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Photosynthetic active radiation (PAR) refers to photons between 400 and 700 nm. These photons drive photosynthesis, providing carbohydrates for plant metabolism and development. Far-red radiation (FR, 701–750 nm) is excluded in this definition because no FR is absorbed by the plant photosynthetic pigments. However, including FR in the light spectrum provides substantial benefits for biomass production and resource-use efficiency. We investigated the effects of continuous FR addition and end-of-day additional FR to a broad white light spectrum (BW) on carbohydrate concentrations in the top and bottom leaves of sweet basil (Ocimum basilicum L.), a species that produces the raffinose family oligosaccharides raffinose and stachyose and preferentially uses the latter as transport sugar. Glucose, fructose, sucrose, raffinose, and starch concentrations increased significantly in top and bottom leaves with the addition of FR light. The increased carbohydrate pools under FR light treatments are associated with more efficient stachyose production and potentially improved phloem loading through increased sucrose homeostasis in intermediary cells. The combination of a high biomass yield, increased resource-use efficiency, and increased carbohydrate concentration in leaves in response to the addition of FR light offers opportunities for commercial plant production in controlled growth environments. [ABSTRACT FROM AUTHOR]

The impact of 316L stainless steel discolouration on chloride induced pitting corrosion was quantified. Several degrees of discolouration were produces by chemical passivation, simulated rouging or anodic polarization. Discolouration was characterized by spectral analysis in the visible and NIR range (400-1000 nm). Pitting corrosion resistance was quantified by pitting potential measurements. The spectra were used to indicate whether pitting or transpassive behaviour would occur using chemometric classification techniques with 88% accuracy. In addition, pitting potentials were predicted combining chemometrics and survival analysis techniques with a 90 mV RMS error in cross-validation. XPS analysis linked pitting resistance with the Cr/Fe balance. • 316L surface discolouration can be quantified by Vis-NIR spectral measurements. • The occurrence of pitting was predicted by application of chemometric classification. • The pitting potential was predicted by application of chemometric survival analysis. • Transpassive outcomes are included by combining chemometric and survival regression. [ABSTRACT FROM AUTHOR]

The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows. [ABSTRACT FROM AUTHOR]

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone–based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which modelbased indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied. [ABSTRACT FROM AUTHOR]

Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns. [ABSTRACT FROM AUTHOR]

Mechanical damage of fresh fruit occurs throughout the postharvest supply chain leading to poor consumer acceptance and marketability. In this review, the mechanisms of damage development are discussed first. Mathematical modeling provides advanced ways to describe and predict the deformation of fruit with arbitrary geometry, which is important to understand their mechanical responses to external forces. Also, the effects of damage at the cellular and molecular levels are discussed as this provides insight into fruit physiological responses to damage. Next, direct measurement methods for damage including manual evaluation, optical detection, magnetic resonance imaging, and X-ray computed tomography are examined, as well as indirect methods based on physiochemical indexes. Also, methods to measure fruit susceptibility to mechanical damage based on the bruise threshold and the amount of damage per unit of impact energy are reviewed. Further, commonly used external and interior packaging and their applications in reducing damage are summarized, and a recent biomimetic approach for designing novel lightweight packaging inspired by the fruit pericarp. Finally, future research directions are provided. Mathematical modeling has been increasingly used to calculate damage to fruit. Cell and molecular mechanisms response to fruit damage is an under-explored area. Susceptibility measurement of different mechanical forces has received attention. Customized design of reusable and biodegradable packaging is a hot topic of research. [ABSTRACT FROM AUTHOR]

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. [ABSTRACT FROM AUTHOR]

As lameness detection in dairy cattle using visual locomotion scoring is cumbersome and subjective, research efforts are dedicated to develop automatic lameness detection systems. ‘Tender hoof placement’ and the distribution of the body weight over the four legs are possible lameness indicators, but no research exists on how to derive from automatically measured gait characteristics. This study aims to derive new variables related to the (i) landing, full weight bearing and lifting phases of a stance time and the (ii) time spent on combinations of legs during the different phases of the gait cycle from cow gait recordings on a dedicated pressure mat, known as the Gaitwise. Data of 9 non-lame, 11 mildly lame and 12 severely lame cows were gathered. For all measurements, each variable was calculated per leg or combination of legs, after which the group means of each variable were compared between the three lameness statuses using a one-way ANOVA analysis. Landing and lifting variables indicated that the proportion of time for hoof placement and hoof lifting during the total stance time was longer in lame cows, and that the proportion of full weight bearing time was shorter. Lame cows were thus more careful to place and retract the hind feet in the case of a hind-lame leg. Support time variables indicated that lame cows increased the percentage triple support time (i.e time spent with three feet on the ground during walking) and lowered the percentage double support (i.e. time spent with two legs on the ground). Also, double support combinations on the same side of the body were preferred above diagonal combinations. The newly defined gait variables indeed reflect tenderness of hoof placement and body weight distribution and hence seem useful for discriminating between non-lame, mildly lame and severely lame cows. However, several of these interesting variables may have to be combined to obtain automatic lameness detection with sufficient accuracy. [ABSTRACT FROM AUTHOR]

While previous research has shown the potential of automatic lameness detection by means of a pressure mat, these systems are currently not adopted in practice due to their high cost and low on-farm applicability. Therefore, the aim of this study was to investigate to what level the size (0.61 × 4.88 m) and resolution (0.0127 × 0.0127 m) of the pressure mat can be reduced without significant loss in lameness detection performance. To this end, standard gait variables were calculated based on adapted datasets in which the available data had been reduced to simulate the effects of a decreasing mat length and sensor resolution. These extracted gait variables were then used in a linear discriminant analysis to classify cows as non-lame, mildly lame or severely lame. This analysis indicated that the measurement zone length must be at least 3.28 m to successfully monitor one complete gait cycle, while the size of each individual sensing element should not be larger than 2.58 ∗ 10 −3 m 2 to avoid an increase in the misidentification of imprints. When these limits were taken into account, the obtained overall lameness detection accuracy was not worse than that of the original system. [ABSTRACT FROM AUTHOR]

