Model Asosiasi Perubahan Warna pada Indikator Kemasan Cerdas dan Perubahan Mutu Produk.

AN ASSOCIATION MODEL OF THE CHANGING COLOR
INDICATOR OF SMART PACKAGING AND PRODUCT
QUALITY

ELFA SUSANTI THAMRIN

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015

DECLARATION OF ORIGINALITY
AND COPYRIGHT TRANSFER*
Hereby, I declare that the thesis entitled An Association Model of The
Changing Color Indicator of Smart Packaging and Product Quality is my own
work under supervision of Dr Endang Warsiki, STP MSi and Dr Eng Taufik
Djatna, STP MSi. It has never previously been published in any university. All of
incorporated originated references from other published as well as unpublished
papers are stated clearly in the text as well as in the references.
Hereby, I state that the copyright to this paper is transferred to Bogor
Agricultural University.

Bogor, May 2015

Elfa Susanti Thamrin
Student ID F351120021

SUMMARY
ELFA SUSANTI THAMRIN. An Association Model of The Changing Color
Indicator of Smart Packaging and Product Quality. Supervised by ENDANG
WARSIKI and TAUFIK DJATNA.
Smart packaging is an innovation in the field of packaging that can monitor
and provide information to producers and consumers about the quality of the
packaged product. TTI (Time Temperature Indicator) is one of smart packaging as
an indicator label that can record temperature and time history of a packaged
product during distribution and storage. In Indonesia, there have been several
studies on the indicator labels to monitor the temperature history of the product.
This label is a crucial indicator for customers to get real information and to
minimize the risk dealing with expiry storage that affect the product quality. The
color change was in line with the quality changes of the product during storage.
Nowadays, there is no exact model to relate both of the changing between the
label and the product quality. Thus, association model is required an to relate

discoloration smart packaging indicator with the packaged product quality change.
The objectives of this research are to identify the association parameters, to
select the important attribute which sufficiently describes the relationship and
furthermore this research is to develop an association model of discoloration of
smart packaging indicator with natural dyes and the quality changes.
This research focused on modeling the association of the discoloration
indicator label and the quality change of packaged products and then connecting
between the two. One technique that is used to connect a combination was by
applying association rules of a combination of items. Before the model was
associated, it took an identification of parameters that will be used in the
modeling.
Parameter identification conducted by literature study and collects the
primer or secondary data. Parameter identification may process generate a lot of
parameters, so it necessary to use particular method to select the important
parameter attributes which sufficiently describes the relationship between the
changes of indicator color and the changes of product quality. It used a Relief
(Reliable Eliminated of Feature) method. Relief is an attribute selection algorithm
on binary classification.
At initial stage, data were obtained from a previous research in the form of
data °Hue, the value of L*; a*; b* (the values were to obtain the value of ΔE),

preference (hedonic) value and total colony of dairy products with labels of ERPA
(Aerva sanguinolenta) leaves. The data was discretized to classify the data °Hue,
ΔE, consumer preferences and the total colony into categorical value.
Selection of attributes by using the Relief method showed that there were
three attributes with the highest weight value (W i). The quality parameter of
organoleptic value (preference of consumers) with weight value is 0.423, ΔE
value with weight value is 0.262, and ° Hue with weight value is -0.0103. Those
were the important parameters and sufficiently describing the relationship of
discoloration and product quality changes.
The development of association model using association rules mining
generated 77 rules which had strong relationships between color changes of smart

packaging indicator and product quality changes parameter. The rules were
generated by calculating the support, confidence, lift and bond measure values
from each combination of itemsets. One of the association model which was
obtained from the association analysis with the ARM method is {° Hue = red, ΔE
= low, hedonic = like, colony = safe } → {color indicator = red} with the support
of 30.7% (the occurance is 30.7% of the whole experiment), confidence 100%
(100% probability of the indicator color was red if the °Hue is red, low ΔE value,
consumers like, and safe from contamination), bond 40% (there was a 40% joint

frequency of the relationship), and lift > 1% indicated the color change and quality
changes which correlated positively (the rules can be used). Practically, the
association rules facilitate the consumers and researchers to predict the quality
products which were adhered smart label indicator in computerized.
Smart label initially red in color, became progressively yellow as product
approached the end of shelf life and consumers advised not to consume the
product if the smart label turned to yellow in color. The color changes due to
changes in temperature during storage in the refrigerator because of the activities
which opened and closed the refrigerator repeatedly.
The statistical significance test concluded that all of the rules were
productive rules because they had extremely small
which could
safely reject the null hypothesis. These rules were representative the real condition of
association between discoloration and quality product changes in observed data.
The development of association model changing color indicator of smart
packaging and product quality applied to several types of products which must
keep in cold temperature or cold chain products. The further researches need to
develop the association model in several types of products in predicting the
quality of the product to minimize the cost and time of the research.
Keywords: Smart packaging, Relief, association rules mining, color change,

