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ispartof: ECOLOGICAL INFORMATICS vol:75 status: accepted

As Satsuma mandarin is a multi-layered fruit, the optical properties of the different tissue layers influence the performance of spectroscopy-based quality detection models. Therefore, the variation in the bulk optical properties (BOP) of the inner (juice vesicles) and outer (flavedo) tissue layer of Satsuma mandarin was investigated for different harvest times, fruit sizes and cultivars. Satsuma mandarin fruit from three different cultivars (Iwasaki, Okitsu and Goku Wase) were harvested at three different times around the recommended harvest date and the BOP of the inner and outer tissue layers were estimated from double integrating spheres measurements. Along the harvest time, the absorption peak related to carotenoids in the bulk absorption coefficient (μa) spectrum increased in both tissue layers, while the values attributed to chlorophylls decreased in the flavedo. Moreover, a slightly increasing trend over the harvest time was observed in the bulk scattering coefficient (μs) spectra of juice vesicles, especially at the early stage. The μa values of both tissue layers and the μs values of juice vesicles deviated among different fruit sizes, which can be related to the development stage. Smaller fruit presented a higher carotenoids concentration in juice vesicles, an advanced color change in flavedo throughout the maturation stages and higher μs values in juice vesicles at earlier maturation stages. The μa differences in pigment absorption among different cultivars were related to the maturation order. Earlier maturing cultivars possessed higher μa values related to carotenoids absorption in juice vesicles and advanced color changes in flavedo. The μs values of the flavedo differed among the cultivars and these differences were enhanced during maturation. These insights in the sources of variation in the bulk optical properties of Satsuma mandarin tissues can be used for simulation-based optimal design of an optical measurement configuration and interpretation of the acquired signals in spectroscopy-based quality detection and monitoring.

Vibrational spectroscopy methods are widely investigated as fast and non-destructive alternatives for postharvest quality evaluation. As these methods measure spectral responses at a large number of wavebands correlated to the quality traits of interest, multivariate calibration equations have to be built to estimate the quality traits from the acquired spectra. This paper provides an overview of the most important multivariate data analysis techniques for exploring spectral data, detecting outliers and building calibration models for predicting the quality traits of interest. Both linear and non-linear calibration methods are discussed for quantitative (continuous) and qualitative (discrete) quality traits. For each of the presented methods the theory is explained, followed by illustration of an example case from the postharvest domain and a discussion of applications of this technique for postharvest quality evaluation based on spectral sensors. As spectral preprocessing, careful validation and calibration transfer are crucial aspects for successful implementation of spectral sensors for postharvest quality evaluation, special attention is given to these aspects. Finally, conclusions are drawn and recommendations are made with respect to the steps to take and points of attention for successful calibration. ispartof: POSTHARVEST BIOLOGY AND TECHNOLOGY vol:158 status: Published online

The discrete element method (DEM) offers a powerful tool for simulating the interactions of large numbers of particles. Recently, DEM was used to simulate the interactions of tubular particles. While the existing linear elastic and Hertzian contact models can approximate the reversible compression for small deformations, they are inadequate for larger deformations. Here, the force–deformation behaviour is highly non-linear and plastic. In this study, data based contact models were developed for crop stems. These models combine realistic deformation behaviour with a minimal number of model parameters. Furthermore, the effect of plastic deformation and damage was incorporated in the model. The contact models were successfully used to validate, through comparison of simulations and measurements, individual stem and bulk compression. A good agreement was found between both. These validated DEM contact models for compression of crop stems allow to simulate the processing of large numbers of crop stems. publisher: Elsevier articletitle: A discrete element approach for modelling the compression of crop stems journaltitle: Computers and Electronics in Agriculture articlelink: http://dx.doi.org/10.1016/j.compag.2016.02.018 content_type: article copyright: Copyright © 2016 Elsevier B.V. All rights reserved. ispartof: Computers and Electronics in Agriculture vol:123 issue:123 pages:80-88 status: published

Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy. The funding of this work has been covered by the Universidad Pública de Navarra through the concession of both a predoctoral research grant (Res. 1753/2012) and a mobility grant (Res. 1506/ 2013), by the National Institute for Agricultural and Food Research and Technology (INIA) project: “Mejora genetica de la patata: caracterizacion reol ogica y por tecnología NIRS del material” RTA2013-00006-C03-03, and by the Agency for Innovation by Science and Technology in Flanders (IWT) through the Chameleon (SB-100021) project.

