Quality Prediction of Kiwifruit Based on Near Infrared Spectroscopy

Lee, Jin Su; Kim, Seong-Cheol; Seong, Ki Cheol; Kim, Chun-Hwan; Um, Yeong Cheol; Lee, Seung Koo

doi:10.7235/hort.2012.12139

Cited by 20 publications

(11 citation statements)

References 21 publications

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“…Moghimi et al [ 87 ] predicted the acidity in kiwifruits with transmittance spectra in the 400–1000 nm range. A PLS model based on spectra processed with SNV, median filter and 1st derivative could yield a result with r p of 0.94 and RMSEP of 0.076, close to the values obtained by Lee et al [ 92 ], who used spectra in the 408-2492 nm range and predicted acidity with r p of 0.95 and SEP of 0.28%. The feasibility of Vis/NIR spectroscopy for predicting acidity in kiwifruit was thus proved.…”

Section: Quality Evaluation For Different Fruit Varietiessupporting

confidence: 84%

“…The PLS model based on first derivative spectra could yield the optimal result, with r p of 0.85 and RMSEP of 1.89. A better result was reported by Lee et al [ 92 ], who used spectra in the 408–2492 nm range. Flesh firmness was predicted with r p of 0.94 and SEP of 3.32 N. Lv et al [ 25 ] optimized modeling wavelengths and decreased the number of principal components (PCs) with net analyte processing (NAP).…”

Section: Quality Evaluation For Different Fruit Varietiesmentioning

confidence: 80%

“…The superiority of interactance spectra was confirmed by Schaare and Fraser [ 88 ]. Lee et al [ 92 ] predicted SSC with a broad spectral range (408–2492 nm), and an excellent result was observed ( r p = 0.99; SEP = 0.49°Brix). McGlone et al [ 89 ] found the 800–1000 nm waveband performed well.…”

Section: Quality Evaluation For Different Fruit Varietiesmentioning

confidence: 92%

See 2 more Smart Citations

Fruit Quality Evaluation Using Spectroscopy Technology: A Review

Wang

Peng

Xie

et al. 2015

Sensors

293

146

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An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.

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Section: Quality Evaluation For Different Fruit Varietiessupporting

confidence: 84%

Section: Quality Evaluation For Different Fruit Varietiesmentioning

confidence: 80%

See 1 more Smart Citation

Fruit Quality Evaluation Using Spectroscopy Technology: A Review

Wang

Peng

Xie

et al. 2015

Sensors

293

146

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“…NIR spectral data have information that are concerning the relative proportions of C-H, N-H, and O-H bands which are the main structural constituents of organic molecules [12,13]. Many studies explored the application of NIR spectroscopy on SSC measurement of fruits, such as apple [14,15], kiwifruit [16], and grape [17].…”

Section: Introductionmentioning

confidence: 99%

Apple Variety Identification Using Near-Infrared Spectroscopy

Lü

et al. 2018

Journal of Spectroscopy

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Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. Experimental results show that ELM models performed better on identifying apple variety and geographical origin than others. Especially, the SPA-ELM model could reach 98.33% identification accuracy on the calibration set and 96.67% on the prediction set. This study suggests that it is feasible to identify apple variety and cultivation region by using NIR spectroscopy.

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“…The application of visible and near-infrared spectroscopy for the analysis of fruit has allowed the prediction of chemical composition, notably sugar content (Li et al, 2013;Reita et al, 2008), and textural parameters (Lee et al, 2012;Sánchez et al, 2011), as well as the identification of varieties (Li et al, 2016;Guo et al, 2016) and the measurement of quality-related parameters (Pérez-Marín et al, 2011). This technique is relatively rapid, simple, cost-effective, non-destructive, and environmentally friendly.…”

mentioning

confidence: 99%

Sweet and nonsweet taste discrimination of nectarines using visible and near-infrared spectroscopy

Cortés

Cubero

Blasco

et al. 2017

Postharvest Biology and Technology

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The feasibility of using visible and near-infrared spectroscopy technology combined with multivariate analysis to discriminate cv. 'Big Top' and cv. 'Diamond Ray' nectarines has been studied. These varieties are very difficult to differentiate visually on the production line but show important differences in taste that affects the acceptance by final consumers. The relationship between the diffuse reflectance spectra and the two nectarine varieties was established. Five hundred nectarine samples (250 of each variety) were used for the study. Tests were performed by using a spectrometer capable of measuring in two different spectral ranges (600-1100 nm and 900-1700 nm). These spectral ranges were used to develop two accurate classification models based on linear discriminate analysis (LDA) and partial least squares discriminate analysis (PLS-DA). Later, selection techniques were applied to select the most effective wavelengths. The results showed that the PLS-DA model achieved better accuracy and less latent variables than LDA model, and specifically, good results with 100 % classification accuracy were obtained using only the 600-1100 nm spectral range for the two models and eight selected wavelengths. These results places visible and near-infrared spectrocopy as an accurate classification tool for nectarine varieties with a very similar appearance but different tastes that could be potentially used in an automated inspection system.

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Quality Prediction of Kiwifruit Based on Near Infrared Spectroscopy

Cited by 20 publications

References 21 publications

Fruit Quality Evaluation Using Spectroscopy Technology: A Review

Fruit Quality Evaluation Using Spectroscopy Technology: A Review

Apple Variety Identification Using Near-Infrared Spectroscopy

Sweet and nonsweet taste discrimination of nectarines using visible and near-infrared spectroscopy

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