Evaluation Of Glycemic Index Determination Method.
EVALUATION OF GLYCEMIC INDEX
DETERMINATION METHODS
RATNA SARI LISTYANINGRUM
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
STATEMENT LETTER OF THESIS AND
SOURCE OF INFORMATION*
I declare that the thesis entitled Evaluation of Glycemic Index
Determination Methods is my own work in collaboration with the advisors and
has not been submitted in any form to any college. The sources of information
derived and quoted from published or unpublished works of other authors
mentioned in the text are listed in the bibliography at the end of this thesis.
Hereby I transfer the copyright of this thesis to Bogor Agricultural
University (IPB).
Bogor, February 2016
Ratna Sari Listyaningrum
F251130101
SUMMARY
RATNA SARI LISTYANINGRUM. Evaluation of Glycemic Index
Determination Method. Supervised by: DIDAH NUR FARIDAH and PUSPO
EDI GIRIWONO
The concept of glycemic index (GI) can be used to assist in the selection of
foods in a healthy diet. Food containing low GI tends to lower postprandial blood
glucose and insulin response. Because of the beneficial effect of GI, claims of low
GI have been found in various food products. Therefore, the determination of GI
value should be a concern. The most common method of testing GI is by using
human subjects (in vivo) which has many steps to be evaluated. In Indonesia, there
are two recommendations on GI method from FAO (1998) and BPOM (2011),
however there are some differences in the points of recommendation, including
the least subjects enrolled, reference food trials, and blood sampling points. The
type of the reference food and the test portion are also points that need to be
evaluated. Because of the in vivo method is time consuming, costly, and laborious,
in order to overcome the difficulties of the method, the interest in in vitro
methodology has increased. In evaluating the GI method, need to be seen whether
the method has been able to classify samples by GI category. Therefore, sample
from three classifications of GI is required, thus rice, wheat cookies, native
arrowroot starch cookies, and HMT-modified arrowroot cookies were used.
The objectives of this research were 1) to evaluate the steps in in vivo GI
method based on BPOM (2011) and FAO (1998) recommendations, 2) to examine
the feasibility of using 25 g AVCHO as basic portion, 3) to examine the feasibility
of using white rice (Pandan Wangi) as a reference food, 4) to compare the result
of GI in vivo method with predicted-GI in vitro method, 5) to understand the
correlation of HMT arrowroot starch modification to the GI of product, 6) to
determine GL of the product.
Subjects enrolled in this research need to fulfil the inclusion criteria (healthy,
21-36 years old and BMI 18.5-24.9 kg/m2). In GI test, experiments were carried
out involving three times for glucose testing and once for each sample. Glucose or
samples containing 50 g or 25 g available carbohydrate were consumed within 12
minutes. Blood samples were obtained at 15, 30, 45, 60, 90, and 120 min
consumption of samples. GI values were calculated from IAUC of blood glucose
curve of samples and compared with that of reference food, ignoring the area
below the fasting concentration.
The conclusion from this research were (1) the recommendation that can be
given is to have reference food trials at least twice with at least seven subjects
with seven blood sampling points. (2) need a further study about the use of 25g
AVCHO as the basis portion and the possibility of its GI-grouping, (3) Pandan
Wangi white rice is feasible to be used as a reference food in GI test with
conversion factor 0.74, (4) predicted-GI in vitro using H(90) starch hydrolysis
sampling did not predict the GI value precisely as GI in vivo, (5) HMT
modification did not affect GI value of product, (6) white rice is classified as high
GL food and all of the cookies are classified as low GL food.
Keywords: glycemic index, available carbohydrates, cookies, white rice, method
RINGKASAN
RATNA SARI LISTYANINGRUM. Evaluasi Metode Penentuan Indeks
Glikemik. Dimbimbing oleh: DIDAH NUR FARIDAH dan PUSPO EDI
GIRIWONO
Konsep indeks glikemik (IG) dapat membantu dalam pemilihan makanan
pada diet sehat. Makanan yang mengandung IG rendah cenderung menurunkan
glukosa darah postprandial dan respon insulin. Efek menguntungkan dari IG
tersebut memicu munculnya berbagai produk makanan yang menggunakan klaim
IG rendah. Oleh karena itu, penentuan nilai IG menjadi perhatian penting. Metode
umum pengujian IG adalah dengan menggunakan subjek manusia (in vivo) yang
perlu untuk dievaluasi. Di Indonesia, terdapat dua rekomendasi metode IG, yaitu
dari FAO (1998) dan BPOM (2011), namun terdapat beberapa perbedaan dalam
poin rekomendasi, termasuk jumlah subjek yang diperlukan, uji pangan standar,
dan poin pengambilan sampel darah. Jenis pangan standar dan porsi uji juga
merupakan poin-poin yang perlu dievaluasi. Oleh karena metode in vivo memakan
waktu dan biaya yang cukup mahal, uji IG dengan menggunakan metode in vitro
mulai diteliti. Dalam mengevaluasi metode IG, perlu dilihat apakah metode
tersebut telah mampu mengklasifikasikan sampel berdasarkan kategori IG. Oleh
karena itu, sampel dari tiga klasifikasi IG diperlukan, sehingga beras, cookies
gandum, cookies pati garut, dan cookies garut termodifikasi HMT digunakan.
Tujuan dari penelitian ini adalah 1) untuk mengevaluasi metode in vivo IG
berdasarkan rekomendasi BPOM (2011) dan FAO (1998), 2) untuk menguji
penggunaan 25 g karbohidrat sedia sebagai porsi uji, 3) untuk menguji
penggunaan nasi putih (Pandan Wangi) sebagai pangan standar, 4) untuk
membandingkan hasil dari IG hasil metode in vivo dengan IG hasil prediksi
metode in vitro, 5) untuk memahami korelasi HMT pati garut modifikasi dengan
IG produk, 6) untuk menentukan beban glikemik (BG) produk.
Subjek dalam penelitian ini perlu memenuhi kriteria inklusi (sehat, berusia
21-36 tahun dan Indeks Masa Tubuh 18,5-24,9 kg / m2). Dalam uji IG, percobaan
dilakukan dengan tiga kali pengujian glukosa dan sekali untuk masing-masing
sampel. Glukosa atau sampel yang mengandung 50 g atau 25 g karbohidrat sedia
dikonsumsi dalam waktu 12 menit. Contoh darah diambil pada 15, 30, 45, 60, 90,
dan 120 menit setelah mengkonsumsi sampel. Nilai IG dihitung berdasarkan luas
area di bawah kurva dari sampel dan dibandingkan dengan pangan standar.
Kesimpulan dari penelitian ini adalah (1) rekomendasi yang diberikan
adalah dengan menguji pangan standar minimal dua kali dengan subjek minimal
tujuh dan tujuh poin pengambilan sampel darah. (2) membutuhkan studi lebih
lanjut mengenai penggunaan 25g karbohidrat sedia sebagai porsi uji, (3) nasi putih
Pandan Wangi layak digunakan sebagai pangan uji dalam uji IG dengan faktor
konversi 0,74, (4) Metode prediksi IG in vitro dengan menggunakan persen
hidrolisis pati menit ke-90 (H90) belum dapat memprediksi nilai IG tepat seperti
IG in vivo, (5) tidak terlihat korelasi antara modifikasi HMT dan IG produk, (6)
nasi putih diklasifikasikan sebagai makanan dengan BG tinggi dan semua cookies
diklasifikasikan sebagai makanan dengan BG rendah.
Kata kunci: indeks glikemik, karbohidrat yang tersedia, cookies, nasi putih, metode
© Copy Right IPB, 2016
This work is under copy right of IPB 2016. No part of this work can be copied
without citing the source. Citation is allowable solely for education, research,
scientific paper writing, reporting, assay or review; and the citation will not cause
liability to IPB
Do not publish or copy a part or a whole of thesis without prior inform consent of
IPB
EVALUATION OF GLYCEMIC INDEX
DETERMINATION METHOD
RATNA SARI LISTYANINGRUM
Thesis
as one of the requirements for obtaining a degree of
Master of Science
at Food Science Master Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
Examiner in Thesis Defense: Dr. Ir. Endang Prangdimurti, M.Si
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PREFACE
Praise and gratitude to Allah Subhanahu wa Ta'ala for all His blessings so
this thesis is successfully completed. The research entitled “Evaluation of
Glycemic Index Determination Method” was carried out in Bogor Agricultural
University from October 2014 until April 2015.
Authors would like to express sincere thanks to parents and the whole
family for all the prayers and support and also Dr. Didah Nur Faridah, S.T.P, M.Si
and Puspo Edi Giriwono, PhD as advisory committee for the guidance, helps, and
advices during the time completing this thesis. Author would also like to thank to
Dr. Ir. Endang Prangdimurti, M.Si as an examiner in thesis defense for the
corrections and inputs in improving this thesis.
Expression of thanks are also given to Dean of Postgraduate School of
Bogor Agricultural University, Dr. Ir. Dahrul Syah, MSc.Agr and Head of Food
Science Master Program, Prof. Dr. Ir. Ratih Dewanti-Hariyadi, M.Sc for giving
permission for the research and acknowledgement to this thesis. Thanks to the
staffs of Postgraduate School and Food Science program for the help in
administration of the thesis. Thanks to Bu Antin, Mbak Yane, Pak Rozak, Pak
Yahya, Mbak Irin, and Mbak Ririn as laboratory technicians for the help during
research. Thanks to Mbak Any for the assistance in managing research fund.
