Artificial neural network and shortwave near-infrared light in pineapple internal quality classification
The determination of the internal quality of pineapples is important for grading the fruit quality. The common procedure to determine the internal quality of the fruit is using a digital refractometer that are destructive, expensive, and time-consuming. In this research, a non-destructive appr...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2018
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/7487/1/24p%20MOHAMAD%20NUR%20HAKIM%20JAM.pdf http://eprints.uthm.edu.my/7487/2/MOHAMAD%20NUR%20HAKIM%20JAM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7487/3/MOHAMAD%20NUR%20HAKIM%20JAM%20WATERMARK.pdf |
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| 總結: | The determination of the internal quality of pineapples is important for grading the
fruit quality. The common procedure to determine the internal quality of the fruit is
using a digital refractometer that are destructive, expensive, and time-consuming. In
this research, a non-destructive approach was designed to classify the internal quality
of pineapples using Artificial Neural Network (ANN). The device applied diffuse
reflectance mode using five Near-Infrared (NIR) light with different wavelengths
that were in the range of 780 nm and 940 nm. A photodiode sensor (OPT101) was
used to collect the reflected light from pineapples non-destructively. Firstly, the
reflected NIR light from the top, middle, and bottom sections of a pineapple were
non-destructively acquired using a designed NIR device. After that, the Soluble
Solids Content (SSC) of the pineapple was conventionally acquired using a digital
reflectometer. Next, the relationship between the NIR light and the SSC of
pineapples was established using ANN. The potential outliers were identified and
excluded using box-plot and leave-one-out cross-validation. The effects of the
random seed and the hidden neurons of ANN were investigated to optimize the
classification accuracy. K-fold cross-validation was used to validate the performance
of the classification accuracy. The results indicate that the top, middle, and bottom of
pineapples can be categorized using the proposed NIR approach with the
classification accuracy of 76%, 85%, and 77.78% for training, testing, and K-fold
cross-validation analysis, respectively. Next, when the proposed method was tested
using new pineapples, findings indicate that the predictive model is capable of
achieving 77.62% accuracy without any outlier detection. Thus, the combination of
ANN and shortwave NIR light is able to rapidly, and non-destructively classify the
internal quality of pineapples with satisfactory accuracy |
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