Predicting Oil Content of Fresh Palm Fruit Using Transmission-Mode Ultrasonic Technique
In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
[1] R. Sambantamurthi, K. Sundram, Y.-A. Tan, "Chemistry and
biochemistry of palm oil," Progress in Lipid Research, vol. 39, pp. 507-
558, 2000.
[2] Office of Agricultural Economics, Situation and trends in key
agricultural year 2554, Ministry of Agriculture, Thailand, 2011.
[3] P. Butz, C. Hofmann, B. Tauscher, "Recent developments in
noninvasive techniques for fresh fruit and vegetable internal quality
analysis," J. FoodSci, vol. 70, no. 9, pp. 131-R141, November 2005.
[4] A. Mizrach, "Ultrasonic technology for quality evaluation of fresh fruit
and vegetables in pre- and postharvest processes," Postharvest Biology
and Technology, vol. 48, pp. 315-330, 2008.
[5] A. Mizrach, U. Flitsanov, "Nondestructive ultrasonic determination of
avocado softening process," Journal of Food Engineering, vol. 40, pp.
139-144, 1999.
[6] J. Krautkramer, H. Krautkramer, Ultrasonic testing of materials.,
Springer-Verlag, Heidelberg, Germany, 1990.
[1] R. Sambantamurthi, K. Sundram, Y.-A. Tan, "Chemistry and
biochemistry of palm oil," Progress in Lipid Research, vol. 39, pp. 507-
558, 2000.
[2] Office of Agricultural Economics, Situation and trends in key
agricultural year 2554, Ministry of Agriculture, Thailand, 2011.
[3] P. Butz, C. Hofmann, B. Tauscher, "Recent developments in
noninvasive techniques for fresh fruit and vegetable internal quality
analysis," J. FoodSci, vol. 70, no. 9, pp. 131-R141, November 2005.
[4] A. Mizrach, "Ultrasonic technology for quality evaluation of fresh fruit
and vegetables in pre- and postharvest processes," Postharvest Biology
and Technology, vol. 48, pp. 315-330, 2008.
[5] A. Mizrach, U. Flitsanov, "Nondestructive ultrasonic determination of
avocado softening process," Journal of Food Engineering, vol. 40, pp.
139-144, 1999.
[6] J. Krautkramer, H. Krautkramer, Ultrasonic testing of materials.,
Springer-Verlag, Heidelberg, Germany, 1990.
@article{"International Journal of Information, Control and Computer Sciences:62583", author = "Sutthawee Suwannarat and Thanate Khaorapapong and Mitchai Chongcheawchamnan", title = "Predicting Oil Content of Fresh Palm Fruit Using Transmission-Mode Ultrasonic Technique", abstract = "In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.", keywords = "Non-destructive, ultrasonic testing, oil content, fresh palm fruit, neural network.", volume = "5", number = "9", pages = "1049-4", }