Shared subspace learning via partial Tucker decomposition for hyperspectral image classification.

A tensor-based classification framework, which we refer to as Shared Subspace Tensor Classification (SSTC), is proposed for hyperspectral imaging applications where image-level labels must predict phenomena that distribute heterogeneously across samples. Instead of flattening the natural multi-dimensional structure of hyperspectral data, our approach employs partial Tucker decomposition to learn shared spatial and spectral subspaces across samples, enabling effective dimensionality reduction while preserving crucial relationships between dimensions. Core tensors encoding each sample's projection onto these subspaces provide discriminative features that achieve strong classification performance even with simple classifiers.

Analysis of the fat globule size in raw milk through parametrization of visible and near-infrared spatially resolved spectra.

Fat globule size has a strong impact on the sensory properties of dairy products and should therefore be monitored during milk processing. However, the current measurement techniques cannot meet the demand for real-time monitoring of its size distribution. Therefore, the aim of this study was to evaluate the potential of spatially resolved spectroscopy (SRS) in the visible (Vis) and near-infrared (NIR) region for this purpose.

Detection and quantification of pork adulteration in beef meatballs with Raman spectroscopy and near infrared spectroscopy.

One of the main halal concepts requires that food is free from pork substances. Muslim-majority countries establish halal regulations that require the screening of processed meat products, such as meatballs, are screened for adulteration with pork meat to guarantee appropriate halal certification for consumers. Currently, halal authorities rely on the analysis of DNA, protein, or fat with RT-PCR, LC-MS, or GC-FID, which are reliable but are not suitable for rapid screening of large numbers of samples.

Cost-efficient training of hyperspectral deep learning models for the detection of contaminating grains in bulk oats by fluorescent tagging.

Computer vision based on instance segmentation deep learning models offers great potential for automating many visual inspection tasks, such as the detection of contaminating grains in bulk oats, a nutrient rich grain which is well-tolerated by people suffering from gluten intolerance. Whereas distinguishing foreign objects is often relatively easy with the naked eye, it is much more difficult to distinguish highly similar products, e.g.

PROSAC as a selection tool for SO-PLS regression: A strategy for multi-block data fusion.

Background: Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks.

Terahertz time-domain transmission spectroscopy of water and hydrogel thin films: Extraction of optical parameters and application to agarose gel characterization.

Terahertz time-domain spectroscopy (THz-TDS) is an emerging optical technique that has potential applications in the characterization of (bio)materials. However, the complicated extraction of optical parameters from multi-layered and optically thin samples is a barrier towards its acceptance by applied scientists. Therefore, the aim of this work is to provide a straightforward approach for the extraction of the THz absorption coefficient and index of refraction profiles of aqueous thin films in a window-sample-window configuration, which is ubiquitous in many laboratories (i.

On-farm NIR sensor for milk analysis: Exploitation of bias monitoring and bias correction.

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples.

Classification of industrial tapioca starch hydrolysis products based on their Brix and dextrose equivalent values using near-infrared spectroscopy.

Background: 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.

Results: NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000-4000 cm range.

Near-infrared spectra dataset of milk composition in transmittance mode.

Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time.

Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis.

Context: Sticky trap catches of agricultural pests can be employed for early hotspot detection, identification, and estimation of pest presence in greenhouses or in the field. However, manual procedures to produce and analyze catch results require substantial time and effort. As a result, much research has gone into creating efficient techniques for remotely monitoring possible infestations.

Drought stress triggers alterations of adaxial and abaxial stomatal development in basil leaves increasing water-use efficiency.

The physiological control of stomatal opening by which plants adjust for water availability has been extensively researched. However, the impact of water availability on stomatal development has not received as much attention, especially for amphistomatic plants. Therefore, the acclimation of stomatal development in basil ( L.

Far-Red Light Mediated Carbohydrate Concentration Changes in Leaves of Sweet Basil, a Stachyose Translocating Plant.

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.

The role of chloroplast movement in C4 photosynthesis: a theoretical analysis using a three-dimensional reaction-diffusion model for maize.

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.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory-Part II: Implementation and Validation.

Eggshell strength is a critical quality factor for consumption eggs as it affects the probability of breakage in practice. In this study, a fast and low-cost methodology for the non-destructive determination of eggshell strength is presented. The method utilized a small steel ball to impact the egg and a microphone to analyse the impact characteristics.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory Part I: Theory and Applicability.

In the egg industry, fast and highly reliable quality measurements are crucial. This study presents a novel method based on Hertz contact theory that allows for non-destructive determination of eggshell strength. The goal of the study was to evaluate the material strength (Young's Modulus) and structural strength (stiffness) of eggshells.

Fabrication and characterization of porous tissue-mimicking optical phantoms as a tool for optical sensor validation.

This paper presents porous polydimethylsiloxane (PDMS) optical phantoms with tunable microstructural and optical properties to mimic porous biological tissues (e.g., fruit) during the design and optimization of novel optical setups.

Unsupervised dynamic orthogonal projection. An efficient approach to calibration transfer without standard samples.

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.

Optical Identification of Fruitfly Species Based on Their Wingbeats Using Convolutional Neural Networks.

The spotted wing Drosophila (SWD), , is a significant invasive pest of berries and soft-skinned fruits that causes major economic losses in fruit production worldwide. Automatic identification and monitoring strategies would allow to detect the emergence of this pest in an early stage and minimize its impact. The small size of and similar flying insects makes it difficult to identify them using camera systems.

Mechanical damages and packaging methods along the fresh fruit supply chain: A review.

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.

A feasibility study on nondestructive classification of frozen Atlantic salmon (Salmo salar) fillets based on temperature history at the logistics using NIR spectroscopy.

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.

Application of multivariate data analysis for food quality investigations: An example-based review.

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.

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition.

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring.

Antinutrient to mineral molar ratios of raw common beans and their rapid prediction using near-infrared spectroscopy.

The presence of antinutrients in common beans negatively affects mineral bioavailability. Therefore, this study aimed to predict the antinutrient to mineral molar ratios (proxy-indicators of in vitro mineral bioavailability) of a wide range of raw bean types, using near-infrared (NIR) spectroscopy. Iron, zinc, phytate and tannin concentrations and, antinutrient to mineral molar ratios were determined.

Soil Moisture Levels Affect the Anatomy and Mechanical Properties of Basil Stems ( L.).

As plants would benefit from adjusting and optimizing their architecture to changing environmental stimuli, ensuring a strong and healthy plant, it was hypothesized that different soil moisture levels would affect xylem and collenchyma development in basil ( L. cv. Marian) stems.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry.

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor.