Application eBook
Food & Beverage ScienceTable of contents
Oils and fats
Classiﬁcation of olive oil (Fluorescence) …………………………………………………………… 3
Quantitative analysis of free fatty acids
in vegetable oil (UHPLC-ADAM derivatization) ………………………………………………… 6

Proteins and amino acids
Component analysis of dairy product (FTIR) …………………………………………………… 8
Simultaneous quantitative analysis
of multi-component in food product ………………………………………………………………… 10
Quantitative analysis of amino acids
in functional beverage (HPLC-OPA derivatization) …………………………………………… 13

Carbohydrates
Molecular weight distribution analysis
of condroitin sulfate sodium salt (HPLC-SEC column) ……………………………………… 16
Quantitative analysis of sugars (HPLC) ……………………………………………………………… 19

Fermented foods
Rapid separation of organic acids in beer (HPLC-ODS column)……………………… 22
Rapid separation of nonvolatile amines in wine
(RHPLC-OPA derivatization) ……………………………………………………………………………… 24

Additives
Identiﬁcation test of food additives (FTIR) ………………………………………………………… 26
Analysis of preservatives and sweeteners (HPLC) …………………………………………… 28
Rapid separation of food additives in functional beverage (UHPLC) ……………… 30

Hazards (impurity/natural poisons)Combined analysis of food package ﬁlms (micro IR/micro Raman) ………………… 33
Combined analysis of impurities in food (micro IR/micro Raman) …………………… 35
Time estimation of pest contamination (Circular dichroism) …………………………… 38
2

FOOD SCIENCE APPLICATIONS - OILS AND FATS -Classification of olive oil(Fluorescence)
In order to detect the food frauds and to get the information about species, quality and production area of foods, it ismandatory to analyze the components in food. Since the several components are included in food, the proper informationneeds to be extracted, and the various methods (chromatography, mass spectrometer, etc) are used for separating/analyzing
the components. However, these methods has the disadvantages that analytical instruments are expensive, and much timeand labor is required. In addition, sample pretreatments and skilled analytical techniques are also required.
Spectroscopy analysis, which can evaluate rapidly and easily, is expected to be effective tool, and the analytical method by
Excitation-Emission Matrix (EEM) attracts attention recently. Focusing on the components with fluorophore, it is possible toperform the component estimation and quantitative analysis by extracting each EEM from the mixed EEM of food product.
Its extraction can be done by PARAFAC (Parallel Factor Analysis), which is one of multivariate analysis.
As a measurement example, this article shows the fluorescence measurement result of olive oils (6 kinds of extra virgin olive
oil, 2 kinds of pure olive oil), and also shows the classification result by PARAFAC.

Identification of components in olive oil
EEM after processing (spectral correction and Raman rejection of solvent) are shown in Figure 1.The peak locations inFigure 1 and literature* were used to estimate the components of the samples and are the following: oxidation product(Excitation: 300 to 400 nm, Emission: 320 to 500 nm) and chlorophyll (Excitation: 300 to 700 nm, Emission: 650 to 800
nm).

*Kongbonga YGM, Ghalila H, Onana MB, Majdi Y, Lakhdar ZB, Mezlini H and Ghalila SS, Food and Nutrition Sciences, 2, 692-699, (2011).

3

600

500

400

300
320 400

500

600

700

600

500

400

300
320 400

800

500

600

700

600

500

400

300
320 400

800

500

600

700

Emission wavelength [nm]Emission wavelength [nm]a) EXV olive oil Ab) EXV olive oil Bc) EXV olive oil C

600

500

400

500

600

700

800

600

500

400

300
320 400

500

600

700

800

600

500

400

300
320 400

500

600

700

d) EXV olive oil De) EXV olive oil Ef) EXV olive oil F

700

700
Excitation wavelength [nm]Emission wavelength [nm]Excitation wavelength [nm]Emission wavelength [nm]

600

500

400

500

600

700

800

0

10000

Emission wavelength [nm]300
320 400

800

700
Excitation wavelength [nm]Excitation wavelength [nm]

700

300
320 400

10000

Emi ssion wavelength [nm]700
Excitation wavelength [nm]700
Excitation wavelength [nm]700
Excitation wavelength [nm]Excitation wavelength [nm]

700

800

0

10000

600

500

400

300
320 400

500

600

700

Emission wavelength [nm]Emission wavelength [nm]g) Pure olive oil Gh) Pure olive oil H

800

0Figure 1. EEM of olive oil samples

PARAFAC was performed on the EEM and the number of component spectra was set to 2. Figure 2 shows the component
EEM calculated by PARAFAC. The first component EEM is chlorophyll and the second is an oxidation product.

4

700

650

650
Excitation wavelength [nm]Excitation wavelength [nm]

700

600
550
500
450
400
350

600
550
500
450
400
350

300

400

500
600
700
Emission wavelength [nm]

300

800

400

500
600
700
Emission wavelength [nm]800

Figure 2. First (left) and second (right) component EEM calculated by PARAFAC.

Classification of olive oil
Figure 3 shows the score plot for the first component (chlorophyll) and the second component (an oxidation product).
Regarding the distribution in the extra virgin olive oil, the score for the first component is large while the second is small.
However, the distribution in the pure olive oil shows the score of first component is small while the second is large. This
trend is related to the production process of the olive oil. Extra virgin olive oil is produced by squeezing and filtering the
fruit of the olive without any chemical treatment process, and the acidity of the olive oil is less than 0.8%. On the other
hand, pure olive oil is a blend of virgin olive oil and refined olive oil, so the acidity of pure olive oil is less than 1%. In
addition, the chlorophyll content of pure olive oil is small because it is reduced during the production process for refining
the olive oil.
PARAFAC analysis of EEM can perform the discrimination of food product as well as the identification of component
in food product by verifying the EEM of each component. This technique may also be applied for the food ingredientidentification, the quality control monitoring and the food fraud detection.

x 104
6

EXV olive oil A
EXV olive oil B
EXV olive oil C
EXV olive oil D
EXV olive oil E
EXV olive oil F
Pure olive oil G
Pure olive oil H

Second component

5

Pure olive oil

4

3

2

EXV olive oil

1

0

0

0.5

1

1.5

First component

2

2.5

3

x 10

5

Figure 3. Score plot of the first and second components analyzed by PARAFAC

5FOOD SCIENCE APPLICATIONS - OILS AND FATS -Quantitative analysis of free fatty acids invegetable oil (UHPLC-ADAM derivatization)
HPLC is widely used for quantitative analysis of fatty acids in oil, and their components are generally detected by UV
detector, refractive index detector and evaporative light scattering detector (ELSD). In the case that the amount of target
component is very small, the derivatization method is preferred because it offers much higher sensitivity. Among the various
derivatization methods, 9- Anthryldiazomethane (ADAM) is most commonly used because it reacts with fatty acids easily atroom temperature and enables to highly sensitive and selective analysis.
This article shows the analysis result of free fatty acids in vegetable oil using ADAM derivatization by Ultra High-performance
Liquid Chromatography (UHPLC).

Pre-column derivatization with ADAM reagent
Figure 1 shows the procedure of ADAM derivatization, and Figure 2 shows the reaction mechanism of ADAM reagent withfatty acid. In this time, the fluorophore was attached to fatty acids by pre-column derivatization, which is the method thatthe sample is derivatized before injecting it to the column.
Sample (in ethyl acetate) 200 µL

CHN2
Add 0.1 %(w/v) of ADAM reagent (in etyl acetate) 200 µL

R-COOH+Fatty acid
Keep more than 60 min. at room temperature

Filtrate by 0.2 µm membrane

ADAM

CH2OCOR+

N2

Inject 1 µL to UHPLCADAM-derivatized fatty acid

Figure 1. Procedure of ADAM derivatization

6

Figure 2. Reaction mechanism of ADAM reagent with fatty acid

Analysis of standard fatty acids
Figure 3 shows the chromatograph of the standard fatty acids derivatized with ADAM. 11 different fatty acids wereseparated within 7 minutes.