Changes in the feeding pattern of a pig may indicate disease or welfare issues and can also be strongly related to productivity changes. Automatic measurements of the feeding patterns of group-housed growing-finishing pigs can enable the detection of such feeding related changes. In this paper, a high frequency (HF) Radio Frequency Identification (RFID) system was used to register pigs’ feeding patterns at a commercial-type feed trough. The raw RFID registrations had to be translated into feeding visits, as there were irregular time gaps between the registrations that were inherent to the system for this application. Data from two experiments were used: one used behavioural observations of six pigs during three days with the aim of finding the best method for visit construction (experiment 2, E2), whilst the other experiment used feeding pattern observations of 20 pigs on one day as an independent validation data-set (experiment 1, E1). Three methods for visit construction were proposed: (1) visit criteria (a bout criterion and a minimum duration criterion), (2) a digital filter (using a well-defined time delay and a threshold for feeding) and (3) erosion and dilation operations from mathematical morphology. The best results were obtained using a bout criterion equal to 10 s for two RFID tags per pig or 20 s for one RFID tag per pig. Removal of short visits was found to be unnecessary. This optimal method is equal to a closing operation (a dilation followed by an erosion). For the RFID system with the optimal bout criterion, using two tags per pig, average sensitivity was 83%, specificity 98%, accuracy 97% and precision was 75% for E2. Applying the same bout criterion on the independent test set (E1), resulted in an average sensitivity of 80%, a specificity of 99%, an accuracy of 98% and a precision of 78%. Performance of the method tended to be lower when using one tag per pig. Regression analysis revealed that the observed feeding duration was well-correlated with RFID-based feeding duration (R 2 = 0.86 for two tags per pig, E1 + E2). Other observed variables (number of feeding visits and inter-visit interval) were harder to predict by the RFID system. From these results, there was concluded that feeding patterns can be recorded accurately in an automatic way if the registrations from the RFID system are analysed using an appropriate bout criterion. [ABSTRACT FROM AUTHOR]

By controlling the blood glucose levels of diabetics permanent diabetes-related problems such as blindness and loss of limbs can be delayed or even avoided. Therefore, many researchers have aimed at the development of a non-invasive sensor to monitor the blood glucose level continuously. As non-invasive measurements through the skin, the ear lobe or the gums have proven to be either unreliable or impractical, attention has recently shifted to minimally invasive sensors which measure the glucose content in serum or interstitial fluid. Thanks to the development of on-chip spectrometers minimally invasive, implantable devices are coming within reach. However, this technology does not allow to acquire a large number of wavelengths over a broad range. Therefore, the most informative combination of a limited number of variables should be selected. In this study, Interval PLS (iPLS), Variable Importance in Projection (VIP), Uninformative Variable Elimination (UVE), Bootstrap-PLS coefficients, Moving window, CorXyPLS, Interval Random Frog-PLS and combinations of these methods were used in order to address the question whether the short wave band (800–1500 nm), first overtone band (1500–1800 nm), the combination band (2050–2300 nm) or a combination of them is the most informative region for glucose measurements and which wavebands should be measured within these wavelength ranges. The three different data sets employed focus on the determination of (1) glucose in aqueous solutions over the 1–30 mM range in presence of urea and sodium D -lactate, (2) glucose in aqueous solutions over the 2–16 mM range in presence of icodextrin and urea and (3) glucose in human serum samples. The best results for the first, second and third data sets were obtained by selecting 40, 130 and 20 variables resulting in a PLS model with an RMSEP of 0.56, 0.59 and 1.5 mM, respectively. It was found that the first overtone band is most informative for aqueous solutions, while for glucose measurement of serum samples the combination band was found to be the better choice. [ABSTRACT FROM AUTHOR]

Puffiness is one of the dominant postharvest disorders in easy-peeling citrus cultivars. In this study, the structural changes between healthy and puffy Satsuma mandarin were investigated and the potential of using optical methods for disorder detection was explored. To gain more insight in this disorder, the external appearance and internal quality attributes were first compared between healthy and puffy Iwasaki Satsuma mandarins at three harvest times. Although no consistent differences were observed in the appearance of fruits, the soluble solids content and Brix minus acid values in puffy mandarin were found to be higher compared to the corresponding healthy fruit. The structural properties of the flavedo and albedo tissue layer in the peel were quantified from X-ray CT scans. Whilst no differences were observed in the size of the oil glands in the flavedo, the pore size in the albedo of puffy mandarin was found to be larger with later harvest. The bulk optical properties of the intact fruit were estimated from laser scatter images with a metamodel calibrated on optical phantoms. The reduced scattering coefficient (μ s ') for the intact fruit was found to be lower in puffy mandarin relative to healthy fruit. The distinction between healthy and puffy mandarin based on μ s ' was further validated on Goku Wase Satsuma mandarin. The results obtained indicate that healthy and puffy mandarin can be separated well based on their μ s ' at all the selected wavelengths. This provides a basis for the non-destructive optical detection of puffing disorder at an early stage. [Display omitted] • External and internal attributes were compared between healthy and puffy mandarin. • Thickness of both flavedo and albedo tissue layers was lower in puffy mandarin. • Pore size in the albedo of puffy mandarin was larger at a later harvest time. • The μ s ' of the intact fruit was lower in the puffy than the healthy mandarin. [ABSTRACT FROM AUTHOR]

A rapid, robust, unbiased, and inexpensive discriminant method capable of classifying hazelnut ( Corylus avellana, L.) in compliance with the statements set out by the Commission Regulation (EC) No. 1284/2002 is economically important to the fresh and processed industries. Thus, in this study, the feasibility of high dynamic range (HDR) hyperspectral imaging for hazelnut kernel sorting (cv. Tonda Gentile Romana) of four quality classes (Class Extra, Class I, Class II, and Waste) has been investigated. Two different exposure times (5 and 8 ms) were selected for experiments, and the respective spectra were combined to obtain a HDR over the full spectral range. The illumination setup was optimized to improve the intensity and uniformity of the light along the field of view of the camera. PLS-DA was used to classify the kernels based on their spectra and the spectral pretreatment was optimized through an iterative routine. The performance of each PLS-DA model was defined based on its accuracy, sensitivity, and selectivity rates. All of the selected models provided a very good (>90 %) or good (>80 %) sensitivity and selectivity for the predefined classes. Misclassified kernels were primarily assigned to the low-quality classes (i.e., Class II and Waste). Moreover, the spatial domain was used to evaluate the feasibility of distinguishing hazelnut classes on the basis of their size and shape. It was found that hazelnut dimensions can be used to improve the accuracy of the classification of the kernels. Thanks to this combination of both spectral and spatial information spectral imaging could be used for quality sorting of hazelnuts. [ABSTRACT FROM AUTHOR]