Thanks to the volunteers for the willingness to participate in this research.
Author would also like to thank to Mutiara, Fathma, Fitria, Anand, and Rita
for the great team work during research. Thanks to Isti, Dina, Lorenzia, Karin, and
Eci for the advices and helps during study and research. Thanks to Dila, Alif,
Putri, Happy, Leyla, Astri, Endah, Riza, and Ika for the support during completing
this research. Thanks to friends from IPN and Bateng69 for the support. Last but
not least, thanks to DIKTI through Penelitian Unggulan Dasar program 2014 &
Laboratory of Food Analysis (LDITP) IPB for funding the research.
Hopefully this thesis can be useful and give contribution in food science.
Bogor, February 2016
Ratna Sari Listyaningrum
CONTENT
LIST OF TABLES
v
LIST OF FIGURES
v
LIST OF APPENDICES
v
ABBREVIATION
vi
1 INTRODUCTION
1
Background
1
Problems
3
Hypothesis
3
Objectives
4
Outcome
4
2 MATERIAL AND METHODS
4
Time and Place
4
Materials and Instruments
4
Methods
5
3 RESULT AND DISCUSSION
10
Samples Composition
10
Selection of Blood Glucose Measurement Methods
11
Individual Evaluation
13
Comparison of GI based on Several Reference Food Trials
14
Comparison of GI based on Blood Sampling Points
16
Comparison of BPOM standard and FAO standard for GI test
17
Comparison of 25 g and 50 g available carbohydrate portion
18
White Rice as Reference Food
22
Correlation of HMT Modification with GI
24
Glycemic Load
26
Predicted-GI in vitro
27
5 CONCLUSION AND RECOMMENDATION
30
Conclusion
30
Recommendation
30
BIBLIOGRAPHY
APPENDICES
AUTHOR BIOGRAPHY
30
LIST OF TABLES
1
Formula of cookies
7
2
Solutions in kit assay test
9
3
Statistical analysis
10
4
Composition of samples
11
5
Mean CV of individual glucose trials iAUC
13
6
Correlation between gender, age, BMI and mean of iAUC
14
7
GI value of the samples based on different glucose trials
15
8
GI value of the samples based on blood glucose sampling points
17
9
Recommendations of in vivo-GI test by FAO dan BPOM
18
10
GI based on three points recommendations by FAO and BPOM
19
11
Mean GI of white wice
20
12
Difference value of GI product using 50g and 25g AVCHO
21
13
Correlation of iAUC glucose and rice
22
14
GI value based on glucose and white rice as reference foods
23
15
GI values of several types of rice and possible conversion factor
23
16
Resistant Starch and Glycemic Index of samples
24
17
Correlation between Dietary Fiber, Test Portion, Resistant Starch, and 25
Gycemic Index
18
Glycemic load of samples
27
19
Predicted-GI in vitro of all samples compared with GI in vivo
28
20
Comparison of GI based on in vitro and in vivo methods in several foods
29
LIST OF FIGURES
1
Flow chart of research
2
Comparison of blood glucose measurement based on days of trials and
6
12
methods.
3
iAUC of the same sample and subject with different blood glucose
16
sampling points
4
Blood glucose curves of glucose and rice based on 25 g available
20
carbohydrate
5
Mean iAUC of glucose and rice
21
LIST OF APPENDICES
1
Ethical clearance from Health Ministry of Indonesia
36
2
Informed consent
37
3
Glucose Calculation
40
4
Control Solution Testing
40
5
Blood glucose measurement using glucometer
41
6
Reagents for measuring blood glucose concentration in kit assay
41
7
Statistical analysis of blood glucose concentration by kit assay day 1&2
42
8
Statistical analysis of blood glucose concentration by glucose day1&2
42
9
Statistical analysis of blood glucose concentration by kit assay and
43
glucometer in day1
10
Statistical analysis of blood glucose concentration by kit assay and
43
glucometer in day2
11
Statistical analysis of GI based on reference food trials
44
12
Statistical analyisis of GI based on blood sampling points
46
13
Statistical analysis of GI basedn on BPOM and FAO methods
47
14
Statistical analysis of GI based on AVCHO glucose (25g vs 50g)
51
15
Statistical analysis using Pandan Wangi white rice as reference food
52
16
Statistical Analysis of Resistant Starch
54
ABBREVIATIONS
ADA
American Diabetes Association
ANOVA
Analysis of Variance
AVCHO
Available Carbohydrates
BMI
Body Mass Index
BPOM
Badan Pengawas Obat dan Makanan
CV
Coefficient of Variation
DF
Dietary Fiber
DM
Diabetes Mellitus
FAO
Food and Agricultural Organization
GI
Glycemic Index
GL
Glycemic Load
HDPE
High Density Poly Ethylene
HI
Hydrolysis Index
HMT
Heat Moisture Treatment
iAUC
Incremental Area Under Curve
IDF
International Diabetes Federation
NCD
Non-Communicable Disease
NS
Native Starch
PGI
Predicted Glycemic Index
RDS
Rapid Digestible Starch
RS
Resistant Starch
SCFA
Short Chain Fatty Acid
SD
Standard Deviation
SDS
Slow Digestible Starch
WHO
World Health Organization
1 INTRODUCTION
Background
Non-communicable diseases (NCD) are the leading cause of death globally.
In 2008, from 57 million deaths those occurred globally 36 million deaths were
caused by NCD, such as cardiovascular disease, cancer, diabetes, and chronic lung
disease. According to the WHO report (WHO 2010), approximately 80% of
deaths due to NCD in countries with low and moderate incomes appeared to be
greatly influenced by socio-economic circumstances. NCD can be triggered by
smoking, unhealthy diet, lack of physical exercise, and alcohol consumption.
Unhealthy diet can cause excess energy intake in the body and lead to the risk of
type 2 diabetes. Type 2 diabetes is a chronic metabolic disorder, resulting from
progressive insulin over secretion complicated by insulin resistance and
characterized by abnormal increase in the blood glucose level (ADA 2005).
International Diabetes Federation (2013) estimates that the number of diabetics
will increase from 382 million to 592 million people by 2035 globally with the
majority age under 60. WHO has also predicted that there will be an increase of
diabetics in Indonesia from 8.4 million people in 2000 to 21.3 million people by
2030 (Wild et al. 2004). With this increase, there is an urgency to improve
prevention by consuming low GI foods. Moreover, from 4.6% of diabetes mellitus
(DM) prevalence in productive age in urban Indonesia, 3.5% is considered
undiagnosed (Mihardja et al. 2014). Prediction factors of undiagnosed DM in
Indonesia are age, obesity, hypertension, and smoking habit (Pramono et al. 2010).
The risk of obesity can be decreased by consuming healthy foods. Healthy diet
can be used as an approach to reduce the risk of type 2 diabetes. Selection of food
that does not raise blood sugar levels rapidly can be used as an attempt to keep
blood glucose at a normal level
The concept of glycemic index (GI) can be used to assist in the selection of
foods in a healthy diet. The GI value can be used as an estimation to the likely
effects of food on blood glucose levels when consumed. Food containing low GI
tends to lower postprandial blood glucose and insulin response (Wolever 1992).
Food and Agricultural Organization (FAO 1998) recommends food intake which
have low-GI, especially for diabetics and people who are intolerant to glucose.
Food with low-GI has been proven to give beneficial effect to human health, such
as improving blood glucose control (Wolever et al. 1991, Gilbertson et al. 2001,
Stevenson et al. 2006, Barakatun-Nisak et al. 2009, Moses et al. 2009); reduce the
risk of coronary heart disease (Liu et al. 2000); decrease total fat mass and
increase lean body mass (Bouche et al. 2002). Because of the beneficial effect of
GI, claims of low GI have been found in various food products. Therefore, the
determination of the GI value should be a concern.
The most common method of testing GI is by using human subjects (in vivo).
This method has many steps, such as filing of ethical clearance, preparation of
reference food and samples, selection of test subjects, blood sampling, analytical
methods, and calculation of GI. Blood sampling has been done in order to
calculate the GI value of food. Blood glucose of the samples is estimated using
spectrophotometer or auto analyzer (Brouns et al. 2005).
2
Recently, glucometer has also been used in estimating blood glucose in GI
determination. However, there is controversy regarding the use of glucometer,
because this method is commonly used as a self-monitoring of blood glucose and
not for research purpose (Velangi et al. 2005).
The cost of in vivo GI determination method is quite expensive. Therefore, it
is necessary to evaluate the method in order to get not only an effective method
but also accurate result. In Indonesia, there are two recommendations on GI
method from Food and Agricultural Organization (FAO) (1998) and National
Agency of Drug and Food Control of Indonesia (BPOM) (2011), however there
are some differences in the points of recommendation, including the minimum
number of subjects enrolled, reference food trials, and number of blood sampling
points.
Most of studies of GI use either glucose or white bread as reference food.
The use of white bread as a reference food is based on western diet that is
accustomed white bread as staple food, though for eastern cultures especially
South East Asia people who consume white rice as staple food, rice can be
considered more palatable than white bread or glucose. Both FAO (1998) and
BPOM (2001) recommend that the portion of food tested should contain 50g of
available carbohydrates (AVCHO). However, using 50g of AVCHO in low
density such as rice can lead to large volumes to be ingested.
