{"type": "FeatureCollection", "features": [{"id": "10.1016/j.chemolab.2022.104517", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:16:22Z", "type": "Journal Article", "created": "2022-02-10", "title": "Improved understanding and prediction of pear fruit firmness with variation partitioning and sequential multi-block modelling", "description": "Fruit firmness is a complex trait that develops throughout fruit development, including post-harvest, and is influenced by both ripening and dehydration. There is a wide interest in predicting the firmness with non-destructive sensing techniques such as spectral analyses. However, often used reference techniques, such as acoustic firmness (AF), limited compression (LC) and Magness-Tyler (MT), respond differently to dehydration and ripening. This study aims to detangle how the firmness of \u2018Conference\u2019 pears relates to dehydration and ripening and to model ripening-related firmness using non-destructive sensing. Hereto, a pear fruit matrix was created with varying firmness and dehydration levels. To model fruit firmness (LC and MT) with Vis-NIR spectroscopy and explore whether AF information could complement Vis-NIR spectroscopy, a sequential multi-block analysis was performed. Single block Vis-NIR spectral data were made multi-block by partitioning the variance in spectral data into acoustic-dependent and -independent parts. A variation partitioning based approach was also presented to select the best pre-processing operation for Vis-NIR spectral data modelling. Multi-block regression to predict firmness and classification modelling of pear fruit in different firmness classes was also practised. The obtained results led to enhanced insights into the different fruit firmness measures and the capability of Vis-NIR and acoustic for non-destructive fruit firmness prediction. The results can benefit the scientific community working in the domain of fruit optical spectroscopy and chemometric modelling.", "keywords": ["Fruit quality", "Non-destructive", "0404 agricultural biotechnology", "Dehydration", "Firmness", "Ripening", "04 agricultural and veterinary sciences", "Chemometrics", "Data fusion", "0405 other agricultural sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.chemolab.2022.104517"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Chemometrics%20and%20Intelligent%20Laboratory%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.chemolab.2022.104517", "name": "item", "description": "10.1016/j.chemolab.2022.104517", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.chemolab.2022.104517"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.1016/j.mne.2022.100125", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:17:14Z", "type": "Journal Article", "created": "2022-03-09", "title": "Multispectral imaging flow cytometry for process monitoring in microalgae biotechnology", "description": "In the course of efficient development and optimization of biotechnological processes, the need for methods to track morphological and compositional changes of single cells is increasing. So far, the material composition of cells is determined by chemical analysis of a pooled cell sample, which reflects the average composition of the taken cell collection. Conventional flow cytometry enables the analysis of individuals from a population. However, it cannot resolve such valuable information like morphological details and distribution of molecular compounds inside the cells. This gap is bridged by a combination of imaging flow cytometry and multispectral imaging. The potential of this Multispectral Imaging Flow Cytometry (MIFC) approach has been investigated and confirmed in the presented parameter study on the bioproduction of Astaxanthin (Ax) by the microalgae Haematococcus pluvialis (HP). As far as multispectral imaging in transmission mode, only three spectral channels (446\u00a0nm, 532\u00a0nm, 646\u00a0nm) were used to measure the amount of substance and the molecular distribution of the core components chlorophyll (Chl) and Ax. Both could be clearly separated from the phase-contrast information generated from the cellular structures and morphology. In general, the results from the MIFC method comply with the conventional measurements but extend them for details on the morphology and on compositional changes within the cultivated cell population during the cultivation process and in response to the applied stimuli.", "keywords": ["0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "Algae", "TK7800-8360", "Multispectral", "Process monitoring", "T1-995", "Chemometrics", "Electronics", "Multispectral imaging flow cytometry", "Technology (General)", "Biotechnology"]}, "links": [{"href": "https://doi.org/10.1016/j.mne.2022.100125"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Micro%20and%20Nano%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.mne.2022.100125", "name": "item", "description": "10.1016/j.mne.2022.100125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.mne.2022.100125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-01T00:00:00Z"}}, {"id": "10.1016/j.postharvbio.2021.111739", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:17Z", "type": "Journal Article", "created": "2021-09-20", "title": "Avocado dehydration negatively affects the performance of visible and near-infrared spectroscopy models for dry matter prediction", "description": "Abstract   This study aims to test the hypothesis that skin dehydration can cause the development of cork-like layers in the avocado fruit skin which may negatively affect Vis-NIR spectroscopy. To