quality change

RINGKASAN
ELFA SUSANTI THAMRIN. Model Asosiasi Perubahan Warna pada Indikator
Kemasan Cerdas dan Perubahan Mutu Produk. Dibimbing oleh ENDANG
WARSIKI dan TAUFIK DJATNA.
Kemasan cerdas merupakan suatu inovasi dalam bidang kemasan yang
dapat memantau dan memberikan informasi kepada produsen dan konsumen
perihal kualitas produk yang dikemas. TTI (Time Temperature Indicator)
merupakan salah satu pengembangan kemasan cerdas yang merupakan label
indikator yang dapat mencatat sejarah waktu dan suhu dari suatu produk yang
dikemas selama distribusi dan penyimpanan. Di Indonesia sudah ada beberapa
kajian mengenai label indikator untuk memonitor sejarah suhu dari produk.
Kemasan cerdas merupakan indikator yang sangat penting bagi konsumen untuk
mendapatkan informasi dan untuk meminimalkan resiko yang berhubungan
dengan kadaluarsa penyimpanan yang berdampak pada kualitas produk.
Perubahan warna sejalan dengan terjadinya perubahan mutu produk selama masa
penyimpanan dan saat ini belum ada perhitungan kuantifikasi yang
menghubungkan keduanya. Sehingga, model asosiasi dibutuhkan untuk
menghubungkan antara perubahan warna indikator kemasan cerdas dengan

terjadinya perubahan kualitas produk yang dikemas.
Tujuan dari penelitian ini yaitu mengidentifikasi parameter asosiasi,
memilih atribut yang penting dan cukup untuk menggambarkan hubungan dan
mengembangkan model asosiasi perubahan warna indikator kemasan cerdas
dengan pewarna alami dan perubahan mutu produk.
Penelitian ini berfokus pada pemodelan asosiasi perubahan warna label
indikator kemasan cerdas dan perubahan mutu produk yang dikemas dan
menghubungkan antara keduanya. Salah satu teknik yang dapat digunakan untuk
menghubungkan suatu kombinasi yaitu dengan menerapkan aturan hubungan
asosiatif (Association rules) dari suatu kombinasi item. Sebelum dilakukan
pemodelan asosiasi, dibutuhkan suatu identifikasi parameter-parameter yang akan
digunakan dalam pemodelan.
Identifikasi parameter dapat dilakukan dengan studi literatur dan
pengumpulan data primer atau sekunder. Proses identifikasi parameter mungkin
menghasilkan banyak parameter sehingga dibutuhkan suatu metode untuk
memilih atribut parameter yang penting dan cukup untuk menggambarkan
hubungan antara perubahan warna indikator dan perubahan mutu produk. Peneliti
menggunakan metode Relief (Reliable Eliminated of Feature). Relief merupakan
algoritma pemilihan atribut pada binary classification.
Awalnya, data diperoleh dari penelitian sebelumnya berupa data nilai Hue,

nilai L*; a*; b* (untuk mendapat nilai ∆E), nilai uji organoleptik (kesukaan
konsumen) dan total koloni produk pada penyimpanan produk susu dengan label
indikator kemasan cerdas dari daun erpa. Data didiskretisasi untuk
mengklasifikasikan data Hue, nilai ∆E, nilai kesukaan konsumen dan total koloni
menjadi nilai kategori.
Pemilihan atribut dengan menggunakan metode Relief menunjukkan bahwa
terdapat tiga atribut dengan nilai bobot (Wi) tertinggi. Parameter mutu nilai
organoleptik (kesukaan konsumen) dengan nilai bobot 0,423, ∆E dengan nilai

bobot 0.262, dan nilai °Hue dengan nilai bobot -0,0103. Parameter ini merupakan
parameter yang penting dan cukup menggambarkan hubungan dari perubahan
warna dan perubahan mutu produk.
Pengembangan model asosiasi dengan menggunakan kaidah aturan asosiasi
(Association Rules Mining) membentuk 77 aturan yang memiliki hubungan yang
kuat antara terjadinya perubahan warna label indikator kemasan cerdas dan
perubahan mutu produk. Aturan ini diperoleh dengan perhitungan nilai support,
confidence, lift dan bond dari masing-masing kombinasi itemsets. Proses
kombinasi itemsets hingga kombinasi 5 itemsets. Salah satu model asosiasi yang
dibentuk dari analisa asosiasi dengan metode Association Rules Mining yaitu
{° Hue = red, ΔE = low, hedonic = like, colony = safe } → {color indicator =

red} dengan nilai support 30,7% (kejadian munculnya hubungan adalah 30,7%
dari seluruh percobaan), confidence 100% (100% kemungkinan warna indikator
merah apabila nilai Hue merah, nilai ∆E rendah, konsumen suka, dan aman dari
kontaminasi), bond 40% (terdapat 40% frekuensi bersama dari terjadinya
hubungan keduanya), dan lift > 1% menunjukkan terjadinya perubahan warna dan
perubahan mutu berkorelasi positif (rules tersebut dapat digunakan). Secara
praktis, aturan asosiasi ini dapat memfasilitasi konsumen dan peneliti untuk
memprediksi kualitas prduk yang dilekatkan indikator label cerdas secara
komputerisasi.
Label cerdas pada awalnya berwarna merah, kemudian berangsur-angsur
mejadi warna kuning ketika produk mencapai akhir dari umur simpannya dan
konsumen disarankan untuk tidak mengkonsumsi produk jika label cerdas
berubah menjadi warna kuning. Perubahan warna dikarenakan perubahan suhu
selama penyimpanan didalam kulkas (refrigerator) karena adanya kegiatan
membuka dan menutup kulkas secara berulang-ulang.
Pengujian statistical significance dapat disimpulkan bahwa semua rules
yang sangat
adalah rules yang produktif karena memiliki nilai
kecil yang dapat menolak H0. Rules ini merepresentasikan kondisi nyata dari
asosiasi antara perubahan warna dan perubahan kualitas produk pada data

pengamatan.
Pengembangan model asosiasi antara perubahan warna kemasan cerdas dan
perubahan kualitas produk diaplikasikan pada beberapa jenis produk yang harus
disimpan pada suhu dingin atau produk rantai dingin. Peneliti selanjutnya perlu
mengembangkan model asosiasi pada beberapa jenis produk dalam memprediksi
kualitas produk untuk meminimalkan biaya dan waktu penelitian.
Kata kunci: Kemasan cerdas, Relief, kaidah aturan asosiasi, perubahan warna,
perubahan kualitas

©Copyright 2015 by IPB
All Rights Reserved
No Part or all of this thesis may be excerpted without or mentioning the sources.
Excerption only for research and education use, writing for scientific papers,
reporting, critical writing or reviewing of a problem. Excerption doesn’t inflict a
financial loss in the paper interest of IPB.
No part or all of this thesis may be transmitted and reproduced in any forms
without a written permission from IPB.

