Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra

Kim, Jae-Soo; Mowat, A. D.; Poole, Philip R.; Kasabov, Nikola

doi:10.1016/s0169-7439(00)00070-8

Cited by 67 publications

(36 citation statements)

References 42 publications

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“…Bruise [74] Pear Reflectance Firmness, SSC [59] Reflectance Firmness [61] Interactance Pectin constituent [10] Orange Reflectance SSC, total acidity [25] Reflectance SSC, acidity, firmness [75] N/A SSC [41] Transmittance Sugar content [7] Transmittance Sugar content, acid content [76] Reflectance SSC, titratable acidity [77] Interactance Transmission Transmittance Defect, ripeness [78] Wikifruit Reflectance Dry matter, SSC [9] Interactance Firmness, dry matter, SSC [79] Interactance Dry matter, SSC [80] Reflectance Maturity [81] Reflectance Firmness [82] Interactance Storage disorder [83] Tomato Reflectance Firmness [63] Transmittance (tomato mashed, samples rotated)…”

Section: Discriminationmentioning

confidence: 99%

Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Lin

Ying

2009

Sens. & Instrumen. Food Qual.

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Fruits provide nutrients for human body and are able to prevent sorts of non-communicable diseases. The fruit quality test is an area that both technology and market section concern about. Near infrared spectroscopy (NIR) is a rapid, precise, and non-destructive technique which can be well utilized in determination of fruit quality. This review paper summarizes the theory of NIR analysis, and the fundamental structure of instruments based on NIR for fruit quality assessment. Chemometrics for NIR spectroscopy involving analysis methods of data pre-processing, calibration, model transfer and evaluation, is also included. In recent 11 years, significant progresses were achieved in fruit quality assessment via NIR spectroscopy, which is the main focus in this review. Furthermore, urgent problems in this research field are discussed, expecting to be solved in the near future.

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Section: Discriminationmentioning

confidence: 99%

Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Lin

Ying

2009

Sens. & Instrumen. Food Qual.

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“…The input and output parameters of the training vector are determined as explained in Table 2. For this work we have used the backpropagation neural network because this learning algorithm provides high degrees of robustness and generalisation in classification Kim et al (2000). To find the optimum NN topology, a large number of learning experiments, in a manner similar to Qahwaji and Colak (2007), were carried out.…”

Section: Determining the Type Of The Largest Spot -Pmentioning

confidence: 99%

Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

Colak

Qahwaji

2007

Sol Phys

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Abstract.A hybrid system for the automated detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images is presented in this paper. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and Neural Networks techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rates when compared to the existing manually created catalogues. In addition our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers.

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“…Kim et al(2000) used linear discriminant analysis (LDA) and non-linear techniques based on multilayer perceptrons (MLPs) with variations on back-propagation (BP) learning to classify kiwifruit grown under different conditions, using a range of features extracted from visible-NIR spectra. Results indicate that the performance of non-linear models was significantly better in comparison to the linear model, LDA.…”

Section: Introductionmentioning

confidence: 99%

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

Peng

et al. 2009

J. Zhejiang Univ. Sci. B

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Abstract:The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R c )=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

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Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra

Cited by 67 publications

References 42 publications

Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

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