E. Arendse, O. A. Fawole, L. S. Magwaza, and U. L. Opara, Non-destructive prediction of internal and external quality attributes of fruit with thick rind: a review, J. Food Eng, vol.217, pp.11-23, 2018.

S. Armenta, J. Moros, S. Garrigues, and M. D. Guardia, The use of near-infrared spectrometry in the olive oil industry, Crit. Rev. Food Sci. Nutr, vol.50, issue.6, pp.567-582, 2010.

L. Awhangbo, R. Bendoula, J. M. Roger, and F. Béline, Multi-block SO-PLS approach based on infrared spectroscopy for anaerobic digestion process monitoring, Chemom. Intell. Lab. Syst, vol.196, p.103905, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02610133

R. J. Barnes, M. S. Dhanoa, and S. J. Lister, Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra, Appl. Spectrosc, vol.43, issue.5, pp.772-777, 1989.

Y. Bi, K. Yuan, W. Xiao, J. Wu, C. Shi et al., A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation, Anal. Chim. Acta, vol.909, pp.30-40, 2016.

X. Bian, K. Wang, E. Tan, P. Diwu, F. Zhang et al., A selective ensemble preprocessing strategy for near-infrared spectral quantitative analysis of complex samples, Chemom. Intell. Lab. Syst, vol.197, p.103916, 2020.

A. Biancolillo, K. H. Liland, I. Måge, T. Naes, and R. Bro, Variable selection in multiblock regression, Chemom. Intell. Lab. Syst, vol.156, pp.89-101, 2016.

A. Biancolillo, T. Naes, R. Bro, and I. Måge, Extension of SO-PLS to multi-way arrays: SO-N-PLS, Chemom. Intell. Lab. Syst, vol.164, pp.113-126, 2017.

A. Biancolillo, T. Naes, and M. Cocchi, Chapter 6 -the sequential and orthogonalized PLS regression for multiblock regression: theory, examples, and extensions. Data Handling in Science and Technology 31, pp.157-177, 2019.

S. Bureau, D. Ruiz, M. Reich, B. Gouble, D. Bertrand et al., Application of ATR-FTIR for a rapid and simultaneous determination of sugars and organic acids in apricot fruit, Food Chem, vol.115, issue.3, pp.1133-1140, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02666634

F. Dieterle, A. Ross, G. Schlotterbeck, and H. Senn, Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics, Anal. Chem, vol.78, issue.13, pp.4281-4290, 2006.

J. Engel, J. Gerretzen, E. Szyma?ska, J. J. Jansen, G. Downey et al., Breaking with trends in pre-processing?, TrAC Trends Anal. Chem, vol.50, pp.96-106, 2013.

P. Firmani, A. Nardecchia, F. Nocente, L. Gazza, F. Marini et al., Multiblock classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices, Food Chem, vol.309, p.125677, 2020.

O. Galtier, N. Dupuy, Y. Le-dréau, D. Ollivier, C. Pinatel et al., Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra, Anal. Chim. Acta, vol.595, issue.1, pp.136-144, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01493412

J. Gerretzen, E. Szyma?ska, J. J. Jansen, J. Bart, H. Van-manen et al., Simple and effective way for data preprocessing, 2015.

. Fig, Regression vector from, multiplicative scatter correction (MSC) (solid red), standard normal variate (SNV) (dotted blue) and 2nd derivative (solid green) preprocessed blocks of the apricot dataset, vol.10

P. Mishra, Postharvest Biology and Technology, vol.168, p.111271, 2020.

, selection based on design of experiments, Anal. Chem, vol.87, issue.24, pp.12096-12103

A. M. Gómez-caravaca, R. M. Maggio, and L. Cerretani, Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review, Anal. Chim. Acta, vol.913, pp.1-21, 2016.

Q. Guo, W. Wu, and D. L. Massart, The robust normal variate transform for pattern recognition with near-infrared data, Anal. Chim. Acta, vol.382, issue.1, pp.87-103, 1999.