AN ASSOCIATION MODEL OF THE CHANGING COLOR
INDICATOR OF SMART PACKAGING AND PRODUCT

QUALITY

ELFA SUSANTI THAMRIN

Thesis
as partial fulfillment of the requirements
for the degree of Master of Science
in the Agroindustrial Technology Study Program

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015

Non-committee examiner: Prof. Dr. Ir. Sutrisno, MAgr

Thesis Title : An Association Model of the Changing Color Indicator of Smart
Packaging and Product Quality
Name
: Elfa Susanti Thamrin

Student ID : F351120021

Approved by
Supervisor

Dr Endang Warsiki, STP MSi
Chairman

Dr Eng Taufik Djatna, STP MSi
Member

Acknowleged by

Head of
Agroindustrial Technology
Study Program

Dean of Graduate School

Prof Dr Ir Machfud, MS

Dr Ir Dahrul Syah, MScAgr

Examination date:
April 13th 2015

Passed date:

PREFACE
I would like to thank Allah Subhanahu Wa Ta’ala for all His gifts so that
this research is successfully completed. The theme chosen in the research which
conducted during July 2014 is association model, with the title of An Association
Model of The Changing Color Indicator of Smart Packaging and Product Quality.
I would like to express my sincere gratitude to Dr Endang Warsiki as Chair
of Advisory Committee for her support and encouragement during my study in
Bogor Agricultural University. I am very grateful to Dr Eng Taufik Djatna as
Member of Advisory Committee for his advice and supervision during the thesis
work. I would like to say many thanks to my family H. Thamrin (father) and Hj.
Hartati (mother), Hafiz Fajar Saputra (brother), Miftahul Jannah (sister), Rahmat
Saleh (brother), Muhammad Taufiq for their true and endless love, for never
failing patience and encouragement.
I would like to thank all lecturers and staff of Agroindustrial Technology
Department, all of my colleagues, especially my best friend in Agro-industrial
Technology (Elfira Febriani, Nina Hairiyah, Nova Alemina Sitepu, Eddwina Aidil
Fitria and M. Rafi), colleagues in Computer Laboratory of Agro-industrial
Technology Department (M. Zaki Hadi, Novi Purnama Sari, Riva Aktivia, Hetty
Handayani Hidayat, Aditya Ginantaka, IB Dharma Yogha, Rahmawati, Azri
Firwan, Rohmah, Fajar Munichputranto, Husnul Khotimah, Puspa, Aisyah,
Yudhis, Ikhsan, Imam, Denny, Septian), Teguh Pamungkas, Aziz Rahmad,
Iswahyudi, all of colleagues in Agro-industrial Technology 2012 and 2013, and
colleagues in IMPACS IPB for their support. It has been a pleasure to work with
you.
Hopefully this thesis is useful.

Bogor, May 2015
Elfa Susanti Thamrin

TABLE OF CONTENTS
TABLE OF CONTENTS

v

LIST OF TABLES

vi

LIST OF FIGURES

vi

LIST OF APPENDIX

vi

1 INTRODUCTION
Background of Research
Problem Statement of Research
Objective of Research
Benefit of Research
Boundaries of Research

1
1
2
2
2
2

2 LITERATURE REVIEW
The Color Changes
The Quality Changes
Association Rule Mining
Smart Packaging
Previous Research

3
3
5
6
7
10

3 METHODOLOGY
Research Framework
Identification of Association Parameter
Attribute Selection Analysis
Association Analysis of Discoloration
Model Evaluation

11
11
12
13
14
16

4 RESULT AND DISCUSSION
Identification of Parameter
Selection of Attributes
Association Rules Model
Association Model Evaluation

17
17
18
19
26

5 CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation

28
28
29

REFERENCES

29

APPENDIXES

33

GLOSSARY

51

BIOGRAPHY

53

LIST OF TABLES
1
2
3
4
5
6
7
8
9

Discretization of the data
The maximum weight (W) of variable combinations
The quality parameters that affect the color change indicator
The causal relationship color changes with product quality changes
The combination of 5-itemsets
Association Rules
The matrix of Top-10 Ranking Association Rules
Kinetic model of pasteurized milk color quality changes
Contingency table for Z and Y, conditional on W= X Z

18
18
19
19
21
22
22
26
26

LIST OF FIGURES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Color graph of L*, a*, b* (chrome dan ºhue)
3
Color indicator label (Nofrida 2013)
11
Research Framework
12
Flow diagram of identification association parameter
12
Flow diagram of selection important parameter
13
Flow diagram of association rule stage
16
Flow diagram of the computer program of association rules for smart
packaging
17
Combination process of 2 items
20
The relationship between ln value of total colony and storage times at
refrigerator temperature and room temperature
25
Arrhenius equation plot from ln of total colony
25
Program interface
28
Slopes mathematical equations of relationship between time storage and
discoloration of smart label
41
User selects the value in combo box
49
Running program stated that status product is “Good”
49
Running program stated that status product is to be eaten soon (Eat now!!!) 50
Running program stated that status product has already been spoiled (Bad) 50

