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, pp.99-118, 2007.

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

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.

P. Mishra, Improving moisture and soluble solids content prediction in pear fruit using near-infrared spectroscopy with variable selection and model updating approach, Postharvest Biol. Technol, vol.171, p.111348, 2021.

L. M. Yuan, F. Mao, X. J. Chen, L. M. Li, and G. Z. Huang, Non-invasive measurements of 'Yunhe' pears by vis-NIRS technology coupled with deviation fusion modeling approach, Postharvest Biol. Technol, vol.160, p.111067, 2020.

X. J. Yu, H. D. Lu, and D. Wu, Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging, Postharvest Biol. Technol, vol.141, pp.39-49, 2018.

X. M. He, X. Jiang, X. P. Fu, Y. W. Gao, and X. Q. Rao, Least squares support vector machine regression combined with Monte Carlo simulation based on the spatial frequency domain imaging for the detection of optical properties of pear, Postharvest Biol. Technol, vol.145, pp.1-9, 2018.

J. H. Wang, J. Wang, Z. Chen, and D. H. Han, Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable vis-NIR spectroscopy, Postharvest Biol. Technol, vol.129, pp.143-151, 2017.

S. E. Adebayo, N. Hashim, R. Hass, O. Reich, C. Regen et al., Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear (Pyrus communis 'Conference')-An update when using diffusion theory, Postharvest Biol. Technol, vol.130, pp.56-63, 2017.

S. Travers, M. G. Bertelsen, K. K. Petersen, and S. V. Kucheryavskiy, Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy, Lwt-Food Science and Technology, vol.59, pp.1107-1113, 2014.

A. M. Cavaco, P. Pinto, M. D. Antunes, J. M. Silva, and R. Guerra, Rocha' pear firmness predicted by a Vis/NIR segmented model, Postharvest Biol. Technol, vol.51, pp.311-319, 2009.

T. Sun, H. J. Lin, H. R. Xu, and Y. B. Ying, Effect of fruit moving speed on predicting soluble solids content of 'Cuiguan' pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression, Postharvest Biol. Technol, vol.51, pp.86-90, 2009.

P. Mishra, Close-range hyperspectral imaging of whole plants for digital phenotyping: recent applications and illumination correction approaches, Comput. Electron. Agric, vol.178, p.105780, 2020.

P. Mishra, SPORT pre-processing can improve near-infrared quality prediction models for fresh fruits and agro-materials, Postharvest Biol. Technol, vol.168, p.111271, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02958878

P. Mishra, Two standard-free approaches to correct for external influences on near-infrared spectra to make models widely applicable, Postharvest Biol. Technol, vol.170, p.111326, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02968769

P. Mishra, Partial least square regression versus domain invariant partial least square regression with application to near-infrared spectroscopy of fresh fruit, Infrared Phys. Technol, p.103547, 2020.

P. P. Subedi and K. B. Walsh, Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: method and instrumentation optimisation, Postharvest Biol. Technol, p.111078, 2020.

X. D. Sun, P. Subedi, and K. B. Walsh, Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content, Postharvest Biol. Technol, p.111117, 2020.

X. Sun, P. Subedi, R. Walker, and K. B. Walsh, NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pretreatment, Postharvest Biol. Technol, vol.163, p.111140, 2020.

C. A. Santos, M. Lopo, R. N. Pascoa, and J. A. Lopes, A review on the applications of portable near-infrared spectrometers in the agro-food industry, Appl. Spectrosc, vol.67, pp.1215-1233, 2013.

S. Gabriëls, Non-destructive measurement of internal browning in mangoes using visibleand near-infrared spectroscopy supported by artificial neural network analysis, Postharvest Biol. Technol, p.111206, 2020.

P. Mishra, Improved prediction of 'Kent' mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression, Infrared Phys. Technol, vol.110, p.103459, 2020.