The study of animal feeding behaviour is of interest to understand feeding, to investigate the effect of treatments and conditions or to predict illness. This paper reviews the different steps to undertake when studying animal feeding behaviour, with illustrations for group-housed pigs. First, one must be aware of the mechanisms that control feeding and the various influences that can change feeding behaviour. Satiety is shown to largely influence free feeding ( ad libitum and without an operant condition) in animals, but ‘free’ feeding seems a very fragile process, given the many factors that can influence feeding behaviour. Second, a measurement method must be chosen that is compatible with the goal of the research. Several measurement methods exist, which lead to different experimental set-ups and measurement data. Sensors are available for lab conditions, for research on group-housed pigs and also for on-farm use. Most of these methods result in a record of feeding visits. However, these feeding visits are often found to be clustered into meals. Thus, the third step is to choose which unit of feeding behaviour to use for analysis. Depending on the situation, either meals, feeding visits, other raw data, or a combination thereof can be suitable. Meals are more appropriate for analysing short-term feeding behaviour, but this may not be true for disease detection. Further research is therefore needed. To cluster visits into meals, an appropriate analysis method has to be selected. The last part of this paper provides a review and discussion of the existing methods for meal determination. A variety of methods exist, with the most recent methods based on the influence of satiety on feeding. More thorough validation of the recent methods, including validation from a behavioural point of view and uniformity in the applied methods is therefore necessary. [ABSTRACT FROM AUTHOR]

Fibrous forage and biomass crops such as grass and maize are used at large scale as feed for ruminants and feedstock for biogas production. These crops are typically harvested by chopping them in small particles which are light and suitable for partial pneumatic conveying, where the particle trajectory is determined by their aerodynamic properties. While the aerodynamic properties of many grains have been studied, no reports were found for perennial ryegrass. Chopped ryegrass samples of leaf blades and stems were collected in the fields. After imaging the fresh harvested particles to quantify their size and surface area, their terminal velocity was measured with the fluidised-bed method. The relationship between the mass, projected area and moisture content of the stems and blades with their terminal velocities were modelled. In line with theory, mass and moisture content was positively correlated with terminal velocity, while projected area was negatively correlated with it. Smaller terminal velocities were observed for the blades (1.75 m s−1) compared to the stems (4.44 m s−1). The drag coefficients of grass particles were calculated, and for blades it was negatively correlated with the particle Reynold's number lying within Newton's regime (103 < Re < 3 × 105), whilst no significant correlation was found for stems. The average drag coefficient of blades (0.75) was larger than stems (0.54). No significant effect (p-value <0.01) of moisture content on the drag coefficient of grass particles was observed. This confirms that the drag coefficient is a shape parameter, related to formation of airstreams around the particle. • Aerodynamic properties of ryegrass were quantified in a wind tunnel. • The terminal velocity of grass blades was 61% smaller than that of stems. • Terminal velocities can be predicted from mass, area and moisture content (R2 = 0.77). • Grass particles' drag coefficient was calculated and compared to similar materials. • The drag coefficient of grass blades was 39% larger than that of stems. [ABSTRACT FROM AUTHOR]

Highlights: [•] A HF RFID system was validated for registering feeding pigs. [•] The system was used in simulated commercial farm conditions. [•] Specificity for individual pigs with one or two tags was always above 85%. [•] Sensitivity varied for individual pigs, tags and different time windows used. [•] A sensitivity of 88.58% and a specificity of 98.34% can be reached. [Copyright &y& Elsevier]

Highlights: [•] Molecular structure determined. [•] Molecular descriptors are calculated based on above structure determination. [•] SPA and GA as feature selection method coupled with LDA (linear) and KNN (nonlinear) applied and compared. [•] QSAR classification models were built with linear and nonlinear techniques (only when few descriptors selected). [•] The SPA was found to select few variables leading to good classification models. [Copyright &y& Elsevier]

Quantitative structure-activity relationship ( QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization ( PSO) and genetic algorithm ( GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression ( MLR)] and non-linear [locally weighted regression ( LWR) based on both Euclidean ( E) and Mahalanobis ( M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50. The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction ( RMSEP) for the test sets obtained by PSO/ MLR, GA/ MLR, PSO/ LWRE, PSO/ LWRM, GA/ LWRE, and GA/ LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear. [ABSTRACT FROM AUTHOR]

[Display omitted] • The principles of four methods for food quality analysis are discussed. • Different MVDA methods are illustrated on a common data set as an example-based review. • The most suitable MVDA method depends on the nature of the data set and the objective of the research. • The potential of more advanced multivariate methods should be further explored. These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

The driving tasks of combine harvester operators can be described as being very exhausting because of the high environmental temperatures, long working days and time constraints operators have to deal with. In order to lighten the job, automation is being introduced. In this study, the focus lies on the automation of the cleaning unit. A non-linear prediction model for the sieve losses has been established by means of fuzzy modelling techniques. Excessive sieve losses can be predicted by making use of a differential pressure measurement under the upper sieve section and the derived sieve load signal. The latter signal is a measure for the loadings by grain and chaff on the upper sieve and can thus be linked with the aeration level of the upper sieve section. Validation results revealed that the best position for mounting a pressure sensor is at the rear section of the upper sieve. Grain loss kernels will be blown out by the fan when the loadings on the upper sieve are very low, while a high loading on the upper sieve will result in sieve overload and thus very high sieve losses. [Copyright &y& Elsevier]

Operating a combine harvester can be described as very exhausting because of the high environmental temperatures, long working days and time pressures. In order to lighten the job load, automation technologies have been introduced. In this study, the focus is on the automation of the cleaning unit. A non-linear prediction model for the material other than grain (MOG) content in the grain bin has been established by means of fuzzy modelling techniques. Different model structures were evaluated and best results were obtained when the input data space consists of two input variables (fan speed and load on the upper sieve section) and is subdivided into three local linear sub-models. It was shown that the cleaning section settings such as lower and upper sieve openings had a minor effect on the content of MOG in the grain bin when compared to other variables such as fan speed and the loadings by chaff, straw and grain on the upper sieve. High MOG values can be expected when both the loadings on the upper sieve are high and the fan speed is low. High fan speeds will result in lower MOG values, independent of the load on the upper sieve, but they can also give high losses. [Copyright &y& Elsevier]