LIST OF APPENDIX
1
2
3
4
5
6
7
8
9

Research database in refrigerator temperature (3 ± 2° C)
Binary table
The combination of 2-itemsets
The combination of 3-itemsets
The combination of 4-itemsets
Determination of association rules
Slopes mathematical equations
Statistical significance test
Display design of program

33
34
35
37
38
39
41
42
49

1 INTRODUCTION
Background of Research
Smart packaging is an innovation in the field of packaging that can monitor
and provide information to producers and consumers about the quality of the
packaged product. Smart packaging has an indicator adhered inside or outside of
the packaging, which is able to provide information about the condition of the
packaging and or quality of the food inside (Robetson 2006). TTI (Time
Temperature Indicator) is one of smart packaging in form of a label (indicator)
that can record temperature and time history of a packaged product during
distribution and storage. The indicator will provide information about the
changing of product quality by changing the color label (Wanihsuksombat et al.
2010).
The temperature and time indicators have been applied to reflect exposure
of cold temperatures and frozen products such as marine products (Hasnedi et al.
2010), horticulture (Warsiki and Putri 2012), dairy products (Nofrida et al. 2013),
as well as poultry and meat products (Vaikousi et al. 2009). In addition, TTI also
has been applied to pasteurized and sterilized products as to estimate the shelf life
of food products (Wanihsuksombat et al. 2010).
In Indonesia, there have been several studies on the indicator labels to
monitor the temperature history of the product. Previous research of Warsiki and
Putri (2012) shows that the color of film-indicator with synthetic dyes is changing
along with degradation of the sliced-pineapple quality during storage. The color of
film indicator shifted from bright red to pink because of degradation of acid
content of sliced pineapple during storage. The result of Nofrida‟s work (2013)
shows that the color of film-indicator of smart packaging with natural dyes from
Aerva sanguinolenta stored at 40° C shifted from red to yellow within two hours.
Furthermore, the research showed that the label‟s color yellow in one day at room
temperature, and at refrigerator temperature (3±2 °C), the smart packaging
indicator turned to yellowish red on the 8th day and turned into bright yellow on
the 12th day, whereas, the color in film indicator stored at freezer temperature
changed a bit after 78 days. Smart label was then applied to pasteurized milk
products.
Parameters such as Hue, value of L*; a*; b*, preference (hedonic) value,
total colony and color indicator of smart label are identified as results of literature
study and primary or secondary data collection. Parameter identification process
may generate lot of parameters, so it necessary to use particular method to select
the important parameter attribute which sufficiently describe the relationship
between the changes of indicator color and the changes of product quality. In this
research Relief (Reliable Eliminated of Feature) method is used. Relief is an
attribute selection algorithm on binary classification (Kira and Rendell 1992).
The label of smart packaging is the crucial indicator for customers to get
real-time information and to minimize the risk in dealing with expired product.
The color will change in line with the quality changes of the product during
storage. However until now there is no exact model to show the relation of both

2
parameters. It is required an association between discoloration of smart packaging
indicator and the packaged-product quality-change.
Focus of this research is making an association model of indicator color
changes and the changes of packaged product quality to give a solution of
problems. One technique is used to connect a combination is by applying a‟priori
algorithm. A‟priori algorithm is one of data mining techniques for data retrieval
using association rules of itemsets combination.
Association rules mining (ARM) is commonly referred as market basket
analysis. This is because the use of association rules mining can help
manufacturers or retailers to make decisions in running the business by analyzing
the consumer purchasing behavior. The mechanism of the association rules is to
calculating the value of support, confidence, lift and bond of relationship items. If
the support value is greater than the minimum support and confidence value is
greater than the minimum confidence, it determines an association rules from a
combination of items (Yin et al. 2011).

Problem Statement of Research
Nowadays, there is no exact model to show the relation of the changing
between the label and the product quality. The challenge in this research is to find
a causal relationship between the color change and the quality changes of the
packaged products. The value of parameters; ΔE, °Hue, preference and total are
based on previous researcher‟s result, that have been analyzed by previous
researchers and also to classify the causal relationship.

Objective of Research
The objectives of this research are to identify the association parameters, to
select the important attribute and sufficiently describing the relationship and
furthermore this research is to develop an association model of discoloration of
smart packaging indicator with natural dyes and the quality changes.

Benefit of Research
This research is expected to formulate a relationship model of the changing
color of smart label and product quality. The result is expected to facilitate
prediction of product quality using smart packaging instead of conducting
laboratory test.

Boundaries of Research
This research construct a causal relationship model using secondary data
such as ∆E, Hue, discoloration of indicator label, and product quality changes;
including hedonic value (preference value), and total of colony of packaged
product, which are obtained from previous research. The secondary data is from

3
analysis of smart label which‟s stored in refrigerator temperature (3 ± 2° C) during
12 days. These assumed data are able to generate causal relationship between the
changing color of label and product quality to set the rules which will serve as a
basis in predicting the other products.

