Calibration Transfer Between Portable and Laboratory NIR ...

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transfer models for internal quality estimation from laptop NIR equipment ... under calibration transfer is bounded by the best fitting model obtained for the slave.
Calibration Transfer Between Portable and Laboratory NIR Spectrophotometers P. Barreiro, D. Herrero and N. Hernández* Physical Properties and Advanced Techniques in Agrofood, LPFTAG Rural Engineering Department, E.T.S.I.A. Polytechnic University of Madrid Ciudad Universitaria s/n 28040 Madrid Spain * E-mail: [email protected]

A. Gracia and L. León Área de Mejora y Biotecnología de Cultivos IFAPA “Alameda del Obispo” Junta de Andalucía Av. Menéndez Pidal s/n 14004 Córdoba Spain

Keywords: breeding program, olive, oil content, internal quality, moisture, multivariate calibration techniques Abstract This paper evaluates a range of calibration transfer techniques in order to transfer models for internal quality estimation from laptop NIR equipment (foss 6500) towards a portable prototype (NIROLIVA) in the framework of a breeding program. The best results were obtained for Piecewise Direct Standardization in comparison with Orthogonal Signal Correction and Orthogonal Projection. The best estimation under calibration transfer is bounded by the best fitting model obtained for the slave equipment when working as master. INTRODUCTION There is a large scope for the application of portable NIR spectrometers , among which breeding programs offer most promising results. However, outdoor conditions represent a very hard and restricting environment. The interference of ambient light and fluctuating temperatures have to be carefully taken into account under outdoor conditions, either by minimizing them or by specific measurement with appropriate data processing (Nicolai et al., 2007). Most breeding programs that make use of NIR spectroscopy have started from laptop NIR equipments, and thus large spectral databases and models are available for the estimation of internal quality; in the case of olive breeding Leon et al. (2004) and Mazarro et al. (2006) have a wide experience. Calibration models developed for one spectrometer are as such useless on another device (Nicolai et al., 2007), this point represents a large handicap for the incorporation of new portable devices, even though they might bring further advantages such as nondestructive supervision and faster performance. The lack of transferability of models between equipment is generally assigned as a lack of robustness which is due to the occurrence of spectral shift, model shift, or interactions between the two (Zeaiter et al., 2005). Geometric spectral preprocessing methods such as Multiplicative Scatter Correction (MSC), which correct for the base-line shift curvilinearity and noise-related terms, particularly reduce the spectral shift. On the other hand, Orthogonal Signal Correction (OSC), and Orthogonal Projection (OP) ensure the independence of the model from the spectral shift (Zeaiter et al., 2005). Piecewise Direct Standardization (PDS) is another possibility for calibration transfer which consists of transforming the spectra from a slave instrument as to appear as if originated from the master instrument (Alamar et al., 2007). CIFA Alameda del Obispo (Córdoba, Spain) and LPFTAG (Madrid, Spain) are involved in a joint project aimed at implementing NIR-based models for a in-field portable spectrometer within a olive breeding program. At the first step, the design and construction of a portable device has been carried out using a TG-Cooled NIR I (Hamamatsu) spectrometer (900 and 1700 nm. Proc. IVth IS on Model-IT Eds.: P. Barreiro et al. Acta Hort. 802, ISHS 2008

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The objective of this research is to verify robustness of the portable system, performing a variety of calibration transfer techniques towards a master NIR instrument. MATERIALS AND METHODS Laboratory NIR tests were conducted with a FOSS NIR-System 6500 spectrometer, that is already in use within the breeding program since 2004. The spectra are acquired in the wavelength range between 800 and 2498 nm and the data are recorded by means of ISISCAN software (Figure 1). For the portable equipment a Cooled NIR I of Hamamatsu was selected, which has a Peltier and an electric fan. This device works within a wavelength’s range which is between 900 nm and 1700 nm, and can be plugged to a computer by a USB cable. A continuous light source is used which varies between 360nm and 2000nm, LS-1-LL Tungsten Halogen, of Ocean Optics. It can provide light up to 10000 hours while its weight and its size are very little. The transmission of the light from the source through the olive forward the spectrometer is by means an optical fiber’s guide which is diverged QR400-7-VIS-NIR, and SMA 905 connectors are used. As it has been proved in several studies, the temperatures of both the sample and the spectrophotometer are main factor affecting the quality of the spectra. In order to record these temperatures, a digital card NI USB9211A has been incorporated, that allows gathering the signal corresponding to four thermocouples, which are located in at the sample presentation probe (Figure 2). The control of the spectrometer’s software and the storage of the spectrums are carried out by laptop Dell latitude C400, which at the same time is monitored with a PDA Hp hw6915. The utilization of the PDA allows the use of GPS’ aerial which gives a precise location of every olive’s tree analysed. The battery used for the spectrometer and the light source is NL 2054A22 made by Inspired Energy and its capacity is around 6, 6 Ah. Aiming to recharge the batteries while the analysis is being carried out at the field, a solar panel is incorporated, Iowa PowerFilm F15-600 of 10W of maximum power. On the other hand the laptop and the PDA have their own battery. RESULTS AND DISCUSSION Figure 3 highlights the differences in the responses when using spectrometers of different models and brands. Several PLS models for NIR equipments with regard to standard laboratory references (water content -% f.w.- by oven dehydration and NMR fat quantification -% d.w.-analysis, Table 1) by applying methods that perform reduction of the dimensionality in the variable space such as Partial Least Square (PLS), see Table 1. Spectral differences among equipments (laboratory and portable) are also reflected in the robustness of the models as shown in Figure 4 for the prediction of oil content (% d.w.) In both equipments no more than 5 latent variables should be used as to provide stable models as shown under cross-validation. However, the portable equipment seems to be more sensitive to the nº of latent variables selected for PLS model. Multiplicative scatter correction as referred by Maleki et al. 2007, and spectral interpolation as proposed by Alamar et al, 2007, are used as pre-processing technique before applying calibration transfer procedures (Figure 5). Piecewise Direct Standardization is one of the CT techniques used, being very sensitive to the spectral window size and nº of latent variables used for standardization. Figure 6 shows that excessive noise arises when over fitting the CT model (20 wavelength window and 20 latent variables). External validation is used to test the efficiency of CT. Upper-left graph in Figure 6 shows a comparison of spectra from laptop and portable NIR spectrometers without MSC. Upper-right graph compares the spectra after calibration transfer using PDS. Lower-left graph shows the determination coefficient of the master spectra when predicted by means of slave. Finally, Lower-right graph presents the external validation of moisture content for olives adjusted for the master instrument and 374

