Models to improve the non-destructive analysis of persimmon fruit properties by VIS/NIR spectrometry.

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Models to improve the non-destructive analysis of persimmon fruit properties by VIS/NIR spectrometry.

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2017 nî lūn-bûn

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2017年の論文

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2017年論文

@yue

2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年论文

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2017年论文

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2017年论文

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Models to improve the non-dest ...... rties by VIS/NIR spectrometry.

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Models to improve the non-dest ...... rties by VIS/NIR spectrometry.

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Models to improve the non-dest ...... rties by VIS/NIR spectrometry.

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P1476

Models to improve the non-dest ...... rties by VIS/NIR spectrometry.

@en

P2093

Antonella Tauriello

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Francesco Genovese

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Giovanni Carlo Di Renzo

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P2860

Support vector machine regression (SVR/LS-SVM) - an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data

Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy.

Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison.

Distribution of variables by method of outlier detection

A working guide to boosted regression trees.

Radical scavenging activity and phenolic compounds in persimmon (Diospyros kaki L. cv. Mopan).

Towards combinatorial spectroscopy: the case of minor milk fatty acids determination.

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks.

PLS-regression: a basic tool of chemometrics

Unmasking Multivariate Outliers and Leverage Points

P304

5302-5310

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P31

scholarly article

P356

10.1002/JSFA.8416

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P407

English

P433

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P478

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P50

Giuseppe Altieri

P577

2017-05-08T00:00:00Z

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P698

28488325

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