2 LITERATURE REVIEW
The Color Changes
Color becomes an important thing for human and agro industry product. By
using color, it can appraise the product quality. Color is the important factor in
sensory estimation due to give information about the quality of products
(Setiautami 2013). Three factors are related to the color which is captured by sight
sense; shine source, chemistry and physics object characteristics, and spectrum
sensitivity characteristic (Putri 2012).
There are two methods for color measurement; they are subjective and
objective method. The subjective method is conducted directly by sight sense and
assisted by chromaticity diagram. The second one, the objective color
measurement uses analytical instrument, such as chrome meter and
spectrophotometer. Chrome meter can detect spectral ray deviation from light
emission automatically. The measuring results are in output color system CIE
(Commission Internationale d‟Eclairage), color system Hunter LAB and color
system CIELAB. In a general way, CIELAB system is commonly used by users
(MacDougall 2002). Chrome meter is used for measuring solid sample, whereas
spectrophotometer for measuring liquid samples.
CIE system describes the color using Y symbol, Hunter LAB system
interpreted color as XYZ and CIELAB interpreted color as L*, a*, b* and
additional requirements such as Hue (h), chrome value (C) and total of color
difference (ΔE) (MacDougall 2002).

Figure 1 Color graph of L*, a*, b* (chrome and ºHue)
The color change of smart label TTI is measured by chrome meter which
explained as total of color difference (ΔE) (Wanihsuksombat et al. 2010):

E

( L )2

( a )2

( b )2

(1)

4

Where ΔL* is brightness degree (0) black and (+100) white, Δa* is
difference of greenish degree (-60) and florid degree (+60), Δb* is difference of
deep blue (-60) and yellowness (+60) (Vaikousi et al. 2009; Kim et al. 2012). The
value of Hue shows the chrome degree on chromatic revolution which is seeing
by sight sense.
Dye materials are in two categories, namely natural dye and synthetic dye.
Natural dye is dye material for food product which the materials were took from
the plants (chlorophyll, anthocyanin, bixsin, curcumin, and carotene), animals
(myoglobin and hemoglobin), caramel and mineral dye. Natural dye is safe and
added into the other food materials. The weaknesses of natural dye are instability,
weakness of color effort, and limited of color variations. These cases motivate the
producer to use synthetic dye as addition in food and drink products. Synthetic
dye is soluble easier in the water, more stable, more interesting, distributed the
food color, and brought back the color of base material which turned during
process (Warsiki and Putri 2012). Synthetic dye which is usually found in food
product, namely sunset yellow, tartrazine, carmoisin, and ponceau 4R. Except
that there are rhodamin B and metanil yellow. The synthetic dye is toxic for
human health (Sumarlin 2008).
One of the natural dyes used are anthocyanins. Stability of anthocyanin is
influenced by the structure and anthocyanin concentration, temperature, pH,
oxygen, light, ascorbic acid, sugar and sulfite (Jackman and Smith 1996).
Anthocyanin dominant structure is in the form of the flavium cation core
protonation and electron deficiency in acidic conditions. When the pH value is
increase, flavium cation is unstable and prone to structural transformation into a
colorless compound (kalkon) (Jackman and Smith 1996).
Oxygen causes oxidation of anthocyanin then become colorless compound
that lowers the color stability of anthocyanins (Ningrum 2005). Light has a certain
energy which stimulates the occurrence of photochemical reactions
(photooxidation) are able to cause carbon ring number 2 was opening.
Photooxidation reaction is capable of forming a colorless compound (degradation
indicator) (Nofrida 2013). The increase in temperature resulted in deterioration
and discoloration anthocyanins quickly through the hydrolysis process of
glycosidic anthocyanin and produce open aglycone ring so formed groups‟
carbinol and kalkon which is colorless (Nofrida 2013).
According Nofrida (2013), the increase in the value of L* on the indicator
label with ERPA leaf dye occured due to anthocyanin degradation process under
the influence of temperature, the higher of storage temperature then the faster the
L* value increased because of the color samples are nearly white. Inversely
proportional to the measurement of a* value, the higher of storage temperature
then the faster a* value decreased due to a decrease in the degree of redness
caused an increase in the reaction rate of structural transformation of flavium
cations (red) into kalkon (colorless). While the value of b* increased due to an
increase in the storage temperature. Increased temperature and light cause
anthocyanin compounds are degraded more rapidly becoming kalkon compounds.
ΔE is color change value. The greater of ΔE value indicates decreasing the
intensity of the color is much different to the original color. Sample color changes
indicate the storage time, temperature and light due to degradation of
anthocyanins which cause a color change from red to yellow. In addition, the

5
temperature increase and storage time led to the value °Hue increase (Nofrida
2013).
Synthetic dyes are sensitive to acidic conditions. If the synthetic dye
contacts with the acidic conditions then occur degradation in synthetic dyes
became more faded (Putri 2012).