<Conditions>
Column:

4000001

Eluent A:
Eluent B:
Gradient:
Flow rate:
Column temp. :Detection:

Intensity [µV]

3000002200000X-PressPak V-C18-W(3.0 mmI.D. × 50 mmL, 2.0 µm)
Water/Methanol (5/95)
Acetonitrile/THF (90/10)
High pressure gradient elution
1.0 mL/min.
40 °C
Fluorescence detector(excitation: 365 nm, emission: 412 nm)

3

8

4

100000

9

7

5

10

6

11

0
0

1.0

2.0

3.0
4.0
5.0
Retention time [min.]

6.0

7.0

8.0

Figure 3. Chromatogram of standard fatty acids derivatized with ADAM
1: Caprylic acid (C8), 2: Capric acid (C10), 3: Lauric acid (C12), 4: Linolenic acid (C18:3), 5: Myristic acid(C14), 6: Linoleic acid (C18:2),
7: Palmitic acid (C16), 8: Oleic acid (C18:1), 9: Stearic acid (C18),10: Arachidic acid (C20), 11: Behenic acid (C22)

Analysis of fatty acids in vegetable oils
Figure 4 shows the chromatograms of fatty acids in rice oil and coconut oil derivatized with ADAM, respectively. Table 1
shows quantitative values of fatty acids in rice oil and coconut oil.
300000500000400000200000Intensity [µV]

Intensity [µV]8

100000
6

0

1.0

2.0

200000
1

7
9

4 5

0

3

5

2

7

10

6

0
3.0
4.0
5.0
Retention time [min.]

6.0

7.0

8.0

8

0

1.0

2.0

9

3.0
4.0
5.0
Retention time [min.]

6.0

7.0

Figure 4. Chromatograms of ADAM derivatives in rice oil (left) and coconut oil (right)
Peak numbers and chromatographic conditions are the same as in Figure 3.
Preparation: 1.0 g of oil was dissolved in ethyl acetate, and then its volume was made up to 10 mL. After
that, procedure of ADAM derivatization was followed as in Figure 1.

Table 1. Quantitative values (mg/g) of rice oil and coconut oil
Fatty oil

C8

C10

C12

C14

C16

C18

C18:1

C18:2

C18:3

C20

C22

rice oil

-

-

-

0.010

0.23

0.044

0.41

0.38

0.007

0.020

-

coconut oil

0.017

0.012

0.85

0.45

0.26

0.089

0.20

0.055

-

-

-

7

8.0

FOOD SCIENCE APPLICATIONS - PROTEINS AND AMINO ACIDS -Component analysis of dairy product(FTIR)
It is important to know the components (or ingredients) in foods to solve the problems on the manufacturing process andquality control, and IR spectroscopy is effective tool to get such information. However, since many ingredients are included
in food, IR spectrum which these information is added in is detected, and it is hard to extract the information which the userneeds. In order to get the proper data from measurement data, chemometrics is used frequently. Examples of chemometrics
technique are the principal component analysis (PCA) and the partial least squares (PLS) regression.
PCA is the technique that can perform the dimensionality reduction of the data during minimizing the information loss ofhigh-dimensional data (such as spectra). Its technique enables to find the characteristics (latent variables) from multi-data(spectra), which can perform the identification and classification of data.
Although PLS regression can also perform the dimentionality reduction like PCA, it is the regression analysis utilizing thelatent variables, which are found so that the relation (covariance) between the latent variable and the response variable
is maximized. Its technique can create the robuster model than the general multiple regression analysis, and it is strong
against the noise.
This article shows the component analysis result of dairy product by PCA, and also shows the quantitative analysis result ofprotein in food product by PLS. Measurement data were acquired by IR spectroscopy for these analyses.

Component analysis of milk by PCA
IR spectra were acquired by using ATR. Figure 1 shows the IR difference spectra between milk and water. Milk consists
mainly of 4 components (water, protein, sugar and fat), and approx. 90 % of milk is water. Subtracting water spectrum frommilk spectrum could observe the specific functional group bands of each main component.

8

SugarProtein8
1

Principal component 2 (Sugar)

6

Abs.

Fat

2

3

4
2
0

7

-2
-4

6 4

-6

5

-8
-15
1820

-10

800

Wavenumber [cm-1]

Figure 1. IR difference spectra between milk and water(red: milk, blue: coffee with milk)

-5

0

5

Principal component 1 (Protein)

10

Figure 2. Principal component scores
1. Coffee with milk (A), 2. Coffee with milk (B), 3. Coffee with milk (C), 4.
Milk (D), 5. Milk (E), 6. Milk (F), 7. Milk (G)Next, 7 kinds of commercial milk were measured, and classification of them were performed by using PCA (Figure 2).
Figure 2 indicates that the component ratios of milk samples (No. 4 to 7) are almost the same. Regarding the coffee withmilk (No.1 to 3), it indicates that the sugar ratios of them are almost the same, and they are largely different in the othercomponent ratio.

Quantitative anaylysis of protein in food products
The several food samples including the protein (table

Table 1. Food samples and their protein concentration (on label)

1) were measured by IR spectroscopy. Figure 3 shows

Food sample

Protein concentration

Gouda cheese

18.0 %

similar, there are slight difference among them. Next, PLS

Natural cheese

28.0 %

calibration model were created by using the measured

Worcester sauce

0.9 %the near-IR spectra of them. Although these spectra aredata and the protein concentration value which is
described on the label of each food sample (table 1).
Figure 4 shows the correlation between the spectrum

Yogurt4.2 %Soybean milk yogurt

4.0 %Miso (salted bean-paste)

10.0 %and protein concentration. Its correlation coefficient was
0.994, which means that it is good calibration model.

2.4

35

Soybean milk yogurt

2.0

30

1.0

Predicted value

Abs.

25

Gouda cheese

Miso

20
15
10
5

Worcester sauce

0
-0.1
10000

8000
Wavenumber [cm-1]

6000

Figure 3. Near-IR spectra of food samples

4000

0

10

20

True value (label)

30

Figure 4. PLS calibration model of protein

9FOOD SCIENCE APPLICATIONS - PROTEINS AND AMINO ACIDS -Simultaneous quantitative analysis
of multi-component in food productNear-IR spectroscopy is widely used for quantitative analysis of components in food product. Its method has the feature that
it is possible to perform non-destructive measurement without any special sample preparation. Therefore, not dependingon the status of sample (such as liquid, paste, solid or particle), just putting it on the sampling plate or cell in the analytical
instrument can perform the measurement easily. In addition, since the multivariate analysis can provide the quantitative
information of mult-component from one spectrum simultaneously without splitting the spectrum into several spectra,
applying its technique enables to get a lot of information rapidly and easliy.
This article shows the simultaneous quantitative analysis result of each component in commercial food product by a seriesof near-IR measurement.

Simultaneous quantitative analysis of multi-component in the condensed soup
15 kinds of condensed soup, whose concentration of each component is already known, were prepared, and diffuse
transmission measurements of them were performed by using UV/Vis-NIR spectrophotometer with integrating sphere
unit. Figure 1 shows the spectra of each condensed soup. Although these spectra are similar, there are slight difference
among them. Next, based on the measured data, the calibration model of salt was created by PLS (Figure 2). As a result,the calibration model with high correlation coefficient could be obtained. It means that the salt can be quantified by using
the phenomenon that the peak of water is shifted depending on the salt concenctration even though the salt does nothave the absorption in near-IR region.
Calibration models of other components (water and protein) were also created by using the same method. Table 1 shows
the correlation coefficients of each calibration model. It indicates that the calibration models were created with highcorrelation coefficient, and means that this technique can quantify the multi-components simultaneously.

10

13

1.3

1.0

Abs

Predicted value

10

0.5

5

0
1250

1400

1600
Wavelength [nm]

1800

1856

2

2

5

10

13

True value

Figure 1. Spectra of each condensed soup

Figure 2. PLS calibration model of salt

Table 1. Calibration model of each component and their correlation coefficientComponent

Number ofsamples

Range (%)

Correlation
coefficient

Error ofprediction

Water

15

55.7 to 77.4

0.961

2.04

Protein15

1.0 to 4.7

0.955

0.470

Salt

15

3.1 to 12.3

0.992

0.415

Simultaneous quantitative analysis of multi-component in soybean
Soybeans were measured without destroying them by using Fourier-Transform near-IR spectrometer (FT-NIR). Figure 3
shows the near-IR spectra of each soybean. Although these spectra are similar, there are slight difference among them.

