Wouter Saeys

NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit—A Review

The global citrus industry is continually confronted by new technological challenges to meet the ever-increasing consumer awareness and demand for quality-assured fruit. To face these challenges, recent trend in agribusiness is declining reliance on subjective assessment of quality and increasing adoption of objective, quantitative and non-destructive techniques of quality assessment. Non-destructive instrument-based methods are preferred to destructive techniques because they allow the measurement and analysis of individual fruit, reduce waste and permit repeated measures on the same item over time. A wide range of objective instruments for sensing and measuring the quality attributes of fresh produce have been reported. Among non-destructive quality assessment techniques, near-infrared (NIR) spectroscopy (NIRS) is arguably the most advanced with regard to instrumentation, applications, accessories and chemometric software packages. This paper reviews research progress on NIRS applications in internal and external quality measurement of citrus fruit, including the selection of NIR characteristics for spectra capture, analysis and interpretation. A brief overview on the fundamental theory, history, chemometrics of NIRS including spectral pre-processing methods, model calibration, validation and robustness is included. Finally, future prospects for NIRS-based imaging systems such as multispectral and hyperspectral imaging as well as optical coherence tomography as potential non-destructive techniques for citrus quality assessment are explored.

Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm.

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200 nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding-doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses.

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.

Application of Visible and Near-Infrared Reflectance Spectroscopy (Vis/NIRS) to Determine Carotenoid Contents in Banana (Musa spp.) Fruit Pulp

The analysis of carotenoids is complicated by the tendency of these compounds to react with radical species, leading to oxidative breakdown and isomerization during extraction. Therefore, protocols should be rapid and avoid unnecessary exposure to heat, acids, and so forth. Here, we evaluate the use of visible and near infrared reflectance spectroscopy (Vis/NIRS) to measure carotenoid contents in fruit from 28 Musa (banana and plantain) varieties. Carotenoid contents were first quantified using standardized RP-HPLC protocols, and these results were then used to develop algorithms to predict carotenoid contents from Vis/NIR spectra of the same samples. Cross-validation of the predictive algorithms across a genetically diverse group of varieties demonstrated that correlation coefficients between the HPLC measurements and the Vis/NIRS predictions varied from good for the total carotenoids and beta-carotene fractions (r(2)(cv), 0.84, 0.89) to reasonable for alpha-carotene and cis-carotenes (r(2)(cv), 0.61, 0.66), but there was only a poor correlation (r(2)(cv), 0.30) for the minor lutein component. Nonetheless, since approximately 90% of the Musa carotenoids consist of only alpha- and beta-carotene, results indicate that Vis/NIRS can be used for the high-throughput screening of fruit pulp samples for vitamin A nutritional content on the basis of their total carotenoids content.

Nondestructive Measurement of Fruit and Vegetable Quality

We review nondestructive techniques for measuring internal and external quality attributes of fruit and vegetables, such as color, size and shape, flavor, texture, and absence of defects. The different techniques are organized according to their physical measurement principle. We first describe each technique and then list some examples. As many of these techniques rely on mathematical models and particular data processing methods, we discuss these where needed. We pay particular attention to techniques that can be implemented online in grading lines.

Modeling and control of a spherical rolling robot: A decoupled dynamics approach

This paper presents the results of a study on the dynamical modeling, analysis, and control of a spherical rolling robot. The rolling mechanism consists of a 2-DOF pendulum located inside a spherical shell with freedom to rotate about the transverse and longitudinal axis. The kinematics of the model has been investigated through the classical methods with rotation matrices. Dynamic modeling of the system is based on the Euler-Lagrange formalism. Nonholonomic and highly nonlinear equations of motion have then been decomposed into two simpler subsystems through the decoupled dynamics approach. A feedback linearization loop with fuzzy controllers has been designed for the control of the decoupled dynamics. Rolling of the controlled mechanism over linear and curvilinear trajectories has been simulated by using the proposed decoupled dynamical model and feedback controllers. Analysis of radius of curvature over curvilinear trajectories has also been investigated.

Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: Reflectance or transmittance?