The Quality Changes
Deterioration is a deviation of the product from the first quality. The
degradation of food product is occurred when there are some products digressions
after they are produced (Nurkhoeriyati 2007). The reaction of deterioration
conducts when the product contacts to the environment directly or there is beating
of mechanical. The deterioration occurs when the product contacts to the
environment directly due to contact with the air, oxygen, light, aqueous vapor or
there is temperature change. The degree of deterioration influenced with storage
time, and rate of degradation influenced with environment condition of storage
(Savitri 2000).
Determination the quality of food stuffs generally were on several factors;
taste, color, texture, nutritional value and microbiological properties. Pasteurized
milk is very susceptible to high temperatures, the packaging showed that milk
able to damage faster than the expiration date when stored at temperatures > 5°C.
The quality of pasteurized milk through organoleptic and total plate test which
stored at refrigerator temperature (3 ± 2° C) and room temperature (25 ± 3° C)
(Nofrida 2013). The main cause of milk deterioration is microbes (bacteria), it due
to the high nutrient content of milk become the preferred medium of
microorganisms to grow and thrive. Lactic acid is formed as a result of lactose
fermentation by lactic acid bacteria and other bacterial contamination (E. coli).
The maximum limit of microbial contamination in milk is 5x10 4 colonies / mL
(Nofrida 2013).
Pasteurized milk is milk that has undergone a heating process at a
temperature of 72 °C for 15 seconds, namely High Temperature Short Time
(HTST) or heating at 63 °C-66 °C, namely Low Temperature Low Time (LTLT)
for 30 minutes, then immediately is cooled to 10 °C. Furthermore, it is treated
aseptically and stored at a maximum temperature 4.4 °C to increase the shelf life
of milk. The temperature does not cause spoilage microbes die, but no longer able
to grow and reproduce. During the microbial spoilage are not active, then the milk
remain durable and good to eat (Mulyani 2011).
Pasteurized milk quality testing refers to SNI No 01-3141-1998 which is
testing i.e. color, smell, taste, alcohol test, density, fat content, protein content, the
degree of acid, microbial contamination (TPC, E. coli, Salmonella) and metal
contamination (Mulyani 2011). As a result of a variety of chemical reactions that
occur in food products are accumulative and irreversible during storage, so that at
certain times of the reaction products result unacceptable food quality
(Nurkhoeriyati 2007).
Type parameters or quality attributes are tested to determine the shelf life of
the product depend on the type of product. The products were stored in frozen

6
form (cold chain product) or in cold conditions parameters such as the growth of
microbes (Nurkhoeriyati 2007).
The changes of fruit quality are affected by water content, total acid, fruit
hardness, pH levels and vitamin C. The high water content of fruit makes the fruit
easily damaged if it does not conduct handling during storage. Minimally
processed fruits undergo browning faster because a polyphenol oxidase compound
catalyzes the phenol oxidation to be o-quinone compound. Spontaneously, it
carries out the polymerization reaction to be brown color pigment (melanin). One
way to prevent browning in fruits and vegetables is by cold storage. Total acid of
fruit increased during storage because of the hydrolysis of starch into simple
sugars and then converted into organic acids that cause a decrease in the pH value
on the fruit (Putri 2012).
According to Labuza (1982), the reaction of food quality changes can be
explained by the order of zero and one. The types of deterioration that follow
zero-order kinetics include enzymatic breakdown reactions, enzymatic browning
and oxidation reactions. Degradation of zero-order reaction is constant, the speed
of degradation takes place remains at a constant temperature. The types of
deterioration that followed the first order are rancidity, microbial growth, off
flavor production by microbes in meat, fish and poultry, destruction of vitamins
and protein degradation.
The calculation of reaction kinetics of quality changes based on parameters
of total colony in pasteurized milk by means of looking for ln score of total
colonies in refrigerator temperature 3 ± 2 °C and room temperature 25±3 °C. The
obtained results are then plotted on a graph the relationship between the score ln
as the y-axis and storage time as the x axis. Thereafter, it looks for the value of a
constant (k) quality changes per day were derived from the slope of the regression
equation graph. After the k value is obtained, and then it searched ln k values for
each storage temperature. Hereafter Arrhenius plot is devised by the x-axis
represent the value of 1 / T (K-1) and the y-axis declared ln k value at each storage
temperatures. Linear regressions obtained in this Arrhenius curves were predict
the quality changes of products by using the Arrhenius formula:
Qt Q0 e kt
(2)
Where, Qt is the total colony that has changed the quality, Q0 is the total
initial colony, e is the base value of the natural logarithm, k is a constant, t is time
storage Labuza 1982; Nurkhoeriyati 2007).

Association Rule Mining
Data mining is a generic term which covers research results, techniques and
tools used to extract useful information from large databases (Niu and Chen 2013).
Association rule is one of the most popular data mining techniques widely used
for discovering interesting association and correlation between data elements in a
diverse range of application (Agrawal and Srikant 1994; Kotsiantis and
Kanellopoulus 2006; Shaharanee et al. 2010; Özseyhan et al. 2012; Niu and Chen
2013).
Association rules identify items that are most often bought along with
certain other items by a significant fraction of the customers (Omiecinski 2003).

7
Omiecinski (2003) gave an example “we may find that 95 percent of the
customers who bought bread also bought milk”. A rule may contain more than
one item in the antecedent and the consequent of the rule. Association rules
mining (ARM) is useful for discovering relationships among data and application
to many different domains including market basket and risk analysis in
commercial environments, clinical medicine, epidemiology, crime prevention, and
fluid dynamics (Niu and Chen 2013).
In association rules mining, every rule must satisfy two user specified
constraint; one is a measure of statistical significance called support and the other
a measurement of goodness of the rule called confidence (Omiecinski 2003). But
according to Han and Kamber (2012) to discover interesting rules efficiently,
three main sets of constraints on significance and interest of the rules are defined,
these are support, confidence, and lift. According to the original definition based
on Agrawal and Srikant (1994), support (supp(X,Y)) of a rule is the proportion of
transactions including item sets (X Y) over all transactions. Confidence is the
proportion of transactions that fulfill the rule completely over the ones having
only the left-hand-side of the rule true. Lift measures how much the observed
confidence of the rule deviates from the expected confidence. The other
measurement of association rules in data mining is bond. Bond measure is much
the same with support measure. Bond measure knows about relationship ratio of
items from total transactions of the items, whereas support knew about
relationship of items from over all transactions (Erniyati 2013).
The problem of mining association rules is to generate all rules that have
support and confidence greater than some user specified minimum support and
minimum confidence thresholds (Omiecinski 2003; Niu and Chen 2013). The
minimum support threshold and minimum confidence threshold can be set users or
domain experts (Raorane et al. 2012). Rules that satisfy both a minimum support
threshold and minimum confidence threshold are called strong (Raorane et al.
2012).