1.4

Abs

1.0

0.5

0.3
10000

8000

Wavenumber [cm-1]

6000

4000

Figure 3. Near-IR spectra of each soybean

11Next, PLS calibration models of each component were created by using measured near-IR spectra. Quantitative
information of each component which is measured by wet chemistry method is used for the true value of PLS calibration
model. Figure 4 shows the calibration models which were created simultaneously by using the measured value of othercomponents (water, carbohydrate and fat) as well as protein. These models of each component were created with highcorrelation between the true value and the predicted value, and it means that this technique can quantify the multicomponents simultaneously.
31

14

12

Predicted value

Predicted value

30

10

28

26

8

8

10

True value

12

25
25

14

26

28

42

24

23

40

Predicted value

Predicted value

31

Carbohydrate

Water

22

21

38

36

20

19
19

30

True value

20

21

22

True value

23

24

34
34

Fat

36

38

True value

40

42

ProteinFigure 4. PLS calibration models

FT-NIR system has the following advantage comparing to the other spectroscopy system, and can be used for quantifying
the multi-components in food product simultaneously.
● Excellent wavenumber accuracy● Bright and high throughput● Continuous measurement and monitoring available● Simultaneous measurement in entire region
JASCO can suggest the suitable solution in accordance to the user's demands.

12

FOOD SCIENCE APPLICATIONS - PROTEINS AND AMINO ACIDS -Quantitative analysis of amino acids in functional
beverage (HPLC-OPA derivatization)
Among the various methods for analyzing amino acids, HPLC is widely used as easy and rapid analytical method. Sincealmost amino acid does not have the absorption and fluorescence in the region from UV to visible light, these targets need
to be derivatized by using reagent for detecting with high sensitivity.
As an example of derivatization methods, the derivatization of o-phthalaldehyde (OPA) is well-known, and makes it possible
to attach the fluorophore (chromophore) so that HPLC can analyze them. Its method enables highly sensitive and selective
analysis of each amino acid, and makes it possible to do by general optical detector (UV detector and fluorescence detector).
This article shows the analysis results of OPA-derivatized amino acids by using HPLC with UV detector or fluorescence
detector.

Derivatization with OPA
Figure 1 shows the reaction mechanism of OPA reagent with amino acids. Its reagent has the features that it reacts withamino acids rapidly even at room temperature. Since the reaction rate depends on the amino acid and the derivatives
are begun to decompose after reaction, it operation needs to be done carefully.
O

S
H
H

O

+

RNH₂
Amino acid

+

R’SH

N

R’
R

Thiol
OPA-derivatized amino acidο-phthalaldehyde

Figure 1. Reaction mechanism of OPA reagent with amino acid13

Since OPA-derivatized amino acid has the fluorophore, it is possible to perform high sensitive detection (on the order offmol) by selecting the proper detection wavelength (excitation: 340 to 350 nm, emission: 450 to 460 nm).
In this time, amino acids were analyzed using OPA pre-column derivatization, which is the method that the sample isderivatized before injecting it to the column. The automated pre-column derivatiation function of autosampler was usedbecause it can do the derivatization operation reproducibly. v

Amino acid analysis using fluorescence detection
Setting the excitation wavelength and emission wavelength to 345 nm and 455 nm respectively, the amino acids wereanalyzed. Figure 2 shows the chromatogram of standard amino acids, and Figure 3 shows the chromatogram of functional
beverage. Both could be acquired with good separation.

<Conditions>
Column:150000

10

8
7
5

15
9

10000016

Intensity [µV]

2
3

1

4

Eluent A:
Eluent B:
Gradient:
Flow rate:
Column temp.:
Detection:

14
13

11 12

CrestPak C18S
(4.6 mmI.D. × 150 mmL, 5 µm)
Sodium acetate buffer/methanol/THF (89/10/1)
Methanol/THF (90/10)
High pressure gradient elution
1.0 mL/min.
20 °C
Fluorescence detector(excitation: 345 nm, emission: 455 nm)

17

6
18

50000

0
0

5.0

10.0

15.0
20.0
Retention time [min.]

25.0

30.0

35.0

Figure 2. Chromatogram of standard amino acids
1: Aspartic acid, 2: Glutamic acid, 3: Asparagine, 4: Histidine, 5: Serine, 6: Glutamine, 7: Arginine, 8: Glycine, 9: Threonine, 10: Taurine,
11: Alanine, 12: Tyrosine, 13: Methionine, 14: Valine, 15: Phenylalanine, 16: Isoleucine, 17: Leucine, 18: Lysine

65000

Intensity [µV]

8

5

60000

9

40000

10
11

2

1

20000

14

3

7

4

12

6

13

15

17

18

16

0
0

5.0

10.0

15.0
20.0
Retention time [min.]

25.0

30.0

35.0

Figure 3. Chromatogram of functional beverage
Peak numbers and chromatographic conditions are the same as in Figure 2.
Preparation: Functional beverage was diluted 100 times with 0.01 M HCl, andthen it was filtrated with 0.2 µm membrane. After that, 10 µL was
injected to HPLC.

14

Amino acid analysis using UV absorption detection
OPA-derivatized amino acids can be detected even by UV detector. Setting the absorption wavelength to 338 nm, aminoacids were analyzed. Figure 4 shows the chromatogram of standard amino acids, and Figure 5 shows the chromatogramof functional beverage. Both could be acquired with good separation even by UV detector.
<Conditions>
Column:

15000

18

10000
Intensity [µV]

14
13
3
7

2

1

5000

4 56

89

11

15

CrestPak C18S
(4.6 mmI.D. × 150 mmL, 5 µm)
Sodium acetate buffer/methanol/THF (89/10/1)
Methanol/THF (90/10)
High pressure gradient elution
1.0 mL/min.
20 °C
UV detector(absorption wavelength: 338 nm)

Eluent A:
Eluent B:
Gradient:
Flow rate:
Column temp.:
Detection:

16
17

12

10

0
0

5.0

10.0

15.0
20.0
Retention time [min.]

25.0

30.0

35.0

Figure 4. Chromatogram of standard amino acids
1: Aspartic acid, 2: Glutamic acid, 3: Asparagine, 4: Histidine, 5: Serine, 6: Glutamine, 7: Arginine, 8: Glycine, 9: Threonine, 10: Taurine,
11: Alanine, 12: Tyrosine, 13: Methionine, 14: Valine, 15: Phenylalanine, 16: Isoleucine, 17: Leucine, 18: Lysine

3000

8

7

10 times dilution

Intensity [µV]

2000

11
17

1000

14

16

0

0

5.0

10.0

15.0
20.0
Retention time [min.]

25.0

30.0

35.0

6000
7

100 times dilution

Intensity [µV]

4000

2000

0
0

5.0

10.0

15.0
20.0
Retention time [min.]

25.0

30.0

35.0

Figure 5. Chromatogram of functional beverage
Peak numbers and chromatographic conditions are the same as in Figure 4.
Preparation: Functional beverage was diluted 10 times and 100 times with 0.01 M
HCl respectively. After that, 5 µL was injected to HPLC.

15

FOOD SCIENCE APPLICATIONS - CARBOHYDRATES -Molecular weight distribution analysis ofcondroitin sulfate sodium salt (HPLC-SEC column)
Chondroitin sulfate (MW 20,000 to 50,000), which is one of mucopolysaccharide, has several types (A to E),and are foundin animal connective tissue (cartilage, skin, etc.). Chondroitin sulfate is widely used in the manufacture of health foods
and medicines. In this article, chondroitin sulfate (sodium salt) was analyzed using a SEC (size exclusion chromatography)
column with an exclusion limit of 300,000 molecular weight (pullulan) and detection using refractive index (RI detector). A
pullulan (PL) calibration curve was created from a standard mixture, and the resulting molecular weights calculated from thestandard PL are described below.
Figure 1 shows the chromatogram of standard PL mixture for molecular weight calibration, and Figure 2 shows the one ofchondroitin sulfate sodium salt.

PL-STD1
PL-STD2

15000<Conditions>
Column:5,900
170,000 21,100
9,600
47,100200,000

Eluent A:
Flow rate:
Column temp.:
Detection:

Intensity [µV]

10000

Shodex Asahipak GF-510 HQ(7.5 mmI.D. × 300 mmL, 5 µm)
0.04 M Sodium dihydrogen phosphate anhydrous0.06 M Disodium hydrogen phosphate anhydrous
0.6 mL/min.
30 °C
RI detector

5000

0
0

2.0

4.0

6.0

8.0
10.0
12.0
Retention time [min.]