The size of colloidal particles in food products has a considerable impact on the product's physicochemical, functional and sensory characteristics. Measurement techniques to monitor the size of suspended particles could, therefore, help to further reduce the variability in production processes and promote the development of new food products with improved properties. Visible and near-infrared (Vis/NIR) spectroscopy is already widely used to measure the composition of agricultural and food products. However, this technology can also be consulted to acquire microstructure-related scattering properties of food products. In this study, the effect of the fat globule size on the Vis/NIR bulk scattering properties of milk was investigated. Variability in fat globule size distribution was created using ultrasonic homogenization of raw milk. Reduction of the fat globule size resulted in a higher wavelength-dependency of both the Vis/NIR bulk scattering coefficient and the scattering anisotropy factor. Moreover, the anisotropy factor and the bulk scattering coefficients for wavelengths above 600 nm were reduced and were dominated by Rayleigh scattering. Additionally, the bulk scattering properties could be well (R(2) ≥ 0.990) estimated from measured particle size distributions by consulting an algorithm based on the Mie solution. Future research could aim at the inversion of this model to estimate the particle size distributions from Vis/NIR spectroscopic measurements. publisher: Elsevier articletitle: Effect of ultrasonic homogenization on the Vis/NIR bulk optical properties of milk journaltitle: Colloids and Surfaces B: Biointerfaces articlelink: http://dx.doi.org/10.1016/j.colsurfb.2015.01.004 content_type: article copyright: Copyright © 2015 Elsevier B.V. All rights reserved. ispartof: Colloids and Surfaces B, Biointerfaces vol:126 pages:510-519 ispartof: location:Netherlands status: published

This paper presents the nonlinear modeling of the yaw and longitudinal dynamics of a tractor–trailer system. First, the yaw dynamic models of both the tractor and trailer are derived considering the lateral forces and side-slip angles. In order to be able to calculate the side-slips precisely, the relaxation length approach is preferred. Since the obtained yaw dynamic models are nonlinear, a constrained nonlinear optimization problem is formulated for the parameter estimation. Second, the longitudinal dynamic model for the system is derived based-on the static and dynamic responses. The static model consist of two inputs, the hydrostat position and the diesel engine speed, and one output, the longitudinal speed of the system. Afterwards, a dynamic model is proposed to define the dynamic effect between the output of the static model and the actual longitudinal speed. Third, the mathematical models of the steering mechanisms both for the tractor and trailer are identified. Consequently, a complete nonlinear dynamic model for the tractor–trailer system is obtained. The overall resulting model is thought to provide useful physical insight on such a complex mechatronic system, and can serve as the input for model based controller design. publisher: Elsevier articletitle: Nonlinear modeling and identification of an autonomous tractor–trailer system journaltitle: Computers and Electronics in Agriculture articlelink: http://dx.doi.org/10.1016/j.compag.2014.05.002 content_type: article copyright: Copyright © 2014 Elsevier B.V. All rights reserved. ispartof: Computers and Electronics in Agriculture vol:106 pages:1-10 status: published

In this chapter an overview will be given of non-destructive techniques for measuring quality attributes of horticultural produce. Vision-based techniques to measure external appearance will be discussed, as well as tomographic techniques to inspect internal defects. The emphasis will be on magnetic resonance imaging and X-ray computed tomography. Impact and acoustic impulse response techniques for measuring mechanical properties of fruit will be reviewed. The relationship between destructive and non-destructive measures of firmness will be addressed, as well as the limitations of the so-called firmness index when applied to non-spherical fruit. Spectroscopic techniques to measure quality attributes such as soluble solids content and firmness of fruit and vegetables will be introduced. Novel techniques such as time and spatially resolved spectroscopy for the estimation of light absorption and scattering properties of vegetable tissue, as well as NIR hyperspectral imaging techniques, will be discussed. Finally, novel mass spectrometry based techniques and electronic noses for rapid measurement of aroma will be described.