Because of the in vivo method is time consuming, costly and laborious, in
order to overcome the difficulties of the method, the interest in an in vitro
methodology has increased. Predicted glycemic index (PGI) in vitro method has
been developed by Grandfelt et al. (1992) and Goni et al. (1997) and also has
been used in recent researches. In general, PGI is calculated based on hydrolysis
index (HI) obtained from the percent ratio iAUC of reference food and samples
after hydrolysis for 120 to 180 minutes. Yet Goni et al. (1997) stated that the best
hydrolysis time to estimate the glycemic response is in 90 minute (H90). Study
about the correlation between PGI in vitro calculated based on H90 and GI in vivo
calculated based on IAUC for 90 minutes has not yet been carried out.
Food can be classified based on its GI value: food with low GI (≤55),
intermediate (56-69), and high (≥70) (CDA 2011). In evaluating the GI method,
there need to be seen whether the method is able to classify samples by GI
category. Therefore, samples from three classifications of GI are required. Rice
that has been known to have high GI and wheat cookies that usually have
intermediate GI can be used as samples. For samples with low GI, arrowroot
starch shows potency to be used as sample. Arrowroot has been known to have a
low GI value of 14, compared to yam (90), taro (95), edible canna (105), sweet
potato (179) (Marsono 2002) and suweg (42) (Faridah 2005). Some studies
indicate that heat moisture treatment (HMT) modification can decrease
digestibility of starch (He et al. 2008; Syahbanu 2015). Study by Chung et al.
(2009) showed that 30% moisture content, 2 hrs, 120°C HMT decreased rapid
digestible starch (RDS) of all gelatinized starches. Percent differences of RDS
were 11%, 15%, and 16% for corn, pea, and lentil respectively. Therefore,
arrowroot starch needs to be modified with HMT in the expectation to get lower
GI sample.
3
It needs to take into account that the amount of carbohydrate consumed
affect the glucose response. Thus, GI determination need to be followed by
glycemic load (GL) calculation. GI along with GL can show the effect of
carbohydrate to glycemic response in quality and quantity.
Problems
Increase of diabetics and emergence of products with low GI claims makes
the determination of the GI value a concern. In vivo method to determine GI has
many steps to be evaluated. Controversy regarding the use of glucometer in GI
test should be re-examined by comparing it with previous method which is kit
assay. FAO (1998) recommends that the least number of subjects enrolled in the
GI tests are seven, while BPOM (2011) recommends ten. Recommendation from
FAO (1998) stated that reference food test should be repeated at least three times,
while BPOM (2011) does not give specific recommendation. In blood glucose
sampling points, FAO (1998) requires seven points (at 0, 15, 30, 45, 60, 90, 120
minutes), while BPOM (2011) requires five points (0, 30, 60, 90, 120 minutes).
Hence, it is interesting to know whether the difference in points of
recommendation can affect GI.
The use of white bread as a reference food is considered to be more
palatable than glucose and can affect psychologically. In eastern cultures
especially South East Asia people who consume white rice as staple food, rice can
be considered more palatable than white bread or glucose. The use of Pandan
Wangi as reference food in GI test need to be examined. In GI test, the use of 50g
AVCHO in low density sample such as rice can lead to large volumes having to
be ingested. Therefore, the effect of using smaller AVCHO intake (25g) to GI
needs to be examined. In vitro analysis methods of GI have been proposed as a
predictor of physiological effects of food. The difference in enzymes used and
hydrolysis time was the main interest for the previous studies. In this study, the
point of interest is in whether PGI in vitro calculated based on H90 will give a
similar value with IG in vivo calculated based on IAUC for 90 minutes. In
evaluating the GI method, samples from three classifications of GI is required. For
the low GI sample, arrowroot starch shows potency to be used as ingredient in the
making of cookies. In addition, HMT modification has been showed to decrease
starch digestibility starch. Therefore, the correlation of HMT and GI need to be
studied.
Hypothesis
Hypothesis which will be tested in this research were 1) In vivo GI method
can be efficiently done using at least 7 subjects, single reference food test, and 5
times blood sampling; 2) 25g AVCHO can be used as an alternative amount of
food test portion; 3) White rice (Pandan Wangi) can be used as an alternative
reference food in GI test, 4) HMT starch modification correlate with GI value of
product, 5) Predicted-GI in vitro using percent hydrolysis in minute 90 (H90)
method can be used as a preliminary screening test in GI in vivo test.
4
Objectives
The objectives of this research were 1) to evaluate the steps in in vivo GI
method based on BPOM (2011) and FAO (1998) recommendations (including
reference food trials, blood sampling points, and least of subject enrolled), 2) to
compare iAUC using 50g and 25g AVCHO portion, 3) to examine the feasibility
of using white rice (Pandan Wangi) as a reference food, 4) to understand the
correlation of HMT arrowroot starch modification to the GI of product, 5) to
determine GL of the samples, 6) to compare the result of GI in vivo method with
predicted-GI H(90) in vitro method.
Outcome
This study can provide an inputs to the party that will hold GI test related to
blood glucose sampling method, subjects enrolled, reference food trials, blood
sampling points, reference food type, and AVCHO intake, in order to get an
effective method yet accurate result. It also provides the feasibility of PGI in vitro
method in the initial screening of GI in vivo tests. The other outcome from this
study is the addition of reference in GI values of arrowroot cookies and HMTmodified arrowroot cookies.
2 RESEARCH METHODOLOGY
Time and Place
This research was conducted from October 2014 until April 2015 in
Laboratory of Processing, Laboratory of Chemistry, Laboratory of Biochemistry
of Food Science and Technology Department and Pilot Plant of South East Asian
Food and Agricultural Science and Technology (SEAFAST) Center, Bogor
Agricultural University
Materials and Instruments
Type of rice used in this research was Pandan Wangi (Upper Right Cianjur,
PT Midi Utama Indonesia, Tbk). Cookies was made from wheat (Tepung Segitiga
Bogasari, Indonesia), native arrowroot starch (obtained from Kelompok Wanita
Tani Yogyakarta, Indonesia), and HMT-modified arrowroot starch. Dextrose
monohydrate/glucose (Qinhuangdao Lihua Starch CO, China) was used as
reference food. Kit assay (Glucose Assay Kit, GAGO-20 Sigma, Sigma-Aldrich,
USA) was used in the selection of blood glucose measurement along with
glucometer, lancets, and strips from One Touch Ultra Lifescan (Johnson &
Johnson Company, USA), cotton, and alcohol 70% (PT Ikapharmindo Putramas,
Indonesia). Enzymes used in the analysis were Termamyl (Sigma A3403, SigmaAldrich, USA), protease (Sigma P4630, Sigma-Aldrich, USA), amyloglucosidase
(Sigma A9913, Sigma-Aldrich, USA), pepsin (Sigma P7000, Sigma-Aldrich,
USA), α-amylase (Sigma 10065, Sigma-Aldrich, USA). Instruments used in this
research were centrifuge (Eppendorf 5810R, Germany), spectrophotometer UVVIS (ShimadzuUVmini-1240, Japan), shaking water bath (Burgwedel GFL 1083,
Germany), pH meter (Eutech pH700, Singapore), vacuum-system (B’u’chi B-169,
Switzerland), and analytic scale (Precisa XT220A, Switzerland).
5
Methods
This research was divided into 6 stages, which were 1) Preparation (ethical
clearance, subjects, reference food and samples), 2) Analysis of samples
(proximate, DF, and RS), 3) Selection of blood glucose measurement method, 4)
GI-in vivo test, 5) GL calculation, 6) Predicted-GI in vitro test. Flow chart of
research is described in Figure 1.
Preparation
Ethical Issues
Ethical clearance was obtained from the Ethics Committee, The Health
Ministry of Indonesia (LB.02.01/5.2/KE.142/2014) (Appendix 1).
Subjects
Inclusion criteria in this research were healthy, no history of DM, not
pregnant, no smoking habit, range of age between 21-36 years old and range of
normal body mass index (BMI) 18.5-24.9 kg/m2. BMI was calculated as weight
(kg) divided by height (m2). All subjects had given informed consents to
participate (Appendix 2).
Modification of arrowroot starch
Native arrowroot starch was set up to 20% moisture content by spraying
water. The amount of water was determined by the following equilibrium wet
mass. Starch which has reached 20%wb moisture content was then stirred and
placed in high density polyethylene (HDPE) plastic pouch. The starch was set at
room temperature for one night to homogenize moisture content. Wet starch was
then treated with HMT by heating in an autoclave at a temperature of 1210C for
15 minutes. Furthermore, HMT-Modified Arrowroot Starch was dried by tray
dryer for 2h at 50°C.
(100% - MC1) x SW1 = (100% - MC2) x SW2
MC1: moisture content in the initial condition (%wb)
MC2: the desired moisture content (%wb)
SW1: weight of starch in the initial condition (g)
SW2: weight of starch after reaching the desired moisture content (g)
Production of rice and cookies
Rice was boiled with water to rice ratio 3: 1 in rice cooker. Rice was made on
the same day as the day of testing. Cookies was made with formula in Table 1.
The process of making cookies was started with mixing margarine, fine sugar and
palm sugar. Palm sugar was used because it has been proven that GI of the
mixture of bread using palm sugar as the sweetener was lower than using
sugarcane (Srikaeo & Thongta 2014). Yolk was added into the mixture and mixed
in 2 minutes. Flour or starch, salt, baking soda, vanilla essence and skim milk
powder were added into the mixture and mixed in 8 minutes. The dough then was
sheeted in flat-round shape and baked in oven at 170°C in ±15 minutes. Cookies
were placed in the sealed plastic jar until used.