T. Isaksson and T. Naes, The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy, Appl. Spectrosc, vol.42, issue.7, pp.1273-1284, 1988.

M. Kamruzzaman, Y. Makino, and S. Oshita, Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review, Anal. Chim. Acta, vol.853, pp.19-29, 2015.

R. W. Kennard and L. A. Stone, Computer aided design of experiments, Technometrics, vol.11, issue.1, pp.137-148, 1969.

J. Lammertyn, A. Peirs, J. De-baerdemaeker, and B. Nicola??, Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment, Postharvest Biol. Technol, vol.18, issue.2, pp.121-132, 2000.

R. Lu, R. Van-beers, W. Saeys, C. Li, and H. Cen, Measurement of optical properties of fruits and vegetables: a review, Postharvest Biol. Technol, vol.159, p.111003, 2020.

I. Måge, A. K. Smilde, and F. M. Van-der-kloet, Performance of methods that separate common and distinct variation in multiple data blocks, J. Chemom, vol.33, issue.1, p.3085, 2019.

H. Martens, J. P. Nielsen, and S. B. Engelsen, Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures, Anal. Chem, vol.75, issue.3, pp.394-404, 2003.

B. M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys et al., Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review, Postharvest Biol. Technol, vol.46, issue.2, pp.99-118, 2007.

J. Niimi, O. Tomic, T. Naes, D. W. Jeffery, S. E. Bastian et al., Application of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition, Food Chem, vol.256, pp.195-202, 2018.

B. G. Osborne, Near-Infrared Spectroscopy in Food Analysis. Encyclopedia of Analytical Chemistry, 2006.

C. Pasquini, Near infrared spectroscopy: a mature analytical technique with new perspectives -a review, Anal. Chim. Acta, vol.1026, pp.8-36, 2018.

G. Rabatel, F. Marini, B. Walczak, and J. Roger, VSN: variable sorting for normalization, J. Chemom, vol.34, issue.2, p.3164, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02609665

Å. Rinnan, F. Berg, and S. B. Engelsen, Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends Anal. Chem, vol.28, issue.10, pp.1201-1222, 2009.

J. Roger, F. Chauchard, and V. Bellon-maurel, EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits, Chemom. Intell. Lab. Syst, vol.66, issue.2, pp.191-204, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00464022

J. Roger, A. Biancolillo, and F. Marini, Sequential preprocessing through ORThogonalization (SPORT) and its application to near infrared spectroscopy, Chemom. Intell. Lab. Syst, vol.199, p.103975, 2020.

J. Roger, J. Boulet, M. Zeaiter, and D. N. Rutledge, Pre-processing methods?. Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02586307

W. Saeys, N. N. Do-trong, R. Van-beers, and B. M. Nicolai, Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: a review, Postharvest Biol. Technol, vol.158, 2019.

V. Sierra, N. Aldai, P. Castro, K. Osoro, A. Coto-montes et al., Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy, Meat Sci, vol.78, issue.3, pp.248-255, 2008.

A. K. Smilde, I. Måge, T. Naes, T. Hankemeier, M. A. Lips et al., NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pretreatment, Postharvest Biol. Technol, vol.31, issue.7, p.111140, 2017.

K. B. Walsh, V. A. Mcglone, and D. H. Han, The uses of near infra-red spectroscopy in postharvest decision support: a review, Postharvest Biol. Technol, vol.163, p.111139, 2020.

H. L. Wang, J. Y. Peng, C. Q. Xie, Y. D. Bao, and Y. He, Fruit quality evaluation using spectroscopy technology: a review, Sensors, vol.15, issue.5, pp.11889-11927, 2015.

L. Xu, Y. Zhou, L. Tang, H. Wu, J. Jiang et al., Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration, Anal. Chim. Acta, vol.616, issue.2, pp.138-143, 2008.

P. Mishra, Postharvest Biology and Technology, vol.168, p.111271, 2020.