A class of multivariate calibration methods called augmented classical least squares (ACLS) has been proposed which combines an explicit linear additive model with the predictive power of inverse models, such as principal component regression (PCR) and partial least squares (PLS). Because of its use of the explicit linear additive model, ACLS provides an interesting framework to incorporate different sources of prior information, such as measured pure component spectra, in the model. In this study, the predictive power of ACLS models incorporating different amounts of prior information has been compared to that of PCR and PLS using two examples, a designed experiment and one with biological samples. In both cases, the ACLS models showed predictive power comparable to PLS under idealized validation conditions. When a different interferent structure was present in the validation samples, the predictive power of the inverse models (PCR and PLS) dramatically decreased, with an increase in root-mean-squared error of prediction by a factor of 3.5 for the first example and a factor of 2 in the second example. The incorporation of prior information in the ACLS framework was found to considerably reduce or even completely remove these dramatic effects, especially when the pure component contributions for the interferents were taken into account. [ABSTRACT FROM AUTHOR]

The cleaning process on a combine harvester is a complex process that is influenced by a wide range of parameters such as machine settings, field and crop-related parameters, etc. Because of the high time pressures combine drivers have to deal with, optimal settings for the cleaning section are usually only estimated once for each crop. As a consequence, differences in temporal and site-specific conditions are neglected. No recent literature is available that considers the interaction between the settings of the cleaning section (like e.g. fan speed, lower sieve opening and upper sieve opening) and the material other than grain (MOG) content in the grain bin, which is, however, an important performance parameter of the cleaning shoe. In this study, a combine harvester was equipped with extra sensors that could contain valuable information necessary to predict the performance of the cleaning section. A non-linear genetic polynomial regression technique was used to rank the pool of potential sensors as possible regression variables for a prediction model of the MOG content in the grain bin. This model is important for the automation of the cleaning shoe. Results showed that the MOG content in the grain bin is influenced non-linearly by differences in the amount of biomass on the sieve section and the fan speed, which are also correlated with each other. [Copyright &y& Elsevier]

The cleaning process on a combine harvester is a complex process, which is influenced by a wide range of parameters such as e.g. machine settings, and field- and crop-related parameters. Because of the pressures on time faced by combine operators, optimal settings for the cleaning section are usually estimated only once for each crop, and as a consequence, differences in temporal- and site-specific conditions are neglected. Previous literature showed the importance of making use of the most appropriate settings to reduce grain losses. In order to gain insight into the cleaning process of a combine harvester, machine behaviour was registered for different machine settings. Different measurement devices were installed on a test combine to extract information from the combine harvesting process. In order to rank the candidate input variables as possible regression variables for the prediction of the sieve losses, an input selection methodology based on the principles of natural selection is presented. Results indicate that that the sieve losses are affected in a non-linear manner by differences in the pressure profile of the cleaning section and the upper sieve opening. [Copyright &y& Elsevier]

Abstract: In this paper, an experiment of using a hyperspectral imaging system for bruise detection on ‘Golden Delicious’ apples is reported. The hyperspectral imaging system was built in the wavelength region between 400 and 1000nm. Chemometric tools such as PCA and PLSDA were used to extract and summarize the spectral information from the hyperspectral images. Image processing methods made it possible to segment the region-of-interest according to the spatial features. Classification algorithms based on PCA and PLSDA results were developed, respectively. Their performance with respect to the classification accuracy and feasibility to implement on-line sorting were compared. The chemometric tools are able to extract and summarize the pixel-based information, while the image processing methods provide region-based analysis to efficiently segment differences of the apple surface. This combination of image processing techniques and chemometric tools provides a very promising approach for studying the quality of apples. [Copyright &y& Elsevier]

Incorporating monitoring and forecasting technologies has the potential to enhance the different aspects of winemaking. By monitoring certain chemical parameters, it is possible to evaluate the progress of the fermentation and phenolic extraction, while also detecting deviations from expected behaviour. This study aims to investigate the development of a rapid forecasting method and different strategies to improve the accuracy of process monitoring. The first part involves the introduction of partial least squares (PLS) calibration models able to forecast the trajectories of phenolic extraction during fermentation using grape extract spectra. Additionally, novel methods for reoptimizing calibration models are proposed, with the intention of increasing the prediction accuracy. Whilst this method is transferable to PLS models, principal component regression (PCR) models were used instead as a more simplistic way to test the new methods. First, dynamic PCR linear regression coefficients were adapted over time to capture the compound evolution in the wine matrix. Secondly, PCR calibration models were reoptimized using interpolated spectral and reference data. The forecasting PLS calibration models have shown promising results for total anthocyanin content (mg/L), colour density, polymeric pigments (mg/L), MCP tannins (mg/L), and total phenolic index with overall R2 values on the validation set above 0.6, and low RSMEV values. Using interpolated data to re-optimise models was found to be more effective than global models or those using the calculated regression coefficients methods for predicting the concentration of a particular compound at a certain time point. While R2 values are suboptimal for all considered PCR models, in 72% of the cases analysed, the interpolation method for re-optimisation showed RMSEV values between 6% and 83% lower than those reported for a more traditional PCR model and other re-optimisation strategies. Interpolated models have shown consistency in predicting values, along with good performance metrics, indicating that they could be applied to samples for which calibration models do not exist. • This study uses grape extracts spectral data to predict wine phenolic content. • Time-specific calibration models were optimised to increase prediction accuracy. • Acceptable R2 and RMSEP for phenolic prediction in fermentation were obtained. • Using interpolated data to re-optimise models was found to be effective. [ABSTRACT FROM AUTHOR]

