Design and development of pineapple (Ananas comosus) sweetness detector using NIR spectroscopy / by Angelica D. Colina and Jennylyn D. Dizon.

By:

Colina, Angelica D [author]

Contributor(s):

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : Cavite State University- Main Campus, 2022.Description: ix, 90 pages : illustrations ; 28 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

621.36 C68 2022

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Production credits:

College of Engineering and Information Technology (CEIT)

Abstract: COLINA, ANGELICA D., DIZON, JENNYLYN D. Design and Development of Pineapple (Ananas comosus) Sweetness Detector using NIR Spectroscopy. Undergraduate Project Design. Bachelor of Science in Electronics Engineering. Cavite State University, Indang, Cavite. August 2022. Adviser: Engineer Michael T. Costa. Pineapple has a scientific name Ananas comosus, native to South America and exported to other continents. Judging pineapple's sweetness is difficult. Only experienced farmers can estimate pineapple sweetness by knocking the fruit to measure solid sound, indicating sweetness. Furthermore, there was a device that has been standardized. However, in this method, fruits must be opened to separate the water, which is damaging. This research aimed to design and developed a device capable of determining the level of sweetness in pineapples, allowing for the cost- effective selection of pineapples of the highest quality without the need to open them. The researcher attempted to use the NIRS technique to determine the sweetness level of pineapple, which is widely used in the agricultural sector for high- speed analysis. The Arduino Nano was the main processing unit connected to the input and output devices such as the NIR spectral sensor and the OLED display. It was programmed using the Arduino IDE. In the study, 200 pineapple samples were used. These samples were grouped as 60 % for the training datasets, 20 % for testing, and another 20 % for evaluation. The device can detect pineapple sweetness using NIR spectroscopy and an Arduino Nano integrated with SVM classifiers. The SVM classifier distinguishes sour from sweet pineapple using the six NIRS frequencies as inputs. Based on the results, the device's accuracy has achieved the desired level of about 95% and a scanning speed of 0.2521 seconds on average.

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Holdings
Item type	Current library	Collection	Call number	Materials specified	URL	Status	Notes	Date due	Barcode
Theses / Manuscripts	Ladislao N. Diwa Memorial Library Theses Section	Non-fiction	621.36 C68 2022 (Browse shelf(Opens below))		Link to resource	Room use only	DP-751		00081855

Design Project (Bachelor of Science in Electronics and Communications Engineering) Cavite State University.

Includes bibliographical references.

College of Engineering and Information Technology (CEIT)

COLINA, ANGELICA D., DIZON, JENNYLYN D. Design and Development of
Pineapple (Ananas comosus) Sweetness Detector using NIR Spectroscopy.
Undergraduate Project Design. Bachelor of Science in Electronics Engineering. Cavite
State University, Indang, Cavite. August 2022. Adviser: Engineer Michael T. Costa.
Pineapple has a scientific name Ananas comosus, native to South America and exported
to other continents. Judging pineapple's sweetness is difficult. Only experienced farmers can
estimate pineapple sweetness by knocking the fruit to measure solid sound, indicating
sweetness. Furthermore, there was a device that has been standardized. However, in this
method, fruits must be opened to separate the water, which is damaging. This research aimed
to design and developed a device capable of determining the level of sweetness in pineapples,
allowing for the cost- effective selection of pineapples of the highest quality without the need to
open them.
The researcher attempted to use the NIRS technique to determine the sweetness level of
pineapple, which is widely used in the agricultural sector for high- speed analysis. The Arduino
Nano was the main processing unit connected to the input and output devices such as the NIR
spectral sensor and the OLED display. It was programmed using the Arduino IDE. In the study,
200 pineapple samples were used. These samples were grouped as 60 % for the training
datasets, 20 % for testing, and another 20 % for evaluation. The device can detect pineapple
sweetness using NIR spectroscopy and an Arduino Nano integrated with SVM classifiers. The
SVM classifier distinguishes sour from sweet pineapple using the six NIRS frequencies as
inputs. Based on the results, the device's accuracy has achieved the desired level of about 95%
and a scanning speed of 0.2521 seconds on average.

Submitted to the University Library 09/01/2022 DP-751

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