14.0

16.0

18.0

20.0

Figure 1. Chromatogram of standard PL mixture for molecular weight caligration(Each value on the chromatogram is Mp (peak top molecular weight).)16

15000Chondoroitin sulfate C sodium salt

Intensity [µV]

10000

5000

0
0

2.0

4.0

6.0

8.0
10.0
12.0
Retention time [min.]

14.0

16.0

18.0

20.0

Figure 2. Chromatogram of condroitin sulfate sodium salt
Chromatographic conditions are the same as in Figure 1.

Figure 3. shows the molecular weight calibration curve created by retention volume (elution time) and peak top molecularweight of standard PL.

5.4
5.2
5.0
4.8

LogM

4.6
4.4
4.2
4.0
3.8
3.6
9.0

10.0

11.0
12.0
Retention time [min.]

13.0

14.0

Figure 3. Molecuar weight calibration curve created with standard PL mixture

Figure 4 shows chromatogram of chondroitin sulfate sodium salt and molecular weight calibration curve, and Figure 5
shows the molecular weight distribution caluculation curve. The integral molecular weight distribution curve shows therelationship between molecular weight (logarithmic value) and the percentage of a specific molecular weight in the total ofmolecules. The differential molecular weight distribution curve is used to plot the molecular weight (logarithmic value) on
the x-axis against the weight fraction differentiated from a logarithmic value of molecular weight (dw/d(logM)) on the y-axis.
When normalized, the use of this curve makes it possible to compare a chromatogram and a molecular weight distribution
derived using different columns and measurement conditions.

17

11800

5.4

Molecular weight calibration curve

5.2

10000

5.0
4.8

6000

4.6
4.4

4000

LogM

Intensity [µV]

8000

4.2
2000

4.0
3.8

0
8.0

10.0

12.0
14.0
Retention time [min.]

16.0

18.0

Figure 4. Chromatogram of chondroitin sulfate sodium salt and molecular weight calibration curve of standard PL(The figure in blue represents Mp calculated using standard PL)

1.4

Differential molecular weight distribution curve

100.00Integral molecular weight distribution curve

1.2
80.00

60.00

0.8
0.6

40.00

C. MWD [%]D. MWD

1.0

0.4
20.00

0.2

0.00

0
3.6

3.8

4.0

4.2

4.4

4.6
LogM

4.8

5.0

5.2

5.4

5.6

Figure 5. Molecular weight distribution curves of chondroitin sulfate sodium salt

Table 1 shows the molecular weight caluculation result by standard PL. The molecular weight averages of polymer materials
obtained by size exclusion chromatography (SEC) includes the number-average (Mn), weight average (Mw), z-average (Mz),and viscosity-average (Mv) molecular weights. The distribution of these molecular weight averages bears in general a
relationship such as Mn ≤ Mv ≤ Mw ≤ Mz. In the case where Mn = Mv = Mw = Mz, there is no molecular weight distribution(mono-dispersed).
Table 1. Molecular Weight of Chondroitin Sulfate Sodium Salt

18

Mp

Mn

Mw

Mz

Mv

Mw/Mn

Mz/Mw

46812

36668

57914

85947

57914

1.58

1.48

FOOD SCIENCE APPLICATIONS - CARBOHYDRATES -Quantitative analysis of sugars (HPLC)

HPLC is widely used for quantitative analysis and quality evaluation of sugars in food. Depending on the type of sugar which isseparated, various chromatography such as hydrophilic interaction chromatography (HILIC), size exclustion chromatography(SEC), ion-exchange chromatography and ligand-exchange chromatography are used. Since the sugar does not have theabsorption in the UV region, refractive index detector (RI detector) or evaporative light scattering detector (ELSD) are usedgenerally. Or, the sugars can also be detected by derivatization for attaching fluorophore or chromophore. This article
shows the direct detection example of sugars by RI detector, and also shows the fluorescence detection example by postcolumn derivatization.

Direct detection of sugars by RI detector
RI detector is the versatile detector utilizing the difference between the refractive indices of eluent and sample, and can
detect the sugar even though it does not have UV absorption. In this case, HILIC mode column is generally used.
JASCO RI detector has the following features, and enables to perform stable measurement because it is affected by theexternal environment hardly.
● Baseline stability by cell temperature control● Expandability to semi-micro scale and preparative scale
Figure 1 shows the chromatograms of strawberry jam and orange marmalade, and Table 1 shows the quantitative analysis
result. Monosaccharides (fructose and glucose) and disaccharides (sucrose and maltose) could be separated andquantified very well.

19

<Conditions>
Column:Eluent:Flow rate:
Column temp.:
Detection:

Finepak SIL NH2Acetonitrile/Water (75/25)
1.0 mL/min.
40 °C
RI detector

3

Table 1. Quantitative analysis result

3

1

1
2

4

0

5

StrawberryjamOrangemarmaladeFructose

1.618

1.322

Glucose

2.804

2.609

Sucrose

2.565

2.251

Maltose

0.583

0.873

2

10

15

4

20

0

Retention time [min.]

5

10

15

20

Retention time [min.]Figure 1. Chromatograms of strawberry jam (left) and orange marmalade (right)
1: Fructose, 2: Glucose, 3: Sucrose, 4: Maltose
Preparation: 1.0 g of sample was weighed, and then 5 mL of water was added. After mixing it, it was
filtrated with 0.45 µm membrane. After that, 5 µL was injected to HPLC.

Fluorescence detection by post-column derivatization
In the case that the sample includes the contaminants during the HPLC analysis of sugars, proper qualitative andquantitative analysis cannot be performed because RI detector measures the contaminants as well as the target
components. In addition, even when the amount of target component is very small, it cannot be performed because thesensitivity of its detector is not enough.
Although sugars do not own the chromophore, it can be turned to the derivative with fluorophore by reaction of guanidine
and metaperiodic acid. Applying its properties to HPLC eables to perform highly sensitive and selective detection. This
article shows the fluorescence detection result of sugars by post-column derivatization (the method that the sample
is derivatized after injecting it to the column), whose reproducibility is good. The anion exchange column was usedbecause its column can separate more sugars.
Figure 2 shows the chromatogram of standard sugars, and Figure 3 and 4 show the chromatograms of extract of vegetablestew and the condensed noodle soup, respectively. Each component could be separated with good resolution.

20

2<Conditions>
Column:Eluent A:
Eluent B:
Gradient:
Eluent flow rate:
Column temp. :Reagent:

Finepak GEL SA-121 (6.0 mmI.D. × 100 mmL)
0.25 M borate buffer (pH 8.0)
0.60 M borate buffer (pH 9.0)
Low pressure gradient elution
0.5 mL/min.
80 ºC
100 mM borate + 50 mM guanidine hydrochloride+ 0.5 mM sodium metaperiodate (pH 8.0, adjust with NaOH)/ acetonitrile (80/20)
Reagent flow rate: 0.5 mL/min
Reaction temp. :150 ºC
Detection:
Fluorescence detector(excitation: 310 nm, emission: 415 nm)

3

4

5

6
7

9
8

1

0

20

40

60

Retention time [min.]Figure 2. Chromatogram of standard sugars
1: Sucrose, 2: Maltose, 3: Lactose, 4: Mannose, 5: Fructose, 6: Galactose, 7: Sorbitol,
8: Mannitol, 9: Glucose

5

9

1

2

0

20

60

40
Retention time [min.]Figure 3. Chromatogram of extract of vegetable stew
Peak numbers and chromatographic conditions are the same as in Figure 2.
Preparation: Extract was filtrated with 0.45 µm membrane, an then it was diluted 5
times with water. After that, 20 µL was injected to HPLC.

9

5

1
2

0

6 7

20

40

60

Retention time [min.]Figure 4. Chromatogram of the condensed noodle soup
Peak numbers and chromatographic conditions are the same as in Figure 2.
Preparation: The condensed noodle soup was added to hot water (60 °C), and it was ultrasonicated for 1 minutes.
After it was warmed in hot bath (65 °C) for 5 minutes, it was left for 30 minutes at room temperature.
And then, the supernatant (10 mL) was collected, and it was applied to centrifugal separation (4000rpm, 10 minutes). 6 % perchloric acid (1 mL) and chloroform (1 mL) was added to the obtained
supernatant (1 mL), and it was stirred. After that, the obtained supernatant was filtrated with 0.45 µm
membrane, and 20 µL was injected to HPLC.

