Journal of Scientific & Industrial Research Vol. 58, January 1 999, pp 1 9-24

Determination of Sugars and Organic Acid Concentration in Apple Juices Using Infrared Spectroscopy

Jagdish Tewari, Mona Joshi, Alka Gupta, Ranjana Mehrotra* and Subhas Chandra National Physical Laboratory, Dr K S Krishnan Road, New Delhi I I O 0 1 2

Received: 22 July 1 998; accepted: 1 7 September 1 998

Potential of Fourier Transform IR spectroscopy as an analytical method for determination of sugars and organic acid concentration in apple juice is investigated. Calibration is performed on synthetic samples in mid-IR region using Attenuated Total Reflectance (A TR) mode. Partial Least Square (PLS) and Principle Component Regression (PCR) methods are used to obtain these calibrations for mixtures of sucrose, glucose, fructose, and citric acid in concentration ranges, typically encountered in real apple juices. A comparison is given between the actual values and the predicted values. The calibrations are used to determine the concentration of these components in real samples. The present study assesses the feasibility of the technique for on-line application in fruit processing industries.

Introduction The most important criterion for the final product qual­

ity of fruits and their by-products in fruit processing indus­try is their sugar content. The laboratory methods for the quantitative determination of sugars and organic acids in fruits have been developed and refined over the years. The traditional gravimetric and wet chemical methods are de­structive, expensive, and time-consuming. These methods have proven to be inefficient for a process control in any industry.

. The use of sophisticated techniques like High Perform­ance Liquid Chromatography (HPLC), Magnetic Reso­nance Imaging (MRI) and high- resolution Nuclear Magnetic Resonance (NMR) for monitoring sugars and organic acids for quality control has been reported in l iterature 1-4. These techniques, however, require trained personnel and are uneconomical to perform.

Near IR spectroscopy has been well established as a rapid and nondestructive analytical technique in many agro food industries5.6. Although NIR technique is fast and suitable for process control applications, a major disadvan­tage lies in the intrinsically broad, overlapping bands en­countered in NIR spectra of sugar solution, which limits the accuracy. IR spectroscopy in mid IR region has shown great advantages in predicting sugar contents in both agri-culture and food products7- 10. _

The paper illustrates the potential of Fourier Transform IR Spectroscopy, in mid infrared region, for determination of concentrations of sugars and organic acids in apple juices and also assesses the feasibility of technique for on-line applications in industries. Both, Principle Compo­nent Regression (PCR) and Partial Least Square (PLS) methods have been used to obtain calibration for sucrose, * Author for correspondence

glucose, fructose, and citric acid, based on synthetic sam­ples. The calibration has been applied to predict the con­centrations of different sugars and citric acid in real samples.

Materials and Methods All the FTIR measurements were carried out on a Bio­

Rad 1 75-C (Century series) spectrophotometer equipped with a deuterated triglycine sulphate (DTGS) detector, operating at 4cm-1 resolution and 0.32 cm/s mirror veloc­ity. Two hundred and fifty six interferograms were taken and Fourier Transformed. The instrument was purged with nitrogen gas (grade 1 ) prior to acquisition of spectra in order to minimize spectral contribution due to atmospheric carbon dioxide and water vapors. ATR was adopted as the sampling method for which a horizontal ATR contact sampler equipped with a Zinc Selenide (ZnSe) crystal was used. The sampler affords six reflections to the sample due to 45° angle between beam and the crystal surface.

Single beam spectra were obtained for all the samples and were rotioed against a background spectrum of water to present the spectra in absorbence units. After each sam­ple the A TR cell was thoroughly washed with water.

For calibration, twenty-four standard mixtures of su­crose, glucose, fructose, and citric acid were prepared. Precaution was taken so that the synthetic mixtures repre­sented the real juices and entire concentration range was covered (Table 1 and 2). The samples used for calibration were also used as test samples in order to check the validity of the analysis . A comparison is presented between the results obtained using PCR and PLS methods, employed for quantitative analysis.

20 J SCI IND RES VOL 58 JANURY 1999

Five different commercially available apple juices were multiple spectral intensity can greatly improve the preci-procured for analysis. These samples were used undiluted sion and predictive ability. Twenty-four standard samples with no further preparation. were used for calibrations. In order to model the system

Results and Discussion without over-fitting the concentration data, a cross valida-tion method, leaving out one sample at a time was used.