6
Step 1. Preparation
Filing of ethical clearance
Selection of subjects
Modification of arrowroot starch
Production of rice and cookies
Step 2. Analysis of samples
Proximate, DF, and RS analysis
Step 3. Selection of blood glucose
measurement
Fasting blood glucose measurement (duplicate runs)
Step 4. GI in vivo test
Trials of 50 g glucose (triplicate runs)
GI in vivo tests of samples (50 g AVCHO) (single run each)
Trials of 25 g glucose (single run)
GI in vivo test of rice (25 g avCHO) (triplicate runs)
Step 5. GL calculation
GL calculation
Step 6. Predicted GI in vitro test
Predicted GI in vitro test
Figure 1 Flow chart of research
7
Table 1 Formula of cookies
Item
Wheat flour
Native arrowroot starch
HMT-modified arrowroot starch
Fine sugar
Palm sugar
Margarine
Skim milk powder
Yolk
Salt
Baking soda
Vanilla
Total
Composition (%)
Wheat Cookies NS Cookies HMT Cookies
57.04
57.04
57.04
5.13
5.13
5.13
5.13
5.13
5.13
23.30
23.30
23.30
4.24
4.24
4.24
4.97
4.97
4.97
0.14
0.14
0.14
0.02
0.02
0.02
0.02
0.02
0.02
100
100
100
Analysis of samples
Proximate Analysis
Proximate analysis were measured by AOAC (2012), including water
content (925.10), ash (923.03), protein (960.52), fat (920.39), and carbohydrate
(by difference).
Dietary Fiber
An in vitro method based on the procedure of AOAC (985.29, 2012) was
adopted to determine dietary fiber (DF). Blank was processed through the entire
procedure along with test portions to measure any contribution from reagents to
residue. Samples containing >10% fat were extracted using soxhlet extraction
with hexane as the solvent. Duplicate of fat-free test portions were weighed 1 g
and inserted into a glass beaker, then 50 ml of 0.1 M phosphate buffer pH 6 was
added. Termamyl 100 mL was added and closed with aluminum foil and
incubated at a temperature of 100ºC for 15 minutes. Solutions were cooled to
room temperature, then prepared to pH 7.5 by adding 10 ml 0.275M NaOH and
100 mL of protease solution, incubated at 60°C for 30 minutes in a water bath
shake. The solution was made into pH 4.0 to 4.6 by adding 10 ml of HCl and 300
mL amyloglucosidase 0.325M, incubation at 60 ° C for 30 minutes in a water bath
shake. Ethanol 95% which had previously been heated at a temperature of 60°C
was added 280 ml into the solution and allowed to settle at room temperature for
60 minutes. The solution was filtered using a vacuum filter and washed three
times with 20 ml of ethanol 78%, two times with 10 ml of 95% ethanol, and twice
with 10 ml acetone. The residue was then dried overnight at a temperature of
105ºC. One duplicate was analyzed for protein residue and other was analyzed for
ash residue. Total DF of the samples were calculated using the following formula.
8
TDF (%) =
WR
P
A
B
WT
WR−P− −
WT
�
: average of weight of residue from duplicate samples (mg)
: protein (mg)
: ash (mg)
: weight of blank – weight of blank protein – weight of blank ash
: weight of test portion (before tested)
Resistant Starch
An in vitro method based on the procedure of Goni et al (1996) was adopted
to determine RS with slight modification in buffer and glucose calculation.
Samples contain fat ≥5% were defatted using sohxlet extraction with hexane as
the solvent. Fifty mg of food portion were passed through 80 mesh filter and were
placed into a centrifuge tube. Ten mL of HCI-KCI buffer (pH=l.5) were added.
Then 200 µL of a pepsin solution (4000U/10ml HCI-KCI buffer) were added to
each sample and incubated at 40°C for 1 hour in a shaking water bath. Phosphate
buffer (pH=6.9) were added to reach pH 6-7.
One mL of α-amylase solution (400U α-amylase per mL buffer phosphate)
was added to each centrifuge tubes and incubated for 16 h in a water bath at 37°C
with constant shaking. Samples then were centrifuged (15 min, 3000 g) and the
supernatant were discarded. Samples’ residues were washed twice with 10 mL of
distilled water, and centrifuged again, and discarded. Residues were added with
1.5 mL distilled water and 1.5 mL of 4M KOH, mixed and left for 30 min at room
temperature with constant shaking. Aliquots were added with 2.75 mL 2M HCl
and 1.5 mL of 0.4 M sodium acetate buffer (pH=4.75). Then 80µL of
amyloglucosidase were added to the aliquots and incubated in a water bath for
45min at 60°C with constant shaking. Aliquots were then centrifuged (15 min,
3000 g), supernatants were collected and saved in a volumetric flask. Residues
then were washed at least once with 10 mL of distilled water, centrifuged again,
and combined supernatants with that obtained previously. Volume was adjusted to
10-l00 mL with distilled water. Then 0.5 mL of the solution was tested with
phenol sulphuric acid method (Dubois et al. 1956) to get the glucose content
(Appendix 3). The glucose was converted into starch by multiplying for 0.9.
Blood Glucose Measurement Method Selection
Volunteers were fasted overnight for 10-12 hours. Venous fasting blood
samples were taken from forearm. Blood samples were tested using two methods.
First method was using glucometer. Control solution testing has been done before
blood testing (Appendix 4). Blood sample (1 drop) was placed on analysing strip
of glucometer to obtain blood glucose concentration. Measurements were done
twice and the details was explained in Appendix 5. Second, blood samples (3ml)
were collected into EDTA treated tubes. Centrifugation was done at a speed of
2500 rpm at 40°C for 15 minutes to obtain blood plasma. Plasma glucose
concentrations were estimated using an enzymatic kit in duplicate. Following
solutions (Table 2) were pipetted into appropriately marked test tubes. At zero
time, 2mL of assay reagent were added to the first tube and mixed to start the
reaction.
9
About 30 to 60 second interval were allowed between additions of assay
reagent to each subsequent tube. Each tube was reacted exactly 30 minutes at
37°C. At 30-60 second intervals, then into each tube were added 2mL of 12 N
H2SO4 to stop the reaction. Each tube was mixed thoroughly and then measured
the absorbance against reagent blank at 540nm. Composition of reagents to
measure blood glucose is listed in Appendix 6. Blood glucose concentration of the
samples was calculated using the following formula.
=
�
�
�
Table 2 Solutions in kit assay test
Tubes
Water (mL)
Blank
1
Standard
0.95
Test
0.95
Sample (mL)
0.05
Glucose standard (mL)
0.05
-
Glycemic Index in vivo Test
Experiments were carried out involving glucose testing and sample testing
as shown in Figure 1. Subjects were asked to fast overnight for 10-12 hours.
Glucose or samples containing 50 g or 25 g AVCHO were consumed with 200 ml
of water within 12 minutes. Blood samples were obtained at 15, 30, 45, 60, 90,
and 120 min consumption of samples, using the chosen blood glucose
measurement tools. The tests were done with an interval times at least 3 days. GI
values were calculated from incremental Area Under Curves (iAUC) of blood
glucose curve of samples and compared with that of reference food, ignoring the
area below the fasting concentration.
=
�� �
�� �
×
Glycemic Load
Glycemic load of the samples was calculated using the following formula
by Martin et al. (2008). Serving size of the products was based on serving size of
similar product in the International Table of GI and GL by Foster-Powell et al.
(2002).
=
×
� � ��
�
��
Predicted-Glycemic Index In vitro Test
An in vitro method based on the procedure of Goni et al (1997) was adopted
to determine PGI with slight modification in buffer and glucose calculation. Fifty
mg of food portion were passed through 80 mesh filter. Ten mL of HCI-KCI
buffer (pH=l.5) were added. Then 200 µL of a pepsin solution (4000U/10ml HCIKCI buffer) were added to each sample and incubated at 40°C for 1 hour in a
shaking water bath. Volume was completed with phosphate buffer (pH=6.9) to
reach pH 6-7. Five mL of α-amylase solution (2,6U/5mL phosphate buffer) added
to each sample. Samples were incubated at 37°C in a shaking water bath.
10
One mL aliquot samples were taken at 0 minute and 90 minute. These
aliquots were heated at 100°C for 5 min to inactivate the enzyme. Then 4 mL of
sodium acetate buffer (pH=4.75) were added to each aliquot, and 60µL of
amyloglucosidase were used to hydrolyse the digested starch into glucose after 45
min at 60°C in a shaking water bath. Rice solution was centrifuged 1500g in 5
minutes. Volume was adjusted to 10-l00 mL with distilled water. Then 0.5 mL of
the solution was tested with phenol sulphuric acid method (Dubois et al. 1956) to
get the glucose content. The glucose was converted into starch by multiplying for
0.9.
Statistical Analysis
Data were expressed as mean values. The details of statistical analysis are
written in Table 3. Statistical software used was SPSS® version 20.
Table 3 Statistical Analysis
Data
Comparison of blood glucose measurement
Correlations between gender, age, BMI and
mean of IAUC
Comparison of GI of the samples based on
different glucose trials
Comparison of GI of the samples based on
blood glucose sampling points
Comparison of GI of the samples based on
three points of recommendation
Mean GI of white rice using 25g and 50g
AVCHO portion
Correlation of iAUC glucose and rice
GI value based on glucose and white rice as
reference foods.