test this, dehydration treatment was applied on avocado fruit by storing them at low relative humidity (RH) during ripening treatment. Furthermore, to demonstrate that the hypothesis was not only valid for a single instrument and in general valid for any type of Vis-NIR instrument the avocados were also measured with two different spectrometers i.e., lab-based, and hand-held. Since the two instruments have two different measurement geometries i.e., diffuse reflection and interaction, the study also tests which geometry was best for the measurement of DMC in dehydrated avocados. The results showed that the dehydration of avocado fruit negatively affects the performance of Vis-NIR calibrations compared to the non-dehydrated fruit. The root mean squared error of cross-validation (RMSEcv) on internal test set for dehydrated and non-dehydrated fruit were up to 1.49 % dw/fw and 1.02 % dw/fw, respectively. The hypothesis was true for both lab-based and hand-held instruments, and the root mean squared error of prediction on internal test set were up to 28 % higher for dehydrated fruits. The performance of interaction measurement mode was better (RMSEcv\u2009=\u20090.98 % dw/fw) than the diffuse reflection mode (RMSEcv\u2009=\u20091.21 % dw/fw) for non-dehydrated fruit, however, both modes achieved similar performance (RMSEcv = \u223c1.42 % dw/fw) for dehydrated fruit. The poorer performance of Vis-NIR models on dehydrated avocado fruit can be accepted as a limitation of Vis-NIR spectroscopy for avocado fruit analysis.", "keywords": ["0404 agricultural biotechnology", "04 agricultural and veterinary sciences", "Chemometrics", "0405 other agricultural sciences", "Fruit storage", "Multivariate", "Quality"]}, "links": [{"href": "https://doi.org/10.1016/j.postharvbio.2021.111739"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Postharvest%20Biology%20and%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.postharvbio.2021.111739", "name": "item", "description": "10.1016/j.postharvbio.2021.111739", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.postharvbio.2021.111739"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2022.156582", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:25Z", "type": "Journal Article", "created": "2022-06-14", "title": "Potential of visible and near infrared spectroscopy coupled with machine learning for predicting soil metal concentrations at the regional scale", "description": "Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R<sup>2</sup><sub>p</sub> = 0.76), Co (R<sup>2</sup><sub>p</sub> = 0.77), Cd (R<sup>2</sup><sub>p</sub> = 0.64) and Pb (R<sup>2</sup><sub>p</sub> = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R<sup>2</sup><sub>p</sub> = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R<sup>2</sup><sub>p</sub> = 0.70), while it best predicted Cd with PLSR with an R<sup>2</sup><sub>p</sub> value of 0.67, which is of value for regional survey.", "keywords": ["Spectroscopy", " Near-Infrared", "Support Vector Machine", "RANGE", "Machine", "Machine learning modelling", "learning modelling", "REFLECTANCE SPECTROSCOPY", "CONTAMINATION", "Soil", "Lead", "Soil contamination", "Nickel", "Metals", "Earth and Environmental Sciences", "Soil Pollutants", "Chemometrics", "Cadmium", "Near-infrared spectra"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2022.156582"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2022.156582", "name": "item", "description": "10.1016/j.scitotenv.2022.156582", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2022.156582"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-01T00:00:00Z"}}, {"id": "10.1039/d2ay01215d", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:18:32Z", "type": "Journal Article", "created": "2022-09-23", "title": "Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Light scatterometry combined with chemometrics can be a practical approach for the analysis of size and concentration of microplastics in water.</p></article>", "keywords": ["Polyethylene", "PARTICLE-SIZE DISTRIBUTIONIDENTIFICATIONRELEASEFOOD", "Microplastics", "PARTICLE-SIZE DISTRIBUTION", " IDENTIFICATION", " RELEASE", " FOOD", "Polymethyl Methacrylate", "Polystyrenes", "Reproducibility of Results", "Water", "Chemometrics", "Plastics", "6. Clean water"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/523367/1/Batch%20analysis%20of%20microplastics%20using%20multi-angle%20static%20light%20scattering%20and%20chemometric%20methods.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2022/AY/D2AY01215D"}, {"href": "https://doi.org/10.1039/d2ay01215d"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Analytical%20Methods", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1039/d2ay01215d", "name": "item", "description": "10.1039/d2ay01215d", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1039/d2ay01215d"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.13122321", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:23:11Z", "type": "Dataset", "title": "Near-infrared (NIR) soil spectral library using the NeoSpectra Handheld NIR Analyzer by Si-Ware", "description": "Open AccessUp-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers.  Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by Si-Ware. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices.  This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by Woodwell Climate Research Center. These samples were queried from the USDA NRCS NSSC-KSSL Soil Archives as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the University of Nebraska - Lincoln to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned.  