Figure 1 shows the FT-IR spectra of sucrose, glucose, Given the set of 24 calibration spectra, the peR and PLS fructose, and citric acid, the prime components of apple calibrations on 23 spectra were performed, and using this juice. The bands for different sugars, as well as for citric calibration, the concentrations of the compounds in the r acid are well defined, narrow and distinct. These vibra-tional mode bands for sucrose, glucose, fructose and citric sample left out during calibration were predicted.

acid were identified using pure solutions of each individual The values of the root mean square deviation (RMSD),

component. After careful examination, the region between which is an indication of the average error in the analysis, 900 cm-1 to 1 200 cm-1 was chosen for calibration.Figure 2 for each component i s :

N depicts the family of spectra of synthetic mixtures used for RMSD = [ l /N r (Ai - Bi)2]O.5, calibrations. Partial least square method and principal com-ponent regression method were employed to develop a

i=1

and the square of the correlation coefficient (R2), which multivariate model. Multivariate calibrations are useful in spectral analysis because the simultaneous inclusion of is an indication of the quality of fit, is :

Table I - Predicted concentrations of different components of synthetic mixture with respect to actual values using PCR method

SI No. Sucrose, Glucose, Fructose, Citric acid, �er cent �er cent per cent �er cent

Actual Predicted Actual Predicted Actual Predicted Actual Predicted

1.50 1.54 1.00 1.02 0.50 0.54 0.30 0.30 2 1.60 1.59 1.05 1.04 0.60 0.59 0.31 0.30 3 1.80 1 .70 1.15 1.13 0.80 0.70 0.32 0.32 4 1.90 1.87 1.20 1.22 0.90 0.87 0.33 0.34 5 1.95 1.94 1.25 1.25 0.95 0.94 0.34 0.34 6 2.00 2.01 1.30 1.28 1.00 1.01 0.35 0.35 7 2.10 2.12 1.35 1.34 1.10 1.12 0.36 0.36 8 2.20 2.20 1.40 1.40 1 .20 1.20 0.37 0.37 9 2.30 2.30 1.45 1.46 1.30 1.30 0.37 0.37 10 2.40 2.37 1.50 1.51 1.40 1.37 0.38 0.39 11 2.45 2.45 1.55 1.56 1.45 1.45 0.39 0.39 .>: 12 2.50 2.51 1.60 1.58 1.50 1.51 0.40 0.40 13 2.60 2.61 1.65 1.65 1.60 1.61 0.41 0.41 14 2.70 2.71 1.70 1.70 1.70 1.71 0.41 0.41 15 2.80 2.79 1.75 1.76 1.80 1.79 0.42 0.42 16 2.90 2.89 1.80 1.81 1.90 1.88 0.43 0.43 17 2.95 2.96 1.85 1.87 1.95 1.96 0.44 0.44 18 3.00 3.01 1.90 1.89 2.00 2.01 0.44 0.44 19 3.10 3.13 1.95 1.96 2.10 2.13 0.45 0.45 20 3.20 3.19 2.00 2.00 2.20 2.19 0.46 0.46

21 3.30 3.31 2.10 2.08 2.30 2.31 0.47 0.47 22 3.40 3.43 2.15 2.14 2.40 2.43 0.48 0.48

23 3.50 3.47 2.20 2.19 2.50 2.47 0.49 0.49 24 3.60 3.59 2.25 2.26 2.60 2.59 0.55 0.50

1 C VV I\Kl er at.: I\YYLC J UILC;:) :t l

Table 2 - Predicted concentrations of different components of synthetic mixture with respect to actual values using PLS method

SI No. Sucrose, Glucose, Fructose, Citric acid, per cent per cent per cent per cent

Actual Predicted Actual Predicted Actual Predicted Actual Predicted

1 .50. 1 .55 1 .00 1 .0.2 0..50. 0..55 0..30. 0..30.

2 1 .60. 1 .59 1 .0.5 1 .0.3 0..60. 0..59 0..3 1 0..3 1 l

3 1 .80. 1 .70. 1 . 1 5 1 . 1 2 0..80. 0..70. 0..32 0..3 1

4 1 .90. 1 .87 1 .20. 1 .2 1 0..90. 0..87 0..33 0..33

5 1 .95 1 .93 1 .25 1 .24 0..95 0..94 0..34 0..34

6 2.0.0. 2.01 1 .30 1 .28 1 .00 1 .0. 1 0.35 0.35

7 2. 1 0 2. 1 1 1 .35 1 .34 1 . 1 0. 1 . 1 1 0..36 0..36

8 2.20. 2. 1 9 1 .40 1 .39 1 .20. 1 . 1 9 0..37 0..37

9 2.30 2.32 1 .45 1 .45 1 .30. 1 .32 0..37 0..37

1 0. 2.40. 2.38 1 .50 1 .5 1 1 .40. 1 .38 0..38 0..39

1 1 2.45 2.46 1 .55 1 .56 1 .45 1 .46 0..39 0..39

1 2 2.50 2.50. 1 .60. 1 .59 1 .50 1 .50 0..40. 0..40.