Correlation between DF, Test Portion, RS,
and GI
Statistical Analysis
Independent sample t test
Pearson correlation test
ANOVA followed by Duncan’s
multiple range test (p
DETERMINATION METHODS
RATNA SARI LISTYANINGRUM
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
STATEMENT LETTER OF THESIS AND
SOURCE OF INFORMATION*
I declare that the thesis entitled Evaluation of Glycemic Index
Determination Methods is my own work in collaboration with the advisors and
has not been submitted in any form to any college. The sources of information
derived and quoted from published or unpublished works of other authors
mentioned in the text are listed in the bibliography at the end of this thesis.
Hereby I transfer the copyright of this thesis to Bogor Agricultural
University (IPB).
Bogor, February 2016
Ratna Sari Listyaningrum
F251130101
SUMMARY
RATNA SARI LISTYANINGRUM. Evaluation of Glycemic Index
Determination Method. Supervised by: DIDAH NUR FARIDAH and PUSPO
EDI GIRIWONO
The concept of glycemic index (GI) can be used to assist in the selection of
foods in a healthy diet. Food containing low GI tends to lower postprandial blood
glucose and insulin response. Because of the beneficial effect of GI, claims of low
GI have been found in various food products. Therefore, the determination of GI
value should be a concern. The most common method of testing GI is by using
human subjects (in vivo) which has many steps to be evaluated. In Indonesia, there
are two recommendations on GI method from FAO (1998) and BPOM (2011),
however there are some differences in the points of recommendation, including
the least subjects enrolled, reference food trials, and blood sampling points. The
type of the reference food and the test portion are also points that need to be
evaluated. Because of the in vivo method is time consuming, costly, and laborious,
in order to overcome the difficulties of the method, the interest in in vitro
methodology has increased. In evaluating the GI method, need to be seen whether
the method has been able to classify samples by GI category. Therefore, sample
from three classifications of GI is required, thus rice, wheat cookies, native
arrowroot starch cookies, and HMT-modified arrowroot cookies were used.
The objectives of this research were 1) to evaluate the steps in in vivo GI
method based on BPOM (2011) and FAO (1998) recommendations, 2) to examine
the feasibility of using 25 g AVCHO as basic portion, 3) to examine the feasibility
of using white rice (Pandan Wangi) as a reference food, 4) to compare the result
of GI in vivo method with predicted-GI in vitro method, 5) to understand the
correlation of HMT arrowroot starch modification to the GI of product, 6) to
determine GL of the product.
Subjects enrolled in this research need to fulfil the inclusion criteria (healthy,
21-36 years old and BMI 18.5-24.9 kg/m2). In GI test, experiments were carried
out involving three times for glucose testing and once for each sample. Glucose or
samples containing 50 g or 25 g available carbohydrate were consumed within 12
minutes. Blood samples were obtained at 15, 30, 45, 60, 90, and 120 min
consumption of samples. GI values were calculated from IAUC of blood glucose
curve of samples and compared with that of reference food, ignoring the area
below the fasting concentration.
The conclusion from this research were (1) the recommendation that can be
given is to have reference food trials at least twice with at least seven subjects
with seven blood sampling points. (2) need a further study about the use of 25g
AVCHO as the basis portion and the possibility of its GI-grouping, (3) Pandan
Wangi white rice is feasible to be used as a reference food in GI test with
conversion factor 0.74, (4) predicted-GI in vitro using H(90) starch hydrolysis
sampling did not predict the GI value precisely as GI in vivo, (5) HMT
modification did not affect GI value of product, (6) white rice is classified as high
GL food and all of the cookies are classified as low GL food.
Keywords: glycemic index, available carbohydrates, cookies, white rice, method
RINGKASAN
RATNA SARI LISTYANINGRUM. Evaluasi Metode Penentuan Indeks
Glikemik. Dimbimbing oleh: DIDAH NUR FARIDAH dan PUSPO EDI
GIRIWONO
Konsep indeks glikemik (IG) dapat membantu dalam pemilihan makanan
pada diet sehat. Makanan yang mengandung IG rendah cenderung menurunkan
glukosa darah postprandial dan respon insulin. Efek menguntungkan dari IG
tersebut memicu munculnya berbagai produk makanan yang menggunakan klaim
IG rendah. Oleh karena itu, penentuan nilai IG menjadi perhatian penting. Metode
umum pengujian IG adalah dengan menggunakan subjek manusia (in vivo) yang
perlu untuk dievaluasi. Di Indonesia, terdapat dua rekomendasi metode IG, yaitu
dari FAO (1998) dan BPOM (2011), namun terdapat beberapa perbedaan dalam
poin rekomendasi, termasuk jumlah subjek yang diperlukan, uji pangan standar,
dan poin pengambilan sampel darah. Jenis pangan standar dan porsi uji juga
merupakan poin-poin yang perlu dievaluasi. Oleh karena metode in vivo memakan
waktu dan biaya yang cukup mahal, uji IG dengan menggunakan metode in vitro
mulai diteliti. Dalam mengevaluasi metode IG, perlu dilihat apakah metode
tersebut telah mampu mengklasifikasikan sampel berdasarkan kategori IG. Oleh
karena itu, sampel dari tiga klasifikasi IG diperlukan, sehingga beras, cookies
gandum, cookies pati garut, dan cookies garut termodifikasi HMT digunakan.
Tujuan dari penelitian ini adalah 1) untuk mengevaluasi metode in vivo IG
berdasarkan rekomendasi BPOM (2011) dan FAO (1998), 2) untuk menguji
penggunaan 25 g karbohidrat sedia sebagai porsi uji, 3) untuk menguji
penggunaan nasi putih (Pandan Wangi) sebagai pangan standar, 4) untuk
membandingkan hasil dari IG hasil metode in vivo dengan IG hasil prediksi
metode in vitro, 5) untuk memahami korelasi HMT pati garut modifikasi dengan
IG produk, 6) untuk menentukan beban glikemik (BG) produk.
Subjek dalam penelitian ini perlu memenuhi kriteria inklusi (sehat, berusia
21-36 tahun dan Indeks Masa Tubuh 18,5-24,9 kg / m2). Dalam uji IG, percobaan
dilakukan dengan tiga kali pengujian glukosa dan sekali untuk masing-masing
sampel. Glukosa atau sampel yang mengandung 50 g atau 25 g karbohidrat sedia
dikonsumsi dalam waktu 12 menit. Contoh darah diambil pada 15, 30, 45, 60, 90,
dan 120 menit setelah mengkonsumsi sampel. Nilai IG dihitung berdasarkan luas
area di bawah kurva dari sampel dan dibandingkan dengan pangan standar.
Kesimpulan dari penelitian ini adalah (1) rekomendasi yang diberikan
adalah dengan menguji pangan standar minimal dua kali dengan subjek minimal
tujuh dan tujuh poin pengambilan sampel darah. (2) membutuhkan studi lebih
lanjut mengenai penggunaan 25g karbohidrat sedia sebagai porsi uji, (3) nasi putih
Pandan Wangi layak digunakan sebagai pangan uji dalam uji IG dengan faktor
konversi 0,74, (4) Metode prediksi IG in vitro dengan menggunakan persen
hidrolisis pati menit ke-90 (H90) belum dapat memprediksi nilai IG tepat seperti
IG in vivo, (5) tidak terlihat korelasi antara modifikasi HMT dan IG produk, (6)
nasi putih diklasifikasikan sebagai makanan dengan BG tinggi dan semua cookies
diklasifikasikan sebagai makanan dengan BG rendah.
Kata kunci: indeks glikemik, karbohidrat yang tersedia, cookies, nasi putih, metode
© Copy Right IPB, 2016
This work is under copy right of IPB 2016. No part of this work can be copied
without citing the source. Citation is allowable solely for education, research,
scientific paper writing, reporting, assay or review; and the citation will not cause
liability to IPB
Do not publish or copy a part or a whole of thesis without prior inform consent of
IPB
EVALUATION OF GLYCEMIC INDEX
DETERMINATION METHOD
RATNA SARI LISTYANINGRUM
Thesis
as one of the requirements for obtaining a degree of
Master of Science
at Food Science Master Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
Examiner in Thesis Defense: Dr. Ir. Endang Prangdimurti, M.Si
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PREFACE
Praise and gratitude to Allah Subhanahu wa Ta'ala for all His blessings so
this thesis is successfully completed. The research entitled “Evaluation of
Glycemic Index Determination Method” was carried out in Bogor Agricultural
University from October 2014 until April 2015.
Authors would like to express sincere thanks to parents and the whole
family for all the prayers and support and also Dr. Didah Nur Faridah, S.T.P, M.Si
and Puspo Edi Giriwono, PhD as advisory committee for the guidance, helps, and
advices during the time completing this thesis. Author would also like to thank to
Dr. Ir. Endang Prangdimurti, M.Si as an examiner in thesis defense for the
corrections and inputs in improving this thesis.
Expression of thanks are also given to Dean of Postgraduate School of
Bogor Agricultural University, Dr. Ir. Dahrul Syah, MSc.Agr and Head of Food
Science Master Program, Prof. Dr. Ir. Ratih Dewanti-Hariyadi, M.Sc for giving
permission for the research and acknowledgement to this thesis. Thanks to the
staffs of Postgraduate School and Food Science program for the help in
administration of the thesis. Thanks to Bu Antin, Mbak Yane, Pak Rozak, Pak
Yahya, Mbak Irin, and Mbak Ririn as laboratory technicians for the help during
research. Thanks to Mbak Any for the assistance in managing research fund.
Thanks to the volunteers for the willingness to participate in this research.
