Application of Electronic-Nose for Identification of Ripeness Stage of Durian

Pokhum, C.; Chawengkijwanich, Chamorn; Maolanon, Rungroj

doi:10.17660/actahortic.2010.875.41

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In a research conducted by Pokhum, Chawengkijwanich, and Maolanon (2009), the use of e‐nose for identification of the ripeness stage of durian was investigated. PCA technique was applied for data analysis.…”

Section: Resultsmentioning

confidence: 99%

Detection of ripeness grades of berries using an electronic nose

Aghilinategh

Dalvand

Anvar³

2020

Food Science & Nutrition

View full text Add to dashboard Cite

The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e‐nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e‐nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10–11‐5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.

show abstract

Section: Resultsmentioning

confidence: 99%

Detection of ripeness grades of berries using an electronic nose

Aghilinategh

Dalvand

Anvar³

2020

Food Science & Nutrition

View full text Add to dashboard Cite

show abstract

“…Supriyadi et al [ 148 ] employed a FOX 4000 e-nose to discriminate between ripe and unripe snake fruits ( Salacca edulis Reinw.). Pokhum et al [ 109 ] identified the ripeness stage of durian fruits ( Durio spp.) by use of a MOS e-nose.…”

Section: Electronic-nose Applications For Fruit Aroma Characterizatiomentioning

confidence: 99%

Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading

Baietto

Wilson

2015

Sensors

239

115

View full text Add to dashboard Cite

Fruits produce a wide range of volatile organic compounds that impart their characteristically distinct aromas and contribute to unique flavor characteristics. Fruit aroma and flavor characteristics are of key importance in determining consumer acceptance in commercial fruit markets based on individual preference. Fruit producers, suppliers and retailers traditionally utilize and rely on human testers or panels to evaluate fruit quality and aroma characters for assessing fruit salability in fresh markets. We explore the current and potential utilization of electronic-nose devices (with specialized sensor arrays), instruments that are very effective in discriminating complex mixtures of fruit volatiles, as new effective tools for more efficient fruit aroma analyses to replace conventional expensive methods used in fruit aroma assessments. We review the chemical nature of fruit volatiles during all stages of the agro-fruit production process, describe some of the more important applications that electronic nose (e-nose) technologies have provided for fruit aroma characterizations, and summarize recent research providing e-nose data on the effectiveness of these specialized gas-sensing instruments for fruit identifications, cultivar discriminations, ripeness assessments and fruit grading for assuring fruit quality in commercial markets.

show abstract

“…Kandungan gas tersebut akan meningkat saat durian dalam kondisi matang. Hal ini sesuai dengan teori bahwa durian memiliki kandungan gas alkohol dan bis(ethylthio)methane yang akan semakin kuat aromanya seiring proses kematangan buahnya [15].Setelah diketahui karakteristik data durian dari data latih dan Tabel 1. Maka selanjutnya menguji data dengan menggunakan metode KNN, yakni data yang baru akan dikelompokkan berdasarkan sebaran data tetangga terdekatnya.…”

Section: Pengambilan Data Latihunclassified

Aplikasi Sensor TGS2620 dan MQ138 untuk Mendeteksi Kematangan Buah Durian Berbasis Raspberry Pi 3B

et al. 2021

View full text Add to dashboard Cite

Research has been carried out by application of the TGS 2620 (alcohol level) and MQ 138 (methane level) gas sensors to detect the maturity of durian based on Raspberry Pi 3b. This research was conducted to produce a detection instrument for the level of durian maturity. Durian is inserted into the sample room which contain a gas sensors. The classification method used to determine fruit maturity uses K-Nearest Neighbor (KNN). The KNN test in the durian maturity classification using a gas sensor has accuracy based on maturity level, ripe 93,3%; rare 96.6%; raw 93,3%

show abstract

Application of Electronic-Nose for Identification of Ripeness Stage of Durian

Cited by 5 publications

References 0 publications

Detection of ripeness grades of berries using an electronic nose

Detection of ripeness grades of berries using an electronic nose

Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading

Aplikasi Sensor TGS2620 dan MQ138 untuk Mendeteksi Kematangan Buah Durian Berbasis Raspberry Pi 3B

Contact Info

Product

Resources

About