All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database.  A subset of 1,976 US topsoil samples were used to create Cubist models for 8 soil properties including bulk density (BD, <2mm fraction, 1/3 Bar, units in grams per cubic centimeter), calcium carbonate (CaCO3, <2mm fraction, units in weight percent), clay content (percent), buffered ammonium-acetate exchangeable potassium (Ex. K, units in centimoles of charge per kilogram of soil), pH, sand content (percent), silt content (percent), and soil organic carbon (SOC, estimated after inorganic carbon removal, units in weight percent). Two strategies were evaluated for handling scanner-to-scanner variability: averaging scans per sample (avg) versus retaining replicate scans across all scanners (reps) during model building. Cubist avg models and cubist reps models are provided here for the 8 soil properties outlined in \u201c.qs\u201d file format, and can be opened and worked with in the R programming language. The subset of 1,976 samples has also been provided here for reproducibility (1976_NSlibrary_withmetadata.csv).  The\u00a0repository contains:    Neospectra_database_column_names.csv: describes\u00a0the variables (columns) of site and soil data, and the range of NIR and MIR spectra. Both\u00a0Neospectra_WoodwellKSSL_avg and\u00a0Neospectra_WoodwellKSSL_reps\u00a0share the same columns. The CSV is composed of the file name, column name, type, example, and description with measurement unit.  Neospectra_project_summary.txt: the summary of the project with purpose, the origin of soil samples, instrumentation, and brief SOP.  Neospectra_WoodwellKSSL_avg_MIR.csv: the equivalent MIR spectra of neospectra samples' list\u00a0that was fetched from the KSSL database and formatted to the OSSL specifications.  Neospectra_WoodwellKSSL_avg_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains the averaged spectra for a given scanner and soil sample (1 spectra per scanner per soil sample). Soil and site info is filled within the same soil sample.  Neospectra_WoodwellKSSL_reps_soil+site+NIR.csv:\u00a0soil, site, and Neospectra's NIR. Each row\u00a0contains one\u00a0replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample.  1976_NSlibrary_withmetadata.csv: reproducible matrix for model calibration.  Models:    log..bd_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for log(1+BD).    log..caco3_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for log(1+CaCO3).     clay_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for clay.     log..k.ex_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for log(1+Ex. K).     ph.h2o_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for pH.     sand_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for sand.     silt_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for silt.     log..soc_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average model for log(1+SOC).     log..bd_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: \u00a0Cubist replicate model for log(1+BD).     log..caco3_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for log(1+CaCO3).     clay_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for clay.     log..k.ex_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for log(Ex. K).     ph.h2o_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for pH.     sand_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for sand.     silt_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for silt.     log..soc_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicate model for log(1+SOC).", "keywords": ["13. Climate action", "15. Life on land", "soil analysis", "chemometrics", "6. Clean water", "soil spectral library", "soil spectroscopy"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13122321"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13122321", "name": "item", "description": "10.5281/zenodo.13122321", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13122321"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-30T00:00:00Z"}}, {"id": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:13Z", "type": "Journal Article", "created": "2022-06-14", "title": "Potential of visible and near infrared spectroscopy coupled with machine learning for predicting soil metal concentrations at the regional scale", "description": "Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R<sup>2</sup><sub>p</sub> = 0.76), Co (R<sup>2</sup><sub>p</sub> = 0.77), Cd (R<sup>2</sup><sub>p</sub> = 0.64) and Pb (R<sup>2</sup><sub>p</sub> = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R<sup>2</sup><sub>p</sub> = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R<sup>2</sup><sub>p</sub> = 0.70), while it best predicted Cd with PLSR with an R<sup>2</sup><sub>p</sub> value of 0.67, which is of value for regional survey.", "keywords": ["Spectroscopy", " Near-Infrared", "Support Vector Machine", "RANGE", "Machine", "Machine learning modelling", "learning modelling", "REFLECTANCE SPECTROSCOPY", "CONTAMINATION", "Soil", "Lead", "Soil contamination", "Nickel", "Metals", "Earth and Environmental Sciences", "Soil Pollutants", "Chemometrics", "Cadmium", "Near-infrared spectra"]}, "links": [{"href": "https://doi.org/1854/LU-01GM39KW0F5ENNMCF40YD35GFY"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "name": "item", "description": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01GM39KW0F5ENNMCF40YD35GFY"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-01T00:00:00Z"}}, {"id": "20.500.14243/523367", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:26:34Z", "type": "Journal Article", "created": "2022-09-23", "title": "Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Light scatterometry combined with chemometrics can be a practical approach for the analysis of size and concentration of microplastics in water.