Author would also like to thank to Mutiara, Fathma, Fitria, Anand, and Rita
for the great team work during research. Thanks to Isti, Dina, Lorenzia, Karin, and
Eci for the advices and helps during study and research. Thanks to Dila, Alif,
Putri, Happy, Leyla, Astri, Endah, Riza, and Ika for the support during completing
this research. Thanks to friends from IPN and Bateng69 for the support. Last but
not least, thanks to DIKTI through Penelitian Unggulan Dasar program 2014 &
Laboratory of Food Analysis (LDITP) IPB for funding the research.
Hopefully this thesis can be useful and give contribution in food science.
Bogor, February 2016
Ratna Sari Listyaningrum
CONTENT
LIST OF TABLES
v
LIST OF FIGURES
v
LIST OF APPENDICES
v
ABBREVIATION
vi
1 INTRODUCTION
1
Background
1
Problems
3
Hypothesis
3
Objectives
4
Outcome
4
2 MATERIAL AND METHODS
4
Time and Place
4
Materials and Instruments
4
Methods
5
3 RESULT AND DISCUSSION
10
Samples Composition
10
Selection of Blood Glucose Measurement Methods
11
Individual Evaluation
13
Comparison of GI based on Several Reference Food Trials
14
Comparison of GI based on Blood Sampling Points
16
Comparison of BPOM standard and FAO standard for GI test
17
Comparison of 25 g and 50 g available carbohydrate portion
18
White Rice as Reference Food
22
Correlation of HMT Modification with GI
24
Glycemic Load
26
Predicted-GI in vitro
27
5 CONCLUSION AND RECOMMENDATION
30
Conclusion
30
Recommendation
30
BIBLIOGRAPHY
APPENDICES
AUTHOR BIOGRAPHY
30
LIST OF TABLES
1
Formula of cookies
7
2
Solutions in kit assay test
9
3
Statistical analysis
10
4
Composition of samples
11
5
Mean CV of individual glucose trials iAUC
13
6
Correlation between gender, age, BMI and mean of iAUC
14
7
GI value of the samples based on different glucose trials
15
8
GI value of the samples based on blood glucose sampling points
17
9
Recommendations of in vivo-GI test by FAO dan BPOM
18
10
GI based on three points recommendations by FAO and BPOM
19
11
Mean GI of white wice
20
12
Difference value of GI product using 50g and 25g AVCHO
21
13
Correlation of iAUC glucose and rice
22
14
GI value based on glucose and white rice as reference foods
23
15
GI values of several types of rice and possible conversion factor
23
16
Resistant Starch and Glycemic Index of samples
24
17
Correlation between Dietary Fiber, Test Portion, Resistant Starch, and 25
Gycemic Index
18
Glycemic load of samples
27
19
Predicted-GI in vitro of all samples compared with GI in vivo
28
20
Comparison of GI based on in vitro and in vivo methods in several foods
29
LIST OF FIGURES
1
Flow chart of research
2
Comparison of blood glucose measurement based on days of trials and
6
12
methods.
3
iAUC of the same sample and subject with different blood glucose
16
sampling points
4
Blood glucose curves of glucose and rice based on 25 g available
20
carbohydrate
5
Mean iAUC of glucose and rice
21
LIST OF APPENDICES
1
Ethical clearance from Health Ministry of Indonesia
36
2
Informed consent
37
3
Glucose Calculation
40
4
Control Solution Testing
40
5
Blood glucose measurement using glucometer
41
6
Reagents for measuring blood glucose concentration in kit assay
41
7
Statistical analysis of blood glucose concentration by kit assay day 1&2
42
8
Statistical analysis of blood glucose concentration by glucose day1&2
42
9
Statistical analysis of blood glucose concentration by kit assay and
43
glucometer in day1
10
Statistical analysis of blood glucose concentration by kit assay and
43
glucometer in day2
11
Statistical analysis of GI based on reference food trials
44
12
Statistical analyisis of GI based on blood sampling points
46
13
Statistical analysis of GI basedn on BPOM and FAO methods
47
14
Statistical analysis of GI based on AVCHO glucose (25g vs 50g)
51
15
Statistical analysis using Pandan Wangi white rice as reference food
52
16
Statistical Analysis of Resistant Starch
54
ABBREVIATIONS
ADA
American Diabetes Association
ANOVA
Analysis of Variance
AVCHO
Available Carbohydrates
BMI
Body Mass Index
BPOM
Badan Pengawas Obat dan Makanan
CV
Coefficient of Variation
DF
Dietary Fiber
DM
Diabetes Mellitus
FAO
Food and Agricultural Organization
GI
Glycemic Index
GL
Glycemic Load
HDPE
High Density Poly Ethylene
HI
Hydrolysis Index
HMT
Heat Moisture Treatment
iAUC
Incremental Area Under Curve
IDF
International Diabetes Federation
NCD
Non-Communicable Disease
NS
Native Starch
PGI
Predicted Glycemic Index
RDS
Rapid Digestible Starch
RS
Resistant Starch
SCFA
Short Chain Fatty Acid
SD
Standard Deviation
SDS
Slow Digestible Starch
WHO
World Health Organization
1 INTRODUCTION
Background
Non-communicable diseases (NCD) are the leading cause of death globally.
In 2008, from 57 million deaths those occurred globally 36 million deaths were
caused by NCD, such as cardiovascular disease, cancer, diabetes, and chronic lung
disease. According to the WHO report (WHO 2010), approximately 80% of
deaths due to NCD in countries with low and moderate incomes appeared to be
greatly influenced by socio-economic circumstances. NCD can be triggered by
smoking, unhealthy diet, lack of physical exercise, and alcohol consumption.
Unhealthy diet can cause excess energy intake in the body and lead to the risk of
type 2 diabetes. Type 2 diabetes is a chronic metabolic disorder, resulting from
progressive insulin over secretion complicated by insulin resistance and
characterized by abnormal increase in the blood glucose level (ADA 2005).
International Diabetes Federation (2013) estimates that the number of diabetics
will increase from 382 million to 592 million people by 2035 globally with the
majority age under 60. WHO has also predicted that there will be an increase of
diabetics in Indonesia from 8.4 million people in 2000 to 21.3 million people by
2030 (Wild et al. 2004). With this increase, there is an urgency to improve
prevention by consuming low GI foods. Moreover, from 4.6% of diabetes mellitus
(DM) prevalence in productive age in urban Indonesia, 3.5% is considered
undiagnosed (Mihardja et al. 2014). Prediction factors of undiagnosed DM in
Indonesia are age, obesity, hypertension, and smoking habit (Pramono et al. 2010).
The risk of obesity can be decreased by consuming healthy foods. Healthy diet
can be used as an approach to reduce the risk of type 2 diabetes. Selection of food
that does not raise blood sugar levels rapidly can be used as an attempt to keep
blood glucose at a normal level
The concept of glycemic index (GI) can be used to assist in the selection of
foods in a healthy diet. The GI value can be used as an estimation to the likely
effects of food on blood glucose levels when consumed. Food containing low GI
tends to lower postprandial blood glucose and insulin response (Wolever 1992).
Food and Agricultural Organization (FAO 1998) recommends food intake which
have low-GI, especially for diabetics and people who are intolerant to glucose.
Food with low-GI has been proven to give beneficial effect to human health, such
as improving blood glucose control (Wolever et al. 1991, Gilbertson et al. 2001,
Stevenson et al. 2006, Barakatun-Nisak et al. 2009, Moses et al. 2009); reduce the
risk of coronary heart disease (Liu et al. 2000); decrease total fat mass and
increase lean body mass (Bouche et al. 2002). Because of the beneficial effect of
GI, claims of low GI have been found in various food products. Therefore, the
determination of the GI value should be a concern.
The most common method of testing GI is by using human subjects (in vivo).
This method has many steps, such as filing of ethical clearance, preparation of
reference food and samples, selection of test subjects, blood sampling, analytical
methods, and calculation of GI. Blood sampling has been done in order to
calculate the GI value of food. Blood glucose of the samples is estimated using
spectrophotometer or auto analyzer (Brouns et al. 2005).
2
Recently, glucometer has also been used in estimating blood glucose in GI
determination. However, there is controversy regarding the use of glucometer,
because this method is commonly used as a self-monitoring of blood glucose and
not for research purpose (Velangi et al. 2005).
The cost of in vivo GI determination method is quite expensive. Therefore, it
is necessary to evaluate the method in order to get not only an effective method
but also accurate result. In Indonesia, there are two recommendations on GI
method from Food and Agricultural Organization (FAO) (1998) and National
Agency of Drug and Food Control of Indonesia (BPOM) (2011), however there
are some differences in the points of recommendation, including the minimum
number of subjects enrolled, reference food trials, and number of blood sampling
points.
Most of studies of GI use either glucose or white bread as reference food.
The use of white bread as a reference food is based on western diet that is
accustomed white bread as staple food, though for eastern cultures especially
South East Asia people who consume white rice as staple food, rice can be
considered more palatable than white bread or glucose. Both FAO (1998) and
BPOM (2001) recommend that the portion of food tested should contain 50g of
available carbohydrates (AVCHO). However, using 50g of AVCHO in low
density such as rice can lead to large volumes to be ingested.