In the past decade, hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods for non-destructive food quality analysis and control. This paper demonstrates fast and automatic quantification of food ingredients with hyperspectral imaging in the visible and near-infrared range in combination with chemometrics and image processing techniques. The application of ingredient quantification in bread flour recipes requires high spatial resolution in addition to spectral discrimination power. Our results show that automatic and accurate quantification of all ingredients can be done, reaching pixel discrimination accuracies above 90% and ingredient quantification errors within the required 1% absolute error in weight. The classification accuracy obtained using 15 wavebands on the test images is around 15% higher than what was obtained with colour imaging. • Accurate ingredient discrimination (>90%) with VNIR hyperspectral imaging. • Accurate seed count based on spatial post-processing of classified image. • Accurate quantification of weight in non-seed ingredients. • Increased discrimination accuracy versus color imaging. [ABSTRACT FROM AUTHOR]

• Reflectance, Transmittance, Fluorescence, Raman and integrated imaging is covered. • Instrumentation is discussed with special attention for real-time imaging. • The major steps in hyperspectral data analysis are discussed. • Overview of applications in external and internal quality evaluation. • Review of applications in food safety detection. In the past 20 years, hyperspectral imaging has been widely investigated as an emerging, promising technology for evaluating quality and safety of horticultural products. This technology has originated from remote sensing and joins the domains of machine vision and point spectroscopy to provide superior image segmentation for the detection of defects and contaminations, and to map the chemical composition. Thanks to the advancements in instrumentation and data analysis in the past two decades, hyperspectral imaging technology has evolved into a powerful nondestructive inspection tool and the scope of applications in postharvest quality and safety evaluation has expanded tremendously. In this article, different imaging modes (reflectance, transmittance, fluorescence and Raman) and their combinations, and the potential for real-time acquisition of hyperspectral images at industry relevant speeds are first discussed in terms of their advantages and disadvantages. Next reviewed are different data processing/analysis methods and associated steps from data pre-processing over the spectral and spatial domains to the actual model building and performance evaluation. An overview is then given of hyperspectral imaging applications for external quality and defect evaluation, internal quality and maturity assessment, and food safety detection of horticultural products. Finally, a brief discussion is presented on the challenges and opportunities in future development and application of hyperspectral imaging technology in food quality and safety evaluation of horticultural products. [ABSTRACT FROM AUTHOR]

• Additional green light adds to biomass production in Ocimum basilicum L. • Additional green light increases stem length, leaf length and leaf area in Ocimum basilicum L. • This study confirms the hypothesis that green light induces shade avoidance phenotypes. • This study shows that green light improves plant quality in the cultivation of basil. The consequence of adding green light (505 nm) to a white light spectrum (350–700 nm) and the partial replacement of blue and red light by green light within a white light spectrum were evaluated for Ocimum basilicum L. cv. Marian regarding plant growth and development. Plants were grown in absence of far-red light consequently making green light the only light passing through the leaves. Green light (15 μmol∙m−2∙s−1) was added to a white light spectrum (108 μmol ∙m−2∙s−1) without changing the absolute intensity of the blue and red wavelengths. Adding green light to the spectrum did not affect the chlorophyll content in the leaves, nor the ratio of leaf length over leaf width. An increased biomass production, number of leaves per pot, number of leaves per stem, stem length, and individual leaf area were observed. These features are important quality attributes determining the marketable value of young basil. The partial replacement of blue and red light by green light resulted in a significant increase in biomass production compared to plants produced under a control spectrum at equal light intensity. A relative increase in green light also significantly increased stem length, leaf length and projected single leaf area. This study highlights the growth benefits of green wavelengths that are easily transmitted to the underlying leaves. We evaluated the effects of green light on basil morphology and its potential to induce shade avoidance symptoms. By observing increases in stem and leaf elongation, we have shown that green light could be used to alter the appearance and commercial value of basil. [ABSTRACT FROM AUTHOR]

• Challenges in multivariate calibration of spectroscopic sensors are discussed. • Discussion of methods for quantitative and qualitative calibration. • All methods are illustrated on an example case from the postharvest domain. • Discussion of applications for postharvest quality evaluation. • Special attention is given to spectral preprocessing, careful validation and calibration transfer. 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. [ABSTRACT FROM AUTHOR]

• A fiber-optic spatially-resolved spectroscopy (SRS) setup measured milk NIR spectra. • The setup evaluated 30 different illumination-to-detection distances (1.1–4 mm) • Shorter illumination-to-detection distances were optimal for lactose prediction. • Larger illumination-to-detection distances were optimal for fat and protein. • The accuracy of the composition predictions are well within the ICAR requirements. To support in-line quality control of raw milk and ensure the close monitoring of the metabolic and udder health of dairy cows, we propose a fiber-optic spatially-resolved spectroscopy (SRS) setup. This setup allows to vary the interaction of long-wave near-infrared (LW-NIR) light with the milk by submerging a separate optical illumination and detection fiber into the sample and altering their relative distance to optimize measurements for specific milk component(s). We evaluated this approach for predicting milk fat, protein, and lactose content and determined the optimal illumination-to-detection distances for each milk component. The region between 1.1 and 1.8 mm was optimal for lactose, and between 2.2 and 3.8 mm for fat and protein. These distance ranges resulted in a root-mean-square error of prediction (RMSEP) of less than 0.10 % weight/weight (wt/wt) for milk fat, and lower than 0.13 % (wt/wt) for protein and lactose. Integration of these distances into a fiber-optic SRS reflectance probe would allow to simultaneously determine the fat, protein, and lactose content of raw milk in-line with high accuracy. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor‐intensive and time‐consuming, the objective of this study was to investigate the potential of near‐infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products.NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000–4000 cm−1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes.These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In the search for a healthy indoor climate, chemical cleaning agents are replaced by bacillus strains to create an indoor ecosystem in which these harmless bacteria clean the surfaces by consuming the organic molecules in dust and dirt. To optimise these ecosystems, it is important to monitor the bacterial growth and activity, but the reference methods are time consuming and involve a serious time delay. Therefore, the potential of fluorescence spectroscopy for monitoring the activity of bacillus strains was investigated in this study. We confirmed that fluorescence signals could be detected for Bacillus vegetative cells at a load above 108 cells mL−1. As the fluorescence emission was mostly in the 330–400 nm range after excitation in the 280–290 nm range, we could attribute this signal to the tryptophan residues. The Bacillus fluorescence signals could be distinguished from those released by pure l -tryptophan, plate count agar medium, and Bacillus spores, which were classified with 100% accuracy by PLSDA. In comparison to other microbes, bacillus strains, Escherichia coli (MG1655) and Saccharomyces cerevisiae were found to produce an indistinguishable peak at 340 nm in response to excitation at 280 nm. However, using a hybrid multivariate classifier involving PCA combined with a One-Class SVM on the area-normalised fluorescence emission spectra in response to excitation at 280 nm allowed footprints of Bacillus cells to be identified with a classification accuracy of 78%. • Bacillus subtilis can be detected at above 108 cell mL-1. • Fluorescence of bacillus cells is mainly generated by tryptophan residues. • A hybrid multivariate classifier can discriminate Bacillus strains from other microbes. [ABSTRACT FROM AUTHOR]