21

FOOD SCIENCE APPLICATIONS - FERMENTED FOODS -Rapid separation of orgainic acids in beer
(HPLC-ODS column)
Organic acids in fermented foods are produced as the byproduct during fermentation process of lactic acid bacteria, andare the compounds that produce tastes such as sourness and deliciousness. In addition, they are also expected to havethe oxidation resistant and antibacterial activity, and their functional features attract the attention.
Generally, the ion exclusion column is used for separating the organic acids, and adding the pH indicator (BTB: bromothymolblue) after column enhances the selectivity between target and contamitant compounds (Figure 1). Ion exclusion column
is used for applying to the separation of various organic acids (high/low hydrophobicity). If only organic acids with lowhydrophobicity is evaluated, reversed-phase column is good option of high speed and high sensitivity analysis . This article
shows the separation example of organic acid by using ODS column.
<Conditions>
Column:

Shodex Rspak KC-G + KC-811 × 2(6.0 mmI.D. × 50 mmL)
(8.0 mmI.D. × 300 mmL × 2)
Eluent:2 mM or 3 mM Perchloric acidEluent flow rate:
1.0 mL/min.
Reagent:
0.2 mM BTB + 15 mM Disodium hydrogen phosphate
Reagent flow rate: 1.5 mL/min
Column temp.:
60 ̊C
Detection:
UV detector(Absorption wavelength: 445 nm)

1200000Intensity [µV]2

3

100000
4 5 6

7 8

9 10

11

12

0
0

10

20
Retention time [min.]30

40

Figure 1. Chromatogram of standard organic acids (by ion exclusion column)
1: Citric acid, 2: Malic acid, 3: Succinic acid, 4: Lactic acid, 5: Formic acid, 6: Acetic acid, 7: Pyroglutamic acid, 8: Propionic acid, 9: Isobutyric acid,
10: n-Butyric acid, 11: Isovaleric acid, 12: n-Valeric acid (5.0 mmol/L each)22Analysis of standard organic acids
Figure 2 shows the chromatograms of standard organic acids by ODS column and ion exclusion column. Separation
method by ODS column could separate 10 organic acids in half the measurement time by ion exclusion column (12 min.),and also improved their separation. In addition, it could also enhance the sensitivity (2 to 8 times).
<Ion exclusion column>

Intensity [µV]

20000

1
2

3

10000

4 5
6

7

8 9

10

0
0

10
Retention time [min.]20

26<Conditions (ion exclusion column)>
Column:
Shodex Rspak KC-G + KC-811 × 2(6.0 mmI.D. × 50 mmL)
(8.0 mmI.D. × 300 mmL × 2)
Eluent:2 mM or 3 mM Perchloric acidEluent flow rate:
1.0 mL/min.
Reagent:
0.2 mM BTB + 15 mM Disodium hydrogen phosphate
Reagent flow rate: 1.5 mL/min.
Column temp.:
60 ̊C
Detection:
UV detector(absorption wavelength: 445 nm)<ODS column>
10000

2

3
1

Intensity [µV]

7
8
6

5000

45

Separation time: 1/2

9

10High sensitivity: 2–8 ×

0

0

10
Retention time [min.]20

26<Conditions (ODS column)>
Column:InertSustain AQ-C18(4.0 mmI.D. × 10 mmL, 5 µm)(4.6 mmI.D. × 150 mmL, 5 µm)
Eluent:5 mM Perchloric acidEluent flow rate:
1.0 mL/min.
Reagent:
0.2 mM BTB + 15 mM Disodium hydrogen phosphate
Reagent flow rate: 1.0 mL/min.
Column temp.:
40 ̊C
Detection:
UV detector(absorption wavelength: 445 nm)

Figure 2. Chromatograms of standard organic acids (by ion exclusion column and ODS column)
1: Citric acid, 2: Tartaric acid, 3: Malic acid, 4: Succinic acid, 5: Fumaric acid, 6: Lactic acid, 7: Formic acid, 8: Acetic acid, 9: Pyroglutamic acid, 10:
Propionic acid, 0.5 mM each

Comparison of quantitative values of organic acids in beer
Figure 3 shows the chromatogram of beer, and Table 1 shows the quantitative values of organic acids in beer. Bothmethods could provide the same quantitative values.
<Ion exclusion column>

Intensity [µV]

40000
1
20000
3

4

6

8

Table 1. Quantitative values of organic acids in beer (mM)

9

Ion exclusion column

ODS column

Malic acid

0.19

0.17

0
0

10

20

28

Retention time [min.]<ODS column>

Intensity [µV]

15000
10000
3

5000

8
6

1

9

Lactic acid

0.97

0.98

Acetic acid

0.83

0.95

Citric acid

0.58

0.49

Pyroglutamic acid

0.58

0.69

Succinic acid

0.17

0.16

4

0
0

10

20

28

Retention time [min.]Figure 3. Chromatograms of beer (by ion exclusion column and ODS column)
Peak numbers and chromatographic conditions are the same as in Figure 1.
Preparation: Beer was diluted twice with 1.5 mM perchloric acid, and then it was
filtrated with 0.45 µm membrane.

23

FOOD SCIENCE APPLICATIONS - FERMENTED FOODS -Rapid separation of nonvolatile amines in wine
(RHPLC-OPA derivatization)
Nonvolatile amines are produced during the microbe decay process of proteins and amino acids. Since several nonvolatileamines including histamine causes the food poisoning, their quantitative evaluation should be performed for quality control.
HPLC method using dansyl chloride (DNS-Cl) derivatization is generally used for analyzing the nonvolatile amines. However,its method needs many labors to pretreat the sample, and needs long time to measure the sample (approx. 40 minutes).
Pre-column derivatization by o-phthalaldehyde (OPA) is expected to reduce the complicated pretreatment because it reacts
the sample rapidly and easily and enables to perform the flourescence detection. This article shows the separation example
of nonvolatile amines by using RHPLC (Rapid Separation LC) with OPA derivatization.

Comparison of derivatization methods (DNS-Cl reagent and OPA reagent)
Figure 1 and 2 show the reaction mechanisms of DNS-Cl derivatization and OPA derivatiation, respectively.
N

O
+

SO₂
Cl
Dansyl chloride

RNH₂

H

Amine

H

(Amino acid)

N

-HCl

o-phthalaldehyde

SO₂
NH

R

DNS-Cl derivatized amine
Excitation: 325 nm, Emission: 525 nm

Figure 1. Reaction mechanism of DNS-Cl derivatization

24

O

+

+

RNH₂
Amine

R’SH
Thiol

(Amino acid)

S

R’

N

R

OPA derivatized amine
Excitation: 345 nm, Emission: 455 nm

Figure 2. Reaction mechanism of OPA derivatization

There are many process to perform DNS-Cl derivatization (sample preparation, reaction, extraction, concentration andre-dissolution), and it takes long.time to pretreat it. In the case of OPA derivatization, only setting the autosampler after
preparing the reagent, it runs automatically for reaction process. OPA derivatization is done in one-fourth the process
time of DNS-Cl derivatization.