Because of the in vivo method is time consuming, costly and laborious, in
order to overcome the difficulties of the method, the interest in an in vitro
methodology has increased. Predicted glycemic index (PGI) in vitro method has
been developed by Grandfelt et al. (1992) and Goni et al. (1997) and also has
been used in recent researches. In general, PGI is calculated based on hydrolysis
index (HI) obtained from the percent ratio iAUC of reference food and samples
after hydrolysis for 120 to 180 minutes. Yet Goni et al. (1997) stated that the best
hydrolysis time to estimate the glycemic response is in 90 minute (H90). Study
about the correlation between PGI in vitro calculated based on H90 and GI in vivo
calculated based on IAUC for 90 minutes has not yet been carried out.
Food can be classified based on its GI value: food with low GI (≤55),
intermediate (56-69), and high (≥70) (CDA 2011). In evaluating the GI method,
there need to be seen whether the method is able to classify samples by GI
category. Therefore, samples from three classifications of GI are required. Rice
that has been known to have high GI and wheat cookies that usually have
intermediate GI can be used as samples. For samples with low GI, arrowroot
starch shows potency to be used as sample. Arrowroot has been known to have a
low GI value of 14, compared to yam (90), taro (95), edible canna (105), sweet
potato (179) (Marsono 2002) and suweg (42) (Faridah 2005). Some studies
indicate that heat moisture treatment (HMT) modification can decrease
digestibility of starch (He et al. 2008; Syahbanu 2015). Study by Chung et al.
(2009) showed that 30% moisture content, 2 hrs, 120°C HMT decreased rapid
digestible starch (RDS) of all gelatinized starches. Percent differences of RDS
were 11%, 15%, and 16% for corn, pea, and lentil respectively. Therefore,
arrowroot starch needs to be modified with HMT in the expectation to get lower
GI sample.
3
It needs to take into account that the amount of carbohydrate consumed
affect the glucose response. Thus, GI determination need to be followed by
glycemic load (GL) calculation. GI along with GL can show the effect of
carbohydrate to glycemic response in quality and quantity.
Problems
Increase of diabetics and emergence of products with low GI claims makes
the determination of the GI value a concern. In vivo method to determine GI has
many steps to be evaluated. Controversy regarding the use of glucometer in GI
test should be re-examined by comparing it with previous method which is kit
assay. FAO (1998) recommends that the least number of subjects enrolled in the
GI tests are seven, while BPOM (2011) recommends ten. Recommendation from
FAO (1998) stated that reference food test should be repeated at least three times,
while BPOM (2011) does not give specific recommendation. In blood glucose
sampling points, FAO (1998) requires seven points (at 0, 15, 30, 45, 60, 90, 120
minutes), while BPOM (2011) requires five points (0, 30, 60, 90, 120 minutes).
Hence, it is interesting to know whether the difference in points of
recommendation can affect GI.
The use of white bread as a reference food is considered to be more
palatable than glucose and can affect psychologically. In eastern cultures
especially South East Asia people who consume white rice as staple food, rice can
be considered more palatable than white bread or glucose. The use of Pandan
Wangi as reference food in GI test need to be examined. In GI test, the use of 50g
AVCHO in low density sample such as rice can lead to large volumes having to
be ingested. Therefore, the effect of using smaller AVCHO intake (25g) to GI
needs to be examined. In vitro analysis methods of GI have been proposed as a
predictor of physiological effects of food. The difference in enzymes used and
hydrolysis time was the main interest for the previous studies. In this study, the
point of interest is in whether PGI in vitro calculated based on H90 will give a
similar value with IG in vivo calculated based on IAUC for 90 minutes. In
evaluating the GI method, samples from three classifications of GI is required. For
the low GI sample, arrowroot starch shows potency to be used as ingredient in the
making of cookies. In addition, HMT modification has been showed to decrease
starch digestibility starch. Therefore, the correlation of HMT and GI need to be
studied.
Hypothesis
Hypothesis which will be tested in this research were 1) In vivo GI method
can be efficiently done using at least 7 subjects, single reference food test, and 5
times blood sampling; 2) 25g AVCHO can be used as an alternative amount of
food test portion; 3) White rice (Pandan Wangi) can be used as an alternative
reference food in GI test, 4) HMT starch modification correlate with GI value of
product, 5) Predicted-GI in vitro using percent hydrolysis in minute 90 (H90)
method can be used as a preliminary screening test in GI in vivo test.
4
Objectives
The objectives of this research were 1) to evaluate the steps in in vivo GI
method based on BPOM (2011) and FAO (1998) recommendations (including
reference food trials, blood sampling points, and least of subject enrolled), 2) to
compare iAUC using 50g and 25g AVCHO portion, 3) to examine the feasibility
of using white rice (Pandan Wangi) as a reference food, 4) to understand the
correlation of HMT arrowroot starch modification to the GI of product, 5) to
determine GL of the samples, 6) to compare the result of GI in vivo method with
predicted-GI H(90) in vitro method.
Outcome
This study can provide an inputs to the party that will hold GI test related to
blood glucose sampling method, subjects enrolled, reference food trials, blood
sampling points, reference food type, and AVCHO intake, in order to get an
effective method yet accurate result. It also provides the feasibility of PGI in vitro
method in the initial screening of GI in vivo tests. The other outcome from this
study is the addition of reference in GI values of arrowroot cookies and HMTmodified arrowroot cookies.
2 RESEARCH METHODOLOGY
Time and Place
This research was conducted from October 2014 until April 2015 in
Laboratory of Processing, Laboratory of Chemistry, Laboratory of Biochemistry
of Food Science and Technology Department and Pilot Plant of South East Asian
Food and Agricultural Science and Technology (SEAFAST) Center, Bogor
Agricultural University
Materials and Instruments
Type of rice used in this research was Pandan Wangi (Upper Right Cianjur,
PT Midi Utama Indonesia, Tbk). Cookies was made from wheat (Tepung Segitiga
Bogasari, Indonesia), native arrowroot starch (obtained from Kelompok Wanita
Tani Yogyakarta, Indonesia), and HMT-modified arrowroot starch. Dextrose
monohydrate/glucose (Qinhuangdao Lihua Starch CO, China) was used as
reference food. Kit assay (Glucose Assay Kit, GAGO-20 Sigma, Sigma-Aldrich,
USA) was used in the selection of blood glucose measurement along with
glucometer, lancets, and strips from One Touch Ultra Lifescan (Johnson &
Johnson Company, USA), cotton, and alcohol 70% (PT Ikapharmindo Putramas,
Indonesia). Enzymes used in the analysis were Termamyl (Sigma A3403, SigmaAldrich, USA), protease (Sigma P4630, Sigma-Aldrich, USA), amyloglucosidase
(Sigma A9913, Sigma-Aldrich, USA), pepsin (Sigma P7000, Sigma-Aldrich,
USA), α-amylase (Sigma 10065, Sigma-Aldrich, USA). Instruments used in this
research were centrifuge (Eppendorf 5810R, Germany), spectrophotometer UVVIS (ShimadzuUVmini-1240, Japan), shaking water bath (Burgwedel GFL 1083,
Germany), pH meter (Eutech pH700, Singapore), vacuum-system (B’u’chi B-169,
Switzerland), and analytic scale (Precisa XT220A, Switzerland).
5
Methods
This research was divided into 6 stages, which were 1) Preparation (ethical
clearance, subjects, reference food and samples), 2) Analysis of samples
(proximate, DF, and RS), 3) Selection of blood glucose measurement method, 4)
GI-in vivo test, 5) GL calculation, 6) Predicted-GI in vitro test. Flow chart of
research is described in Figure 1.
Preparation
Ethical Issues
Ethical clearance was obtained from the Ethics Committee, The Health
Ministry of Indonesia (LB.02.01/5.2/KE.142/2014) (Appendix 1).
Subjects
Inclusion criteria in this research were healthy, no history of DM, not
pregnant, no smoking habit, range of age between 21-36 years old and range of
normal body mass index (BMI) 18.5-24.9 kg/m2. BMI was calculated as weight
(kg) divided by height (m2). All subjects had given informed consents to
participate (Appendix 2).
Modification of arrowroot starch
Native arrowroot starch was set up to 20% moisture content by spraying
water. The amount of water was determined by the following equilibrium wet
mass. Starch which has reached 20%wb moisture content was then stirred and
placed in high density polyethylene (HDPE) plastic pouch. The starch was set at
room temperature for one night to homogenize moisture content. Wet starch was
then treated with HMT by heating in an autoclave at a temperature of 1210C for
15 minutes. Furthermore, HMT-Modified Arrowroot Starch was dried by tray
dryer for 2h at 50°C.
(100% - MC1) x SW1 = (100% - MC2) x SW2
MC1: moisture content in the initial condition (%wb)
MC2: the desired moisture content (%wb)
SW1: weight of starch in the initial condition (g)
SW2: weight of starch after reaching the desired moisture content (g)
Production of rice and cookies
Rice was boiled with water to rice ratio 3: 1 in rice cooker. Rice was made on
the same day as the day of testing. Cookies was made with formula in Table 1.
The process of making cookies was started with mixing margarine, fine sugar and
palm sugar. Palm sugar was used because it has been proven that GI of the
mixture of bread using palm sugar as the sweetener was lower than using
sugarcane (Srikaeo & Thongta 2014). Yolk was added into the mixture and mixed
in 2 minutes. Flour or starch, salt, baking soda, vanilla essence and skim milk
powder were added into the mixture and mixed in 8 minutes. The dough then was
sheeted in flat-round shape and baked in oven at 170°C in ±15 minutes. Cookies
were placed in the sealed plastic jar until used.
