Detection of ripeness grades of berries using an electronic nose

Aghilinategh, N.; Dalvand, Mohammad Jafar; Anvar, Adieh

doi:10.1002/fsn3.1788

Cited by 40 publications

(32 citation statements)

References 41 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Oleh karena itu, estimasi kematangan merupakan bagian penting dari penentuan kualitas karena kematangan saat panen dapat mempengaruhi sifat sensorik dan penyimpanan buah. Cara yang mungkin untuk menentukan tingkat kematangan adalah merasakan volatil aromatik yang dilepaskan oleh buah menggunakan hidung elektronik (e-nose) [5].…”

Section: Pendahuluanunclassified

“…Pengembangan hidung elektronik (enose) sudah banyak dilakukan terutama untuk mendeteksi kualitas buah mangga berdasarkan gas yang dihasilkannya [1]. Dari penelitian-penelitian tersebut sensor gas yang digunakan bervariasi, seperti sensor gas etilen [4] dan sensor gas tipe MQ dan TGS [5].…”

Section: Pendahuluanunclassified

“…Sistem e-nose dikembangkan dengan algoritma pembelajaran mesin khusus pengklasifikasi, seperti jaringan saraf tiruan (JST), Principal Component Analysis (PCA), dan analisis diskriminan linier (LDA) [5]. Selain itu terdapat banyak metode pembelajaran mesin lain yang digunakan untuk klasifikasi dalam bidang pertanian misalnya Regresi Linier, Regresi Logistik, Pohon Pemilihan (Decision Tree), Support Vector Machine (SVM), Naïve Bayes, K Nearest Neighbor (KNN), dan Random Forest [6].…”

Section: Pendahuluanunclassified

See 2 more Smart Citations

Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Murad

Sukmawaty

Ansar

et al. 2021

jtim

View full text Add to dashboard Cite

Fruits, including mangoes, produce a wide variety of volatile organic compounds that give them their distinct aroma. Characteristics of fruit aroma is one of the important keys in determining consumer acceptance in the commercial fruit market based on individual preferences. So a possible way to determine the level of ripeness/damage is to feel the distinctive aroma presented by the fruit using a gas sensor. This study aims to build a system that can detect mango damage based on its aroma. The sensors used are TGS 2600, MQ3, MQ4, MQ2, and MQ8 which are connected to the Arduino Mega 2560. The learning model used is an ensemble learning model of Dynamic Classifier Selection (DCS) with Local Class Accuracy (LCA)/DCS-LCA. This algorithm combines Logistic Regression, Selection Tree, Support Vector Machine (SVM), Naïve Bayes, Random Forest, and Neural Networks. The model was then tested with a comparison of the amount of test data and training data of 70%:30%. The test results showed that the overall system Accuracy was 75% and the ability to detect mango fruit damage was 71%. The DCS-LCA classifier model outperforms each of its constituent base classifiers.

show abstract

Section: Pendahuluanunclassified

See 1 more Smart Citation

Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Murad

Sukmawaty

Ansar

et al. 2021

jtim

View full text Add to dashboard Cite

show abstract

“…Strawberry odour, which is strictly related to the consumer’s preference and to the sensory quality of the fruit, is the result of a complex mixture of multiple volatile organic compounds (VOCs) [ 4 , 5 , 7 ]. The profile of VOCs rapidly changes during ripening, but it is also affected by several other factors, including cultivar and pre- and postharvest handlings [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…This technique can simulate the human olfactory sense and generate fingerprints of VOCs in real time [ 21 ]. E-nose sensory data combined with suitable chemometric tools have been successfully applied to detect changes in VOC content in several food matrices for several purposes, including food quality, safety and fraud detection as well as to discriminate the fruit based on its degree of ripening [ 7 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries

Palumbo

Cozzolino

Laurino

et al. 2022

Foods

View full text Add to dashboard Cite

Electronic nose (e-nose), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and image analysis (IA) were used to discriminate the ripening stage (half-red or red) of strawberries (cv Sabrosa, commercially named Candonga), harvested at three different times (H1, H2 and H3). Principal component analysis (PCA) performed on the e-nose, ATR-FTIR and IA data allowed us to clearly discriminate samples based on the ripening stage, as in the score space they clustered in distinct regions of the plot. Moreover, a correlation analysis between the e-nose sensor and 57 volatile organic compounds (VOCs), which were overall detected in all the investigated fruit samples by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), allowed us to distinguish half-red and red strawberries, as the e-nose sensors gave distinct responses to samples with different flavours. Three suitable broad bands were individuated by PCA in the ATR-FTIR spectra to discriminate half-red and red samples: the band centred at 3295 cm−1 is generated by compounds that decline, whereas those at 1717 cm−1 and at 1026 cm−1 stem from compounds that accumulate during ripening. Among the chemical parameters (titratable acidity, total phenols, antioxidant activity and total soluble solid) assayed in this study, only titratable acidity was somehow correlated to ATR-FTIR and IA patterns. Thus, ATR-FTIR spectroscopy and IA might be exploited to rapidly assess titratable acidity, which is an objective indicator of the ripening stage.

show abstract

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

Bakhshipour

2023

Food Science & Nutrition

View full text Add to dashboard Cite

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e‐nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e‐nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion‐based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion‐based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e‐nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.

show abstract

Detection of ripeness grades of berries using an electronic nose

Cited by 40 publications

References 41 publications

Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

Contact Info

Product

Resources

About