Inspired by the rapid progress and existing limitations in surface plasmon resonance (SPR) biosensing technology, we have summarized the recent trends in the fields of both chip-SPR and fiber optic (FO)-SPR biosensors during the past five years, primarily regarding smart layers design, multiplexing, continuous monitoring and in vivo sensing. Versatile surface chemistries, biomaterials and nanomaterials have been utilized thus far to generate smart layers on SPR platforms and as such achieve oriented immobilization of bioreceptors, improved fouling resistance and sensitivity enhancement, collectively aiming to improve the biosensing performance. Furthermore, often driven by the desires for time- and cost-effective quantification of multiple targets in a single measurement, efforts have been made to implement multiplex bioassays on SPR platforms. While this aspect largely remains difficult to attain, numerous alternative strategies arose for obtaining parallel analysis of multiple analytes in one single device. Additionally, one of the upcoming challenges in this field will be to succeed in using SPR platforms for continuous measurements and in vivo sensing, and as such match up other biosensing platforms where these goals have been already conquered. Overall, this review will give insight into multiple possibilities that have become available over the years for boosting the performance of SPR biosensors. However, because combining them all into one optimal sensor is practically not feasible, the final application needs to be considered while designing an SPR biosensor, as this will determine the requirements of the bioassay and will thus help in selecting the essential elements from the recent progress made in SPR sensing. Image 1 • Continuous progress in material sciences promotes SPR biosensors development. • Smart layers improve receptor orientation, fouling resistance and sensitivity. • Challenging multiplexing is complemented by parallel analysis on SPR biosensors. • Continuous and in vivo sensing with SPR biosensors remains yet to be attained. [ABSTRACT FROM AUTHOR]

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort, and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control, PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared with the current state of the art. [ABSTRACT FROM AUTHOR]

Chloroplasts movement within mesophyll cells in C4 plants is hypothesized to enhance the CO2 concentrating mechanism, but this is difficult to verify experimentally. A three-dimensional (3D) leaf model can help analyse how chloroplast movement influences the operation of the CO2 concentrating mechanism. The first volumetric reaction–diffusion model of C4 photosynthesis that incorporates detailed 3D leaf anatomy, light propagation, ATP and NADPH production, and CO2, O2 and bicarbonate concentration driven by diffusional and assimilation/emission processes was developed. It was implemented for maize leaves to simulate various chloroplast movement scenarios within mesophyll cells: the movement of all mesophyll chloroplasts towards bundle sheath cells (aggregative movement) and movement of only those of interveinal mesophyll cells towards bundle sheath cells (avoidance movement). Light absorbed by bundle sheath chloroplasts relative to mesophyll chloroplasts increased in both cases. Avoidance movement decreased light absorption by mesophyll chloroplasts considerably. Consequently, total ATP and NADPH production and net photosynthetic rate increased for aggregative movement and decreased for avoidance movement compared with the default case of no chloroplast movement at high light intensities. Leakiness increased in both chloroplast movement scenarios due to the imbalance in energy production and demand in mesophyll and bundle sheath cells. These results suggest the need to design strategies for coordinated increases in electron transport and Rubisco activities for an efficient CO2 concentrating mechanism at very high light intensities. [ABSTRACT FROM AUTHOR]

Banana and its dried products are one of the most common foods consumed all over the world. Quality attributes of dried banana slices are typically evaluated using time-consuming and labor intensive methods which do not allow to evaluate each individual slice. Replacement of the current destructive methods for quality evaluation of dried banana slices by fast, non-destructive methods would provide large added value to the food industry. Therefore, the aim of this study was to monitor the moisture content, texture and color of banana slices during the drying process in a fast and non-destructive way. VNIR hyperspectral reflectance imaging in the 400–1000 nm range was selected for this purpose. Thanks to a cross-polarized configuration the effects of glare or specular reflection on the banana slice surfaces in the hyperspectral diffuse reflectance images was largely reduced. Reference quality attributes for all the slices (moisture content, texture and color ( L*, a*, b* values) obtained using conventional destructive methods and colorimeter were predicted from their corresponding average reflectance spectra extracted from the hyperspectral images by means of partial least squares regression (PLSR). The PLSR calibration models were validated on samples which had not been used for model calibration. The results were very good for water content ( R 2 P  = 0.97, RMSEP  = 0.05 kg water/kg DM), quite good for and b* value ( R 2 P  = 0.83, RMSEP  = 1.95), and reasonable for texture ( R 2 P  = 0.66, RMSEP  = 11.8 N), a* value ( R 2 P  = 0.53, RMSEP  = 1.32) and L* value ( R 2 P  = 0.61, RMSEP  = 5.92). Subsequently, these calibration models were used to predict those quality attributes at pixel level for the validation slices to visualize the spatial distribution of these quality parameters at different stages during the drying process. The obtained results clearly indicate the potential of cross-polarized hyperspectral reflectance imaging for non-destructive monitoring of the quality attributes of banana slices during drying. [ABSTRACT FROM AUTHOR]