J. Qin, Hyperspectral Imaging for Food Quality Analysis and Control, pp.129-172, 2010.

P. Mishra, M. S. Asaari, A. Herrero-langreo, S. Lohumi, B. Diezma et al., Close range hyperspectral imaging of plants: a review, vol.164, pp.49-67, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02607157

K. B. Walsh, J. Blasco, M. Zude-sasse, and X. Sun, Visible-NIR 'point' spectroscopy in postharvest fruit and vegetable assessment: the science behind three decades of commercial use, Postharvest Biol. Technol, vol.168, p.111246, 2020.

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

Q. Liu, K. Sun, N. Zhao, J. Yang, Y. R. Zhang et al., Information fusion of hyperspectral imaging and electronic nose for evaluation of fungal contamination in strawberries during decay, Postharvest Biol. Technol, vol.153, pp.152-160, 2019.

F. Mendoza, R. F. Lu, and H. Y. Cen, Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content, Postharvest Biol. Technol, vol.73, pp.89-98, 2012.

L. Zhou, C. Zhang, Z. Qiu, and Y. He, Information fusion of emerging non-destructive analytical techniques for food quality authentication: a survey, Trac. Trends Anal. Chem, vol.127, p.115901, 2020.

V. Steinmetz, F. Sevila, and V. Bellon-maurel, A methodology for sensor fusion design: application to fruit quality assessment, J. Agric. Eng. Res, vol.74, pp.21-31, 1999.
URL : https://hal.archives-ouvertes.fr/hal-02577519

A. K. Smilde, I. Måge, T. Naes, T. Hankemeier, M. A. Lips et al., Common and distinct components in data fusion, J. Chemometr, vol.31, p.2900, 2017.

M. Alinaghi, H. C. Bertram, A. Brunse, A. K. Smilde, and J. A. Westerhuis, Common and distinct variation in data fusion of designed experimental data, Metabolomics, vol.16, p.2, 2019.

Y. Song, J. A. Westerhuis, and A. K. Smilde, Separating common (global and local) and distinct variation in multiple mixed types data sets, J. Chemometr, vol.34, p.3197, 2020.

P. Mishra, New data preprocessing trends based on ensemble of multiple preprocessing techniques, TrAC Trends Anal. Chem, p.116045, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02968798

P. Mishra, Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques, Talanta, vol.223, p.121693
URL : https://hal.archives-ouvertes.fr/hal-02968467

P. Mishra, Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques, J. Pharmaceut. Biomed. Anal, p.113684, 2020.

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

A. Biancolillo, R. Bucci, A. L. Magrì, A. D. Magrì, and F. Marini, Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication, Anal. Chim. Acta, vol.820, pp.23-31, 2014.

A. Biancolillo, I. Måge, and T. Naes, Combining SO-PLS and linear discriminant analysis for multi-block classification, Chemometr Intell Lab, vol.141, pp.58-67, 2015.

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.

A. Biancolillo, F. Marini, and J. Roger, So-CovSel, A novel method for variable selection in a multiblock framework, J. Chemometr, vol.34, p.3120, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02609248

J. M. Roger, B. Palagos, D. Bertrand, and E. Fernandez-ahumada, CovSel: variable selection for highly multivariate and multi-response calibration Application to IR spectroscopy, Chemometr. Intell. Lab. Syst, vol.106, pp.216-223, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00635415

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

W. Saeys, N. N. Trong, R. Van-beers, and B. M. Nicolai, Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: a review, Postharvest Biol. Technol, p.110981, 2019.

S. Wold, M. Sjostrom, and L. Eriksson, PLS-regression: a basic tool of chemometrics, Chemometr Intell Lab, vol.58, pp.109-130, 2001.

P. Mishra, J. M. Roger, D. N. Rutledge, A. Biancolillo, F. Marini et al., A Chemometric Graphical User Interface for Multi-Block Data Visualisation, Regression, Classification, Variable Selection and Automated Pre-processing, p.104139, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02959982

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