validated for the slave. The highest coefficient of determination is obtained for PDS window of 20 wavelengths and 5 latent variables in the model which yield the best result obtained when fitting the moisture model directly for the portable equipment under cross validation. OSC as proposed by Sjöblom in 1998 and OP as referred by Roger et al. in 2008 have also been tested in the framework of this research however the results were always poorer that for PDS. CONCLUSIONS Among the calibration techniques tested only Piecewise Direct Standardization, with or without Multiplicative Scattering Correction, give promising results for the transfer of models between laptop and portable NIR spectrometers. PDS parameters have to be carefully selected as to optimize CT performance. Literature Cited Alamar, M.C., Bobelyn, E., Lammertyn, J., Nicolaï, B.M. and Moltó, E. 2007. Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple. Postharvest Biology and Technology 45(1):38-45. León, L., Garrido-Varo, A. and Downey, G. 2004. Parent and harvest year effects on nearinfrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits. Journal of Agriculture and Food Chemistry 52:4957-4962. Maleki, M.R., Mouazen, A.M., Ramon, H. and De Baerdemaeker, J. 2007.Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy. Biosystems Engineering 96(3):427-433. Mazarro, M., Gracia, A., Barranco, D. and León, L. 2006. Utilizacion de la técnica NIRS para la selección de genotipos en programas de mejora genética de olivo. III Congreso de mejora genética de plantas. Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I. and Lammertyn, J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology 46(2):99118. Preys, S., Roger, J.M. and Boulet, J.C. 2008. Robust calibration using orthogonal projection and experimental design. Application to the correction of the light scattering effect on turbid NIR spectra. Chemometrics and Intelligent Laboratory Systems 91(1):28-33. Roger, J.-M., Chauchard, F. and Bellon-Maurel, V. 2003. EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemometrics and Intelligent Laboratory Systems 66(2):191-204. Sjöblom, J., Svensson, O., Josefson, M., Kullberg, H. and Wold, S. 1998.An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra. Chemometrics and Intelligent Laboratory Systems 44(1-2):229-244. Zeaiter, M., Roger, J.M. and Bellon-Maurel, V. 2006. Dynamic orthogonal projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR-based monitoring of wine fermentations. Chemometrics and Intelligent Laboratory Systems 80(2):227-235.

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Tables Table 1. Results of calibration. Foss. Moisture content HFR (% f.w.) Fat Content (% d.w.)

Set Calibration Validation Calibration Validation

R2 0.91 0.89 0.86 0.86

RMSEC 1.73 2.50 1.24 1.82

Figurese

Fig. 1. Laptop spectrometer foss 6500 used as master for calibration transfer.

Fig. 2. NIROLIVA working in field.

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RMSEP 2.09 2.45 1.49 1.76

Fig. 3. Spectra as acquired by the laptop NIR equipment (foss acting as master) and the portable device (Hamamatsu operating as slave).

Fig. 4. Performance of PLS models developed for foss (master) and portable (slave). The determination coefficient (r2) and the standard error of calibration (SEC) and prediction (SEP) under cross validation are presented for the estimation of oil content.

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Fig. 6. Parameters used to evaluate the quality of the calibration transfer: upper-left comparison of spectra from laptop and portable NIR spectrometers without MSC; upper-right comparison of spectra after calibration transfer using PDS; lower-left determination coefficient of the master spectra when predicted by means of slave; lower-right comparison of external validation of HFR model adjusted for the master and validated for the slave. The highest coefficient of determination is obtained for PDS window of 20 wavelengths and 5 latent variables in the model.

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