Insight in the transmission of light through intact fruit is important to optimize and interpret the signals acquired with spectroscopy methods such as tunable diode laser absorption spectroscopy (TDLAS). To this end, we quantified the bulk optical properties (BOP) of skin, outer cortex, inner cortex, and core tissue of three pear (Pyrus communis) cultivars (Thimo , Cepuna , and Conference) using double integrating spheres measurements in combination with the inverse adding doubling method. Similar trends and values were observed for the bulk optical properties of the three cultivars. A clear chlorophyll-a absorption feature was present in the bulk absorption coefficient (μ a) at 690 nm for the skin samples. At 761 nm, the bulk scattering coefficient (μ s) decreased from the skin towards the core region, for example from 256 ± 34 cm−1 to 146 ± 22 cm−1 for Conference pears. The observed anisotropy factor (g) of the skin was lower than these of the other tissue layers (0.87 ± 0.026 vs. 0.94 ± 0.08 for Conference). Next, Monte Carlo voxelized media (MCVM) simulations were performed based on these BOP values and validated against measurements with a TDLAS setup for gas in scattering media absorption spectroscopy (GASMAS) at 761 nm. In these experiments, the sample thickness was reduced by sequentially slicing the pear from the skin towards the center. Light transmittance through an intact pear at the height of the core region was found to be very low (1.9 × 10−4 ± 8.5 × 10−5%) and exponentially increased around 100-fold when the sample thickness decreased from 64 mm to 24 mm. The simulated transmittance profiles based on the measured BOP values at 761 nm followed the same trend as the measured GASMAS transmittance as a function of the fruit thickness. This shows that simulations based on these measured BOP values can be used to optimize the measurement configuration for O 2 monitoring in intact pear fruit with GASMAS. • The bulk optical properties of pear tissue layers: skin, outer cortex, inner cortex, and core. • 3D Monte Carlo simulation of diffuse transmittance through an intact pear. • Comparison of simulated and measured diffuse transmittance vs pear sample thicknesses. • Simulations provided a valuable tool for optimizing the amplification of GASMAS detectors in diffuse transmittance mode. [ABSTRACT FROM AUTHOR]

Driven by increasing food demand and the urgent need for intelligent land use, the size and complexity of agricultural machinery has increased significantly. Monitoring the operation of machines during long working days is a very challenging task for the operators. Automatic monitoring systems can lighten the load and also pave the way towards fully autonomous machines. This paper proposes an automatic condition monitoring system for detecting operation anomalies on agricultural machine components with reciprocating motion, and applies it to a use-case on a combine harvester header cutter bar. Cyclostationary analysis techniques are employed to develop filtering algorithms to extract informative features, which are monitored through the implementation of statistical process control (SPC) using control charts. Together with a comparison of several filtering and feature extraction techniques, an analysis is provided on the influence of sensor type and position on anomaly detection performance. Filtering benefit was found to be highly dependent on the considered sensor type and its location, with increases in Matthews correlation coefficient (MCC) ranging from 0 to 50%, resulting in maximal MCC values of 1. Suitable feature calculation resulted in average prediction performance improvements over 10% in MCC values for nearly all considered sensor types and their locations. These results highlight the importance of intelligent sensor selection for condition monitoring purposes on agricultural machinery and the added value of SPC involving cyclostationary analysis techniques for anomaly detection. • Knife impacts are detected with a Matthews correlation coefficient of 0.995. • Sensors mounted closer to the cutter bar performed best. • The proposed synchronous filters clearly improved performance. • The standard deviation per rotation captured the signal response during impact well. [ABSTRACT FROM AUTHOR]

Ethiopia is well known for its use of an ard plough dating from antiquity – maresha – which fractures and disturbs the soil. However, hardly any notable progress of experimental research on this animal drawn tillage tool in the field has been made. The attendant problems in current practise are soil- maresha interaction, viz., uneven oxen strength along with different pace of walking, uncontrolled implement behaviour, and field conditions. Taking stock of the experimental research on animal drawn tillage tools in general, most of the documented works on the dynamics of the interaction between soil and animal drawn tillage tools tend to rely on trial-and-error based on factors mainly based on experience and cultural context. As such, no research tailored to systematically handle the link between maresha plough and soil bin experiments exists. To this aim, this study developed a mobile in-situ soil bin facility in which the system was calibrated, tested, and evaluated under outdoor experimental conditions, wherein online measurements of draught, speed, and depth of tillage were carried out. The insights and observations gained from the experimentation were discussed and reported in terms of smooth run, overload, cyclic forces, zero speed with minimal force, stoppage, speed measurement with no force, force measurement with no speed, and low speed with low force. [ABSTRACT FROM AUTHOR]

Ethiopian farmers have been using an ox-drawn breaking plough, known as ard plough – maresha, for thousands of years. Maresha is a pointed, steel-tipped tine attached to a draught pole at an adjustable shallow angle. It has narrow side-wings, attached to the left and right side of it, to push soil to either side without inverting. The aim of this paper is to explore the effect of side-wings on draught using a field soil bin test facility. To this end, a mobile and an in-situ soil bin test system, for online measurements of draught, was designed and developed. This research considered tool geometry ( maresha plough with and without side-wings) and rake angle (shallow – 8°, medium deep – 15°, and deep – 24°, representing primary, secondary and tertiary tillage processes in Ethiopia, respectively). Maresha plough with side-wings has greater contact area, between the moving soil and tool, than its wingless counterpart. When the ploughshare surface and soil slide relative to one another, the draught expected to increase with contact area, as adhesion and friction resistance increases with area. However, experimental analysis indicated that the maresha with side-wings required less draught compared to maresha without side-wings ( ρ < 0.001) . This might be attributed to the effect of side-wings on crack propagation by a wedging effect to enhance and facilitate subsequent ploughing. This paper also dealt with the effect of rake angle on draught. Though the depth setup was getting smaller d 1 < d 2 < d 3 for the successive tillage runs, analysis showed increment in draught force ( ρ < 0.001) with rake angle. This might be attributed to higher soil compaction that comes with depth and downward force resulting from repeated use of maresha every season to the same depth for thousand years. Although more and rigorous studies should be undertaken considering soil, tool, and operational parameters to arrive at conclusive results, this paper gave some insights regarding effect of side-wings on maresha plough and rake angle on draught. This shows that there is still room for improvement of maresha plough geometry for minimum draught requirement and optimum soil manipulation. [ABSTRACT FROM AUTHOR]