Analysis of standard nonvolatile amines
Figure 3 shows the chromatograms of standard nonvolatile amines by HPLC method and RHPLC method, respectively.
RHPLC method could separate 6 nonvolatile amines in one-third the measurement time of conventional HPLC method(13 min.), and also improved their separation. In addition, it could also enhance the sensitivity (1 to 35 times).
<DNS-Cl derivatization - HPLC><Conditions (HPLC)>
Column:Inertsil ODS-SP (4.6 mmI.D. × 250 mmL, 5 µm)
Eluent:Water/Acetonitrile (35/65), Isocratic
Flow rate:
1.2 mL/min.
Column temp.:
40 ºC
Detection:
Fluorescence detector(exciation: 325 nm, emission: 525 nm)

Intensity [µV]

150000100000

IS
Put
Cad

50000

Spd
Tym

Him

0
0

10

20
Retention time [min.]

30<OPA derivatization - RHPLC<Conditions (RHPLC)>
Column:Sunshell C18 (2.1 mmI.D. × 50 mmL, 2.6 µm)
Eluent A:
1M Citrate buffer 3.5 mL in Water
Eluent B:
1M Citrate buffer 3.5 mL in water/acetonitrile/ethanol (5/4/1)
Gradient:
High pressure gradient elution
Flow rate:
1.0 mL/min.
Column temp.:
40 ºC
Detection:
Fluorescence detector(excitation: 345 nm, emission: 455 nm)

Him

Intensity [µV]

20000

IS

Separation time: 1/3

Spd
Put
Cad

Tym

10000

High sensitivity: 1–35 ×

0
0

10

20
Retention time [min.]30

Figure 3. Chromatograms of nonvolatile amines (by HPLC and RHPLC)
Put: Putrescine, Cad: Cadaverine, Him: Histamine, IS: 1,8-diaminooctane, Tym: Tyramine, Spd: Spermidine

Comparison of quantitative values of nonvolatile amines in wine
Figure 4 shows the chromatogram of wine, and Table 1 shows the quantitative values of nonvolatile amines in wine. Bothmethods could provide the same quantitative values.
<DNS-Cl derivatization -HPLC>

80000
IS

Cad

300000Him
200000

Cad
Him

100000

<STD>
Tym

IS
Tym
IS

Cad
Him

0
0

5.0

10.0

15.0
20.0
Retention time [min.]

60000

Spd

<100 % wine>
Spd

Intensity [µV]

400000Intensity [µV]

<OPA derivatization - RHPLC>

Put

40000

Tym

Spd

25.0

30.0

Tym Spd Put Cad
Put

20000

<10 % wine>

IS

Him

Him

Tym Spd

0

<STD>
IS

Cad

<10 % wine>
0

1.0

2.0

3.0

4.0
5.0
6.0
Retention time [min.]

7.0

8.0

Figure 4. Chromatograms of wine (by HPLC and RHPLC)
Peak numbers and chromatographic conditions are the same as in Figure 3.
Preparation: Wine was diluted 10 times with water, and then it was filtrated with 0.45 µm membrane.

Table 1. Quantitative values of nonvolatile amines in wine (µM)
HPLC

UHPLC

Him

25.2

24.9

Tym

32.0

28.1

Cad

3.95

5.07

Put

226.4

203.1

Spd

4.13

4.11

25

9.0

FOOD SCIENCE APPLICATIONS - ADDITIVES -Identification test of food additives(FTIR)
Manufacturing or using the food additives, each country has settled the reguration for their identification, purity and quality,
and controls them strictly so that the additives which does not conform to the regulation are not sold/used. Food/additive
manufacturers need to verify whether their products conform to the regulation, and also need to confirm if they conform to
the regulation the import country has settled when exporting them.
Test methods are described clearly in the regulation, and indicates that several additives should be identified by "Infrared
spectrometry". Although comparing the measurement spectrum of sample with the reference spectrum or the measurement
spectrum of the reference standard confirms its identification, the skilled technique is required to analyze the spetrum data.
As the one of solutions, using the comparison algorithm enables to evaluate the identification objectively. The examples ofcomparison algorithms are shown as below.
● Euclidean distanceIt evaluates the summation of the square of absorbance difference at each wavenumber. Its algorithm is used forcomparing the spectra including the broad bands.
● Euclidean distance after differentiation
It evaluates the summation of the square of absorbance difference at each wavenumber after differentiation. Its
algorithm is used for comparing the spectra including the baseline drift, or for comparing the spectra including the
sharp peaks.
● Correlation coefficient
It evaluates the correlation between spectra. Its algorithm is used for comparing the spectra including muchnoise.
This article shows the evaluation examples of p-methylacetophenone (used as flavor) and aspartame (used as sweetener)in accordance with the test methods that Japanese authorities specify (Japan's Specifications and Standards for Food
Additives).
26

Identification test of p-methylacetophenone
The liquid sample was held between two KBr windows, and was measured by FTIR instrument (Liquid Film Method).
Figure 1 shows the measurement result.
The regulation says that the both spectra should exhibit similar intensities of absorption at the same wavenumbers whencomparing with the reference spectrum. Selecting the comparison algorithm depending on the situation, quantifying thesimilarity of database enables to distinguish the slight difference between spectra easily even if they look very similar.
110"Euclidean distance after differentiation" was used as100

the comparison algorithm, whose score is 0 when thesimilarity is high. Since two spectra were compared after
be compared accurately. In order to clarify the criteria,
several samples were measured and the euclidean%Tdifferentiation to offset the slope of baseline, they could
50

distances between sample and reference were calculated
beforehand. As a result, its criteria was set to 60.
The score of measurement result shown in Figure 1 is

0
4000

3000

2000

1000

800

-1

37.29, which could be judged as "PASS" objectively

Wavenumber [cm ]Figure 1. IR spectra of p-methylacetophenone

because its score is lower than the criteria.

(green: sample, blue: reference standard)

Identification test of aspartame
The paste sample was produced by mixing the triturated sample with liquid paraffin. Its paste was held between two KBr
windows, and was measured by FTIR instrument (Paste Method). Figure 2 shows the measurement result.
The regulation says that the spectrum should exhibit absorption bands at specific wavenumbers. Its test can be performed
objectively by setting the wavenumbers and tolerances at software.
100

Table 1. Test result
801226.50%T

1736.58
1663.30

699.071376.9360

Peak wavenumber(tolerance)

Measurement value

Pass/Fail1737.00 cm-1 (5.00 cm-1)

1736.58 cm-1

Pass

1666.00 cm-1 (5.00 cm-1)

1663.30 cm-1

Pass

1379.00 cm-1 (5.00 cm-1)

1376.93 cm-1

Pass

1226.50 cm

-1

Pass

699.07 cm

-1

Pass

-1

-1

-1

-1

1227.00 cm (5.00 cm )699.00 cm (5.00 cm )

40
30
4000

3000

2000

1000

400

Wavenumber [cm-1]

Figure 2. IR spectrum of aspartame

JASCO can provide the FTIR system to evaluate the products objectively, which does not need the skilled technique toanalyze IR spectra, This system has the following functions, which enables to perform the product evaluation easily androutinely.
● Database registration of IR spectrum of reference standards● Quantification of spectrum comparison between IR spectra of reference and sample● 7 kinds of comparison algorithm in accordance with the comparison purpose● Objective evaluation by pass/fail judgement function● CFR Part 11 available
27

FOOD SCIENCE APPLICATIONS - ADDITIVES -Analysis of preservatives and sweeteners(HPLC)
Food additives serve useful functions to enrich the consumer's food life, and are essential ingredients during food processing.
There are various additives: 1) sweeteners/color additives/spices for improving the flavor, appearance and colorness offood, 2)preservatives for preventing the food spoilage and the changes in color, flavor or texture, and for maintaining the
freshness, 3)nutrients for fortification.
However, since almost food additives may have adverse health effects depending on the amount to be used, the authorities
in each country strictly study, regulate and monitor them for safe use. In the case of FDA, when evaluating the safty of a
substance and whether it should be approved, FDA considers: 1) the composition and properties of the substance, 2) theamount that would typically be consumed, 3) immediate and long-term health effects, 4) various safety factors. The evaluation
determines an appropriate level of use that includes a built-in safety margin - a factor that allows for uncertainty about thelevels of consumption that are expected to be harmless. Food manufacturers need to verify whether their products conformto the regulation, and also need to confirm if they conform to the regulation the import country has settled when exportingthem.
Japan also regulates the manufacture, use and sale of the food additives, and publishes the specifications and standards forfood, food additives, etc. (Food Sanitation Act). In addition, the analysis methods for each food additive are also published
for their compliance check, and HPLC method is specified in many case of analysis of food additive. This article show the
HPLC measurement examples of preservatives and sweetener according to the analysis method that Japanese authorities
publish.
*U.S. FOOD & DRUG ADMINISTRATION (2010, April), Overview of Food Ingredients, Additives & Colors, https://www.fda.gov/food/food-ingredients-packaging/overview-food-ingredients-additives-colors

Analysis of p-hydroxybenzoate esters
p-hydroxybenzoate esters are added to foods as preservatives because it has the antibacterial activity against various
bacteria. Figure 1 shows the chromatogram of standard p-hydroxybenzoate esters. 5 components were eluted with goodresolution within 10 minutes.
28

6000

1

Eluent:Flow rate:
Column temp.:
Detection:

2

4000
Intensity [µV]

<Conditions>
Column :

3
2000

4

CrestPak C18S
(4.6 mmI.D. × 150 mmL, 5 µm)
Acetonitrile/5 mmol/L Citrate buffer (pH 4.1) (60/40)
1.0 mL/min.
40 ºC
UV detector(absorption wavelength: 270 nm)

5

0

0

2.0

4.0

6.0
8.0
Retention time [min.]

