Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple

Alamar, M. Carmen; Bobelyn, Els; Lammertyn, Jeroen; Nicolaı̈, Bart; Moltó, Enrique

doi:10.1016/j.postharvbio.2007.01.008

Cited by 61 publications

(38 citation statements)

References 36 publications

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“…Therefore, a calibration model is required to establish the relationship between the component of interest and the spectrum. In fact, a combination of near infrared spectroscopy and calibration model has been successfully implemented in non-destructive SSC assessment of various fruits such as apple (Ventura et al, 1998;Peirs et al, 2003;Liu and Ying, 2004;Alamar et al, 2007;Peng and Lu, 2007;Zou et al, 2007;Fan et al, 2009;Zhu et al, 2009), citrus (Lu et al, 2006;Zude et al, 2008), loquat (Fu et al, 2009) mango He, 2009), mangosteen (Teerachaichayut et al, 2007), melon (Long, 2005;Tian et al, 2007), and pineapple (Pathaveerat et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, numerous studies have been published concerning the use of linear models [e.g., multiple linear regression (MLR) (Ventura et al, 1998;Peng and Lu, 2007;Jha and Garg, 2010) and partial least square (PLS) (Peirs et al, 2003;Xing et al, 2005;Alamar et al, 2007;Zou et al, 2007;Fan et al, 2009;Zhao et al, 2009;Zhu et al, 2009;Jha and Garg, 2010)] for the internal quality measurement of apples, using spectroscopic technology. However, the implementation of nonlinear models [e.g., least squares support vector machine (LS-SVM) and least squares support vector regression (LS-SVR) (Liu et al, 2010)] has been reported to be better than that of linear models (e.g., PLS).…”

Section: Introductionmentioning

confidence: 99%

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

Chia

Rahim

2012

J. Zhejiang Univ. Sci. B

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Abstract:Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chia

Rahim

2012

J. Zhejiang Univ. Sci. B

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“…A calibration model transfer for soluble solids contents of apple from a Fourier transform based spectrophotometer to a diode array (DA) spectrophotometer was tested working well, obtaining root mean squared error of prediction (RMSEP) of 0.85°Brix on a data set of 477 Jonagored apples. The necessity of calibration transfer procedures between two DA spectrophotometers of the same type and model had also been found [43].…”

Section: Quantitative Analysismentioning

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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“…The same authors obtained similar results for the SSC of mandarin with RMSEPs of around 0.3°Brix. 49 Alamar et al 50 applied the PDS algorithm for calibration transfer between a diode array and a Fourier transform near-infrared (FT-NIR) system as well as between two diode array systems of the same model for predicting SSC of apple. They obtained an RMSEP of around 0.7°Brix after PDS.…”

Section: Calibration Transfermentioning

confidence: 99%

Vibrational Spectroscopy Techniques in the Quality Assessment of Fruits and Vegetables

Polshin

Lammertyn

Nicolaı̈

2001

Handbook of Vibrational Spectroscopy

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Fruits and vegetables play an important role in the human diet worldwide. Safety and quality are important considerations for the purchase and consumption of fresh raw products. Since the consumers increasingly insist on a very high fruit and vegetable quality nowadays, it is also necessary to provide information about the origin and traceability of these products. One of the alternatives to traditional wet chemistry and human evaluation strategies, which allow quality assessment and control of fruits and vegetables, is vibrational spectroscopy. During the last decade, several vibrational spectroscopic methods have been developed, and have become more widely adopted by the agricultural and food sectors. These methods offer the possibility to rapidly measure several properties of the sample with minimal sample preparation and often to perform nondestructive quality evaluation. In this chapter, an overview of near‐infrared, mid‐infrared, and Raman spectrometries and infrared thermography is given for use in measuring quality attributes of horticultural produce.

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Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple

Cited by 61 publications

References 36 publications

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

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

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

Vibrational Spectroscopy Techniques in the Quality Assessment of Fruits and Vegetables

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