Partial Least Squares (PLS) is by far the most popular regression method for building multivariate calibration models for spectroscopic data. However, the success of the conventional PLS approach depends on the availability of a ‘representative data set’ as the model needs to be trained for all expected variation at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of inverse calibration approaches such as conventional PLS can deteriorate. This underscores the need for calibration methods that are capable of building multivariate calibration models which can be robustified against the unexpected variation in the concentrations and the correlations of the known interferents in the test set. Several methods incorporating ‘a priori’ information such as pure component spectra of the analyte of interest and/or the known interferents have been proposed to build more robust calibration models. In the present study, four such calibration techniques have been benchmarked on two data sets with respect to their predictive ability and robustness: Net Analyte Preprocessing (NAP), Improved Direct Calibration (IDC), Science Based Calibration (SBC) and Augmented Classical Least Squares (ACLS) Calibration. For both data sets, the alternative calibration techniques were found to give good prediction performance even when the interferent structure in the test set was different from the one in the calibration set. The best results were obtained by the ACLS model incorporating both the pure component spectra of the analyte of interest and the interferents, resulting in a reduction of the RMSEP by a factor 3 compared to conventional PLS for the situation when the test set had a different interferent structure than the one in the calibration set. publisher: Elsevier articletitle: Efficient use of pure component and interferent spectra in multivariate calibration journaltitle: Analytica Chimica Acta articlelink: http://dx.doi.org/10.1016/j.aca.2013.03.045 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved. ispartof: Analytica Chimica Acta vol:778 pages:15-23 ispartof: location:HUNGARY, Budapest status: published

This chapter presents the recent developments in advanced optical methods for exploring food microstructure. The chapter first discusses the basics of light propagation in food and the main limitations of classical approaches (e.g. continuous wave near infrared (NIR), colorimetry) for the measurement of the optical properties of food. It then describes the physical principles, the technological solutions and the advantages of optical coherence tomography, and of space- and time-resolved reflectance spectroscopy. The chapter includes examples of applications and an overview of future prospects. © 2013 Woodhead Publishing Limited All rights reserved.

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.

The use of diffuse reflectance visible and near infrared (Vis/NIR) spectroscopy was explored as a non-destructive technique to predict ‘Nules Clementine’ mandarin fruit susceptibility to rind breakdown (RBD) disorder by detecting rind physico-chemical properties of 80 intact fruit harvested from different canopy positions. Vis/NIR spectra were obtained using a LabSpec® spectrophotometer. Reference physico-chemical data of the fruit were obtained after 8 weeks of storage at 8 °C using conventional methods and included RBD, hue angle, colour index, mass loss, rind dry matter, as well as carbohydrates (sucrose, glucose, fructose, total carbohydrates), and total phenolic acid concentrations. Principal component analysis (PCA) was applied to analyse spectral data to identify clusters in the PCA score plots and outliers. Partial least squares (PLS) regression was applied to spectral data after PCA to develop prediction models for each quality attribute. The spectra were subjected to a test set validation by dividing the data into calibration (n = 48) and test validation (n = 32) sets. An extra set of 40 fruit harvested from a different part of the orchard was used for external validation. PLS-discriminant analysis (PLS-DA) models were developed to sort fruit based on canopy position and RBD susceptibility. Fruit position within the canopy had a significant influence on rind biochemical properties. Outside fruit had higher rind carbohydrates, phenolic acids and dry matter content and lower RBD index than inside fruit. The data distribution in the PCA and PLS-DA models displayed four clusters that could easily be identified. These clusters allowed distinction between fruit from different preharvest treatments. NIR calibration and validation results demonstrated that colour index, dry matter, total carbohydrates and mass loss were predicted with significant accuracy, with residual predictive deviation (RPD) for prediction of 3.83, 3.58, 3.15 and 2.61, respectively. The good correlation between spectral information and carbohydrate content demonstrated the potential of Vis/NIR as a non-destructive tool to predict fruit susceptibility to RBD.