The composition of produced milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of the corresponding cow. Therefore, online measurement of milk components during milking 2 or more times per day would provide knowledge about the current health and nutritional status of each cow individually. This information provides a solid basis for optimizing cow management. The potential of visible and near-infrared (Vis/NIR) spectroscopy for predicting the fat, crude protein, lactose, and urea content of raw milk online during milking was, therefore, investigated in this study. Two measurement modes (reflectance and transmittance) and different wavelength ranges for Vis/NIR spectroscopy were evaluated and their ability to measure the milk composition online was compared. The Vis/NIR reflectance measurements allowed for very accurate monitoring of the fat and crude protein content in raw milk (R(2)>0.95), but resulted in poor lactose predictions (R(2)<0.75). In contrast, Vis/NIR transmittance spectra of the milk samples gave accurate fat and crude protein predictions (R(2)>0.90) and useful lactose predictions (R(2)=0.88). Neither Vis/NIR reflectance nor transmittance spectroscopy lead to an acceptable prediction of the milk urea content. Transmittance spectroscopy can thus be used to predict the 3 major milk components, but with lower accuracy for fat and crude protein than the reflectance mode. Moreover, the small sample thickness (1mm) required for NIR transmittance measurement considerably complicates its online use.

Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers

Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal yield. Autonomous navigation of an agricultural vehicle involves the control of different dynamic subsystems, such as the yaw angle dynamics and the longitudinal speed dynamics. In this study, a proportional-integral-derivative controller is used to control the longitudinal velocity of the tractor. For the control of the yaw angle dynamics, a proportional-derivative controller works in parallel with a type-2 fuzzy neural network. In such an arrangement, the former ensures the stability of the related subsystem, while the latter learns the system dynamics and becomes the leading controller. In this way, instead of modeling the interactions between the subsystems prior to the design of a model-based control, we develop a control algorithm which learns the interactions online from the measured feedback error. In addition to the control of the stated subsystems, a kinematic controller is needed to correct the errors in both the x- and the y- axis for the trajectory tracking problem of the tractor. To demonstrate the real-time abilities of the proposed control scheme, an autonomous tractor is equipped with the use of reasonably priced sensors and actuators. Experimental results show the efficacy and the efficiency of the proposed learning algorithm.

Near Infrared Spectroscopy for Agricultural Materials: An Instrument Comparison:

The selection of a spectrophotometer for the measurement of constituents of agricultural materials with acceptable accuracy and cost effectiveness requires a comparative study of the performance of different spectrophotometers. Four commercially available spectrophotometers were evaluated, based on measurements performed on three agricultural materials. These spectrophotometers, differing mainly in wavelength range and measurement principles, comprised a diode array (DA) of 300–1700 nm, a combination of diode array and scanning monochromator (DASM) of 350–2500 nm, a Fourier transform (FT) of 750–2500 nm and a scanning monochromator (SM) of 400–2500 nm spectrophotometers. They were used to measure the moisture content of soil, the chemical constituents of hog manure and to detect bruising in apples. Three spectral pre-treatments were considered. Calibrations were developed using partial least squares (PLS) regression with the leave-one-out cross-validation technique for soil and manure and principal component analysis (PCA) for apple. The four instruments provided good predictions for soil moisture content, with the largest coefficient of determination (r2) values between 0.84–0.86 and with the largest ratio of prediction to deviation (RPD) of standard deviation (SD) to root mean square error of cross-validation (RMSECV) ranging from 2.53 to 2.75. The DASM and SM were comparable and slightly better than the DA and FT. For hog manure, total nitrogen was predicted more accurately with the four instruments (r2 = 0.83–0.89 and RPD = 2.43–3.01) than the phosphorus (r2 = 0.66–0.85 and RPD = 1.72–2.61) and potassium (r2 = 0.70–0.84 and RPD = 1.83–2.50). The order of accuracy of the four spectrophotometers for the measurement of total nitrogen was: FT–SM–DA–DASM. For phosphorus and potassium the order was: DA–DASM–FT–SM and SM–FT–DASM–DA, respectively. The DA performed better than the DASM and FT for discrimination of bi-colour and single-colour bruised apple, respectively. Therefore, selection of a spectrophotometer depends mainly on the type of material analysed and the constituent to be measured. Wavelengths above 1700 nm were found unnecessary for the applications considered and the DA spectrophotometer was of sufficient accuracy, as it is robust, significantly cheaper and can be used in the field for on-line measurements.

Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.