1 3 2.60. 2.6 1 1 .65 1 .65 1 .60. 1 .6 1 0..4 1 0..4 1

1 4 2.70. 2.70. 1 .70. 1 .70. 1 .70. 1 .70. 0..4 1 0..4 1

1 5 2.80. 2.78 1 .75 1 .76 1 .80. 1 .78 0..42 0.42

1 6 2.90. 2.87 1 .80. 1 .8 1 1 .90. 1 .87 0..43 0..43

1 7 2.95 2.96 1 .85 1 .87 1 .95 1 .96 0..44 0..44

1 8 3.00 3.0.1 1 .90 1 .89 2.00. 2.0.1 0..44 0..44

1 9 3 . 1 0 3 . 1 2 1 .95 1 .96 2. 1 0 2. 1 2 0..45 0..45 '1 20. 3.20. 3 . 1 9 2.00 2.00 2.20. 2. 1 9 0..46 0.46

2 1 3.30. 3.3 1 2. 1 0. 2.0.8 2.30. 2.32 0..47 0..47

22 3.40. 3.42 2. 1 5 2 . 14 2.40. 2.42 0..48 0..48

23 3.50. 3.48 2.20. 2. 1 8 2.50. 2.48 0..49 0..49

24 3.60. 3.59 2.25 2.26 2.60. 2.59 0..50. 0..50.

N N given in Table I and Table 2, respectively. The predicted A R2 = I, (Ai - C)2/I, (Bi - C)2, values agree closely with the actual values. i=1 i=1 Figure 3 (a-d) shows the plots of actual concentrations

where Ai is the actual concentration of the analyte in the of sucrose, glucose, fructose, and citric acid in synthetic sample i, Bi represents the estimated concentration of mixture vs the concentrations determined using the PCR the analyte in the sample i, C is the mean of the actual method. The linearity of the plots obtained once again concentrations in the prediction set and N is the total highlights the validity of technique used. number of the samples used in the prediction set. In In order to test the feasibility of quantitative analysis Table 3, RMSD and R2 values have been summarized. five real samples of apple juices (AP I to AP5) were taken The values of RMSD and R2 are observed to be some- and analyzed. A typical ATR spectrum in the range 900 what simi lar for peR and PLS method in all instances. cm-I to 1 250 cm-I of real apple juice is shown in Figure 4. The values obtained for R2 is satisfactory for all the The spectrum very well matches with those of synthetic cases. No significant advantages were found with the mixtures used for calibration. The concentrations of differ-application of two different methods used for calibra- ent components as determined by PCR and PLS methods tion. are shown in Table 4. The values indicate the quality of

The predicted concentrations, using PeR and PLS of fruit taken as far as concentration of various sugars and synthetic samples with reference to the actual values are citric acid is concerned.

22

0.85

0. 8

0.75

III 0.7 0 c. 0 0.65 .D '-0 III

.D 0.6 <t

0.55

0. 5

! 0.45

1300

J SCI IND RES VOL 58 JANURY 1 999

1250 1200 1 150 1 100 1050

Wavenumber (cm- I )

... ... , .

..... / ..

1000 950

Figure I - Ff-IR spectra of different components of apple juice (C-Citric acid; F-Fructose; G-Glucose; and S-Sucrose)

0.9

0.8

III 0 0. 7 c. 0

.D '-0

- 1/1 0.6 .D <t

0. 5

0 . 4

1250 __ 1 200 1 15 0 1 1 00 1050 1000 9 5 0

Waven u m b er ( e m-I )

Figure 2 - Family of Ff-IR spectra of synthetic mixture with different concentrations of components

)

y

4 .0

II) 3. 5 U) 0 . ... 3 .0 u :;, II)

"0 2.5 Q) ..-. U "0 2 .0 II) ... a.

1 .5

3.0 I\) U) 2 .5 0 -. '« � ... 2 .0 ....

"0 I\) t . 5 -.�

"0 I\) 1 . 0 ...

