrations were

performed through MPLS regression. After MSPE

analysis it can be concluded that a strong calibration

model was obtained and that the main source of error

was an unexplained error, accounting for more than

87% for all three components.

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