6
Step 1. Preparation
Filing of ethical clearance
Selection of subjects
Modification of arrowroot starch
Production of rice and cookies
Step 2. Analysis of samples
Proximate, DF, and RS analysis
Step 3. Selection of blood glucose
measurement
Fasting blood glucose measurement (duplicate runs)
Step 4. GI in vivo test
Trials of 50 g glucose (triplicate runs)
GI in vivo tests of samples (50 g AVCHO) (single run each)
Trials of 25 g glucose (single run)
GI in vivo test of rice (25 g avCHO) (triplicate runs)
Step 5. GL calculation
GL calculation
Step 6. Predicted GI in vitro test
Predicted GI in vitro test
Figure 1 Flow chart of research
7
Table 1 Formula of cookies
Item
Wheat flour
Native arrowroot starch
HMT-modified arrowroot starch
Fine sugar
Palm sugar
Margarine
Skim milk powder
Yolk
Salt
Baking soda
Vanilla
Total
Composition (%)
Wheat Cookies NS Cookies HMT Cookies
57.04
57.04
57.04
5.13
5.13
5.13
5.13
5.13
5.13
23.30
23.30
23.30
4.24
4.24
4.24
4.97
4.97
4.97
0.14
0.14
0.14
0.02
0.02
0.02
0.02
0.02
0.02
100
100
100
Analysis of samples
Proximate Analysis
Proximate analysis were measured by AOAC (2012), including water
content (925.10), ash (923.03), protein (960.52), fat (920.39), and carbohydrate
(by difference).
Dietary Fiber
An in vitro method based on the procedure of AOAC (985.29, 2012) was
adopted to determine dietary fiber (DF). Blank was processed through the entire
procedure along with test portions to measure any contribution from reagents to
residue. Samples containing >10% fat were extracted using soxhlet extraction
with hexane as the solvent. Duplicate of fat-free test portions were weighed 1 g
and inserted into a glass beaker, then 50 ml of 0.1 M phosphate buffer pH 6 was
added. Termamyl 100 mL was added and closed with aluminum foil and
incubated at a temperature of 100ºC for 15 minutes. Solutions were cooled to
room temperature, then prepared to pH 7.5 by adding 10 ml 0.275M NaOH and
100 mL of protease solution, incubated at 60°C for 30 minutes in a water bath
shake. The solution was made into pH 4.0 to 4.6 by adding 10 ml of HCl and 300
mL amyloglucosidase 0.325M, incubation at 60 ° C for 30 minutes in a water bath
shake. Ethanol 95% which had previously been heated at a temperature of 60°C
was added 280 ml into the solution and allowed to settle at room temperature for
60 minutes. The solution was filtered using a vacuum filter and washed three
times with 20 ml of ethanol 78%, two times with 10 ml of 95% ethanol, and twice
with 10 ml acetone. The residue was then dried overnight at a temperature of
105ºC. One duplicate was analyzed for protein residue and other was analyzed for
ash residue. Total DF of the samples were calculated using the following formula.
8
TDF (%) =
WR
P
A
B
WT
WR−P− −
WT
�
: average of weight of residue from duplicate samples (mg)
: protein (mg)
: ash (mg)
: weight of blank – weight of blank protein – weight of blank ash
: weight of test portion (before tested)
Resistant Starch
An in vitro method based on the procedure of Goni et al (1996) was adopted
to determine RS with slight modification in buffer and glucose calculation.
Samples contain fat ≥5% were defatted using sohxlet extraction with hexane as
the solvent. Fifty mg of food portion were passed through 80 mesh filter and were
placed into a centrifuge tube. Ten mL of HCI-KCI buffer (pH=l.5) were added.
Then 200 µL of a pepsin solution (4000U/10ml HCI-KCI buffer) were added to
each sample and incubated at 40°C for 1 hour in a shaking water bath. Phosphate
buffer (pH=6.9) were added to reach pH 6-7.
One mL of α-amylase solution (400U α-amylase per mL buffer phosphate)
was added to each centrifuge tubes and incubated for 16 h in a water bath at 37°C
with constant shaking. Samples then were centrifuged (15 min, 3000 g) and the
supernatant were discarded. Samples’ residues were washed twice with 10 mL of
distilled water, and centrifuged again, and discarded. Residues were added with
1.5 mL distilled water and 1.5 mL of 4M KOH, mixed and left for 30 min at room
temperature with constant shaking. Aliquots were added with 2.75 mL 2M HCl
and 1.5 mL of 0.4 M sodium acetate buffer (pH=4.75). Then 80µL of
amyloglucosidase were added to the aliquots and incubated in a water bath for
45min at 60°C with constant shaking. Aliquots were then centrifuged (15 min,
3000 g), supernatants were collected and saved in a volumetric flask. Residues
then were washed at least once with 10 mL of distilled water, centrifuged again,
and combined supernatants with that obtained previously. Volume was adjusted to
10-l00 mL with distilled water. Then 0.5 mL of the solution was tested with
phenol sulphuric acid method (Dubois et al. 1956) to get the glucose content
(Appendix 3). The glucose was converted into starch by multiplying for 0.9.
Blood Glucose Measurement Method Selection
Volunteers were fasted overnight for 10-12 hours. Venous fasting blood
samples were taken from forearm. Blood samples were tested using two methods.
First method was using glucometer. Control solution testing has been done before
blood testing (Appendix 4). Blood sample (1 drop) was placed on analysing strip
of glucometer to obtain blood glucose concentration. Measurements were done
twice and the details was explained in Appendix 5. Second, blood samples (3ml)
were collected into EDTA treated tubes. Centrifugation was done at a speed of
2500 rpm at 40°C for 15 minutes to obtain blood plasma. Plasma glucose
concentrations were estimated using an enzymatic kit in duplicate. Following
solutions (Table 2) were pipetted into appropriately marked test tubes. At zero
time, 2mL of assay reagent were added to the first tube and mixed to start the
reaction.
9
About 30 to 60 second interval were allowed between additions of assay
reagent to each subsequent tube. Each tube was reacted exactly 30 minutes at
37°C. At 30-60 second intervals, then into each tube were added 2mL of 12 N
H2SO4 to stop the reaction. Each tube was mixed thoroughly and then measured
the absorbance against reagent blank at 540nm. Composition of reagents to
measure blood glucose is listed in Appendix 6. Blood glucose concentration of the
samples was calculated using the following formula.
=
�
�
�
Table 2 Solutions in kit assay test
Tubes
Water (mL)
Blank
1
Standard
0.95
Test
0.95
Sample (mL)
0.05
Glucose standard (mL)
0.05
-
Glycemic Index in vivo Test
Experiments were carried out involving glucose testing and sample testing
as shown in Figure 1. Subjects were asked to fast overnight for 10-12 hours.
Glucose or samples containing 50 g or 25 g AVCHO were consumed with 200 ml
of water within 12 minutes. Blood samples were obtained at 15, 30, 45, 60, 90,
and 120 min consumption of samples, using the chosen blood glucose
measurement tools. The tests were done with an interval times at least 3 days. GI
values were calculated from incremental Area Under Curves (iAUC) of blood
glucose curve of samples and compared with that of reference food, ignoring the
area below the fasting concentration.
=
�� �
�� �
×
Glycemic Load
Glycemic load of the samples was calculated using the following formula
by Martin et al. (2008). Serving size of the products was based on serving size of
similar product in the International Table of GI and GL by Foster-Powell et al.
(2002).
=
×
� � ��
�
��
Predicted-Glycemic Index In vitro Test
An in vitro method based on the procedure of Goni et al (1997) was adopted
to determine PGI with slight modification in buffer and glucose calculation. Fifty
mg of food portion were passed through 80 mesh filter. Ten mL of HCI-KCI
buffer (pH=l.5) were added. Then 200 µL of a pepsin solution (4000U/10ml HCIKCI buffer) were added to each sample and incubated at 40°C for 1 hour in a
shaking water bath. Volume was completed with phosphate buffer (pH=6.9) to
reach pH 6-7. Five mL of α-amylase solution (2,6U/5mL phosphate buffer) added
to each sample. Samples were incubated at 37°C in a shaking water bath.
10
One mL aliquot samples were taken at 0 minute and 90 minute. These
aliquots were heated at 100°C for 5 min to inactivate the enzyme. Then 4 mL of
sodium acetate buffer (pH=4.75) were added to each aliquot, and 60µL of
amyloglucosidase were used to hydrolyse the digested starch into glucose after 45
min at 60°C in a shaking water bath. Rice solution was centrifuged 1500g in 5
minutes. Volume was adjusted to 10-l00 mL with distilled water. Then 0.5 mL of
the solution was tested with phenol sulphuric acid method (Dubois et al. 1956) to
get the glucose content. The glucose was converted into starch by multiplying for
0.9.
Statistical Analysis
Data were expressed as mean values. The details of statistical analysis are
written in Table 3. Statistical software used was SPSS® version 20.
Table 3 Statistical Analysis
Data
Comparison of blood glucose measurement
Correlations between gender, age, BMI and
mean of IAUC
Comparison of GI of the samples based on
different glucose trials
Comparison of GI of the samples based on
blood glucose sampling points
Comparison of GI of the samples based on
three points of recommendation
Mean GI of white rice using 25g and 50g
AVCHO portion
Correlation of iAUC glucose and rice
GI value based on glucose and white rice as
reference foods.
Correlation between DF, Test Portion, RS,
and GI
Statistical Analysis
Independent sample t test
Pearson correlation test
ANOVA followed by Duncan’s
multiple range test (p