Smart Packaging
Ahvenainen (2003) asserted “modern packaging consist of two types,
namely active packaging to change the condition of the packed food to extend
shelf life or to improve safety or sensory properties, while maintaining the quality
of the packaged food, and smart packaging as systems monitor the condition of
packaged foods to give information about the packaged food during transport and
storage”. Smart packaging is packaging which senses and informs the condition
of the product. Thus, the term can be used in a broad sense including features
concerning product identify, authenticity, and traceability, tamper evidence and
theft protections as well as safety and quality issues (Kuswandi et al. 2011).
Consumers increasingly need to know what ingredients or components are
in the product and how the product should be stored and used. Smart labeling and
sticker, for example, will be capable of communicating directly to the customer
via thin film devices providing visual information. Another important need is
consumer security assurance, particularly for perishable food products. The

8
question is whether, for example, a slice fruits is safe to use or consume, and
currently this is answered by “best before” date stamping (Kuswandi et al. 2011)
The intelligent packaging or smart packaging includes indicators to be used
for quality control of packed food. They can be external indicators, for example
indicators which are adhered outside the packages (time temperature indicator),
and so called internal indicators which are placed inside the package, either to the
head-space of the package or adhered into the lid. Some of indicators which are
placed inside, namely oxygen indicators for indication of oxygen or package leak,
carbon dioxide indicators, microbial growth indicators and pathogen indicators
(Ahvenainen 2003).
Time and temperature indicator was already applied to represent the
products which have cool and frozen storage such marine products (Taoukis et al.
1999; Mendoza 2003; Hasnedi et al. 2010), horticulture product (Bobelyn et al.
2006; Warsiki and Putri 2012), dairy product (Nofrida et al. 2013), poultry and
meat product (Vaikousi et al. 2009; Ellouze et al. 2010). Color change informs
that the products be in the temperature should not be or in unsuited storage
(Warsiki and Setiautami 2013). A time temperature indicator (TTI) was applied
on pasteurization and sterilization products and predicted the shelf life of food
products (Wanihsuksombat et al. 2010).
Time-temperature indicator has working mechanism based on different
principle namely chemical, physical, and biological. For chemical or physical
response, it is based on chemical reaction or physical change towards time and
temperature. While for biological response, it is based on the change in biological
activity, such as microorganism, spores, enzymes towards time or temperature
(Kuswandi et al.2011).
Time temperature indicator that has been commercialized namely 3M TM
Monitor MarkTM, Fresh-Check® TTI, CheckPoint® TTI, OnVu TTI, TT Sensor
TTI, Timestrips® TTI (Kuswandi et al. 2011; Pavelková 2013).
The 3MTM Monitor MarkTM is diffusion-based indicator label and is on the
color change of an oxidable chemical system controlled by temperature-dependent
permeation through a film (Pavelková 2013). A film strip separates the wick from
the reservoir that is removed at the activation stage. At this point, the porous wick,
white in color, is shown in the window. Upon exposure to a temperature
exceeding the critical temperature, the substance melts and begins to diffuse
through the porous wick, causing a blue coloring to appear (Kuswandi et al. 2011).
Response of the indicator is measured by the progression of the blue dye along the
track, and this is complete when all five windows are blue. The working principle
is based on the melting and diffusion of the blue dye. The label change color when
exposed to higher than recommended storage temperature and will also change as
the product reaches the end of shelf life.
The Fresh-Check® TTI is based on a solid state polymerization reaction,
resulting in a highly colored polymer. The response of the TTI is the color change
measurable as a decrease in reflectance (Pavelková 2013). The mechanism of
Fresh-Check TTI is based on the color change of a polymer formulated from
diacetylene monomers (Kuswandi et al. 2011). The indicator consists of a small
circle of a polymer surrounded by a printed reference ring. The polymer, which
starts lightly colored, gradually darken depends on the color that tend to reflect the
cumulative exposure to temperature, thus the higher the temperature, the more