10.0

12.0

Figure 1. Chromatogram of standard p-hydroxybenzoate esters
1: Methyl p-hydroxybenzoate, 2: Ethyl p-hydroxybenzoate, 3: Propyl p-hydroxybenzoate,
4: Isobutyl p-hydroxybenzoate, 5: Butyl p-hydroxybenzoate

Analysis of bensoic acid, sorbic acid and dehydroacetic acid
Benzoic acid, sorbic acid and dehydroacetic acid are also added to foods as preservatives. Figure 2 shows thechromatogram of standard mixture. 3 components were eluted with good resolution within 9 minutes.
<Conditions>
Column:

12000
1
10000

Eluent:Flow rate:
Column temp.:
Detection:

Intensity [µV]

2

3

5000

CrestPak C18S
(4.6 mmI.D. × 150 mmL, 5 µm)
5 mmol/L Citrate buffer (pH 4.1) / Acetonitrile /Methanol (70/20/10)
1.0 mL/min.
40 ºC
UV detector(absorption wavelength: 230 nm)

0
0

2.0

4.0
6.0
Retention time [min.]

8.0

10.0

Figure 2. Chromatogram of standard mixture (benzoic acid, sorbic acid and dehydroacetic acid)
1: Benzoic acid, 2: Sorbic acid, 3: Dehydroacetic acid

Analysis of saccharin sodium
Saccharin sodium is used as sweetreener. Figure 3 shows the chromatogram of standard saccharin sodium.
1200

Saccharin sodium<Conditions>
Column:1000

Intensity [µV]

Eluent:Flow rate:
Column temp.:
Detection:

CrestPak C18S
(4.6 mmI.D. × 150 mmL, 5 µm)
0.05 mol/L Phosphate buffer (pH 6.9)/Methanol (90/10)
1.0 mL/min
40 ºC
UV detector(absorption wavelength: 230 nm)

500

0
0

2.0

4.0
6.0
Retention time [min.]

8.0

10.0

Figure 3. Chromatogram of saccharin sodium

29FOOD SCIENCE APPLICATIONS - ADDITIVES -Rapid separation of food additives in functional
beverage (UHPLC)
HPLC is widely used for component evaluation of the additives in food because it is easy to operate. Increasing the numberof sample, the demands that a lot of samples want to be measured in shorter time than the conventional method haveincreased. In addition, since the absorption wavelength is different depending on the components, it may be difficult tomonitor all components at one absorption wavelength if there are a lot of components.
Using the PDA detector for Ultra High-performance Liquid Chromatography (UHPLC), which can perform high-speed dataacquisition (100 spectra/sec.), enables to measure the sample in shorter time than the conventional method. The retention
time is not varied even at any absorption wavelength, and it is possible to perform the proper qualification analysis bycomparing the UV spectra of unknown sample acquired from PDA detector with the one of standard sample.
This article shows the UHPLC analysis result of food additives in functional beverage.

Analysis of standard food additives
Figure 1 shows the chromatogram and contour plot of standard food additives. 12 components were clearly separated
within 5 minutes.

30

210 nm
240 nm

8000003

Intensity [µV]

600000<Conditions>
Column:Eluent A:
Eluent B:
Gradient:
Flow rate:
Column temp.:
Detection:

1
92400000

10

5

8

4

200000

11

12

ZORBAX Eclipse Plus C18(3 mmI.D. x 50 mmL, 1.8 µm)
0.3% Phosphoric acid solution
Acetonitrile
High pressure gradient elution
0.8 mL/min.
40 ºC
PDA detector(absorption wavelength: 200 to 650 nm)

7
6

0
0

1.0

2.0

8.08e+04

3.0
4.0
Retention time [min.]3.82e+05

5.0

5.83e+05

6.0

7.84e+05

200
61

Wavelength [nm]

300

2
3

7 8

10

11

12

9

400

4

500

600

0

1.0

5

2.0

3.0
4.0
Retention time [min.]

5.0

6.0

Figure 1. Chromatogram of standard food additives
1: Acesulfame K (0.2mg/mL), 2: p-Hydroxybenzoic acid (0.05mg/mL), 3: Caffeine(0.05mg/mL), 4: Vitamin B2 sodium phosphate(Riboflavin sodium
phosphate) (0.1mg/mL), 5: Vitamin B2(Riboflavin) (0.1mg/mL), 6: Aspartame (0.1mg/mL), 7: Benzoic acid (0.05mg/mL), 8: Methyl p-hydroxybenzoate(0.05mg/mL), 9: Propyl gallate (0.05mg/mL), 10: Ethyl p-hydroxybenzoate (0.05mg/mL), 11: Propyl p-hydroxybenzoate (0.05mg/mL), 12: Butylp-hydroxybenzoate (0.05mg/mL)

Analysis of food additives in functional beverage
Figure 2 and 3 shows the chromatogram and the contour plot of functional beverage, respectively. Figure 4 shows thechromatograms at 4 different wavelengths.
2800000

3

210 nm
240 nm

2400000

Intensity [µV]2000000
1600000
7
1200000
800000101400000

4

9
5

0
0

1.0

2.0

3.0
4.0
Retention time [min.]

5.0

6.0

Figure 2. Chromatogram of functional beverage
Peak numbers and chromatographic conditions are the same as in Figure 1.
Preparation: Functional beverage was filtrated with 0.2 µm membrane.

312.87e+05

1.31e+06

1.99e+06

2.67e+06

200

1

Wavelength [nm]

300

10

7

5

3

9

400

4

500

600

0

1.0

2.0

3.0
4.0
Retention time [min.]

5.0

6.0

Figure 3. Chromatogram of functional beverage (contour plot)
Peak numbers and chromatographic conditions are the same as in Figure 1.

2800000

14000003<240 nm>

1000000
Intensity [µV]

Intensity [µV]2000000

1000000

71

4

3500000

1

10

4

10

9
9

0

0
0

1.0

2200000

2.0

3.0
4.0
Retention time [min.]

6.0

0

1.0

4

2.0

3.0
4.0
Retention time [min.]4<270 nm>Intensity [µV]

1000000

5.0

20000032000000

Intensity [µV]7<210 nm>5.0

6.0<400 nm>

100000

10
7 9

5

0

0
0

1.0

2.0

3.0
4.0
Retention time [min.]

5.0

6.0

0

1.0

2.0

3.0
4.0
Retention time [min.]Figure 4. Chromatograms of functional beverage at 4 different wavelengths
Peak numbers and chromatographic conditions are the same as in Figure 1.

32

5.0

6.0

FOOD SCIENCE APPLICATIONS - HAZARDS -Conbined analysis of food package films(micro IR/micro Raman)
Package films have the roles to protect the food quality and to extend the time limitation of food freshness/consumption.
On the other hand, the defects of package films lead to food deterioration, which may cause the accidents that it injures
humans and things. Therefore, its R&D are being progressed constantly, and quality control in manufacturing process need
to be performed based on the thought of HACCP (Hazard Analysis and Critical Control Point).
There are a lot of materials for packaging (metal, glass, paper etc.), and the plastic material is widely used for food packaging
due to high safety and hygiene. IR microscope and Raman microscope are powerful tool to evaluate the impurities andthe conformation of packaging film. Two instrument have mutual complementary relationship, and obtaining both spectra
can provide the various information. In addition, analyzing the spectrum data by chemometrics can extract the component
information properly. Visualizing the component distribution, it enables to provide detailed information of contamination,
and also to provide the effective breakthroughs for R&D.
This article shows the evaluation example of packaging film.