</p></article>", "keywords": ["Polyethylene", "PARTICLE-SIZE DISTRIBUTIONIDENTIFICATIONRELEASEFOOD", "Microplastics", "PARTICLE-SIZE DISTRIBUTION", " IDENTIFICATION", " RELEASE", " FOOD", "Polymethyl Methacrylate", "Polystyrenes", "Reproducibility of Results", "Water", "Chemometrics", "Plastics", "6. Clean water"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/523367/1/Batch%20analysis%20of%20microplastics%20using%20multi-angle%20static%20light%20scattering%20and%20chemometric%20methods.pdf"}, {"href": "https://biblio.vub.ac.be/vubirfiles/97942982/88500032.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2022/AY/D2AY01215D"}, {"href": "https://doi.org/20.500.14243/523367"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Analytical%20Methods", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14243/523367", "name": "item", "description": "20.500.14243/523367", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/523367"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "20.500.14017/fd1879c3-ab01-40b3-a4f5-12da309de638", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:26:32Z", "type": "Journal Article", "created": "2022-09-23", "title": "Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Light scatterometry combined with chemometrics can be a practical approach for the analysis of size and concentration of microplastics in water.</p></article>", "keywords": ["Polyethylene", "PARTICLE-SIZE DISTRIBUTIONIDENTIFICATIONRELEASEFOOD", "Microplastics", "PARTICLE-SIZE DISTRIBUTION", " IDENTIFICATION", " RELEASE", " FOOD", "Polymethyl Methacrylate", "Polystyrenes", "Reproducibility of Results", "Water", "Chemometrics", "Plastics", "6. Clean water"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/523367/1/Batch%20analysis%20of%20microplastics%20using%20multi-angle%20static%20light%20scattering%20and%20chemometric%20methods.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2022/AY/D2AY01215D"}, {"href": "https://doi.org/20.500.14017/fd1879c3-ab01-40b3-a4f5-12da309de638"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Analytical%20Methods", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/fd1879c3-ab01-40b3-a4f5-12da309de638", "name": "item", "description": "20.500.14017/fd1879c3-ab01-40b3-a4f5-12da309de638", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/fd1879c3-ab01-40b3-a4f5-12da309de638"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "3199553515", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:27:24Z", "type": "Journal Article", "created": "2021-09-20", "title": "Avocado dehydration negatively affects the performance of visible and near-infrared spectroscopy models for dry matter prediction", "description": "Abstract   This study aims to test the hypothesis that skin dehydration can cause the development of cork-like layers in the avocado fruit skin which may negatively affect Vis-NIR spectroscopy. To test this, dehydration treatment was applied on avocado fruit by storing them at low relative humidity (RH) during ripening treatment. Furthermore, to demonstrate that the hypothesis was not only valid for a single instrument and in general valid for any type of Vis-NIR instrument the avocados were also measured with two different spectrometers i.e., lab-based, and hand-held. Since the two instruments have two different measurement geometries i.e., diffuse reflection and interaction, the study also tests which geometry was best for the measurement of DMC in dehydrated avocados. The results showed that the dehydration of avocado fruit negatively affects the performance of Vis-NIR calibrations compared to the non-dehydrated fruit. The root mean squared error of cross-validation (RMSEcv) on internal test set for dehydrated and non-dehydrated fruit were up to 1.49 % dw/fw and 1.02 % dw/fw, respectively. The hypothesis was true for both lab-based and hand-held instruments, and the root mean squared error of prediction on internal test set were up to 28 % higher for dehydrated fruits. The performance of interaction measurement mode was better (RMSEcv\u2009=\u20090.98 % dw/fw) than the diffuse reflection mode (RMSEcv\u2009=\u20091.21 % dw/fw) for non-dehydrated fruit, however, both modes achieved similar performance (RMSEcv = \u223c1.42 % dw/fw) for dehydrated fruit. The poorer performance of Vis-NIR models on dehydrated avocado fruit can be accepted as a limitation of Vis-NIR spectroscopy for avocado fruit analysis.", "keywords": ["0404 agricultural biotechnology", "04 agricultural and veterinary sciences", "Chemometrics", "0405 other agricultural sciences", "Fruit storage", "Multivariate", "Quality"]}, "links": [{"href": "https://doi.org/3199553515"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Postharvest%20Biology%20and%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3199553515", "name": "item", "description": "3199553515", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3199553515"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Chemometrics&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Chemometrics&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Chemometrics&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Chemometrics&offset=10", "hreflang": "en-US"}], "numberMatched": 10, "numberReturned": 10, "distributedFeatures": [], "timeStamp": "2026-06-23T23:41:53.176377Z"}