9
rapidly the polymer change in color Consumers are advised not to consume or
purchase the product, regardless of the “use-by” date. This indicator may be
applied to packages of perishable products to ensure consumers at point-ofpurchase and at home that the product is still fresh. These indicators have been
used on fruit cake, lettuce, milk, chilled food (Kuswandi et al. 2011; Pavelková
2013).
The CheckPoint® TTI is a simple adhesive label on enzymatic system. These
labels react to time and temperature in the same way that food product react, and
thus give a signal about the state of freshness and remaining shelf-life. The TTI is
based on a color change caused by a pH decrease that is the result of a controlled
enzymatic hydrolysis of a lipid substrate. Hydrolysis of the substrate causes acid
release and the pH drop is translated into a color change of a pH indicator from
deep green to bright yellow to orange red (Pavelková 2013). This CheckPoint®
devices consists of a bubble-like dot containing two compartments; one for the
enzyme solution, lipase plus pH indicating dye and the other one for the substrate,
consisting primarily of triglycerides. The dot activated at the beginning of the
monitoring period by application of pressure on the plastic bubble, which breaks
the seal between compartments. The ingredients are mixed and as the reaction
proceed a pH change results in a color change (Kuswandi et al. 2011). There are
two configurations i.e., CheckPoint®I (single dot) is used for transmit temperature
monitoring of cartoons and pellets of product. The other one is CheckPoint ®III
(triple dot) is especially used in the wholesale distribution chain and incorporate
three graded responses in a single label (Kuswandi et al. 2011).
The OnVu TTI is a newly introduced solid state reaction TTI. It is based on
photosensitive compounds; organic pigments e.g. benzylpyridines, that change
color with time at rates determined by temperature. The TTI labels consist of a
heart shaped „apple‟ motif containing an inner heart shape. The image is stable
until activated by UV light from an LED lamp, when the inner heart changes to a
deep blue color. A filter is then added over the label to prevent it being recharged.
The blue inner heart changes to white as a function of time and temperature. The
system can be applied as a label or printed directly onto the package (Pavelková
2013).
The TT Sensor TTI is based on a diffusion–reaction concept. A polar
compound diffuses between two polymer layers and the change of its
concentration causes the color change of a fluorescent indicator from yellow to
bright pink (Pavelková 2013).
Timestrips® TTI are smart labels that monitor how long a product has been
open or how long it has been in use. Timestrips® TTI is activated by squeezing a
start button which moves the liquid into direct contact with the membrane
(Kuswandi et al. 2011).
Fresh-Check® and CheckPoint® as freshness indicators for fish and meat are
based on pH change. Practically, these label prepared by entrapping within a
polymer matrix a pH sensitive dye (e.g. bromocresol green) that responds, through
visible color changes to the spoilage volatile compounds that contribute to a
quantity known as total volatile basic nitrogen (Kuswandi et al. 2011).
The other smart packaging devices except time temperature indicator (TTI)
are oxygen indicator, carbondioxide indicator (package leak e.g. MAP), microbial

10
growth indicator (freshness indicator, pH dye indicator for perishable food),
pathogen indicator, and RFID (Ahvenainen 2003).
ripeSenseTM using a sensor label that reacts to the aromas released by fruit as
it ripens. The sensor initially red and graduates to orange and finally yellow. This
sensor has already applied for pears, and can also be applied as ripeness indicator
for kiwifruit, melon, mango, avocado and other stone fruit etc (Kuswandi et al.
2011).
Modified atmosphere packaging (MAP), in these cases, the atmosphere of
packaged is not air but consists of a lowered level of O 2 and a heightened level of
CO2. The MAPs for non-respiring food typically has a high concentration of CO2
(20-80%) and a low concentration of O2 (0-2%). It has been claimed that the color
change of the O2 indicators used in MAPs containing acidic CO2 gas is not
definite enough (Kuswandi et al. 2011).
Biosensors such as conducting polymers can also be used by detecting the
gases released during microbe metabolism. The biosensors are formed through
inserting conducting nanoparticles into an insulting matrix, where the change in
resistance correlates to the amount of gas released (Kuswandi et al. 2011).
RFID is a very crucial factor in modern supply chains where large amounts
of raw material may be coming from different regions to be processed in one site,
and then distributed to consumers. It is widely envisioned that RFID tags are
expected to replace barcodes that are commonly used today (Kuswandi et al.
2011).
Previous Research
Research development about smart packaging is already conducted and
applied on several products which susceptible of temperature and light. Hasnedi et
al. (2010) detected spoilage of tilapia fillet using color indicator of smart
packaging Bromothymol Blue with quality parameters including TVBN (Total
Volatile Basic Nitrogen) value and TBC (Total Bacterial Counts) value. The
developments of color indicator of smart packaging by using natural and synthetic
dye have already done. Warsiki and Putri (2012) made smart packaging with red
cherry dye and adhered on slice pineapple which packed using styrofoam with
discoloration parameters including ∆E and Hue value and quality product change
parameters including water content, C vitamin content, total acid, pH value, and
weight decrease of product. Natural dye from bit fruit (Warsiki and Setiautami
2013) and ERPA leaves (Nofrida 2013) already used in the making of color
indicator which applied on pasteurization milk by conducting the analysis of color
changes including ∆E and Hue value, whereas product change parameters
including preference of consumers and total colony.
ERPA leaf color indicator is stable at low temperatures and changing colors
very quickly when stored at room temperature or outdoor temperature with solar
radiation. This indicator is in accordance with pasteurized milk because this
product should be stored in the freezer and refrigerator temperature. The
increasing total microbial colonies on pasteurized milk during storage change the
color of smart label (Nofrida 2013).

11

Figure 2 Color indicator (Nofrida 2013)
Total acid at slice pineapple was higher so that the pH of the fruit was lower
caused synthetic dyes on the smart label degraded so that the indicator color
became faded (Putri 2012).
The color change of smart label was applied to the fillet of tilapia because of
the volatile base component chemical which result of degradation process in
tilapia fillet meat tends to increase during decomposition of fish (Hasnedi et al.
2010). Indicator color changed from yellow to dark yellow to bluish green.
But, researches of smart packaging are still limited in specifically analysis
on color indicator which applied in certain product. The analysis in laboratory
scale needs time consuming and expensive. Thus, it needs a model which able to
detect and predict the packaged product condition.

3 METHODOLOGY
Research Framework
The research framework activities can be seen in Figure 3. The stages of
research began by identifying the parameters which was used in the association
analysis by collecting secondary data from previous research, then selected the
important attributes parameters and analyzed the association to see the pattern of
the relationship between color changes and quality changes. The research
framework was illustrated in the following figure.
This research was conducted at the Laboratory of Computer on Department
of Agro-industrial Technology. This research began in July 2014 to January 2015.

12
Start

Identify the association
parameters

Select the important
parameter

Develop the
association model

Ranking of influential
parameter

Evaluate the model

Association Ru