Combined analysis of multi-layer film
Cross-section piece of multi-layer film was created, and its same position was measured by IR microscope and Raman
microscope. Figure 1 shows the observation image of cross-section of multi-layer film, and Figure 2 and 3 show the
principal component spectra calculated by MCR (Multivariate Curve Resolution) and chemical images calculated by thescore of the principal component spectra. As a result of both principal component spectra and chemical images, thedistribution status of polypropylene (PP), polyethylene (PE) and polyethylene terephthalate (PET) could be visualized.
Cellulose with large infrared activity was specifically detected by IR microscope, and titanium oxide (TiO2) was specificallydetected as a very thin layer (several µm) by Raman microscope. Since cellulose is not used in this film, it indicatesthat there is the high possibility that cellulose is mixed as foreign matter,. By using both systems, the complementary
information was acquired.
33

50 µmFigure 1. Observation view

Cellulose (pink)Abs.

PP (blue)

PE (green)

PET (aqua)
4000

50 µm

3000

2000

1000

Wavenumber [cm-1]

Figure 2. Principal component spectra (right) and chemical image (left) (IR microscope)

TiO2 (red)Int.

PP (blue)

PE (green)

PET (aqua)
50 µm

3000

2000

1000

500

Raman shift [cm-1]

Figure 3. Principal component spectra (right) and chemical image (left) (Raman microscope)

JASCO can provide innovative accessory, IQ frame, which enables to obtain Infrared and Raman information in thesame area quickly and conveniently. Its accessory is useful for microscopic analysis between different facilities and remeasurement of samples that are removed once from the stage as well as the combined analysis of IR spectroscopy and
Raman spectroscopy.
34

FOOD SCIENCE APPLICATIONS - HAZARDS -Combined analysis of impurities in food(micro IR/micro Raman)
Recalls by the contaminant problems in food product occurs all around the world, and some authorities disclose the detailsof them. There are various reasons why the food products have been recalled, which include the contaminations of rubber
and resin by deteriorating the parts in product line equipment or by human error. Therefore, it is important to specify thereason of contamination to manufacture the food products safely.
Recently, some countries obligates the manufacturers to have the hygiene control by HACCP (Hazard Analysis and CriticalControl Point), and they are addressing it with the strict quality control so that the recall does not occur.
IR microscope and Raman microscope are powerful tool to evaluate the impurities and the conformation of packaging film.
Two instrument have mutual complementary relationship, and obtaining both spectra can provide the various information.
In addition, analyzing the spectrum data by chemometrics can extract the component information properly, which can be
the evidence to find out the causes of product defect.
This article shows the evaluation example of the plastic particles by using IR microscope and Raman microscope.

Analysis of plastic particles by IR microscope
The sample sprayed with plastic particles was measured by IR microscope. Figure 1 shows the observation image, andFigure 2 shows the principal component spectra calculated by MCR (Multivariate Curve Resolution) and chemical image
calculated by the score of the principal component spectra. As a result, protein was found in addition to four plastics:
polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), and polystyrene (PS).

35

30 µmFigure 1. Observation view

PET (green)Abs.

PE (red)PP (blue)

PS (aqua)Protein (yellow)
30 µm
4000

3000

2000

700

Wavenumber [cm-1]

Figure 2. Principal component spectra (right) and chemical image (left) (IR microscope)

Analysis of plastic particles by Raman microscope
Utilizing IQ frame, Raman measurement was performed at the same position measured by IR microscope. Figure 3
shows the principal component spectra calculated by MCR (Multivariate Curve Resolution) and chemical image by
the peak height of the key band. As a result, carbon was found in addition to the four plastics previously identified
by IR: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), and polystyrene (PS). In analysis by IR
microscope, protein was detected in addition to the four plastics, whereas in analysis by Raman microscope, carbon
was detected without protein detection. These results are obtained because Raman spectroscopy has low detection
sensitivity for protein, but it is good for carbon analysis.

36

PET (green)

PE (red)Int.

PP (blue)

PS (aqua)

Carbon (white)

30 µm
3500

2000

300

Raman shift [cm-1]

Figure 3. Principal component spectra (right) and chemical image (left) (Raman microscope)Overlay of imaging results (IR and Raman)
As a result of the each sample imaging shown in Figure 2 and 3, only five components could be detected in the plastic
particle sample by either IR or Raman spectroscopy separately. However, the results of combined analysis indicated thatsix different components could be detected and imaged.
In order to visualize the complete analytical information, both sets of imaging data were overlaid (Figure 4), this showed theprotein detected by IR analysis together with the five other components, polyethylene terephthalate (PET), polyethylene(PE), polypropylene (PP), polystyrene (PS) and carbon detected by Raman analysis.
These results indicate that using the strong points of both IR and Raman analysis can be combined to be more usefulthan a single analytical technique alone.

30 µm

Figure 4. Overlaid image of IR and Raman

37

FOOD SCIENCE APPLICATIONS - HAZARDS -Time estimation of pest contamination(Circular dichroism)
Pest contamination occurs anytime (from farm to table). When the problem occurs, the manufacturers need to estimate thepoint where the pests have been added to, and need to improve the product processing as soon as possible.
Monitoring the protein denaturation of pest can be one of clues to estimate the time of contamination. Utilizing the behavior
that the high order structure of protein varies irreversibly during heating it, it is feasible that the time of pest contamination
about the heat treated food is estimated by checking whether the denaturation occurs or not (Figure 1).

Contamination

Heating

Completion

Contamination

Figure 1. Time estimation of pest contamination(Top: contamination during the food processing, Bottom: contamination after food processing)38

Since CD spectrometer can detect the change of high order structure of protein, it is expected as the effective tool toestimate the time of pest contamination.
This article evaluated whether CD spectrometer can provide the clues of pest contamination by using protein extracted
from pest.

CD spectrum changes of pest femur extract
Figure 2 shows the CD spectra of the pest femur extract and commercial myosin before/after heating. Both spectra
are similar very well, and it indicates that there are many myosins in pest femur extract. Figure 3 shows the result of
secondary structure estimation analysis by CD spectrum of myosin, which indicates that the ratio of secondary structure
was varied during heating.
20000

myosinpest femur extract

Mol. Ellip.

51.7 %

α-helix

10000

35.5 %

β-sheet0β-turn-10000Random-20000
196.8

220

240

260

Wavelength [nm]Figure 2. CD spectrum changes(blue: non-heating, red: heating)Figure 3. Secondary structure analysis (blue: non-heating, red: heating)

Denaturation temperature of myosin
Figure 4 shows the temperature monitoring result of myosin. As a result, the denaturation temperature of myosin was
46.2 °C, and it indicates that it is possible to estimate the time of pest contamination if the heating treatment temperature
is higher than 46.2 °C. If the denaturation is not confirmed, it is estimated that the pest has been added to the productafter completing it.
-7000

Mol. Ellip.

-10000-15000-19000
10

20

40

60

85.1

Temperature [°C)

Figure 4. Temperature monitoring of myosin

Since the structure of myosin is almost same as the one in many species, it is applicable to many pests. In addition, since
the denaturation temperature is 46.2 °C, it also applicable to the manufacturing process with wide sterilizing temperature.
It is expected that its estimation by CD measurement is applied for the heat treated food (such as canned food, retort
pouch).
39

JASCO CORPORATION

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Tel: +81-42-649-5177 Fax:+81-42-646-4515 Web: www.jasco.co.jp
Japan

JASCO INTERNATIONAL CO., LTD.

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Tel: +81-42-649-3247 Fax: +81-42-649-3518 Web: www.jascoint.co.jp/english/
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Tel: +39-039-9215811 Fax: +39-039-9215835 Web: www.jasco-europe.com
JASCO Deutschland www.jasco.de | JASCO UK www.jasco.co.uk | JASCO France www.jascofrance.fr
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Tel: +86-21-6888-7871 Fax: +86-21-6888-7879 Web: www.jasco-global.com
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CERTIFIED

Products described herein are
designed and manufactured by
ISO-9001 and ISO-14001 certified
JASCO Corporation
E6402-2103

The contents of this brochure are for reference and illustrative purposes only. Information, descriptions, and specifications in thispublication are subject to change without notice. JASCO assumes no responsibility and will not be liable for any errors or omissions
contained herein or for incidental, consequential damages or losses in connection with the furnishing, performance or use of this material.