Development of a Calibration Equation with Temperature Compensation for Determining the Brix Value in Intact Peaches

Kawano, Seiji; Abe, Hideyuki; Iwamoto, Mutsuo

doi:10.1255/jnirs.71

Cited by 118 publications

(82 citation statements)

References 3 publications

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“…We conclude, in agreement with Miyamoto and Kitano (1995) and (Kawano et al 1995), that samples scanned at a range of temperatures should be included in the calibration population in order for the model to be robust in prediction of samples varying in sample temperature. The issue of calibration population design to incorporate robustness for sample temperature without loss of general validation accuracy (i.e.…”

Section: Discussionsupporting

confidence: 83%

“…Prediction robustness in terms of bias was indeed increased for models developed across several temperatures, whereas accuracy [RMSEP(C)] was similar to that of single temperature calibration models, for both DM and TSS (Table 2). Kawano et al (1995) also found that incorporation of samples across a temperature range in a (MLR) calibration allowed prediction of TSS with a high degree of accuracy and minimal bias.…”

Section: Calibration Robustness: Temperaturementioning

confidence: 86%

“…We hypothesise that calibration models for DM would be more sensitive to temperature than models for TSS, as DM models may reflect water content. Kawano et al (1995) noted that as sample fruit (peach) temperature increased, so did absorption at 841 and 966 nm (water bands), resulting in a bias in the prediction of TSS. Miyamoto and Kitano (1995) noted that when a calibration developed from spectra collected from mandarin fruit at 20 • C was used to predict the same fruit at 6, 15, and 25 • C, RMSEP [presumably RMSEP(C)] was constant but bias increased linearly with temperature.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

Guthrie

Reid²,

Walsh

2005

Aust. J. Agric. Res.

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Abstract. The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population ('model updating') were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.Additional keyword: non-invasive.

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Section: Discussionsupporting

confidence: 83%

Section: Calibration Robustness: Temperaturementioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

Guthrie

Reid²,

Walsh

2005

Aust. J. Agric. Res.

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“…An apparent amylose content calibration model with particle size compensation To overcome the error due to particle size, an apparent amylose content calibration model with particle size compensation was developed using a selfcorrective compensation method. This method follows the same approach as the temperature compensation method (Kawano et al, 1995;Chen et al, 2003). Rice flour sample particle size is therefore handled as an ingredient by the partial least squares (PLS) method.…”

Section: Resultsmentioning

confidence: 99%

Amylose Analysis of Rice Flour Using Near-Infrared Spectroscopy with Particle Size Compensation

Zhang

Miao

Takahashi

et al. 2011

FSTR

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We present the use of near-infrared (NIR) spectroscopy with particle size compensation to analyze the apparent amylose content in rice flour. NIR spectra (1100 − 2500 nm) of rice flour samples with different particle sizes were acquired in the reflectance mode. Initially, the effect of rice flour particle size on the determination of amylose content using NIR data was examined. The bias introduced by particle size was discussed. Subsequently, in order to obtain an authentic and accurate apparent amylose content analysis from the NIR data, a calibration model that compensates for particle size was investigated. As a result, a possible automatic method that removes the influence of particle size on NIR data used for determining the apparent amylose content of rice flour was developed with relatively good precision, with standard error of prediction (SEP) of < 1.53% and bias of 0.5%.

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“…Therefore, if a calibration model for the C2 contents in RPP or BPP, developed by using a NIR spectra set measured at a particular temperature, can be used to predict the C2 contents in RPP or BPP from spectra measured at different temperatures, it would be very effective for saving both cost and labor. Kawano et al 32 investigated the influence of a change in sample temperature on This paper reports on a simultaneous prediction method for ethylene (C2) content and the melt temperature in melt state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. The NIR spectra of RPP and BPP in melt states were measured by a FT-NIR on-line monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction of Ethylene Content and Melt Temperature in Melt-state Polypropylene by Near-Infrared Spectroscopy and Chemometrics

Watari

2007

ANAL. SCI.

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IntroductionRecently, on-line process analysis based on near-infrared (NIR) spectroscopy has become a popular technology for many chemical and polymer industries. 1-31 An on-line monitoring system with optical fiber based on NIR spectroscopy is very suitable for use in a hazardous area. In addition to the above advantages, NIR spectroscopy can be applied to samples in various physical states, shapes and thicknesses. [1][2][3][4][5] Optical-fiber NIR spectroscopy has the possibility of allowing direct polymerization analyses, real-time monitoring of physical and/or chemical properties of molten polymers, and the quality control of polymers. Many researchers have been involved in on-line polymer process analyses by optical-fiber NIR spectroscopy, and have produced numerous publications. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][23][24][25][26][27][28][29][30][31] For realizing NIR on-line monitoring for molten polymers, further developments have been studied from the standpoints of hardware and software.Especially, from the software standpoint, robust calibration models, where changes in predicted values from on-line spectra due to various kinds of process disturbances are very small, must be developed. Moreover, it has been expected to develop precise calibration models using sample data and spectra with small size because of savings in costs and labor.We have started a series of studies to solve some of the abovementioned software problems. [16][17][18][19][20][21][22] In our first study of the series, we investigated the on-line monitoring of the density of linear low density polyethylene (LLDPE) in a real plant by NIR spectroscopy and chemometrics. 16,17 In a second study, predicting the ethylene (C2) content in melt random and block polypropylene (RPP and BPP) based on the NIR spectra of the molten polymer system was investigated. 18 A third study was concerned with the influence of a change in the sample temperature on predicting the C2 content in the melt state RPP and BPP and its compensation method. 19 In a fourth study, the possibility of improving calibration models for the vinylacetate (VA) concentration in melt-state ethylene-vinylacetate copolymers (EVA) for an on-line monitoring was reported. 20 A fifth study described a practical calibration correction method for an on-line NIR molten polymer system. 21 In a sixth study, 22 variations in the predicted values from the NIR spectra of samples in vials by using a calibration model developed from spectra of samples in vials were investigated, and modified MSC was proposed as a method for the continuous monitoring of the path-length in vials.The purpose of the present study is to demonstrate the simultaneous prediction of the C2 content and the melt temperature in melt RPP and BPP, and to investigate the effect of a modified MSC to variations in the predicted C2 content in the melt PP from spectra measured at different temperatures from that measured spectra set for the calibration model.The C2 content measurement in RPP and BPP is one of...

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Development of a Calibration Equation with Temperature Compensation for Determining the Brix Value in Intact Peaches

Cited by 118 publications

References 3 publications

Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

Amylose Analysis of Rice Flour Using Near-Infrared Spectroscopy with Particle Size Compensation

Simultaneous Prediction of Ethylene Content and Melt Temperature in Melt-state Polypropylene by Near-Infrared Spectroscopy and Chemometrics

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