Non-destructive evaluation of the internal fruit quality of black mulberry (<i>Morus nigra</i> L.) using visible-infrared spectroscopy and genetic algorithm

Soltanikazemi, Maryam; Mehdizadeh, Saman Abdanan; Heydari, Mokhtar

doi:10.1080/10942912.2016.1238930

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Informasi mengenai tingkat kemanisan buah merupakan hal yang penting untuk diketahui terutama pada buah melon golden, karena hal ini berkaitan dengan penerimaan konsumen terhadap produk tersebut. Berbagai penelitian tentang penggunaan Vis-SWNIRS untuk memprediksi total padatan terlarut buah telah dilakukan pada sawo (Kusumiyati et al, 2018), pisang (Zude, 2003), apel (Pissard et al, 2012), murbei hitam (Soltanikazemi et al, 2017), jeruk (Jamshidi, Minaei, Mohajerani, & Ghassemian, 2012), jambu (Kusumiyati et al, 2020 (Sharabian et al, 2014).…”

Section: Pendahuluanunclassified

PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

Hadiwijaya

Kusumiyati²,

Munawar³

2020

JPS

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Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

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Section: Pendahuluanunclassified

PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

Hadiwijaya

Kusumiyati²,

Munawar³

2020

JPS

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“…The determination and monitoring of quality properties of fresh produce are often forgotten or missed in traditional supply chain management. However, non-destructive methods have been developed over the last decade and have shown potential for application during storage and supply chain [7][8][9][10]. Simultaneously, information and communication technology (ICT) and the Internet of Things (IoT) in various aspects of agribusiness have been studied and reported, especially in developed countries [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Traceability and Quality Monitoring Improvement Throughout Carrot Supply Chain With The Implementation of Internet-Of-Things

Kailaku

Djatna

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Karo Highlands in North Sumatra, Indonesia, provides for a significant portion of horticultural products for domestic and overseas markets. Carrot suffers up to 50% postharvest loss throughout the supply chain. Poor supply chain management and lack of quality monitoring limits competitiveness and aggravates postharvest loss. Non-destructive techniques integrated with Internet-of-Things (IoT) is essential in the modern food supply chain. Implementing these technologies needs to be strategic to achieve benefits without increasing the supply chain’s burden. The study aims to identify the carrot supply chain's situation, design the IoT-based mechanism for traceability and quality monitoring based on the critical points where the postharvest loss occurs in the supply chain, and determine and calculate influential variables formulation of the proposed mechanism regarding resources limitations. The study designs a business process for the IoT architecture implementation and utilizes Goal Programming to evaluate and optimize the performance of the IoT architecture into the supply chain. The implementation design involves farmers, suppliers, and exporting firms to operate the measurement and monitoring devices and accessible information for stakeholders. A minimum set of IoT devices was determined for a particular minimum product flow and required minimum information flow to achieve the optimum postharvest loss reduction.

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“…Near infrared spectroscopy (NIRS) has several advantages over other techniques in terms of the evaluation of fruit internal quality because it is non-destructive, easily applied, and allows for rapid measurement and minimal sample preparation (Pasquini, 2018). NIRS has been applied for the non-destructive assessment of the internal quality of many kinds of fruits including apple (Fan et al, 2019), pomelo (Puangsombut et al, 2012), mangosteen (Teerachaichayut et al, 2007), black mulberry (Soltanikazemi et al, 2017), and apricot (Buyukcan and Kavdir, 2017). There have been a number of investigations applying NIRS for fruit classification.…”

Section: Introductionmentioning

confidence: 99%

Detection of spongy pulp in guava using light properties and near infrared spectroscopy

Noypitak,

Jaitrong,

Terdwongworakul

2021

IFRJ

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Spongy pulp defect in guava is recognised by a dry-looking pulp with a brown colour, and not acceptable to consumers which causes substantial loss in value. Current detection of spongy pulp uses visual assessment of the flesh, which is half cut from the sample. The present work aimed to develop a classifying model based on non-destructive technique for the detection of spongy guava. Guava samples harvested at full maturity were determined for visible light properties, visible light reflectance, and near infrared reflectance. The light properties and light reflectance of guava peel were used to derive a classification model which was then compared with a near infrared reflectance model, which in turn provided absorbance of the flesh and peel using stepwise discriminant analysis. The models were used to classify the guavas into normal and spongy flesh groups, which were assigned with reference to the visual assessment on half cut samples. The classification accuracy for the model using gloss and light reflectance at 650 nm (chlorophyll b) was 90.4%. However, the model developed from the near infrared absorbance provided better accuracy (92.7%). It appeared that the largest wavenumber at 4,721 cm-1 contributed to the total sugar content, which implied that spongy and normal guavas had different total sugar contents in the flesh. The present work demonstrated the potential of near infrared spectroscopy to discriminate spongy from normal guavas. However, the accuracy of the classification could be further improved by analysing more samples from the next season.

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Non-destructive evaluation of the internal fruit quality of black mulberry (Morus nigra L.) using visible-infrared spectroscopy and genetic algorithm

Abstract: View related articles View Crossmark data Citing articles: 1 View citing articles Non-destructive evaluation of the internal fruit quality of black mulberry (Morus nigra L.) using visible-infrared spectroscopy and genetic algorithm

Cited by 8 publications

References 38 publications

PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

Traceability and Quality Monitoring Improvement Throughout Carrot Supply Chain With The Implementation of Internet-Of-Things

Detection of spongy pulp in guava using light properties and near infrared spectroscopy

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