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Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network

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@article{IJASEIT3143,
   author = {Mohamad Nur Hakim Jam and Kim Seng Chia},
   title = {Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1389--1394},
   keywords = {neural network; pineapple; near infrared light; internal quality},
   abstract = {

One of the important internal qualities of pineapples is the total soluble solid content (SSC). Normally, the SSC can be evaluated using a reflectometer that is destructive and time-consuming. This research investigates the relationship between the reflected near infrared light and the internal quality of pineapples non-destructively. Five light emitted diodes (LEDs) that are in the range between 750 nm and 950 nm were used as the light source. The photodiode (OPT101) sensor was used to collect the light from the pineapple. The digital reflectometer was used to determine the reference SSC. The Near-infrared (NIR) data and the digital refractometer data were used to build the predictive model. The relationship between the near infrared light and the SSC of the pineapple was determined using artificial neural network predictive model. The internal quality of pineapples was determined using five NIR data wavelengths, the result points out that the k-fold cross-validation accurate classification was 75.56%. Besides, findings indicate that the artificial neural network that used four wavelengths that were 780 nm, 850 nm, 870 nm, and 940 nm achieved better classification than that used five wavelengths that included 910 nm. Thus, the artificial neural network coupled with NIR light is promising to be used to classify the internal quality of pineapples non-destructively. 

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3143},    doi = {10.18517/ijaseit.7.4.3143} }

EndNote

%A Jam, Mohamad Nur Hakim
%A Chia, Kim Seng
%D 2017
%T Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network
%B 2017
%9 neural network; pineapple; near infrared light; internal quality
%! Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network
%K neural network; pineapple; near infrared light; internal quality
%X 

One of the important internal qualities of pineapples is the total soluble solid content (SSC). Normally, the SSC can be evaluated using a reflectometer that is destructive and time-consuming. This research investigates the relationship between the reflected near infrared light and the internal quality of pineapples non-destructively. Five light emitted diodes (LEDs) that are in the range between 750 nm and 950 nm were used as the light source. The photodiode (OPT101) sensor was used to collect the light from the pineapple. The digital reflectometer was used to determine the reference SSC. The Near-infrared (NIR) data and the digital refractometer data were used to build the predictive model. The relationship between the near infrared light and the SSC of the pineapple was determined using artificial neural network predictive model. The internal quality of pineapples was determined using five NIR data wavelengths, the result points out that the k-fold cross-validation accurate classification was 75.56%. Besides, findings indicate that the artificial neural network that used four wavelengths that were 780 nm, 850 nm, 870 nm, and 940 nm achieved better classification than that used five wavelengths that included 910 nm. Thus, the artificial neural network coupled with NIR light is promising to be used to classify the internal quality of pineapples non-destructively. 

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3143 %R doi:10.18517/ijaseit.7.4.3143 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 4 %@ 2088-5334

IEEE

Mohamad Nur Hakim Jam and Kim Seng Chia,"Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1389-1394, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.3143.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Jam, Mohamad Nur Hakim
AU  - Chia, Kim Seng
PY  - 2017
TI  - Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1389
EP  - 1394
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - neural network; pineapple; near infrared light; internal quality
N2  - 

One of the important internal qualities of pineapples is the total soluble solid content (SSC). Normally, the SSC can be evaluated using a reflectometer that is destructive and time-consuming. This research investigates the relationship between the reflected near infrared light and the internal quality of pineapples non-destructively. Five light emitted diodes (LEDs) that are in the range between 750 nm and 950 nm were used as the light source. The photodiode (OPT101) sensor was used to collect the light from the pineapple. The digital reflectometer was used to determine the reference SSC. The Near-infrared (NIR) data and the digital refractometer data were used to build the predictive model. The relationship between the near infrared light and the SSC of the pineapple was determined using artificial neural network predictive model. The internal quality of pineapples was determined using five NIR data wavelengths, the result points out that the k-fold cross-validation accurate classification was 75.56%. Besides, findings indicate that the artificial neural network that used four wavelengths that were 780 nm, 850 nm, 870 nm, and 940 nm achieved better classification than that used five wavelengths that included 910 nm. Thus, the artificial neural network coupled with NIR light is promising to be used to classify the internal quality of pineapples non-destructively. 

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3143 DO - 10.18517/ijaseit.7.4.3143

RefWorks

RT Journal Article
ID 3143
A1 Jam, Mohamad Nur Hakim
A1 Chia, Kim Seng
T1 Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1389
OP 1394
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 neural network; pineapple; near infrared light; internal quality
AB 

One of the important internal qualities of pineapples is the total soluble solid content (SSC). Normally, the SSC can be evaluated using a reflectometer that is destructive and time-consuming. This research investigates the relationship between the reflected near infrared light and the internal quality of pineapples non-destructively. Five light emitted diodes (LEDs) that are in the range between 750 nm and 950 nm were used as the light source. The photodiode (OPT101) sensor was used to collect the light from the pineapple. The digital reflectometer was used to determine the reference SSC. The Near-infrared (NIR) data and the digital refractometer data were used to build the predictive model. The relationship between the near infrared light and the SSC of the pineapple was determined using artificial neural network predictive model. The internal quality of pineapples was determined using five NIR data wavelengths, the result points out that the k-fold cross-validation accurate classification was 75.56%. Besides, findings indicate that the artificial neural network that used four wavelengths that were 780 nm, 850 nm, 870 nm, and 940 nm achieved better classification than that used five wavelengths that included 910 nm. Thus, the artificial neural network coupled with NIR light is promising to be used to classify the internal quality of pineapples non-destructively. 

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3143 DO - 10.18517/ijaseit.7.4.3143