Q.. 0.5

TEW ARI et al.: APPLE JUICES

2.4 II) 2.2 �2 .0 u � L8 :,; 1 ,6 � 1 .4 � 1 . 2 ... a. 1 .0

23

0.8 � __ �-L __ �� __ �� __ -L __ � 1 .5

c

0.5

2 .0 2 .5 3.0 Actua l : sucrose

3.5 4.0 0.8 1 .0 1 .2 1.4 1 .6 1.8 2.0 2.2 2.4

1 .0 1.5

. :EO 5 · . 0 •

2.0 2.5 3.0

o o

'':; . _ . 'u -gOA -.�

A ctual : f r u c t ose

Actual : g lucose

0.3 0 .35 0.4 0.45 0.5 Actual : ci t ric acid

Figure 3 - Plots of actual concentration of component vs the predicted concentrations using PCR method

1.2 r-��--'-------=------:----:-----___ __

J . I

t

. 0.9-II) o c .x 0.8 ... o � � 0.7

0.6

0.4

Wavenumber (cm- I )

Figure 4 - Typical Ff-IR spectra of real apple juice

950 900

24 1 SCI IND RES VOL 58 lANDRY 1999

Table 3 - Statistical parameters of PLS and PCR methods than the synthetic mixtures so that the contribution of all

PCR PLS the components in apple juices can be incorporated in

Components RMSD R2 RMSD R2 calibration. Once the technique is automated, it will be

many-fold faster than other methods. The important char-Sucrose .0269 .998 .027 1 .998 acteristic of the present study is that the components of Glucose .01 25 .999 .01 2 1 .999 apple juices can be determined without any physical or

Fructose .0269 .998 .0271 .998 chemical separation. The technique holds tremendous po- ) Citric acid .0033 .997 .0029 .997

tential for fruit processing industry. The potential of this type of calibration for on-line analysis is being explored using a fibre optic probe. The preliminary tests indicate quite promising results. -

Table 4 - Predicted results for different variety of apple juices

Sample Method Sucrose, Glucose, Fructose,peCitric References

name per cent per cent r cent acid, 1 Me Feeters R F, Thompson R L & Fleming H P, J Food Sci,

per cent 58(4) ( 1993) pp 832-4.

API PCR 2.23 19 1 .3328 1 .23 19 0.3985 2 Ni Q X & Eads T M, J Agric Food Chern, 41(7) ( 1993) pp 1 035-40. 'r--

PLS 2.4 193 1 .5362 1 .4 193 0.3868

AP2 PCR 2.2824 2. 1364 1 .2824 0.4033 3 Cho S I, Stroshine R L, Baianu I C & Kurtz G W, Trans ASAE,

PLS 2.4667 1 .5650 1 .4667 0.39 13 36(4) ( 1 993) pp 1 2 1 7-2 1 .

AP3 PCR 1 .8993 1 . 1 806 0.8993 0.3642 4 Ni Q X & Eads T M, J Agric Food Chern, 41(7) ( 1993) pp

PLS 2. 1 948 1 .4000 1 . 1 948 0.365 1 1079-83.

AP4 PCR 2.6329 1 .5579 1 .6329 0.4449 5 Delwieh S R & Norris K H, Cereal Chern, 70(1 ) ( 1993) pp 29-35.

PLS 2.8900 1 .8225 1 .8900 0.4320

AP5 PCR 2.33 16 1 .3808 1 .33 1 6 0.4290 6 Isaksson T & Kowalski B, Appl Spectrosc, 47 ( 1993) pp 702-9.

PLS 2.4429 1 .55 1 1 1 .4429 0.3889 7 De Lene M, Boulou J C, Dupuy N, Meurens M, Huvenne J P Y

& Legrand P, Appl Spectrosc, 47(8) ( 1993) pp 1 1 87-9 1 .

Conclusions 8 Kemsley E K, Wilson R H, Poulter G & Day I L, Appl IR technique appears to be a suitable method for accu- Spectrosc, 47( 10) ( 1 993) pp 1 65 1 -54.

rate and fast determination of sugar content in apple juices. 9 Kellner R, Lendl B , Wells 1 & Worsefold P J, Appf Spectrosc, The results are highly promising and the approach can be

developed into a completely automated on line technique 51(2) ( 1997) pp 227-35.

where no sample preparation is required. However, the 1 0 Dupuy N , Huvenne J P & Legrand P, lnd Alirnent Agric,

calibration is needed to be performed on real samples rather 110(1 -2) ( 1993) pp 5-10.