High speed data processing for online food quality inspection using hyperspectral imaging (HSI) is challenging as over hundred spectral images have to be analyzed simultaneously. In this study, a real-time pixel based early apple bruise detection system based on HSI in the shortwave infrared (SWIR) range has been developed. This systems consists of a novel, homogeneous SWIR illumination unit and a line scan camera. The system performance was tested on Jonagold apples bruised less than two hours before scanning. Partial least squares-discriminant analysis was used to discriminate bruised pixel spectra from sound pixel spectra. As the glossiness of many fruit and vegetables limits the accuracy in the detection of defects, several reflectance calibrations and pre-processing techniques were compared for glare correction and maximizing the signal to noise ratio. With the best combination of first derivative and mean centering, followed by image post-processing, this system was able to detect fresh bruises in thirty apples with 98% accuracy at the pixel level with a processing time per apple below 200 ms. [ABSTRACT FROM AUTHOR]

Calibration transfer has been traditionally performed in the context of transferring models between instruments using standard samples. Recently, new methodologies and applications have shown that transfer techniques can be adopted to achieve calibration transfer between other types of domains, such as product form, variant or seasonality. In addition, to achieving a higher efficiency for calibration transfer, it is desirable to perform the transfer without the need for standard samples or new reference analyses. Therefore, we propose a method for unsupervised calibration transfer based on the orthogonalization for structural differences between domains. The method has been successfully applied to one simulated dataset and two real datasets. In the studied cases, the proposed methodology allowed to achieve a successful transfer of calibration models and enabled the interpretation of the interferences responsible for the degradation of the original calibration models when transferred to the new domain. [Display omitted] [ABSTRACT FROM AUTHOR]

Optimization of controlled atmosphere (CA) storage of pome fruit crucially depends on understanding of respiratory gas exchange. Different fruit properties are involved in describing gas transfer and respiration kinetics, which can vary between cultivars, fruit batches and individual fruit. Determination of these properties currently requires a combination of different time-consuming techniques and data analysis procedures. This study introduces a more efficient single method for O 2 gas exchange characterization of fruit based on the non-destructive pathlength-resolved GASMAS (gas in scattering media absorption spectroscopy) technique. GASMAS was used to measure the absolute intercellular O 2 concentration in fruit of different apple cultivars (Malus x domestica Borkh.) with distinct porosity including 'Jonagold', 'Braeburn', and 'Nicoter'. To resolve the ambiguity between variation in pathlength and O 2 concentration inherent to GASMAS, the pathlength travelled through pores cavities was quantified through a second GASMAS measurement at the absorption line of H 2 O vapour. Dynamic experiments were then performed in which the fruit was subjected to varying external O 2 concentrations while measuring the internal O 2 response. The observed dynamic profiles of external and internal O 2 concentration were fitted with a lumped -respiration model to estimate the effective mass transfer coefficient, maximum respiration rate and Michaelis Menten constant of intact fruit. The model showed a good fit (R 2 >0.95) with the measured data and reliable estimates for the model parameters. The proposed method provides a novel and more efficient way to estimate the gas exchange properties of fruit that are essential for optimizing CA storage conditions. Future challenges for the proposed method are with applications to other gasses such as CO 2 and ethylene. • Non-destructive and quantitative internal O 2 measurement of apple using GASMAS. • GASMAS measurement on an intact apple in diffuse transmittance mode. • Fast determination of O 2 exchange and respiration kinetic parameters of apple. [ABSTRACT FROM AUTHOR]

Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near‐infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS‐DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four‐class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two‐class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein‐ and water‐related changes were found important to distinguish the fillets. Based on these findings, the four‐class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two‐class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. Practical Application: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output. [ABSTRACT FROM AUTHOR]

'Braeburn' apples are susceptible to internal browning disorders when stored under controlled atmosphere (CA) conditions with unfavorable gas compositions. The progression of CA-related disorders in apple tissues is dynamic, noting a decrease in porosity during early storage due to cellular breakdown and pore flooding, and an increase in porosity in later stages due to structural collapse and cavity formation. Utilizing grating-based X-ray dark-field radiography, which leverages X-ray small-angle scattering to detect microstructural changes below the pixel scale, this study assesses the technique's efficacy in identifying internal disorders in 'Braeburn' apples at both early and later stages. A machine learning approach was applied to compare the diagnostic capabilities of dark-field imaging with those of X-ray absorption radiography at identical image resolutions. Results indicate that for early-stage disordered fruit detection, X-ray dark field radiography is 10 % more accurate than absorption radiography, regardless of the machine learning classifiers that were applied. In the later stage of browning, dark-field imaging performs similarly to absorption imaging. High-resolution micro-computed tomography scans suggested that the distinct detection performance of dark-field imaging may be attributed to the more pronounced microstructural differences between healthy and early-stage defective tissues than those between healthy and later-stage defective tissues. The insights from this work will guide the application of X-ray dark-field systems in fruit quality assurance, particularly in detecting internal disorders. • X-ray dark-field radiography is more suited for detecting low-porosity defects. • X-ray absorption radiography is more effective for identifying high-porosity defects. • Changes in X-ray dark-field image are closely linked with microstructural changes. [ABSTRACT FROM AUTHOR]

Abstract: The preparation of polysulfone (PSf) solvent resistant nanofiltration (SRNF) flat sheet membranes was investigated. Membranes were prepared via the phase inversion process by means of immersion precipitation. The membrane formation process was studied at the level of the casting solution composition (polymer concentration) as well as on the level of post-casting (evaporation time). Membrane preparation and filtration were performed in a high-throughput fashion. The performance of the synthesized membranes was tested in SRNF by measuring iso-propanol permeances and rejections of Rose Bengal. Rejections varied from 37% to 92% when the polymer concentration in the casting solution was increased from 13 to 25wt% and permeance decreased from 9.7 to 0.07L/m2 h bar. When evaporation time increased from 0 to 120s, rejections increased from 47% to 76% and permeance decreased from 2.37 to 0.08L/m2 h bar. Scanning electron microscopy (SEM) was used to investigate the morphology of the membrane cross sections. Viscosity measurements and light transmittance experiments were conducted in order to describe precipitation kinetics of PSf solutions. Depending on the evaporation time, polymer precipitation took place after 4–10s. Viscosity changed from 0.14 to 15.4Pas when the polymer concentration varied from 13 to 29wt%. Cloudpoint measurements were performed to determine the precipitation curve on a quasi-ternary diagram. The measurements were used to try to better understand the thermodynamic and kinetic aspects of membrane formation. [Copyright &y& Elsevier]

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. [ABSTRACT FROM PUBLISHER]